Patent Application
20220313149
The following relates to wearable devices and data processing, including pregnancy mode profile configuration.
Some wearable devices may be configured to collect physiological data from users, including heart rate, motion data, temperature data, photoplethysmogram (PPG) data, and the like. In some cases, some wearable devices may provide sleep goals, activity goals, and other messaging to a user based on acquired physiological data in order to assist the user with improving their overall health. However, conventional techniques for providing sleep goals, activity goals, and other messaging may be inaccurate, and may lead to detrimental health effects in some cases. As such, some conventional techniques for providing sleep goals, activity goals, and other messaging may be improved.
Some wearable devices may be configured to collect physiological data from users, including heart rate, motion data, temperature data, photoplethysmogram (PPG) data, and the like. In some cases, some wearable devices may provide goals and other messaging to a user based on acquired physiological data in order to assist the user with improving their overall health. For example, some wearable devices may provide daily sleep goals, daily step goals, or daily calorie consumption goals based on the user's overall health and goals. However, conventional techniques for providing goals and other messaging may be inaccurate, and may lead to detrimental health effects in some cases. For example, when a user is pregnant, the user's normal “healthy” goals and related messaging may not be applicable due to the user's altered condition as a result of the pregnancy.
Accordingly, aspects of the present disclosure are directed to techniques which tailor goals, physiological parameter baselines (e.g., expectations for sleep, temperature, heart rate), health-related messaging, and other user guidance provided to a user (e.g., via a graphical user interface (GUI)) based on “operational modes” associated with the user. In particular, aspects of the present disclosure are directed to computing devices and applications (e.g., wearable devices) that measure user physiological parameters, process the measured parameters, and provide outputs to users (e.g., via a GUI). The computing devices and applications may operate in a variety of different operational modes (e.g., normal mode (e.g., non-pregnant mode), pregnancy mode, postpartum mode) that define different device or application functionality. In particular, the various operational modes may be associated with different goals, messaging, and the like. The different goals, messages, and the like may be associated with sleep metrics, activity metrics, Readiness metrics, and related scores. As such, techniques described herein may enable wearable devices to tailor health-related guidance to users in accordance with different operational states, where the operational states may be determined based on user inputs, physiological data acquired from the user, or both.
For example, some aspects of the present disclosure describe techniques for providing guidance to a user during pregnancy and postpartum (e.g., pregnancy mode, postpartum mode). For instance, systems and methods described herein may provide guidance during each stage (e.g., trimester) of pregnancy from conception to birth and recovery from pregnancy after birth (e.g., postpartum). Applications (e.g., mobile health applications) may provide guidance to their users about healthy habits and behaviors so that users may optimize their performance. In some implementations, devices/applications may provide guidance in a periodic manner (e.g., each morning), in a random manner, or prompted by data driven triggers. The guidance may be provided in a specified context, in a personalized form, and at a time when the user is receptive to the guidance.
During pregnancy, and for a time following pregnancy, it may feel inappropriate for a user to receive guidance, targets, and/or charts related to improving physical activity performance and/or avoiding naps when the focus for the user should be on optimizing the user's health throughout pregnancy. During pregnancy, and immediately following, instead of aiming at improved performance, the user may wish to recover and get back to normal physical, sleep, and mental performance. In some cases, the data collected by wearable devices or mobile health applications may be able to detect the onset of pregnancy, but it may be more difficult to automatically detect when a user's condition has returned to normal levels.
The devices or systems of the present disclosure may adjust guidance provided to users in accordance with different operational modes, such as during pregnancy (e.g., pregnancy mode). In some implementations, the devices or systems may compensate for the conditions in which parameters, such as body temperature, heart rate, heart rate variability (HRV) (and corresponding parameters) return to normal (e.g., non-pregnancy) levels earlier than the symptoms disappear. The devices or systems may also compensate to determine when it would be ideal for the human body to return to normal mental or physical levels. For example, the techniques may compensate for a delay in getting back to normal after a period of stress, illness, pregnancy, and the like. Additionally, the devices or systems may be able to compensate for pregnancy related complications or miscarriage following some health-related recommendations. Accordingly, the devices or systems described herein may balance the guidance in a variety of conditions.
In some implementations, an application (e.g., mobile health app) may present daily targets, sleep improvement programs, training programs, and nutritional guidance. The guidance may be based on physiological data acquired via a wearable device, user inputs (e.g., user inputted “tags”), and the like. The targets provided by the application may remain relatively constant from day-to-day, or may vary according to a predefined schedule. In some implementations, an application may alter a sleeping or activity program based on measured parameters (e.g., physiological parameters, detected menstrual cycle parameters, pregnancy, etc.). In some cases, an application may also adjust single day sleep targets and activity targets based on a user's readiness status, which may be calculated based on a previous night's sleep, sleep debt, previous day's activity, resting heart rate, and/or body temperature measurements.
In some implementations, an application may include an operational mode that can be enabled or disabled automatically or manually. The operational mode may be configured such that the application experience changes towards being more fitting to the pregnancy of the user. The operational mode may also be configured such that the application experience omits some or all other health-related targets, particularly those of physical activity and training targets. When a wearable device measures parameters that indicate added stress or potential illness symptoms, the operational mode may be initiated. In some implementations, the application (e.g., via a GUI) may ask the user if they wish to start a special operational mode in the application that will help them concentrate on pregnancy. This operational mode may be referred to as a “pregnancy mode.” In some implementations, the user may activate the pregnancy mode from a menu of the application. This type of activation may be used when the need for the pregnancy mode arises from a confirmation that is not detected automatically by wearable biosignal measurements.
After pregnancy mode is ended, the application health-related guidance may be gradually adjusted towards normal guidance. In some implementations, the termination of pregnancy mode may be triggered by the user (e.g., manually). In some implementations, the application may prompt the user to end the pregnancy mode. In some implementations, the application may terminate pregnancy mode automatically, or prompt the user automatically (e.g., after a number of body status signals like temperature, heart rate, breathing rate, and the like, have been normalized for a predefined period of time). In some implementations, the adjustment may be done in relation to the length of the pregnancy and/or other symptoms or observed signals. This period of time after birth may be referred to as a “postpartum mode.”
Throughout the postpartum period and the following gradual return to the normal guidance, observations related to body signals may be interpreted for users mainly in light of recovery. The selection of health-related content presented to users may depend on the special operational modes described herein (e.g., pregnancy mode and postpartum mode). Modifying sleep and activity targets (e.g., decreasing activity targets and increasing sleep targets) and changing messaging (e.g., providing customized guidance and pregnancy-related coaching) during pregnancy mode and postpartum mode may assist a user in increasing life quality and recovery during and after pregnancy.
In one example, during pregnancy mode, it may be particularly fitting to avoid high intensity exercises. Accordingly, during pregnancy mode, some or all physical activity-related targets may be disabled. In some implementations, instead of a minimum target, the nature of the target may be inverted so that the target is set as a maximum target that should not be exceeded. Returning to normal guidance during pregnancy mode may include adjusting daily activity targets (e.g., calories, active minutes, or steps) by starting from zero, or a lowered target, and ending at normal targets. The adjustment may be based on the amount of time that has elapsed during the pregnancy mode and/or the level of stress or pregnancy complications. The adjustment may be implemented using a weighted average described herein.
Pregnancy mode and postpartum mode may feature a custom set of messages (e.g., daily messages) which are designed to guide the users to shift their focus to pregnancy adjusted goals and targets. For example, during the pregnancy mode messaging period, the application may highlight metrics that can react to strain, such as resting heart rate, HRV, body temperature, sleep efficiency, and total sleep time. After pregnancy mode has been switched off and the user enters postpartum mode, the messaging may gradually start guiding the user back to their normal sleeping and training routines and targets.
During both pregnancy mode and postpartum mode, the measurements upon which the messages are based may be taken over consecutive days. The messages may also emphasize metrics and trends that are the most relevant for the specific user's pregnancy. In pregnancy mode and postpartum mode, instead of providing activity goals and training feedback, activity guidance may encourage the user to focus on rest and recovery, but still break up sedentary time.
Techniques described herein may enable health-related guidance (e.g., sleep targets, activity targets, expected physiological parameter baselines) provided to a user to be tailored in accordance with one or more “operational modes” associated with the user and/or wearable device. For example, during pregnancy, users may experience many changes to their body including sleep, temperature, and the like. In some systems, these changes may not be currently reflected in the algorithms, thus making the majority of insights and recommendations inaccurate for pregnant users. In such cases, the system may configure a pregnancy mode profile to improve the user experience for pregnant users.
The system may adapt user scores (e.g. Readiness, sleep, activity) and include personalized features (e.g., stories, tags, messages, insights) to be more appropriate and engaging for pregnant users. For example, when a user indicates that she is pregnant, the user may choose to enter a “pregnancy mode” where physiology, sleep, and Activity Scores are viewed according to the stage of pregnancy. In such cases, the system may provide insights and contextual tags relevant to pregnancy and provide pregnancy-specific features. The system may add value to the pregnancy space by providing predictive analysis that may not be available without a clinician's help.
While much of the present disclosure is described in the context of a “non-pregnant mode,” “pregnancy mode,” and “postpartum mode,” this is solely for illustrative purposes, and is not to be regarded as a limitation of the present disclosure. In this regard, techniques described herein may be used to tailor guidance (e.g., sleep targets, sleep messages, activity targets, activity messages) provided to users during any number of operational modes including, but not limited to, a normal mode, a rest mode, a recovery mode, a training mode (e.g., marathon training mode, a football season training mode), an illness mode (e.g., COVID-19 mode, flu mode), a surgery mode (e.g., pre-surgery mode, post-surgery mode), a travel mode (e.g., pre- and post-time zone changes), a vacation mode (e.g., holiday mode), a menstrual cycle mode, a menopause mode, a daylight savings mode, and the like.
Moreover, while much of the present disclosure is described in the context of tailoring “targets associated with one or more physiological metrics,” and “messages associated with the one or more physiological metrics” to users based on activated operational modes, techniques described herein may be used to adjust any health-related targets and messaging provided to users based on activated operational modes. Other health-related guidance which may be tailored based on operational modes may include expectations, targets, and baselines for any physiological parameter (e.g., sleep baseline, temperature baseline, respiratory rate baseline, heart rate baseline, activity baseline), as well as expectations, targets, and baselines for scores (e.g., Activity Score, Sleep Score, Readiness Score) and behavioral characteristics (e.g., movement, activity).
As described herein, a system may be configured to transition between operational modes based on manual user inputs received from a user. Additionally, or alternatively, the system may automatically transition between operational modes based on physiological data acquired from the user and/or other data (e.g., ovulation, expected menstrual cycles).
Aspects of the disclosure are initially described in the context of systems supporting physiological data collection from users via wearable devices. Additional aspects of the disclosure are described in the context of example process flows, an example timing diagram, example GUIs, and the like. Aspects of the disclosure are further illustrated by and described with reference to apparatus diagrams, system diagrams, and flowcharts that relate to pregnancy mode profile configuration.
The electronic devices may include any electronic devices known in the art, including wearable devices 104 (e.g., ring wearable devices, watch wearable devices, etc.), user devices 106 (e.g., smartphones, laptops, tablets). The electronic devices associated with the respective users 102 may include one or more of the following functionalities: 1) measuring physiological data, 2) storing the measured data, 3) processing the data, 4) providing outputs (e.g., via GUIs) to a user 102 based on the processed data, and 5) communicating data with one another and/or other computing devices. Different electronic devices may perform one or more of the functionalities.
Example wearable devices 104 may include wearable computing devices, such as a ring computing device (hereinafter “ring”) configured to be worn on a user's 102 finger, a wrist computing device (e.g., a smart watch, fitness band, or bracelet) configured to be worn on a user's 102 wrist, and/or a head mounted computing device (e.g., glasses/goggles). Wearable devices 104 may also include bands, straps (e.g., flexible or inflexible bands or straps), stick-on sensors, and the like, that may be positioned in other locations, such as bands around the head (e.g., a forehead headband), arm (e.g., a forearm band and/or bicep band), and/or leg (e.g., a thigh or calf band), behind the ear, under the armpit, and the like. Wearable devices 104 may also be attached to, or included in, articles of clothing. For example, wearable devices 104 may be included in pockets and/or pouches on clothing. As another example, wearable device 104 may be clipped and/or pinned to clothing, or may otherwise be maintained within the vicinity of the user 102. Example articles of clothing may include, but are not limited to, hats, shirts, gloves, pants, socks, outerwear (e.g., jackets), and undergarments. In some implementations, wearable devices 104 may be included with other types of devices such as training/sporting devices that are used during physical activity. For example, wearable devices 104 may be attached to, or included in, a bicycle, skis, a tennis racket, a golf club, and/or training weights.
Much of the present disclosure may be described in the context of a ring wearable device 104. Accordingly, the terms “ring 104,” “wearable device 104,” and like terms, may be used interchangeably, unless noted otherwise herein. However, the use of the term “ring 104” is not to be regarded as limiting, as it is contemplated herein that aspects of the present disclosure may be performed using other wearable devices (e.g., watch wearable devices, necklace wearable device, bracelet wearable devices, earring wearable devices, anklet wearable devices, and the like).
In some aspects, user devices 106 may include handheld mobile computing devices, such as smartphones and tablet computing devices. User devices 106 may also include personal computers, such as laptop and desktop computing devices. Other example user devices 106 may include server computing devices that may communicate with other electronic devices (e.g., via the Internet). In some implementations, computing devices may include medical devices, such as external wearable computing devices (e.g., Holter monitors). Medical devices may also include implantable medical devices, such as pacemakers and cardioverter defibrillators. Other example user devices 106 may include home computing devices, such as internet of things (IoT) devices (e.g., IoT devices), smart televisions, smart speakers, smart displays (e.g., video call displays), hubs (e.g., wireless communication hubs), security systems, smart appliances (e.g., thermostats and refrigerators), and fitness equipment.
Some electronic devices (e.g., wearable devices 104, user devices 106) may measure physiological parameters of respective users 102, such as photoplethysmography waveforms, continuous skin temperature, a pulse waveform, respiration rate, heart rate, heart rate variability (HRV), actigraphy, galvanic skin response, pulse oximetry, and/or other physiological parameters. Some electronic devices that measure physiological parameters may also perform some/all of the calculations described herein. Some electronic devices may not measure physiological parameters, but may perform some/all of the calculations described herein. For example, a ring (e.g., wearable device 104), mobile device application, or a server computing device may process received physiological data that was measured by other devices.
In some implementations, a user 102 may operate, or may be associated with, multiple electronic devices, some of which may measure physiological parameters and some of which may process the measured physiological parameters. In some implementations, a user 102 may have a ring (e.g., wearable device 104) that measures physiological parameters. The user 102 may also have, or be associated with, a user device 106 (e.g., mobile device, smartphone), where the wearable device 104 and the user device 106 are communicatively coupled to one another. In some cases, the user device 106 may receive data from the wearable device 104 and perform some/all of the calculations described herein. In some implementations, the user device 106 may also measure physiological parameters described herein, such as motion/activity parameters.
For example, as illustrated in
In some implementations, the rings 104 (e.g., wearable devices 104) of the system 100 may be configured to collect physiological data from the respective users 102 based on arterial blood flow within the user's finger. In particular, a ring 104 may utilize one or more LEDs (e.g., red LEDs, green LEDs) that emit light on the palm-side of a user's finger to collect physiological data based on arterial blood flow within the user's finger. In some implementations, the ring 104 may acquire the physiological data using a combination of both green and red LEDs. The physiological data may include any physiological data known in the art including, but not limited to, temperature data, accelerometer data (e.g., movement/motion data), heart rate data, HRV data, blood oxygen level data, or any combination thereof.
The use of both green and red LEDs may provide several advantages over other solutions, as red and green LEDs have been found to have their own distinct advantages when acquiring physiological data under different conditions (e.g., light/dark, active/inactive) and via different parts of the body, and the like. For example, green LEDs have been found to exhibit better performance during exercise. Moreover, using multiple LEDs (e.g., green and red LEDs) distributed around the ring 104 has been found to exhibit superior performance as compared to wearable devices that utilize LEDs that are positioned close to one another, such as within a watch wearable device. Furthermore, the blood vessels in the finger (e.g., arteries, capillaries) are more accessible via LEDs as compared to blood vessels in the wrist. In particular, arteries in the wrist are positioned on the bottom of the wrist (e.g., palm-side of the wrist), meaning only capillaries are accessible on the top of the wrist (e.g., back of hand side of the wrist), where wearable watch devices and similar devices are typically worn. As such, utilizing LEDs and other sensors within a ring 104 has been found to exhibit superior performance as compared to wearable devices worn on the wrist, as the ring 104 may have greater access to arteries (as compared to capillaries), thereby resulting in stronger signals and more valuable physiological data.
The electronic devices of the system 100 (e.g., user devices 106, wearable devices 104) may be communicatively coupled to one or more servers 110 via wired or wireless communication protocols. For example, as shown in
The system 100 may offer an on-demand database service between the user devices 106 and the one or more servers 110. In some cases, the servers 110 may receive data from the user devices 106 via the network 108, and may store and analyze the data. Similarly, the servers 110 may provide data to the user devices 106 via the network 108. In some cases, the servers 110 may be located at one or more data centers. The servers 110 may be used for data storage, management, and processing. In some implementations, the servers 110 may provide a web-based interface to the user device 106 via web browsers.
In some aspects, the system 100 may detect periods of time during which a user 102 is asleep, and classify periods of time during which the user 102 is asleep into one or more sleep stages (e.g., sleep stage classification). For example, as shown in
In some aspects, the system 100 may utilize circadian rhythm-derived features to further improve physiological data collection, data processing procedures, and other techniques described herein. The term circadian rhythm may refer to a natural, internal process that regulates an individual's sleep-wake cycle, that repeats approximately every 24 hours. In this regard, techniques described herein may utilize circadian rhythm adjustment models to improve physiological data collection, analysis, and data processing. For example, a circadian rhythm adjustment model may be input into a machine learning classifier along with physiological data collected from the user 102-a via the wearable device 104-a. In this example, the circadian rhythm adjustment model may be configured to “weight,” or adjust, physiological data collected throughout a user's natural, approximately 24-hour circadian rhythm. In some implementations, the system may initially start with a “baseline” circadian rhythm adjustment model, and may modify the baseline model using physiological data collected from each user 102 to generate tailored, individualized circadian rhythm adjustment models that are specific to each respective user 102.
In some aspects, the system 100 may utilize other biological rhythms to further improve physiological data collection, analysis, and processing by phase of these other rhythms. For example, if a weekly rhythm is detected within an individual's baseline data, then the model may be configured to adjust “weights” of data by day of the week. Biological rhythms that may require adjustment to the model by this method include: 1) ultradian (faster than a day rhythms, including sleep cycles in a sleep state, and oscillations from less than an hour to several hours periodicity in the measured physiological variables during wake state; 2) circadian rhythms; 3) non-endogenous daily rhythms shown to be imposed on top of circadian rhythms, as in work schedules; 4) weekly rhythms, or other artificial time periodicities exogenously imposed (e.g. in a hypothetical culture with 12 day “weeks”, 12 day rhythms could be used); 5) multi-day ovarian rhythms in women and spermatogenesis rhythms in men; 6) lunar rhythms (relevant for individuals living with low or no artificial lights); and 7) seasonal rhythms.
The biological rhythms are not always stationary rhythms. For example, many women experience variability in ovarian cycle length across cycles, and ultradian rhythms are not expected to occur at exactly the same time or periodicity across days even within a user. As such, signal processing techniques sufficient to quantify the frequency composition while preserving temporal resolution of these rhythms in physiological data may be used to improve detection of these rhythms, to assign phase of each rhythm to each moment in time measured, and to thereby modify adjustment models and comparisons of time intervals. The biological rhythm-adjustment models and parameters can be added in linear or non-linear combinations as appropriate to more accurately capture the dynamic physiological baselines of an individual or group of individuals.
In some aspects, the respective devices of the system 100 may support techniques for a pregnancy mode profile configuration in accordance with multiple operational modes of the user 102 and/or wearable device 104. For example, the wearable device 104-a associated with the user 102-a may acquire physiological data from the user, including heart rate data, motion data, temperature data, and the like. During a non-pregnant operational mode, the system may provide health-related guidance to the user based on the user's physiological activity and overall health, including normal sleep targets (e.g., overall sleep goals, REM sleep goals, deep sleep goals, etc.), sleep messages (e.g., encouraging users to reach their sleep targets, messages congratulating users for reaching their sleep targets), physical activity targets (e.g., step goals, calorie goals, standing goals, sleep/rest targets), normal activity messages (e.g., messages encouraging users to reach their physical activity targets, messages congratulating users for reaching their physical activity targets), Readiness targets, and Readiness messages.
Continuing with the same example, the system 100 may identify a trigger to transition from the non-pregnant operational mode to a different operational mode, such as pregnancy mode. For example, the system 100 may identify that the user is pregnant, and may therefore identify a trigger to transition from the non-pregnant operational mode to the pregnancy operational mode. The pregnancy operational mode may be configured to promote rest for the user 102-a, and may therefore be associated with lowered/reduced physical activity targets and related activity messages (e.g., messages encouraging the user 102-a to rest or take a nap). In some cases, the pregnancy operational mode may be configured to promote sleep for the user 102-a, and may therefore be associated with higher/increased sleep targets and related sleep messages (e.g., messages encouraging the user 102-a to go to bed early). As such, upon transitioning to the pregnancy mode, the system 100 may tailor sleep targets, sleep messages, physical activity targets, activity messages, Readiness targets, and Readiness message to allow the user 102-a to prepare for upcoming phases of pregnancy.
For example, the system 100 may provide different health-related guidance to the user, including adjusted sleep targets (e.g., overall sleep goals, REM sleep goals, deep sleep goals, etc.), adjusted sleep messages (e.g., encouraging users to reach their sleep targets, messages congratulating users for reaching their sleep targets), adjusted physical activity targets (e.g., step goals, calorie goals, standing goals, sleep/rest targets), adjusted activity messages (e.g., messages encouraging users to reach their physical activity targets, messages congratulating users for reaching their physical activity targets), adjusted Readiness targets, and adjusted Readiness messages. In some cases, the system 100 may provide different health-related guidance to the user, including adjusted illness detection thresholds. For example, the system may detect elevated temperature, heart rate, respiratory rate, or a combination thereof which may be associated with an illness. However, the system may identify that the user is pregnant and adjust the illness detection thresholds such that the system refrains from identifying the illness and notifying the user of the illness because the user is pregnant. In such cases, the adjusted targets and messages may be adjusted for pregnancy and different from the non-pregnant mode targets and messages.
In some cases, the system 100 may identify the trigger to switch between operational modes (e.g., switch from non-pregnant mode to pregnancy mode, and vice versa) based on user inputs received from the user 102-a. For example, the system may receive a user input from the user 102-a where the user input includes an indication that the user 102-a is pregnant. Additionally, or alternatively, the system 100 may automatically identify triggers for switching between operational modes. For example, the system 100 may recognize that the user is pregnant based on physiological data acquired from the wearable device 104-a. As such, the system 100 may automatically switch between operational modes and/or prompt the user 102-a to confirm or deny a switch between operational modes (e.g., display a message: “It looks like you may be pregnant. Switch to pregnancy mode?”).
It should be appreciated by a person skilled in the art that one or more aspects of the disclosure may be implemented in a system 100 to additionally or alternatively solve other problems than those described above. Furthermore, aspects of the disclosure may provide technical improvements to “conventional” systems or processes as described herein. However, the description and appended drawings only include example technical improvements resulting from implementing aspects of the disclosure, and accordingly do not represent all of the technical improvements provided within the scope of the claims.
In some aspects, the ring 104 may be configured to be worn around a user's finger, and may determine one or more user physiological parameters when worn around the user's finger. Example measurements and determinations may include, but are not limited to, user skin temperature, pulse waveforms, respiratory rate, heart rate, HRV, blood oxygen levels, and the like.
The system 200 further includes a user device 106 (e.g., a smartphone) in communication with the ring 104. For example, the ring 104 may be in wireless and/or wired communication with the user device 106. In some implementations, the ring 104 may send measured and processed data (e.g., temperature data, photoplethysmogram (PPG) data, motion/accelerometer data, ring input data, and the like) to the user device 106. The user device 106 may also send data to the ring 104, such as ring 104 firmware/configuration updates. The user device 106 may process data. In some implementations, the user device 106 may transmit data to the server 110 for processing and/or storage.
The ring 104 may include a housing 205 that may include an inner housing 205-a and an outer housing 205-b. In some aspects, the housing 205 of the ring 104 may store or otherwise include various components of the ring including, but not limited to, device electronics, a power source (e.g., battery 210, and/or capacitor), one or more substrates (e.g., printable circuit boards) that interconnect the device electronics and/or power source, and the like. The device electronics may include device modules (e.g., hardware/software), such as: a processing module 230-a, a memory 215, a communication module 220-a, a power module 225, and the like. The device electronics may also include one or more sensors. Example sensors may include one or more temperature sensors 240, a PPG sensor assembly (e.g., PPG system 235), and one or more motion sensors 245.
The sensors may include associated modules (not illustrated) configured to communicate with the respective components/modules of the ring 104, and generate signals associated with the respective sensors. In some aspects, each of the components/modules of the ring 104 may be communicatively coupled to one another via wired or wireless connections. Moreover, the ring 104 may include additional and/or alternative sensors or other components that are configured to collect physiological data from the user, including light sensors (e.g., LEDs), oximeters, and the like.
The ring 104 shown and described with reference to
The housing 205 may include one or more housing 205 components. The housing 205 may include an outer housing 205-b component (e.g., a shell) and an inner housing 205-a component (e.g., a molding). The housing 205 may include additional components (e.g., additional layers) not explicitly illustrated in
The outer housing 205-b may be fabricated from one or more materials. In some implementations, the outer housing 205-b may include a metal, such as titanium, that may provide strength and abrasion resistance at a relatively light weight. The outer housing 205-b may also be fabricated from other materials, such polymers. In some implementations, the outer housing 205-b may be protective as well as decorative.
The inner housing 205-a may be configured to interface with the user's finger. The inner housing 205-a may be formed from a polymer (e.g., a medical grade polymer) or other material. In some implementations, the inner housing 205-a may be transparent. For example, the inner housing 205-a may be transparent to light emitted by the PPG light emitting diodes (LEDs). In some implementations, the inner housing 205-a component may be molded onto the outer housing 205-b. For example, the inner housing 205-a may include a polymer that is molded (e.g., injection molded) to fit into an outer housing 205-b metallic shell.
The ring 104 may include one or more substrates (not illustrated). The device electronics and battery 210 may be included on the one or more substrates. For example, the device electronics and battery 210 may be mounted on one or more substrates. Example substrates may include one or more printed circuit boards (PCBs), such as flexible PCB (e.g., polyimide). In some implementations, the electronics/battery 210 may include surface mounted devices (e.g., surface-mount technology (SMT) devices) on a flexible PCB. In some implementations, the one or more substrates (e.g., one or more flexible PCBs) may include electrical traces that provide electrical communication between device electronics. The electrical traces may also connect the battery 210 to the device electronics.
The device electronics, battery 210, and substrates may be arranged in the ring 104 in a variety of ways. In some implementations, one substrate that includes device electronics may be mounted along the bottom of the ring 104 (e.g., the bottom half), such that the sensors (e.g., PPG system 235, temperature sensors 240, motion sensors 245, and other sensors) interface with the underside of the user's finger. In these implementations, the battery 210 may be included along the top portion of the ring 104 (e.g., on another substrate).
The various components/modules of the ring 104 represent functionality (e.g., circuits and other components) that may be included in the ring 104. Modules may include any discrete and/or integrated electronic circuit components that implement analog and/or digital circuits capable of producing the functions attributed to the modules herein. For example, the modules may include analog circuits (e.g., amplification circuits, filtering circuits, analog/digital conversion circuits, and/or other signal conditioning circuits). The modules may also include digital circuits (e.g., combinational or sequential logic circuits, memory circuits etc.).
The memory 215 (memory module) of the ring 104 may include any volatile, non-volatile, magnetic, or electrical media, such as a random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically-erasable programmable ROM (EEPROM), flash memory, or any other memory device. The memory 215 may store any of the data described herein. For example, the memory 215 may be configured to store data (e.g., motion data, temperature data, PPG data) collected by the respective sensors and PPG system 235. Furthermore, memory 215 may include instructions that, when executed by one or more processing circuits, cause the modules to perform various functions attributed to the modules herein. The device electronics of the ring 104 described herein are only example device electronics. As such, the types of electronic components used to implement the device electronics may vary based on design considerations.
The functions attributed to the modules of the ring 104 described herein may be embodied as one or more processors, hardware, firmware, software, or any combination thereof. Depiction of different features as modules is intended to highlight different functional aspects and does not necessarily imply that such modules must be realized by separate hardware/software components. Rather, functionality associated with one or more modules may be performed by separate hardware/software components or integrated within common hardware/software components.
The processing module 230-a of the ring 104 may include one or more processors (e.g., processing units), microcontrollers, digital signal processors, systems on a chip (SOCs), and/or other processing devices. The processing module 230-a communicates with the modules included in the ring 104. For example, the processing module 230-a may transmit/receive data to/from the modules and other components of the ring 104, such as the sensors. As described herein, the modules may be implemented by various circuit components. Accordingly, the modules may also be referred to as circuits (e.g., a communication circuit and power circuit).
The processing module 230-a may communicate with the memory 215. The memory 215 may include computer-readable instructions that, when executed by the processing module 230-a, cause the processing module 230-a to perform the various functions attributed to the processing module 230-a herein. In some implementations, the processing module 230-a (e.g., a microcontroller) may include additional features associated with other modules, such as communication functionality provided by the communication module 220-a (e.g., an integrated Bluetooth Low Energy transceiver) and/or additional onboard memory 215.
The communication module 220-a may include circuits that provide wireless and/or wired communication with the user device 106 (e.g., communication module 220-b of the user device 106). In some implementations, the communication modules 220-a, 220-b may include wireless communication circuits, such as Bluetooth circuits and/or Wi-Fi circuits. In some implementations, the communication modules 220-a, 220-b can include wired communication circuits, such as Universal Serial Bus (USB) communication circuits. Using the communication module 220-a, the ring 104 and the user device 106 may be configured to communicate with each other. The processing module 230-a of the ring may be configured to transmit/receive data to/from the user device 106 via the communication module 220-a. Example data may include, but is not limited to, motion data, temperature data, pulse waveforms, heart rate data, HRV data, PPG data, and status updates (e.g., charging status, battery charge level, and/or ring 104 configuration settings). The processing module 230-a of the ring may also be configured to receive updates (e.g., software/firmware updates) and data from the user device 106.
The ring 104 may include a battery 210 (e.g., a rechargeable battery 210). An example battery 210 may include a Lithium-Ion or Lithium-Polymer type battery 210, although a variety of battery 210 options are possible. The battery 210 may be wirelessly charged. In some implementations, the ring 104 may include a power source other than the battery 210, such as a capacitor. The power source (e.g., battery 210 or capacitor) may have a curved geometry that matches the curve of the ring 104. In some aspects, a charger or other power source may include additional sensors that may be used to collect data in addition to, or which supplements, data collected by the ring 104 itself. Moreover, a charger or other power source for the ring 104 may function as a user device 106, in which case the charger or other power source for the ring 104 may be configured to receive data from the ring 104, store and/or process data received from the ring 104, and communicate data between the ring 104 and the servers 110.
In some aspects, the ring 104 includes a power module 225 that may control charging of the battery 210. For example, the power module 225 may interface with an external wireless charger that charges the battery 210 when interfaced with the ring 104. The charger may include a datum structure that mates with a ring 104 datum structure to create a specified orientation with the ring 104 during 104 charging. The power module 225 may also regulate voltage(s) of the device electronics, regulate power output to the device electronics, and monitor the state of charge of the battery 210. In some implementations, the battery 210 may include a protection circuit module (PCM) that protects the battery 210 from high current discharge, over voltage during 104 charging, and under voltage during 104 discharge. The power module 225 may also include electro-static discharge (ESD) protection.
The one or more temperature sensors 240 may be electrically coupled to the processing module 230-a. The temperature sensor 240 may be configured to generate a temperature signal (e.g., temperature data) that indicates a temperature read or sensed by the temperature sensor 240. The processing module 230-a may determine a temperature of the user in the location of the temperature sensor 240. For example, in the ring 104, temperature data generated by the temperature sensor 240 may indicate a temperature of a user at the user's finger (e.g., skin temperature). In some implementations, the temperature sensor 240 may contact the user's skin. In other implementations, a portion of the housing 205 (e.g., the inner housing 205-a) may form a barrier (e.g., a thin, thermally conductive barrier) between the temperature sensor 240 and the user's skin. In some implementations, portions of the ring 104 configured to contact the user's finger may have thermally conductive portions and thermally insulative portions. The thermally conductive portions may conduct heat from the user's finger to the temperature sensors 240. The thermally insulative portions may insulate portions of the ring 104 (e.g., the temperature sensor 240) from ambient temperature.
In some implementations, the temperature sensor 240 may generate a digital signal (e.g., temperature data) that the processing module 230-a may use to determine the temperature. As another example, in cases where the temperature sensor 240 includes a passive sensor, the processing module 230-a (or a temperature sensor 240 module) may measure a current/voltage generated by the temperature sensor 240 and determine the temperature based on the measured current/voltage. Example temperature sensors 240 may include a thermistor, such as a negative temperature coefficient (NTC) thermistor, or other types of sensors including resistors, transistors, diodes, and/or other electrical/electronic components.
The processing module 230-a may sample the user's temperature over time. For example, the processing module 230-a may sample the user's temperature according to a sampling rate. An example sampling rate may include one sample per second, although the processing module 230-a may be configured to sample the temperature signal at other sampling rates that are higher or lower than one sample per second. In some implementations, the processing module 230-a may sample the user's temperature continuously throughout the day and night. Sampling at a sufficient rate (e.g., one sample per second or one sample per minute) throughout the day may provide sufficient temperature data for analysis described herein.
The processing module 230-a may store the sampled temperature data in memory 215. In some implementations, the processing module 230-a may process the sampled temperature data. For example, the processing module 230-a may determine average temperature values over a period of time. In one example, the processing module 230-a may determine an average temperature value each minute by summing all temperature values collected over the minute and dividing by the number of samples over the minute. In a specific example where the temperature is sampled at one sample per second, the average temperature may be a sum of all sampled temperatures for one minute divided by sixty seconds. The memory 215 may store the average temperature values over time. In some implementations, the memory 215 may store average temperatures (e.g., one per minute) instead of sampled temperatures in order to conserve memory 215.
The sampling rate, which may be stored in memory 215, may be configurable. In some implementations, the sampling rate may be the same throughout the day and night. In other implementations, the sampling rate may be changed throughout the day/night. In some implementations, the ring 104 may filter/reject temperature readings, such as large spikes in temperature that are not indicative of physiological changes (e.g., a temperature spike from a hot shower). In some implementations, the ring 104 may filter/reject temperature readings that may not be reliable due to other factors, such as excessive motion during 104 exercise (e.g., as indicated by a motion sensor 245).
The ring 104 (e.g., communication module) may transmit the sampled and/or average temperature data to the user device 106 for storage and/or further processing. The user device 106 may transfer the sampled and/or average temperature data to the server 110 for storage and/or further processing.
Although the ring 104 is illustrated as including a single temperature sensor 240, the ring 104 may include multiple temperature sensors 240 in one or more locations, such as arranged along the inner housing 205-a near the user's finger. In some implementations, the temperature sensors 240 may be stand-alone temperature sensors 240. Additionally, or alternatively, one or more temperature sensors 240 may be included with other components (e.g., packaged with other components), such as with the accelerometer and/or processor.
The processing module 230-a may acquire and process data from multiple temperature sensors 240 in a similar manner described with respect to a single temperature sensor 240. For example, the processing module 230 may individually sample, average, and store temperature data from each of the multiple temperature sensors 240. In other examples, the processing module 230-a may sample the sensors at different rates and average/store different values for the different sensors. In some implementations, the processing module 230-a may be configured to determine a single temperature based on the average of two or more temperatures determined by two or more temperature sensors 240 in different locations on the finger.
The temperature sensors 240 on the ring 104 may acquire distal temperatures at the user's finger (e.g., any finger). For example, one or more temperature sensors 240 on the ring 104 may acquire a user's temperature from the underside of a finger or at a different location on the finger. In some implementations, the ring 104 may continuously acquire distal temperature (e.g., at a sampling rate). Although distal temperature measured by a ring 104 at the finger is described herein, other devices may measure temperature at the same/different locations. In some cases, the distal temperature measured at a user's finger may differ from the temperature measured at a user's wrist or other external body location. Additionally, the distal temperature measured at a user's finger (e.g., a “shell” temperature) may differ from the user's core temperature. As such, the ring 104 may provide a useful temperature signal that may not be acquired at other internal/external locations of the body. In some cases, continuous temperature measurement at the finger may capture temperature fluctuations (e.g., small or large fluctuations) that may not be evident in core temperature. For example, continuous temperature measurement at the finger may capture minute-to-minute or hour-to-hour temperature fluctuations that provide additional insight that may not be provided by other temperature measurements elsewhere in the body.
The ring 104 may include a PPG system 235. The PPG system 235 may include one or more optical transmitters that transmit light. The PPG system 235 may also include one or more optical receivers that receive light transmitted by the one or more optical transmitters. An optical receiver may generate a signal (hereinafter “PPG” signal) that indicates an amount of light received by the optical receiver. The optical transmitters may illuminate a region of the user's finger. The PPG signal generated by the PPG system 235 may indicate the perfusion of blood in the illuminated region. For example, the PPG signal may indicate blood volume changes in the illuminated region caused by a user's pulse pressure. The processing module 230-a may sample the PPG signal and determine a user's pulse waveform based on the PPG signal. The processing module 230-a may determine a variety of physiological parameters based on the user's pulse waveform, such as a user's respiratory rate, heart rate, HRV, oxygen saturation, and other circulatory parameters.
In some implementations, the PPG system 235 may be configured as a reflective PPG system 235 in which the optical receiver(s) receive transmitted light that is reflected through the region of the user's finger. In some implementations, the PPG system 235 may be configured as a transmissive PPG system 235 in which the optical transmitter(s) and optical receiver(s) are arranged opposite to one another, such that light is transmitted directly through a portion of the user's finger to the optical receiver(s).
The number and ratio of transmitters and receivers included in the PPG system 235 may vary. Example optical transmitters may include light-emitting diodes (LEDs). The optical transmitters may transmit light in the infrared spectrum and/or other spectrums. Example optical receivers may include, but are not limited to, photosensors, phototransistors, and photodiodes. The optical receivers may be configured to generate PPG signals in response to the wavelengths received from the optical transmitters. The location of the transmitters and receivers may vary. Additionally, a single device may include reflective and/or transmissive PPG systems 235.
The PPG system 235 illustrated in
The processing module 230-a may control one or both of the optical transmitters to transmit light while sampling the PPG signal generated by the optical receiver. In some implementations, the processing module 230-a may cause the optical transmitter with the stronger received signal to transmit light while sampling the PPG signal generated by the optical receiver. For example, the selected optical transmitter may continuously emit light while the PPG signal is sampled at a sampling rate (e.g., 250 Hz).
Sampling the PPG signal generated by the PPG system 235 may result in a pulse waveform that may be referred to as a “PPG.” The pulse waveform may indicate blood pressure vs time for multiple cardiac cycles. The pulse waveform may include peaks that indicate cardiac cycles. Additionally, the pulse waveform may include respiratory induced variations that may be used to determine respiration rate. The processing module 230-a may store the pulse waveform in memory 215 in some implementations. The processing module 230-a may process the pulse waveform as it is generated and/or from memory 215 to determine user physiological parameters described herein.
The processing module 230-a may determine the user's heart rate based on the pulse waveform. For example, the processing module 230-a may determine heart rate (e.g., in beats per minute) based on the time between peaks in the pulse waveform. The time between peaks may be referred to as an interbeat interval (IBI). The processing module 230-a may store the determined heart rate values and IBI values in memory 215.
The processing module 230-a may determine HRV over time. For example, the processing module 230-a may determine HRV based on the variation in the IBIs. The processing module 230-a may store the HRV values over time in the memory 215. Moreover, the processing module 230-a may determine the user's respiratory rate over time. For example, the processing module 230-a may determine respiratory rate based on frequency modulation, amplitude modulation, or baseline modulation of the user's IBI values over a period of time. Respiratory rate may be calculated in breaths per minute or as another breathing rate (e.g., breaths per 30 seconds). The processing module 230-a may store user respiratory rate values over time in the memory 215.
The ring 104 may include one or more motion sensors 245, such as one or more accelerometers (e.g., 6-D accelerometers) and/or one or more gyroscopes (gyros). The motion sensors 245 may generate motion signals that indicate motion of the sensors. For example, the ring 104 may include one or more accelerometers that generate acceleration signals that indicate acceleration of the accelerometers. As another example, the ring 104 may include one or more gyro sensors that generate gyro signals that indicate angular motion (e.g., angular velocity) and/or changes in orientation. The motion sensors 245 may be included in one or more sensor packages. An example accelerometer/gyro sensor is a Bosch BM1160 inertial micro electro-mechanical system (MEMS) sensor that may measure angular rates and accelerations in three perpendicular axes.
The processing module 230-a may sample the motion signals at a sampling rate (e.g., 50 Hz) and determine the motion of the ring 104 based on the sampled motion signals. For example, the processing module 230-a may sample acceleration signals to determine acceleration of the ring 104. As another example, the processing module 230-a may sample a gyro signal to determine angular motion. In some implementations, the processing module 230-a may store motion data in memory 215. Motion data may include sampled motion data as well as motion data that is calculated based on the sampled motion signals (e.g., acceleration and angular values).
The ring 104 may store a variety of data described herein. For example, the ring 104 may store temperature data, such as raw sampled temperature data and calculated temperature data (e.g., average temperatures). As another example, the ring 104 may store PPG signal data, such as pulse waveforms and data calculated based on the pulse waveforms (e.g., heart rate values, IBI values, HRV values, and respiratory rate values). The ring 104 may also store motion data, such as sampled motion data that indicates linear and angular motion.
The ring 104, or other computing device, may calculate and store additional values based on the sampled/calculated physiological data. For example, the processing module 230 may calculate and store various metrics, such as sleep metrics (e.g., a Sleep Score), activity metrics, and readiness metrics. In some implementations, additional values/metrics may be referred to as “derived values.” The ring 104, or other computing/wearable device, may calculate a variety of values/metrics with respect to motion. Example derived values for motion data may include, but are not limited to, motion count values, regularity values, intensity values, metabolic equivalence of task values (METs), and orientation values. Motion counts, regularity values, intensity values, and METs may indicate an amount of user motion (e.g., velocity/acceleration) over time. Orientation values may indicate how the ring 104 is oriented on the user's finger and if the ring 104 is worn on the left hand or right hand.
In some implementations, motion counts and regularity values may be determined by counting a number of acceleration peaks within one or more periods of time (e.g., one or more 30 second to 1 minute periods). Intensity values may indicate a number of movements and the associated intensity (e.g., acceleration values) of the movements. The intensity values may be categorized as low, medium, and high, depending on associated threshold acceleration values. METs may be determined based on the intensity of movements during a period of time (e.g., 30 seconds), the regularity/irregularity of the movements, and the number of movements associated with the different intensities.
In some implementations, the processing module 230-a may compress the data stored in memory 215. For example, the processing module 230-a may delete sampled data after making calculations based on the sampled data. As another example, the processing module 230-a may average data over longer periods of time in order to reduce the number of stored values. In a specific example, if average temperatures for a user over one minute are stored in memory 215, the processing module 230-a may calculate average temperatures over a five minute time period for storage, and then subsequently erase the one minute average temperature data. The processing module 230-a may compress data based on a variety of factors, such as the total amount of used/available memory 215 and/or an elapsed time since the ring 104 last transmitted the data to the user device 106.
Although a user's physiological parameters may be measured by sensors included on a ring 104, other devices may measure a user's physiological parameters. For example, although a user's temperature may be measured by a temperature sensor 240 included in a ring 104, other devices may measure a user's temperature. In some examples, other wearable devices (e.g., wrist devices) may include sensors that measure user physiological parameters. Additionally, medical devices, such as external medical devices (e.g., wearable medical devices) and/or implantable medical devices, may measure a user's physiological parameters. One or more sensors on any type of computing device may be used to implement the techniques described herein.
The physiological measurements may be taken continuously throughout the day and/or night. In some implementations, the physiological measurements may be taken during 104 portions of the day and/or portions of the night. In some implementations, the physiological measurements may be taken in response to determining that the user is in a specific state, such as an active state, resting state, and/or a sleeping state. For example, the ring 104 can make physiological measurements in a resting/sleep state in order to acquire cleaner physiological signals. In one example, the ring 104 or other device/system may detect when a user is resting and/or sleeping and acquire physiological parameters (e.g., temperature) for that detected state. The devices/systems may use the resting/sleep physiological data and/or other data when the user is in other states in order to implement the techniques of the present disclosure.
In some implementations, as described previously herein, the ring 104 may be configured to collect, store, and/or process data, and may transfer any of the data described herein to the user device 106 for storage and/or processing. In some aspects, the user device 106 includes a wearable application 250, an operating system (OS), a web browser application (e.g., web browser 280), one or more additional applications, and a GUI 275. The user device 106 may further include other modules and components, including sensors, audio devices, haptic feedback devices, and the like. The wearable application 250 may include an example of an application (e.g., “app”) that may be installed on the user device 106. The wearable application 250 may be configured to acquire data from the ring 104, store the acquired data, and process the acquired data as described herein. For example, the wearable application 250 may include a user interface (UI) module 255, an acquisition module 260, a processing module 230-b, a communication module 220-b, and a storage module (e.g., database 265) configured to store application data.
The various data processing operations described herein may be performed by the ring 104, the user device 106, the servers 110, or any combination thereof. For example, in some cases, data collected by the ring 104 may be pre-processed and transmitted to the user device 106. In this example, the user device 106 may perform some data processing operations on the received data, may transmit the data to the servers 110 for data processing, or both. For instance, in some cases, the user device 106 may perform processing operations that require relatively low processing power and/or operations that require a relatively low latency, whereas the user device 106 may transmit the data to the servers 110 for processing operations that require relatively high processing power and/or operations that may allow relatively higher latency.
In some aspects, the ring 104, user device 106, and server 110 of the system 200 may be configured to evaluate sleep patterns for a user. In particular, the respective components of the system 200 may be used to collect data from a user via the ring 104, and generate one or more scores (e.g., Sleep Score, Readiness Score) for the user based on the collected data. For example, as noted previously herein, the ring 104 of the system 200 may be worn by a user to collect data from the user, including temperature, heart rate, HRV, and the like. Data collected by the ring 104 may be used to determine when the user is asleep in order to evaluate the user's sleep for a given “sleep day.” In some aspects, scores may be calculated for the user for each respective sleep day, such that a first sleep day is associated with a first set of scores, and a second sleep day is associated with a second set of scores. Scores may be calculated for each respective sleep day based on data collected by the ring 104 during the respective sleep day. Scores may include, but are not limited to, Sleep Scores, Readiness Scores, and the like.
In some cases, “sleep days” may align with the traditional calendar days, such that a given sleep day runs from midnight to midnight of the respective calendar day. In other cases, sleep days may be offset relative to calendar days. For example, sleep days may run from 6:00 pm (18:00) of a calendar day until 6:00 pm (18:00) of the subsequent calendar day. In this example, 6:00 pm may serve as a “cut-off time,” where data collected from the user before 6:00 pm is counted for the current sleep day, and data collected from the user after 6:00 pm is counted for the subsequent sleep day. Due to the fact that most individuals sleep the most at night, offsetting sleep days relative to calendar days may enable the system 200 to evaluate sleep patterns for users in such a manner that is consistent with their sleep schedules. In some cases, users may be able to selectively adjust (e.g., via the GUI) a timing of sleep days relative to calendar days so that the sleep days are aligned with the duration of time in which the respective users typically sleep.
In some implementations, each overall score for a user for each respective day (e.g., Sleep Score, Readiness Score) may be determined/calculated based on one or more “contributors,” “factors,” or “contributing factors.” For example, a user's overall Sleep Score may be calculated based on a set of contributors, including: total sleep, efficiency, restfulness, REM sleep, deep sleep, latency, timing, or any combination thereof. The Sleep Score may include any quantity of contributors. The “total sleep” contributor may refer to the sum of all sleep periods of the sleep day. The “efficiency” contributor may reflect the percentage of time spent asleep compared to time spent awake while in bed, and may be calculated using the efficiency average of long sleep periods (e.g., primary sleep period) of the sleep day, weighted by a duration of each sleep period. The “restfulness” contributor may indicate how restful the user's sleep is, and may be calculated using the average of all sleep periods of the sleep day, weighted by a duration of each period. The restfulness contributor may be based on a “wake up count” (e.g., sum of all the wake-ups (when user wakes up) detected during different sleep periods), excessive movement, and a “got up count” (e.g., sum of all the got-ups (when user gets out of bed) detected during the different sleep periods).
The “REM sleep” contributor may refer to a sum total of REM sleep durations across all sleep periods of the sleep day including REM sleep. Similarly, the “deep sleep” contributor may refer to a sum total of deep sleep durations across all sleep periods of the sleep day including deep sleep. The “latency” contributor may signify how long (e.g., average, median, longest) the user takes to go to sleep, and may be calculated using the average of long sleep periods throughout the sleep day, weighted by a duration of each period and the number of such periods (e.g., consolidation of a given sleep stage or sleep stages may be its own contributor or weight other contributors). Lastly, the “timing” contributor may refer to a relative timing of sleep periods within the sleep day and/or calendar day, and may be calculated using the average of all sleep periods of the sleep day, weighted by a duration of each period.
By way of another example, a user's overall Readiness Score may be calculated based on a set of contributors, including: sleep, sleep balance, heart rate, HRV balance, recovery index, temperature, activity, activity balance, or any combination thereof. The Readiness Score may include any quantity of contributors. The “sleep” contributor may refer to the combined Sleep Score of all sleep periods within the sleep day. The “sleep balance” contributor may refer to a cumulative duration of all sleep periods within the sleep day. In particular, sleep balance may indicate to a user whether the sleep that the user has been getting over some duration of time (e.g., the past two weeks) is in balance with the user's needs. Typically, adults need 7-9 hours of sleep a night to stay healthy, alert, and to perform at their best both mentally and physically. However, it is normal to have an occasional night of bad sleep, so the sleep balance contributor takes into account long-term sleep patterns to determine whether each user's sleep needs are being met. The “resting heart rate” contributor may indicate a lowest heart rate from the longest sleep period of the sleep day (e.g., primary sleep period) and/or the lowest heart rate from naps occurring after the primary sleep period.
Continuing with reference to the “contributors” (e.g., factors, contributing factors) of the Readiness Score, the “HRV balance” contributor may indicate a highest HRV average from the primary sleep period and the naps happening after the primary sleep period. The HRV balance contributor may help users keep track of their recovery status by comparing their HRV trend over a first time period (e.g., two weeks) to an average HRV over some second, longer time period (e.g., three months). The “recovery index” contributor may be calculated based on the longest sleep period. Recovery index measures how long it takes for a user's resting heart rate to stabilize during the night. A sign of a very good recovery is that the user's resting heart rate stabilizes during the first half of the night, at least six hours before the user wakes up, leaving the body time to recover for the next day. The “body temperature” contributor may be calculated based on the longest sleep period (e.g., primary sleep period) or based on a nap happening after the longest sleep period if the user's highest temperature during the nap is at least 0.5° C. higher than the highest temperature during the longest period. In some aspects, the ring may measure a user's body temperature while the user is asleep, and the system 200 may display the user's average temperature relative to the user's baseline temperature. If a user's body temperature is outside of their normal range (e.g., clearly above or below 0.0), the body temperature contributor may be highlighted (e.g., go to a “Pay attention” state) or otherwise generate an alert for the user.
In some aspects, the system 200 may support techniques for a pregnancy mode profile configuration in accordance with multiple operational modes of the user and/or wearable device 104. For example, the wearable device 104 may acquire physiological data from a user, including heart rate data, motion data, temperature data, and the like. During a non-pregnant operational mode, the system may provide health-related guidance to the user based on the user's physiological activity and overall health, including sleep targets (e.g., overall sleep goals, REM sleep goals), sleep messages (e.g., messages encouraging users to reach their sleep targets, messages congratulating users for reaching their sleep targets), physical activity targets (e.g., step goals, calorie goals, standing goals) and normal activity messages (e.g., messages encouraging users to reach their physical activity targets, messages congratulating users for reaching their physical activity targets).
Continuing with the same example, the system 200 may identify a trigger to transition from the non-pregnant operational mode to a different operational mode, such as a pregnancy mode. For example, the system 200 may identify that the user is pregnant, and may therefore identify a trigger to transition from the non-pregnant operational mode to the pregnancy operational mode. The pregnancy operational mode may be configured to adjust the sleep targets, activity targets, and the like, and may therefore be associated with adjusted sleep targets and physical activity targets and related messages (e.g., messages encouraging the user to rest or take a nap). As such, upon transitioning to the pregnancy mode, the system 200 may tailor sleep targets, sleep messages, physical activity targets, and activity messages to help facilitate the user's pregnancy and allow the user to prepare for the different stages of pregnancy.
In some cases, the system 200 may identify the trigger to switch between operational modes (e.g., switch from the non-pregnant mode to pregnancy mode, and vice versa) based on user inputs received from the user. For example, the user may input that they received a positive pregnancy test or that they are pregnant via the user device 106. Additionally, or alternatively, the system 200 may automatically identify triggers for switching between operational modes. For example, the system 200 may detect that the user is pregnant based on physiological data acquired from the wearable device 104 (e.g., increased temperature, increased heart rate, etc.). As such, the system 200 may automatically switch between operational modes and/or prompt the user to confirm or deny a switch between operational modes.
For example, as noted previously herein, the ring 104 of the system 200 may be worn by a user to collect data from the user, including temperature, heart rate, respiratory data, HRV data, and the like. The ring 104 of the system 200 may collect the physiological data from the user based on temperature sensors and measurements extracted from arterial blood flow (e.g., using PPG signals). The physiological data may be collected continuously. In some implementations, the processing module 230-a may sample the user's temperature continuously throughout the day and night. Sampling at a sufficient rate (e.g., one sample per second or one sample per minute) throughout the day and/or night may provide sufficient temperature data for analysis described herein. In some implementations, the ring 104 may continuously acquire temperature data (e.g., at a sampling rate). In some examples, even though temperature is collected continuously, the system 200 may leverage other information about the user that it has collected or otherwise derived (e.g., sleep stage, activity levels, illness onset, etc.) to select a representative temperature for a particular day that is an accurate representation of the underlying physiological phenomenon.
In contrast, systems that require a user to manually take their temperature each day and/or systems that measure temperature continuously but lack any other contextual information about the user may select inaccurate or inconsistent temperature values for their pregnancy detection, leading to inaccurate detections and decreased user experience. In contrast, data collected by the ring 104 may be used to accurately determine when the user is pregnant and configure the pregnancy mode profile configuration.
As described previously herein, the system 200 may support any number of operational modes, where each individual operational mode may be associated with a set of sleep targets and/or sleep messages, physical activity targets and/or set of activity messages, and the like which are tailored to the respective operational mode. Moreover, in some implementations, a single operational mode may include multiple sets of sleep targets and/or sleep messages, physical activity targets and/or multiple sets of activity messages, and the like, where the system 200 may be configured to select between the respective sets of sleep targets and/or sleep message, activity targets and/or sets of activity messages, and the like based on one or more parameters or characteristics, including acquired physiological data, manual user inputs, and reasons/motivations/causes for the system 200 operating in the respective operational mode.
For example, the system 200 may operate in a “pregnancy mode” in cases where the user is pregnant as well as in cases where the user is experiencing a pregnancy complication or experiencing a miscarriage. In such cases, the system 200 may utilize different sets of sleep targets/sleep messages, activity targets/activity messages, and the like for the user when they are pregnant as compared to when the user is experiencing a miscarriage (e.g., activity targets may be higher when the user is in pregnancy mode due to a pregnancy as compared to activity targets for when the user is in pregnancy mode due to a miscarriage). In other words, the system 200 may utilize different subsets of sleep targets/sleep message, activity targets/activity messages, and the like associated with a given operational state based on a “cause,” or motivation, for the user/system 200 operating within the respective operational state.
Transitions between operational modes may be further shown and described with reference to
At 305, an application (e.g., wearable application) may operate in a non-pregnant mode according to normal (e.g., non-pregnant) parameters, such as normal sleep and activity parameters that promote sleep and activity in a healthy individual. For example, while operating in the non-pregnant mode (e.g., first operational mode), the system may provide a first set of targets associated with one or more physiological metrics and a first set of messages associated with the one or more physiological metrics to the user in accordance with the non-pregnant mode of an application associated with a wearable device of the user. In some examples, the system may provide a first set of sleep and/or activity targets and a first set of sleep and/or activity messages to the user in accordance with the non-pregnant mode of an application associated with a wearable device of the user. The one or more physiological metrics may include sleep metrics, activity metrics, Readiness metrics, or a combination thereof.
At 310, the application may determine whether to transition from the non-pregnant mode to the pregnancy mode. In other words, the application may identify a trigger (or lack thereof) to switch from a first operational mode (e.g., non-pregnant mode) to a second operational mode (e.g., pregnancy mode). For example, the application may determine whether to transition to the pregnancy mode based on user input and/or measured physiological parameters that indicate the user has transitioned (or is expected to transition) to a pregnant state where the user is pregnant. In this regard, triggers for transitioning between operational states may be identified based on manual user inputs, automatically identified based on acquired data, or both. For example, the system may receive, via the user device, a user input including an indication that the user is pregnant and identify the trigger to transition to the pregnancy mode in response to receiving the indication.
In some cases, the system may receive physiological data including at least temperature data. In such cases, the system may identify that the temperature data satisfies a temperature threshold for the user. In such cases, the system 200 may identifying the trigger to transition from the non-pregnant mode to the pregnancy mode in response to the temperature data satisfying the temperature threshold for the user.
At 315, the application may operate in the pregnancy mode according to pregnancy parameters, such as an increased set of sleep parameters and/or reduced/eliminated set of physical activity parameters. For example, while operating in the pregnancy mode (e.g., second operational mode), the system may provide a second set of targets associated with the one or more physiological metrics and a second set of messages associated with the one or more physiological metrics to the user in accordance with the pregnancy mode. In some examples, the second set of targets may be configured to encourage sleep, where the second set of messages are configured to promote or encourage the user to meet the second set of targets. The second set of targets and second set of messages may be adjusted for pregnancy and different from the first set of targets and the first set of messages based on the pregnancy mode. In some implementations, targets associated with the one or more physiological metrics during the pregnancy mode may be increased relative to the targets associated with the one or more physiological metrics in the non-pregnant mode.
In such cases, a pregnancy mode may adjust other health-related expectations and algorithms used to calculate scores for a user. For example, a pregnancy mode may adjust expectations associated with an amount/type of sleep a pregnant user should get, and adjust expectations associated with other physiological parameters, such as respiration rate, resting heart rate, body temperature, and the like. In this regard, by adjusting expectations associated with physiological parameters for pregnant users, the system may more accurately calculate scores (e.g., Activity Scores, Sleep Scores, Readiness Scores) based on normal, expected physiological responses experienced by pregnant users.
In some examples, the system may provide a second set of activity targets and a second set of activity messages that may be configured to encourage rest and promote or encourage the user to meet the second set of activity targets. In some implementations, physical activity targets during the pregnancy mode may be reduced relative to the physical activity targets in the non-pregnant mode. For instance, a pregnancy mode may reduce activity intensity expectations, but may increase movement reminders provided to the user, which may better align with physical expectations for a pregnant user. Additionally, or alternatively, physical activity targets may be silenced, turned off, or otherwise deactivated during the pregnancy mode to encourage the user to rest.
At 320, the application may determine whether to transition from the pregnancy mode to the postpartum mode. In other words, the system may identify a trigger (or lack thereof) to transition from the second operational mode (e.g., pregnancy mode) to a third/intermediate operational mode (e.g., postpartum mode). For example, the application may determine whether to transition based on user input and/or measured physiological parameters that indicate the user has given birth. In this example, the postpartum mode may include an intermediate operational mode between the pregnancy mode and the non-pregnant mode (e.g., the system transitions from the pregnancy mode to the postpartum mode before transitioning from the postpartum mode to the non-pregnant mode). In some cases, the system may transition from the pregnancy mode to the postpartum mode based on identifying the second trigger. The second trigger may be an example of a user indication that the user is in a postpartum state. For example, the system may receive, via the user device, a user input comprising an indication that the user is in a postpartum state.
At 325, the application may operate in the postpartum mode according to postpartum parameters, such as sleep levels that increase or decrease from the pregnancy mode and activity levels that increase or decrease from the pregnancy mode toward the normal mode sleep and activity levels, respectively. For example, while operating in the postpartum mode (e.g., third/intermediate operational mode), the system may provide a third set of targets associated with the one or more physiological metrics and a third set of messages associated with the one or more physiological metrics to the user in accordance with the postpartum mode. In some examples, the third set of targets may be configured to encourage sleep, where the third set of messages are configured to promote or encourage the user to meet the third set of targets. The third set of targets and third set of messages may be adjusted for postpartum and different from the second set of targets and the second set of messages based on the postpartum mode.
In some examples, while operating in the postpartum mode (e.g., third/intermediate operational mode), the system may provide a third set of activity targets and a third set of activity messages to the user in accordance with the postpartum mode. In this example, the third set of activity targets may be configured to encourage recovery, where the third set of activity messages are configured to promote or encourage the user to meet the third set of activity targets.
In such cases, the system may support additional or alternative operational modes associated with a pregnancy mode, including operational modes which help guide a user back to their normal activity levels and physiological parameters following pregnancy (e.g., during postpartum). As such, the system may support one or more intermediary modes between a pregnancy mode and a normal/non-pregnant mode, including a post-natal recovery mode, a post-natal ramp-up mode, and the like. For example, a post-natal recovery mode may tailor guidance provided to the user which is intended to facilitate rest and recovery in order to help the user recover from pregnancy.
At 330, the application may determine whether to transition from the postpartum mode to the non-pregnant mode. In other words, the system may identify a trigger (or lack thereof) to transition from the third/intermediate operational mode (e.g., postpartum mode) back to the first operational mode (e.g., non-pregnant mode). For example, the application may determine whether to transition based on user input, a length of the postpartum mode (e.g., a duration of time spent in the third operational mode), a length of the pregnancy mode (e.g., a duration of time spent in the second operational mode), and/or measured physiological parameters included within the received physiological data that indicate the user is no longer pregnant.
In such cases, the system may identify the trigger (e.g., third trigger) based on a duration of time spent in the second operational mode, a duration of time spent in the third operational mode, measured physiological parameters included within the received physiological data collected via the wearable device that indicate the user is no longer pregnant, scores for the user, or a combination thereof. In general, the system may transition from the postpartum mode to the non-pregnant mode based on identifying that the user is no longer pregnant (e.g., based on physiological data and/or scores).
In some implementations, in addition to providing different sets of Sleep Scores/sleep message and/or Activity Scores/activity messages to users based on the corresponding operational states, the system may be configured to calculate scores for the user (e.g., Sleep Score, Readiness Score, Activity Score) differently while operating in accordance with the respective operational states. For example, in some cases, the system may calculate scores (e.g., Sleep Score, Readiness Score, Activity Score) using a first algorithm (or first set of weights) while operating in the first operational state, and may calculate scores using a second algorithm (or second set of weights) while operating in the second operational state.
For instance, a first algorithm for score calculation associated with anon-pregnant operational mode may result in a decrease in a user's Readiness Score if the user takes a nap late in the evening. Comparatively, a second algorithm for score calculation associated with a pregnancy operational mode may result in an increase in the user's Readiness Score if the user takes a nap in the late evening. This is consistent with the pregnancy mode prioritizing rest and sleep for the user. In some cases, the system may calculate scores using a third algorithm (or third set of weights) while operating in the third operational mode.
As shown in
Additionally, the system may be configured to provide a set of sleep and activity messages to the user, where the sleep and activity messages are associated with (e.g., correspond to) the non-pregnant mode 405-a. In other words, the system may provide non-pregnant messaging 420 to the user, where the non-pregnant messaging 420 includes messages that promote the set of sleep targets and activity targets associated with the non-pregnant mode 405-a. The non-pregnant messaging 420 may include messages associated with the respective scores.
For example, a message associated with the user's Activity Score 415-a may include: “Keep yourself active throughout the day, balance between training and recovery days.” By way of another example, a message associated with the user's Readiness Score 415-b may include: “Balance activity and rest, push your boundaries when you're ready,” where a message associated with the user's Sleep Score 415-c may include: “Sufficient and consistent sleep is the key to good readiness.” In some cases, the system may determine, during a first time interval corresponding to the first operational mode (e.g., non-pregnant mode 405-a), one or more scores associated with the user using a first algorithm and based on the received physiological data.
Continuing with reference to the process flow 400, the system may detect a change in one or more physiological parameters for the user at 425 (e.g., changes in biosignal data). For example, the system may detect elevated body temperature, or elevated resting heart rate. In such cases, the system may determine an automatic pregnancy mode trigger 430 (e.g., a trigger which is not based on a user input). As such, the system may toggle pregnancy mode on at 435 (e.g., transition from the non-pregnant mode 405-a to the pregnancy mode 405-b) in response to the automatic pregnancy mode trigger 430. In additional or alternative cases, the system may receive subjective user feedback 440. For example, a user may input (e.g., via the user device) one or more messages or “tags” which indicate that the user is pregnant. Manual user inputs may enable the system to identify triggers for switching between operational states in cases where the user is pregnant, but where acquired physiological data has not changed significantly. In such cases, the system may identify a manual pregnancy mode trigger 445 (e.g., a trigger based on a manual user input). As such, the system may toggle pregnancy mode on at 435 (e.g., transition from the non-pregnant mode 405-a to the pregnancy mode 405-b) in response to the manual pregnancy mode trigger 445. In some cases, the system may toggle pregnancy mode on at 435 based on receiving a pregnancy status from an application associated with the system. For example, if the user is using a fertility tracking application and acknowledges pregnancy on the fertility tracking application, the system may receive a notification from the fertility tracking application that the user is pregnant. In such cases, inputs from other applications may enable the system to identify triggers for switching between operational states in cases where the user is pregnant.
Upon activating the pregnancy mode 405-b, the system may adjust sleep targets and/or sleep messages, activity targets and/or activity messaging, and the like which may be provided to the user. Additionally, or alternatively, the system may adjust how it calculates scores for the user (e.g., switch to a different algorithm for calculating Sleep Scores, Readiness Scores, Activity Scores, etc.). For example, the system may modify and/or disable activity goals, contributors, and/or Activity Score calculation at 450. Similarly, the system may modify Readiness Score contributors and insights at 455, and may modify sleep insights at 460. Subsequently, the system may provide messaging to the user at 465, where the messaging (e.g., messages associated with the one or more physiological metrics) are associated with the pregnancy mode 405-b. That is, the system may provide messaging which promotes sleep, activity, and the like based on performing the actions at 450, 455, and 460. In some cases, the system may determine, during a second time interval corresponding to the first operational mode (e.g., pregnancy mode 405-b), one or more scores associated with the user being pregnant and using a second algorithm different than the first algorithm and based on the received physiological data.
The messaging for pregnancy mode 465 may include messages intended to promote rest during the pregnancy mode 405-b, and may be based on the user's respective scores. For example, a message associated with the user's Activity Score while in the pregnancy mode 405-b may include: “Concentrate on rest.” By way of another example, a message associated with the user's Readiness Score while in pregnancy mode 405-b may include: “Concentrate on rest and recovery to achieve your peak readiness,” where a message associated with the user's Sleep Score while in the pregnancy mode 405-b may include: “All rest is good rest.”
Subsequently, the system may toggle pregnancy mode off at 470. In other words, the system may identify a trigger to transition from the pregnancy mode 405-b to another operational mode (e.g., non-pregnant mode 405-a, postpartum mode 405-c). As described previously herein, the system may toggle pregnancy mode 405-b off at 470 based on identifying a trigger, where the trigger may be based on a user input (e.g., manual user input) and/or automatically identified based on received physiological data and/or calculated scores.
Upon activating the postpartum mode 405-c, the system may adjust sleep targets and/or sleep messaging, activity targets and/or activity messaging, and the like which may be provided to the user. Additionally, or alternatively, the system may adjust how it calculates scores for the user (e.g., switch to a different algorithm for calculating Sleep Scores, Readiness Scores, Activity Scores, etc.). For example, the system may gradually normalize Sleep Score (e.g., Sleep Score calculation), sleep goals, and/or sleep contributors at 485. Similarly, the system may gradually normalize Readiness Score (e.g., Readiness Score calculation), readiness contributors, and readiness insights at 480, and may gradually normalize activity insights at 475.
The system may be configured to provide messaging to the user at 490, where the messaging (e.g., sleep messages, activity messages, and the like) are associated with the postpartum mode 405-c. That is, the system may provide messaging which promotes rest and recovery based on performing the actions at 475, 480, and 485. For example, a message associated with the user's Activity Score while in the postpartum mode 405-c may include: “Start easy.” By way of another example, a message associated with the user's Readiness Score while in postpartum mode 405-c may include: “Keep taking it easy, but you can start with light activities,” where a message associated with the user's Sleep Score while in the postpartum mode 405-c may include: “Keep paying attention to rest and sleep.”
In some cases, the system may be configured to provide messaging to the user at 490, where the messaging is adjusted based on the user's health and/or fitness status prior to and/or at the time of getting pregnant. For example, the system may provide messaging which promotes continued exercise and activity for users who were active pre-pregnancy. In some examples, a message associated with a user who was less active or inactive pre-pregnancy may promote the user to not increase their activity. In such cases, the system may provide activity training guidance based on the user's health and/or fitness status, previous life-style habits, activity trends or a combination thereof.
Subsequently, the system may toggle postpartum mode 405-c off and return to non-pregnant mode 405-a at 495. In other words, the system may identify a trigger to transition from the postpartum mode 405-c to another operational mode (e.g., non-pregnant mode 405-a). As described previously herein, the system may toggle postpartum mode 405-c off and switch to non-pregnant mode 405-a at 495 based on identifying a trigger, where the trigger may be based on a user input (e.g., manual user input) and/or automatically identified based on received physiological data and/or calculated scores.
For the purposes of the present disclosure, the term “pregnancy program” may be used to refer to a longer-term health/pregnancy program associated with a user that is trying to become pregnant, is pregnant, or was previously pregnant. Example pregnancy programs may include but are not limited to: a sleep program, an exercise training program, a nutrition program, and the like, which may be tailored to the unique needs of pregnancy. Each program may also include additional programs (e.g., sub-category programs). Pregnancy programs may be used to determine targets associated with the one or more physiological metrics and related messaging associated with the one or more physiological metrics for the user that may take into account the additional needs during pregnancy or at certain times throughout pregnancy. In some cases, pregnancy programs may be used to determine sleep targets, related sleep messaging, activity targets, and related activity messaging for the user during or after pregnancy.
In some implementations, operational modes may be used to selectively modify targets/messaging associated with the one or more physiological metrics within each respective program. For example, a user may be actively engaged in a sleep training program, and the system may selectively modify sleep targets/messaging provided to the user throughout the sleep training program as the system transitions between different operational modes (e.g., non-pregnant mode, pregnancy mode, postpartum mode) throughout the duration of the sleep training program.
The process flow 500 illustrates a control module 505 which may be implemented via one or more components of the system (e.g., wearable device, user device, servers). The control module 505 may include or support multiple operational modes, such as a non-pregnant mode, a pregnancy mode, and a postpartum mode. The non-pregnant, pregnancy, and postpartum modes are only example modes. As such, other implementations may include different and/or additional operational modes. For example, other implementations may include four operational modes: a healthy mode, an acute health condition mode, a postpartum from an acute health condition mode, and a chronic health condition mode. The two or more programs/operational modes may be centrally controlled by the central controlling block (e.g., control module 505). The control module 505 (e.g., with three operational modes) may set rules for the respective operational modes/pregnancy programs. The effect of the prevailing operational mode (e.g., pregnancy mode, postpartum mode, non-pregnant mode) may be visible in several different pregnancy programs/operational modes simultaneously (e.g., by modifying targets and messages given to users).
For example, as shown in the process flow 500, a first pregnancy program may be associated with a first set of targets associated with the one or more physiological metrics and a first set of messages associated with the one or more physiological metrics, where a second pregnancy program may be associated with a second set of targets associated with the one or more physiological metrics and a second set of messages associated with the one or more physiological metrics. In such cases, upon identifying a transition between operational modes, the system (e.g., control module 505) may selectively modify the respective targets and/or messages of the respective pregnancy programs based on the operational state. The modified targets and/or modified messages for the respective pregnancy program(s) may then be provided to the user (e.g., via the user device).
A pregnancy program may encourage regular sleep scheduling and taking naps. In some implementations, when the pregnancy mode is activated for a respective pregnancy program, the control module 505 may set a rule for maximum training amounts or intensity for the pregnancy program. Additionally, the control module 505 may also credit the user for actions (e.g., other than training) that enhance recovery, such as taking naps. In a specific example, the system may multiply the numeric daily activity target to be 50-100% of the normal, and increase recommendation for relaxing activities by 100%. The control module 505 may be configured to select and/or modify different message types and control the presentation of messages for different activities within a respective pregnancy program. For example, the control module 505 may not show a message about negative long-term effects of naps, but instead may show a message about their immediate positive effects. Accordingly, in some implementations, the control module 505 may increase the targeted amount of sleep and increase the priority/occurrence rate of positive messages related to sleep and recovery contributions when operating in the pregnancy or postpartum modes (e.g., regardless of the activated pregnancy program).
In some implementations, the pregnancy mode may be activated based on measured signals, such as elevated temperature, elevated breathing rate, resting heart rate, decreased HRV, or the like. In some implementations, the pregnancy mode may be activated in response to user input, such as user input that indicates pregnancy. In some implementations, pregnancy mode may enable a follow-up of symptoms (e.g., using specific tags).
Pregnancy mode may transition to postpartum mode. During postpartum mode, the rules/settings may gradually be returned to non-pregnant mode. For example, the rules may change by x %/day until the non-pregnant level is reached (e.g., until the difference <10%).
Pregnancy mode may transition to postpartum mode in a variety of ways. For example, pregnancy mode may transition to postpartum mode in response to a default time, such as a default elapsed time (e.g., 12 weeks) and/or a specified future date. As another example, pregnancy mode may transition to postpartum mode when measured parameters have returned to normal. In some cases, the control module 505 may add on a set period of time (e.g., 12 weeks) after measured parameters have returned to normal. As another example, pregnancy mode may transition to postpartum mode in response to user input, such as a manual input that indicates the user has given birth (e.g., that the user is in a postpartum state). As another example, pregnancy mode may transition to postpartum mode when a health alert/risk indicator has disappeared. In some cases, the control block may add on a set period of time (e.g., 3 days) after the health alert/risk indicator has disappeared.
The length of postpartum mode may be calculated based on a variety of factors. In some implementations, the length of the postpartum mode may be based on the length of pregnancy mode, a type of pregnancy (e.g., whether there were pregnancy complications as indicated through measured physiological parameters or user-input tags), a type of birth (e.g., vaginal versus cesarian), or any combination of these factors. In some examples, the length of the postpartum mode may be set to a multiple of the length of the pregnancy mode (e.g., ⅓ of the pregnancy mode). In this example, the time multiplier may be age-dependent, where older people may have a larger multiplier (e.g., an increased recovery time). For example, a 20-year-old user may have postpartum time=⅓ of pregnancy time. As another example, a 40-year-old user may have postpartum time=½ of pregnancy time. In some implementations, the time multiplier may be based on measured physiological values (e.g., temperature, heart rate, HRV, respiratory rate, etc.).
As described in further detail herein, the system may be configured to generate a pregnancy mode profile configuration. In some cases, the user's sleep efficiency pattern throughout the night may be an indicator that may characterize pregnancy (e.g., enable the configuration of the pregnancy mode profile). For example, sleep efficiency during the night may identify the indication of pregnancy. As such, the timing diagram 600 illustrates a relationship between a user's sleep efficiency and a time (e.g., over a plurality of weeks and/or months). In this regard, the solid line illustrated in the timing diagram 600 may be understood to refer to the “sleep data 605.” The user's sleep data 605 may be an example of the sleep efficiency. The dashed vertical lines illustrated in the timing diagram 600 may be understood to refer to the “pregnancy onset 610” and “indication of birth 620.” The dotted vertical lines illustrated in the timing diagram 600 may be understood to refer to the “trimester indication 615.”
In some cases, the system (e.g., ring 104, user device 106, server 110) may receive physiological data associated with a user from a wearable device. The physiological data may include at least sleep data 605. The system may determine a time series of the sleep data 605 taken over a plurality of days based on the received sleep data 605. The system may process original time series sleep data 605 in response to receiving the sleep data 605.
The sleep data 605 may be continuously collected by the wearable device. The physiological measurements may be taken continuously throughout the night. For example, in some implementations, the ring may be configured to acquire physiological data (e.g., temperature data, sleep data, heart rate, HRV data, respiratory rate data, MET data, and the like) continuously in accordance with one or more measurement periodicities throughout the entirety of each day/sleep day. In other words, the ring may continuously acquire physiological data from the user without regard to “trigger conditions” for performing such measurements.
The timing diagram 600 may illustrate a sleep trajectory for a user whose pregnancy lasted full term (e.g., 40 weeks). For example, the timing diagram 600 may illustrate that the sleep data 605 at the pregnancy onset 610 may be relatively the same (e.g., similar to) the sleep data 605 prior to pregnancy onset 610 and after pregnancy onset 610 (e.g., at the second trimester indication 615-a, the third trimester indication 615-b, or both). The pregnancy may span from the pregnancy onset 610 to the indication of birth 620. The first trimester may span from the pregnancy onset 610 to the second trimester indication 615-a. The second trimester may span from the second trimester indication 615-a to the third trimester indication 615-b. The third trimester may span from the third trimester indication 615-b to the indication of birth 620. The postpartum period may begin at the indication of birth 620.
In some cases, the sleep data 605 may decrease between the third trimester indication 615-b and the indication of birth 620. In such cases, the sleep data 605 between the third trimester indication 615-b and the indication of birth 620 may be representative of a local minimum. The system may identify one or more local minimum of the time series of the sleep data 605 based on determining the time series. The sleep data 605 may then increase between the third trimester indication 615-b and the indication of birth 620 such that the sleep data 605 may be greater than the local minimum. The sleep data 605 at the indication of birth 620 may be less than the sleep data 605 at the pregnancy onset 610. The timing diagram 600 may be used by the system for sleep training and/or sleep coaching, as described with reference to
In some implementations, the user may experience an increased amount of total time awake overnight (e.g., during sleep) throughout the first trimester and the third trimester. For example, the system may identify that the total time awake during a sleep period at the pregnancy onset 610 is less than the total time awake during the sleep period after pregnancy onset 610 (e.g., during the first trimester, the third trimester, or both).
In some cases, the user may experience an increased amount of total REM sleep throughout the second trimester and the third trimester. In such cases, the system may identify that the total REM sleep at the pregnancy onset 610 is less than the total REM sleep after pregnancy onset 610 (e.g., during the second trimester, the third trimester, or both). In some cases, the system may identify the total REM sleep during the first trimester is less than the total REM sleep at pregnancy onset 610. In such cases, the total REM sleep during the first trimester may be less than the total REM sleep during the second trimester, the third trimester, or both.
The user may experience a decreased amount of total time asleep throughout the second trimester and the third trimester. For example, the system may identify that the total time asleep during the first trimester may be greater than the total time asleep during the second trimester, the third trimester, or both. In some cases, the system may identify that the total time asleep at the pregnancy onset 610 may be less than the total time asleep during the first trimester. In such cases, the user may experience an increased amount of total time asleep after the pregnancy onset 610 and throughout the first trimester and then may experience a decreased amount of total time asleep after the first trimester and throughout the second trimester and the third trimester.
During the user's pregnancy, the amount of time awake during the sleep period, amount of total REM sleep, the amount of total time asleep, or a combination thereof may be used by the system for sleep training and/or sleep coaching, as described with reference to
The GUI 700 may illustrate application pages 705 which may be displayed to a user. For example, GUI 700 illustrates an application page 705 which displays measured biosignal data (e.g., acquired physiological data), which may be used to trigger a switch between pregnancy programs and/or operational states. In some cases, the system may be configured to determine normal or baseline levels of physiological parameters for the user (e.g., baseline physiological data). In such cases, the system may be configured to identify significant deviations from the normal/baseline levels to act as triggers to switch between operational modes of the system, as described herein.
In some examples, the GUI 700 illustrates a series of application pages 705 which may be displayed to a user via the GUI 700. The server of the system may cause the GUI 700 of the user device (e.g., mobile device) to display inquiries of whether the user activates the period mode and wants to track their period (e.g., via application page 705-a). In such cases, the system may generate a personalized cycle tracking experience on the GUI 700 of the user device to detect an indication of pregnancy and track the user's menstrual cycle based on the contextual tags and user questions.
Continuing with the examples above, prior to detecting the indication of the pregnancy, the user may be presented with an application page 705-a upon opening the wearable application. The application page 705-a may display a request to activate the period mode and enable the system to track the menstrual cycle (e.g., thereby enabling the detection of a pregnancy). In such cases, the application page 705-a may display an invitation card where the users are invited to enroll in the menstrual cycle tracking applications. The application page 705-a may display a prompt to the user to verify whether the menstrual cycle may be tracked or dismiss the message if the menstrual cycle is not tracked. The system may receive an indication of whether the user selects to opt-in to tracking the menstrual cycle or opt-out to tracking the menstrual cycle.
The user may be presented with an application page 705-a upon selecting “yes” to tracking the menstrual cycle. The application page 705-a may display a prompt to the user to verify the main reason to track the cycle (e.g., period, ovulation, pregnancy, etc.). In such cases, the application page 705-a may prompt the user to confirm the intent of tracking the menstrual cycle. For example, the system may receive, via the user device, a confirmation of the intended use of the tracking system.
In some cases, the user may be presented with an application page 705-a upon confirming the intent. The application page 705-a may display a prompt to the user to verify the average cycle length (e.g., duration between a first day of a first menstrual cycle and a first day of a second menstrual cycle). In some cases, the application page 705-a may display a prompt to the user to indicate whether the user experiences irregular cycles in which an average cycle length may not be determined. For example, the system may receive, via the user device, a confirmation of the average cycle length, cycle regularity, or both.
The user may be presented with an application page 705-a upon inputting the average cycle length or irregular cycle. The application page 705-a may display a prompt to the user to verify the last cycle start date (e.g., a first day of the most recent menstrual cycle). The application page 705-a may display a prompt to the user to indicate whether the user may be unable to identify the last cycle start date. For example, the system may receive, via the user device, a confirmation of the last cycle start date.
In some cases, the user may be presented with an application page 705-a upon confirming the last cycle start date. The application page may display a prompt to the user to verify whether the user uses hormonal contraceptives in use. For example, the system may receive, via the user device, a confirmation of whether hormonal contraceptives are in use.
In cases where the user dismisses the prompt on application page 705-a, the prompt may disappear, and the user may input an indication of pregnancy. In some cases, the system may display via message a prompt asking the user if the user is pregnant or suggests switching to an alternative mode (e.g., pregnancy mode, rest mode) or deactivating period mode. In such cases, the system may recommend the user switch from period mode to a pregnancy mode based on detecting the indication of pregnancy.
In some implementations the server of the system may cause the GUI 700 of the user device (e.g., mobile device) to display inquiries of whether the user activates the pregnancy mode and wants to track their pregnancy (e.g., via application page 705-b). In such cases, the system may generate a personalized cycle tracking experience on the GUI 700 of the user device to track the pregnancy based on the contextual tags and user questions.
Continuing with the examples above, the user may be presented with an application page 705-b upon opening the wearable application. The application page 705-b may display a request to activate the pregnancy mode and enable the system to track the pregnancy. In such cases, the application page 705-b may display an invitation card where the users are invited to enroll in the pregnancy tracking applications. The application page 705-b may display a prompt to the user to verify whether the pregnancy may be tracked or dismiss the message if the pregnancy is not tracked. The system may receive an indication of whether the user selects to opt-in to tracking the pregnancy or opt-out to tracking the pregnancy.
The user may be presented with an application page 705-b upon selecting “yes” to tracking the pregnancy. The application page 705-b may display a prompt to the user to verify the main reason to track the pregnancy (e.g., overall health, pregnancy complications, sleep, activity, etc.). In such cases, the application page 705-b may prompt the user to confirm the intent of tracking the pregnancy. For example, the system may receive, via the user device, a confirmation of the intended use of the tracking system.
In some cases, the user may be presented with an application page 705-b upon confirming the intent. The application page 705-b may display a prompt to the user to verify the date of a user's last menstrual cycle, an estimated date of conception, a date of a first positive pregnancy test, an estimated due date, or a combination thereof. For example, the system may receive, via the user device, a confirmation of the user's last menstrual cycle, an estimated date of conception, a date of a first positive pregnancy test, an estimated due date, or a combination thereof. In some cases, the application page 705-b may display a prompt to the user to indicate whether the user may be unable to identify the user's last menstrual cycle, an estimated date of conception, a date of a first positive pregnancy test, an estimated due date, or a combination thereof.
In some cases, the user may be presented with an application page 705-b upon confirming the information. The application page may display a prompt to the user to verify whether the user experiences one or more pregnancy complications. For example, the system may receive, via the user device, a confirmation of whether the user experience pregnancy complications.
In some implementations, the server of system may cause the GUI 700 of the user device (e.g., mobile device) to display the indication of pregnancy (e.g., via application page 705-b). In such cases, the system may output the detected indication of pregnancy on the GUI 700 of the user device to indicate that the user is pregnant and/or experiencing a first day pregnancy.
In cases where a user's pregnancy may be identified, as described herein, the server may transmit a message 820 to the user, where the message 820 is associated with the pregnancy for the user. In some cases, the server may transmit a message 820 to a clinician, a fertility specialist, a care-taker, a partner of the user, or a combination thereof. In such cases, the system may present application page 805-a on the user device associated with the clinician, the fertility specialists, the care-taker, the partner, or a combination thereof.
Messages 820 may be modified during pregnancy mode. In some implementations, pregnancy mode may include custom messages. For example, pregnancy mode may feature a custom set of daily messages 820 that are designed to guide the users to shift their focus to certain stages of their pregnancy. During the pregnancy mode messaging period, the system may highlight metrics than may react to strain, such as resting heart rate, HRV, body temperature, sleep efficiency, and total sleep time.
For example, the user may receive message 820, which may include a request to input symptoms associated with the pregnancy, educational content associated with the pregnancy, an adjusted set of sleep targets, an adjusted set of activity targets, and the like. For example, the message 820 may indicate a date of conception (e.g., estimated conception date is April 28th), a range of dates of likely conception (e.g., estimated conception date April 27-29th), a due date (e.g., January 25th), a range of dates of the predicted due date (e.g., estimated due date January 21-29th), or a combination thereof. In such cases, the range may include the day of the estimated date and the day before and after the estimated date. The messages 820 may be configurable/customizable, such that the user may receive different messages 820 based on the pregnancy, as described previously herein.
For example, the message 820 may include trimester-specific physiological insights associated with pregnancy, a recommended wake time during which the user wakes up, a recommended bedtime during which the user goes to sleep, a recommended sleep duration, a recommended time of day during with the user rests, or a combination thereof. In such cases, pregnancy mode may provide trimester-specific physiology insights. For example, the first trimester insights may include that many users experience a rise in their nighttime temperature and breath rate during the first trimester. In some cases, a change heart rate and HRV may be identified during the first trimester. The second trimester insights may include that many users may experience a decrease in their nighttime temperature and breath rate during the second trimester relative to the first trimester. In some cases, an increase in heart rate and HRV during the second trimester may be identified. The third trimester insights may include that many users may experience a decrease in their nighttime temperature and breath rate during the third trimester relative to the second trimester. In some cases, an increase in heart rate and HRV during the third trimester may be identified.
In some cases, the messages 820 may include physiological trends associated with the pregnancy. For example, the physiological trends may include an increase in the user's heart rate throughout pregnancy, an increase in temperature throughout pregnancy, an increase in breath rate throughout pregnancy, or a combination thereof.
In such cases, in pregnancy mode, the user's personalized messaging 820, Readiness Score, illness detection, activity targets, sleep coaching/insights, symptom tags, and user experience may be better adjusted for or tailored to the week/trimester of their pregnancy. The outputs may be driven by algorithms specific to an understanding of pregnancy and may be personalized to the particular pregnancy of a given user. In some implementations, the tags surfaced to users to indicate symptoms or contextual experiences may differ based on the user's indication that the user is pregnant and has an interest in pregnancy mode.
In some implementations, specific recommendations for comfortable sleep and activity may be gauged to the phases of pregnancy. In some implementations, continuous skin temperature data collected by the device(s) (e.g., a ring) may carry information that reflects aspects of immune, metabolic, and hormonal function relevant to pregnancy. This information may be employed to detect and predict pregnancy-related events, including pregnancy losses. An algorithm may take into account deviations from a user's own prior history as well as deviations from a typical pregnancy profile to surface tags and messages 820 indicating that deviations are occurring. For example, messages 820 may include “Your temperature is higher/lower than usual” and “Have you discussed this with your physician?” The example tags may include “cramps,” “fever,” “chills,” “fatigue,” “nausea,” and “bleeding,” as described with reference to
As shown in
The application page 805 may indicate one or more parameters of the pregnancy, including a temperature, heart rate, HRV, sleep data, and the like experienced by the user via the graphical representation 815. The graphical representation 815 may be an example of the timing diagram 600, as described with reference to
Application page 805-a may also include message 820 that includes insights, recommendations, and the like associated with the pregnancy. The server of the system may cause the GUI 800 of the user device to display a message 820 associated with the pregnancy. In such cases, the user device may display recommendations and/or information associated with the pregnancy via message 820. In some implementations, the user device and/or servers may generate alerts 810 associated with the pregnancy which may be displayed to the user via the GUI 800 (e.g., application page 805-a). In particular, messages 820 generated and displayed to the user via the GUI 800 may be associated with one or more characteristics (e.g., timing, trimester, etc.) of the pregnancy.
In some cases, the user may log symptoms via user input 825. For example, the system may receive user input (e.g., tags) to log symptoms associated with the pregnancy (e.g., nausea, fatigue, tiredness, headaches, migraine, pain, etc.). The system may recommend tags to the user based on user history and the pregnancy. In some cases, the system may cause the GUI 800 of the user device to display pregnancy symptom tags based detecting the pregnancy.
In some examples the message 820 may display a recommendation of how the user may adjust their lifestyle throughout the pregnancy. In some examples, if the user tags “fatigue” on week 8 of the pregnancy, the system may display via message 820 a prompt that suggests logging “fatigue” via user input 825 on the days after the user tags “fatigue.” In other examples, the system may recommend a time (e.g., calendar day) for the user to be active or estimate a restorative time throughout the pregnancy.
In some implementations, the system may provide additional insight regarding the user's pregnancy. For example, the application pages 805-a may indicate one or more physiological parameters (e.g., contributing factors) that support the detection of the user's pregnancy, such as increased temperature, and the like. In other words, the system may be configured to provide some information or other insights regarding the pregnancy. Personalized insights may indicate aspects of collected physiological data (e.g., contributing factors within the physiological data) which were used to generate the messages 820 associated with the pregnancy.
In some implementations, the system may be configured to receive user inputs 825 regarding pregnancy in order to train classifiers (e.g., supervised learning for a machine learning classifier) and improve pregnancy profile configuration techniques. For example, the user may receive user input 825, such as an onset of symptoms, a confirmation of the pregnancy, and the like. These user inputs 825 may then be input into the classifier to train the classifier. In other words, the user inputs 825 may be used to validate, or confirm, the pregnancy. In some cases, the system may provide, to the user device, the second set of targets associated with the one or more physiological metrics and the second set of messages associated with the one or more physiological metrics in response to inputting the physiological data into the machine learning classifier.
In some cases, blood volume may increase progressively throughout pregnancy. This may lead to swelling of the fingers, which may make a ring device uncomfortable to wear. In some implementations, a ring application may assess swelling and discomfort by prompting users with tags or insights around the time when most pregnant users need to change the finger on which they wear their ring. For example, a ring application may deliver a personalized message 820 to recommend ways to manage continuing to wear the ring during pregnancy by suggesting finger-switching strategies, such as suggesting a move from the dominant hand to the non-dominant hand and from larger to smaller fingers.
In some implementations, the algorithm tailoring the timing of the message 820 delivery may incorporate information from user self-reports (e.g., frequent tags like “swelling”) or Bluetooth-connected scales about the trajectory of weight gain. In some implementations, devices may utilize the low frequency HRV component, which may reflect baroreflex sensitivity, to index swelling and incorporate this algorithmically as a feature in a risk-prediction algorithm or an algorithm to determine the timing for surfacing messages 820 to the user suggesting solutions like finger switching strategies.
Upon detecting the indication of pregnancy on application page 805-a, the GUI 800 may display a calendar view 830 that may indicate a current date 835 that the user is viewing application page 805-a, a date range including the day when the pregnancy is detected, a date range including the day when conception is estimated, a date range including the day when the due date 840 is estimated, or a combination thereof. For example, the date range may encircle the calendar days using a dashed line configuration, the current date 835 may encircle the calendar day, and the detected day of pregnancy and/or estimated conception/due date may be encircled. The calendar view 830 may also include a message including the current date 835 and indication of the day of the user's pregnancy (e.g., that the user is 37 weeks pregnant).
In some implementations, the trend graphs of signals, such as temperature, heart rate, HRV, and respiration rate may surface information relevant to help users screen for or manage various pregnancy-related complications. In some implementations, the calendar view 830 may be used to indicate other reproductive health features, such as cycle-tracking, and may be subsequently used to indicate events or milestones relevant to pregnancy, such as starting different trimesters, developmental milestones in the pregnancy, or reminders for major prenatal screening check-ins with a health provider and relevant derived read-outs, figures, or summaries.
In some implementations, the application may use the knowledge of when a user became pregnant to estimate their due date 840. For example, the application may ask for confirmation using the calendar view 830 and use this understanding to prompt the user to learn more about the timing of relevant events by tailoring appropriate messaging. For example, as labor and birth approaches, the application may send messages 820 and provide relevant educational materials. Example messages surfaced to users may include “Due date 840 is approaching, have you packed your hospital bag?” In some examples, educational materials surfaced to users may include breathing techniques in labor, suggestions for labor positions, discussing expectations with a partner, and preparing for postpartum. The activity goals may be modified and messages 820 may change from “Walk 10 miles to reach your goal” to “Try to remain active as much as possible throughout pregnancy.” The sleep goals may be modified and messages 820 may change from “Try to go to bed early” to “Try to keep your sleep hygiene in mind when bedtime approaches.”
Additionally, in some implementations, the application pages 905 may display one or more scores (e.g., Sleep Score, Readiness Score 910, etc.) for the user for the respective day. Moreover, in some cases, the pregnancy may be used to update (e.g., modify) one or more scores associated with the user (e.g., Sleep Score, Readiness Score 910, Activity Score, etc.), as described herein. That is, data associated with the pregnancy may be used to update the scores for the user for the following calendar day after which the pregnancy was confirmed. As may be seen by comparing application page 905-a and application page 905-b, the Readiness Score 910 provided to the user within non-pregnant mode and pregnancy mode may be different.
In some cases, the Readiness Score 910 may be updated based on the pregnancy. For example, an elevated body temperature relative to a temperature baseline for the user may cause the system to alert the user about their body signals (e.g., elevated body temperature). In such cases, the Readiness Score 910-b may indicate to the user to “pay attention” based on elevated body temperatures or elevated heart rate. If the Readiness Score 910 changes for the user, the system may implement the pregnancy mode for users whose symptoms may be severe and may benefit from adjusted sleep, activity, and Readiness guidance for a couple of days. In other examples, the Readiness Score 910 may be updated based on the Sleep Score and elevated body temperatures. That is, the system may determine that the user is pregnant and may adjust (e.g., increase or decrease) the Readiness Score 910 and/or Sleep Score to offset the effects of the pregnancy.
In some cases, the messages displayed to the user via the GUI 900 of the user device may indicate how the pregnancy affected the overall scores (e.g., overall Readiness Score 910, Sleep Score, Activity Score, etc.) and/or the individual contributing factors. For example, a message may indicate “Your resting heart rate indicates that you might not be recovered well” or “From your recovery metrics it looks like your body is still doing ok, so some light activity can help relieve the symptoms.”
The messages may provide suggestions for the user in order to improve their general health and pregnancy. For example, the message may indicate “If you feel really low on energy, why not save time for a nap this afternoon,” or “Since you are feeling fatigued and nauseous, devote today for rest.” In such cases, the messages displayed to the user may provide targeted insights to help the user adjust their lifestyle during a portion of their pregnancy. For users whose body signals (e.g., body temperature, heart rate, HRV, and the like) may react to the phase of pregnancy, the system may display low activity goals around the start of pregnancy. In such cases, accurately detecting the indication of pregnancy may increase the accuracy and efficiency of the Readiness Score 910 and Activity Scores.
Additionally, in some implementations, calculation of Readiness Scores 910 may also take into account the enabled operational mode such that the system gives different weights to the parameters that better indicate body status. In other words, the system may calculate Readiness scores 910/Sleep Scores/Activity Scores differently (e.g., using different algorithms, using different weights) based on which operational state is enabled. For example, with respect to sleep health programs during pregnancy mode, in some implementations, the role of naps may be positive in pregnancy mode, and may be interpreted and communicated as contributing to improved health. In non-pregnant mode, naps may not be typically recommended, as they can spoil normal circadian rhythms. In other words, a system may calculate sleep and Readiness Scores 910 differently in non-pregnant mode and pregnancy mode such that a nap will have a different effect on the user's sleep and Readiness Scores 910 while in non-pregnant mode as compared to pregnancy mode.
Application page 905-b illustrated in
The application page 905-b may illustrate how different operational modes may be used to change how a tagging feature (e.g., tag 915) is used. For example, in some cases, the system may emphasize or otherwise encourage users to utilize tags 915 more in the pregnancy mode and postpartum mode as compared to the non-pregnant mode. Moreover, the application page 905-b shows how suggested tags 915 may be presented to the user.
As noted previously herein, the system may utilize tags 915 inputted/selected by a user to identify triggers for switching between operational states. For example, if a user selects a “pregnancy” tag 915, the system may switch to a pregnancy operational state, where the sleep targets, sleep messages, activity targets, and activity messaging provided to the user are configured to promote healthy pregnancy sleep and activity. In this example, the sleep targets and sleep messaging may change throughout the pregnancy as the user progresses throughout her pregnancy.
In some cases, users may be able to select from a set of pre-configured tags 915. In other cases, a user may be able to input custom tags 915 or insights. Tags 915 may be related to nutrition, caffeine, lifestyle, activities, health, and the like. For example, the tags 915 may be an example of contextual tags for pregnancy, labor onset (e.g., water broke), birth, contractions (e.g., Braxton hicks), pregnancy complications, symptoms, and the like.
Moreover, activity messages may be modified from one operational mode to another (e.g., modified during postpartum mode). For example, after pregnancy mode has been switched off (automatically or manually) and the user enters postpartum mode, the messaging may gradually start guiding the user back to their normal training routines and targets.
As shown in
In some cases, the GUI 1000 may display actigraphy-based features (e.g. total sleep, restfulness, awakenings, etc.) in the application pages 1005. The system may determine that during the first trimester, the user may experience longer times to fall asleep, increased restlessness, and increased amounts of light sleep. The system may determine that during the second trimester, the user may sleep more than usual, experience differences in the user's sleep stages, or both. The system may determine during the third trimester that the user may experience increased awakenings, increased amounts of REM sleep, or both.
In some implementations, upon activating the pregnancy mode, the user may be presented with the application page 1005-b upon opening the wearable application. As shown in
The messages from the user device associated with the sleep coach may include educational content (e.g., video, audio, text, etc.) associated with the sleep targets/goals and graphical representations of the physiological data associated with the user's sleep patterns. For example, the message from the sleep coach may indicate to the user that “Your Sleep Score is looking low, this could mean you aren't getting enough deep sleep.” In some examples, the graphical representation may include a timing diagram associated with the user's daytime heart rate that indicates “Seems that your resting heart rate has been higher than average the whole day. Want to take a moment of downtime?”
In some implementations, upon activating the pregnancy mode, the user may be presented with the application page 1005-c upon opening the wearable application. As shown in
The messages may include an overall progress of the user based on the second set of targets associated with the one or more physiological metrics. For example, the application page 1005-c may include an upcoming schedule of the user's educational content provided by the system and recommended by the sleep coach. In such cases, the user may read, watch, or listen to the sleep coach curriculum in the application page 1005-c. The upcoming schedule of the user's education content may include a recommended video, audio, or text and indicate a title of the educational content, a link to the educational content, an estimated duration to complete the educational content, a recommended week to view the educational content, or a combination thereof.
The sleep coaching may be tailored to pregnancy and preparation for postpartum. The educational materials may be based on gestational stage (e.g., current state of pregnancy) and include what to expect regarding temperature, sleep, respiratory rate, heart rate and HRV. In some cases, allowing the user to learn about sleep management may decrease the likelihood of postpartum depression. The application pages 1005 may include insights and recommendations regarding breastfeeding, stress management coaching, connection with other pregnant users at a similar pregnancy stage, or a combination thereof.
As shown in
In some cases, the system may cause the GUI 1100 of the user device to display recommended associated with the sleep, activity, or nutrition targets that are adjusted for pregnancy based on the second operational mode. In such cases, the application page 1105-a may include a recommend tag to try a new habit and indicate how many times the user may try the new habit for the week. The recommended tag may also indicate how the recommended tag relates to the user's goals and which goal the tag is associated with. In such cases, the coach may send a custom plan using tags to the user. For example, the system may receive a custom plan, from a user device associated with the coach, and display the custom plan to the user.
In some implementations, upon activating the pregnancy mode, the user may be presented with the application page 1105-b upon opening the wearable application. As shown in
In some cases, the system may cause the GUI 1100 of the user device to display one or more graphical representation to the user. In some examples, the graphical representation may include a timing diagram associated with the user's physiological data. The graphical representation may include trends (e.g., patterns) identified by the system and indicate how the tag affected the user's physiological data. For example, the graphical representation may indicate “You tried Custom tag 1 five times last week, and it may have impacted your resting heart rate.” In other examples, the graphical representation may indicate “You tried Custom tag 2 three times last week, but also increased Custom tag 3, so the impact on your data is unclear.”
The input module 1210 may provide a means for receiving information such as packets, user data, control information, or any combination thereof associated with various information channels (e.g., control channels, data channels, information channels related to illness detection techniques). Information may be passed on to other components of the device 1205. The input module 1210 may utilize a single antenna or a set of multiple antennas.
The output module 1215 may provide a means for transmitting signals generated by other components of the device 1205. For example, the output module 1215 may transmit information such as packets, user data, control information, or any combination thereof associated with various information channels (e.g., control channels, data channels, information channels related to illness detection techniques). In some examples, the output module 1215 may be co-located with the input module 1210 in a transceiver module. The output module 1215 may utilize a single antenna or a set of multiple antennas.
For example, the wearable application 1220 may include a data acquisition component 1225, a first guidance component 1230, a user input component 1235, an operational mode component 1240, a second guidance component 1245, or any combination thereof. In some examples, the wearable application 1220, or various components thereof, may be configured to perform various operations (e.g., receiving, monitoring, transmitting) using or otherwise in cooperation with the input module 1210, the output module 1215, or both. For example, the wearable application 1220 may receive information from the input module 1210, send information to the output module 1215, or be integrated in combination with the input module 1210, the output module 1215, or both to receive information, transmit information, or perform various other operations as described herein.
The data acquisition component 1225 may be configured as or otherwise support a means for receiving physiological data associated with a user from a wearable device. The first guidance component 1230 may be configured as or otherwise support a means for providing, to a user device associated with the user, a first set of targets associated with one or more physiological metrics and a first set of messages associated with the one or more physiological metrics based at least in part on the received physiological data, the first set of targets and the first set of messages associated with a first operational mode of an application associated with the wearable device that is associated with the user. The user input component 1235 may be configured as or otherwise support a means for receiving, via the user device, a user input comprising an indication of the user being pregnant. The operational mode component 1240 may be configured as or otherwise support a means for identifying a trigger to transition from the first operational mode to a second operational mode of the application associated with the wearable device that is associated with the user based at least in part on receiving the indication. The second guidance component 1245 may be configured as or otherwise support a means for providing, to the user device based at least in part on identifying the trigger, a second set of targets associated with the one or more physiological metrics and a second set of messages associated with the one or more physiological metrics that are adjusted for pregnancy and different from the first set of targets and the first set of messages based at least in part on the second operational mode.
The data acquisition component 1325 may be configured as or otherwise support a means for receiving physiological data associated with a user from a wearable device. The first guidance component 1330 may be configured as or otherwise support a means for providing, to a user device associated with the user, a first set of targets associated with one or more physiological metrics and a first set of messages associated with the one or more physiological metrics based at least in part on the received physiological data, the first set of targets and the first set of messages associated with a first operational mode of an application associated with the wearable device that is associated with the user. The user input component 1335 may be configured as or otherwise support a means for receiving, via the user device, a user input comprising an indication of the user being pregnant. The operational mode component 1340 may be configured as or otherwise support a means for identifying a trigger to transition from the first operational mode to a second operational mode of the application associated with the wearable device that is associated with the user based at least in part on receiving the indication. The second guidance component 1345 may be configured as or otherwise support a means for providing, to the user device based at least in part on identifying the trigger, a second set of targets associated with the one or more physiological metrics and a second set of messages associated with the one or more physiological metrics that are adjusted for pregnancy and different from the first set of targets and the first set of messages based at least in part on the second operational mode.
In some examples, the user score component 1350 may be configured as or otherwise support a means for determining, during a first time interval corresponding to the first operational mode, one or more scores associated with the user using a first algorithm and based at least in part on the received physiological data. In some examples, the user score component 1350 may be configured as or otherwise support a means for determining, during a second time interval corresponding to the second operational mode, the one or more scores associated with the user being pregnant and using a second algorithm different from the first algorithm and based at least in part on the received physiological data.
In some examples, the one or more scores comprise a Sleep Score, a Readiness Score, an Activity Score, or any combination thereof.
In some examples, the operational mode component 1340 may be configured as or otherwise support a means for identifying a second trigger to transition away from the second operational mode. In some examples, the operational mode component 1340 may be configured as or otherwise support a means for transitioning from the second operational mode to a third operational mode of the application associated with the wearable device that is associated with the user based at least in part on the second trigger, wherein the third operational mode comprises an intermediary mode for transitioning from the second operational mode to the first operational mode. In some examples, the second guidance component 1345 may be configured as or otherwise support a means for providing, to the user device based at least in part on transitioning to the third operational mode, a third set of targets associated with the one or more physiological metrics and a third set of messages associated with the one or more physiological metrics that are adjusted for postpartum and different from the second set of targets and the second set of messages based at least in part on the third operational mode.
In some examples, to support identifying the second trigger, the user input component 1335 may be configured as or otherwise support a means for receiving, via the user device, a user input comprising an indication that the user is in a postpartum state.
In some examples, the operational mode component 1340 may be configured as or otherwise support a means for identifying a third trigger to transition from the third operational mode to the first operational mode. In some examples, the operational mode component 1340 may be configured as or otherwise support a means for transitioning from the third operational mode to the first operational mode based at least in part on the third trigger. In some examples, the first guidance component 1330 may be configured as or otherwise support a means for providing, to the user device based at least in part on transitioning to the first operational mode, the first set of targets and the first set of messages based at least in part on the first operational mode.
In some examples, identifying the third trigger is based at least in part on a duration of time spent in the second operational mode, a duration of time spent in the third operational mode, measured physiological parameters included within the received physiological data that indicate the user is no longer pregnant, or a combination thereof.
In some examples, the first operational mode comprises a non-pregnant mode. In some examples, the second operational mode comprises a pregnancy mode. In some examples, the third operational mode comprises a postpartum mode.
In some examples, the operational mode component 1340 may be configured as or otherwise support a means for identifying a second trigger to transition away from the second operational mode. In some examples, the operational mode component 1340 may be configured as or otherwise support a means for transitioning from the second operational mode to the first operational mode based at least in part on the second trigger. In some examples, the first guidance component 1330 may be configured as or otherwise support a means for providing, to the user device based at least in part on transitioning to the first operational mode, the first set of targets and the first set of messages based at least in part on the first operational mode.
In some examples, the physiological data comprises temperature data, and the data acquisition component 1325 may be configured as or otherwise support a means for identifying that the temperature data satisfies a temperature threshold for the user, wherein identifying the trigger is based at least in part on the temperature data satisfying the temperature threshold for the user.
In some examples, the first operational mode comprises a non-pregnant mode and the second operational mode comprises a pregnancy mode. In some examples, the first set of targets comprise targets associated with the user when the user is in a non-pregnant state. In some examples, the second set of targets comprise a set of adjusted targets associated with the user when the user is pregnant. In some examples, the second set of messages are configured to promote the set of adjusted targets.
In some examples, the user input component 1335 may be configured as or otherwise support a means for receiving, via the user device, a user input comprising a date of a user's last menstrual cycle, an estimated date of conception, a date of a first positive pregnancy test, an estimated due date, an actual birth date, or a combination thereof, wherein identifying the trigger is based at least in part on receiving the user input.
In some examples, the user interface component 1355 may be configured as or otherwise support a means for causing a graphical user interface of the user device to display pregnancy symptom tags based at least in part on the second operational mode.
In some examples, the user interface component 1355 may be configured as or otherwise support a means for causing a graphical user interface of the user device to display a message associated with the second set of targets and the second set of messages that are adjusted for pregnancy based at least in part on the second operational mode.
In some examples, the message further comprises trimester-specific physiological insights associated with pregnancy, a recommended wake time during which the user wakes up, a recommended bedtime during which the user goes to sleep, a recommended sleep duration, a recommended time of day during with the user rests, a request to input symptoms associated with pregnancy, educational content associated with pregnancy, or a combination thereof.
In some examples, the second guidance component 1345 may be configured as or otherwise support a means for inputting the physiological data into a machine learning classifier, wherein providing, to the user device, the second set of targets and the second set of messages is based at least in part on inputting the physiological data into the machine learning classifier.
In some examples, the one or more physiological metrics comprise one or more sleep metrics.
In some examples, the wearable device comprises a wearable ring device.
In some examples, the wearable device collects the physiological data from the user based on arterial blood flow.
The communication module 1410 may manage input and output signals for the device 1405 via the antenna 1415. The communication module 1410 may include an example of the communication module 220-b of the user device 106 shown and described in
In some cases, the device 1405 may include a single antenna 1415. However, in some other cases, the device 1405 may have more than one antenna 1415, which may be capable of concurrently transmitting or receiving multiple wireless transmissions. The communication module 1410 may communicate bi-directionally, via the one or more antennas 1415, wired, or wireless links as described herein. For example, the communication module 1410 may represent a wireless transceiver and may communicate bi-directionally with another wireless transceiver. The communication module 1410 may also include a modem to modulate the packets, to provide the modulated packets to one or more antennas 1415 for transmission, and to demodulate packets received from the one or more antennas 1415.
The user interface component 1425 may manage data storage and processing in a database 1430. In some cases, a user may interact with the user interface component 1425. In other cases, the user interface component 1425 may operate automatically without user interaction. The database 1430 may be an example of a single database, a distributed database, multiple distributed databases, a data store, a data lake, or an emergency backup database.
The memory 1435 may include RAM and ROM. The memory 1435 may store computer-readable, computer-executable software including instructions that, when executed, cause the processor 1440 to perform various functions described herein. In some cases, the memory 1435 may contain, among other things, a BIOS which may control basic hardware or software operation such as the interaction with peripheral components or devices.
The processor 1440 may include an intelligent hardware device, (e.g., a general-purpose processor, a DSP, a CPU, a microcontroller, an ASIC, an FPGA, a programmable logic device, a discrete gate or transistor logic component, a discrete hardware component, or any combination thereof). In some cases, the processor 1440 may be configured to operate a memory array using a memory controller. In other cases, a memory controller may be integrated into the processor 1440. The processor 1440 may be configured to execute computer-readable instructions stored in a memory 1435 to perform various functions (e.g., functions or tasks supporting a method and system for sleep staging algorithms).
For example, the wearable application 1420 may be configured as or otherwise support a means for receiving physiological data associated with a user from a wearable device. The wearable application 1420 may be configured as or otherwise support a means for providing, to a user device associated with the user, a first set of targets associated with one or more physiological metrics and a first set of messages associated with the one or more physiological metrics based at least in part on the received physiological data, the first set of targets and the first set of messages associated with a first operational mode of an application associated with the wearable device that is associated with the user. The wearable application 1420 may be configured as or otherwise support a means for receiving, via the user device, a user input comprising an indication of the user being pregnant. The wearable application 1420 may be configured as or otherwise support a means for identifying a trigger to transition from the first operational mode to a second operational mode of the application associated with the wearable device that is associated with the user based at least in part on receiving the indication. The wearable application 1420 may be configured as or otherwise support a means for providing, to the user device based at least in part on identifying the trigger, a second set of targets associated with the one or more physiological metrics and a second set of messages associated with the one or more physiological metrics that are adjusted for pregnancy and different from the first set of targets and the first set of messages based at least in part on the second operational mode.
By including or configuring the wearable application 1420 in accordance with examples as described herein, the device 1405 may support techniques for improved communication reliability, reduced latency, improved user experience related to reduced processing, reduced power consumption, more efficient utilization of communication resources, improved coordination between devices, longer battery life, improved utilization of processing capability.
The wearable application 1420 may include an application (e.g., “app”), program, software, or other component which is configured to facilitate communications with a ring 104, server 110, other user devices 106, and the like. For example, the wearable application 1420 may include an application executable on a user device 106 which is configured to receive data (e.g., physiological data) from a ring 104, perform processing operations on the received data, transmit and receive data with the servers 110, and cause presentation of data to a user 102.
At 1505, the method may include receiving physiological data associated with a user from a wearable device. The operations of 1505 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1505 may be performed by a data acquisition component 1325 as described with reference to
At 1510, the method may include providing, to a user device associated with the user, a first set of targets associated with one or more physiological metrics and a first set of messages associated with the one or more physiological metrics based at least in part on the received physiological data, the first set of targets and the first set of messages associated with a first operational mode of an application associated with the wearable device that is associated with the user. The operations of 1510 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1510 may be performed by a first guidance component 1330 as described with reference to
At 1515, the method may include receiving, via the user device, a user input comprising an indication of the user being pregnant. The operations of 1515 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1515 may be performed by a user input component 1335 as described with reference to
At 1520, the method may include identifying a trigger to transition from the first operational mode to a second operational mode of the application associated with the wearable device that is associated with the user based at least in part on receiving the indication. The operations of 1520 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1520 may be performed by an operational mode component 1340 as described with reference to
At 1525, the method may include providing, to the user device based at least in part on identifying the trigger, a second set of targets associated with the one or more physiological metrics and a second set of messages associated with the one or more physiological metrics that are adjusted for pregnancy and different from the first set of targets and the first set of messages based at least in part on the second operational mode. The operations of 1525 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1525 may be performed by a second guidance component 1345 as described with reference to
At 1605, the method may include receiving physiological data associated with a user from a wearable device. The operations of 1605 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1605 may be performed by a data acquisition component 1325 as described with reference to
At 1610, the method may include providing, to a user device associated with the user, a first set of targets associated with one or more physiological metrics and a first set of messages associated with the one or more physiological metrics based at least in part on the received physiological data, the first set of targets and the first set of messages associated with a first operational mode of an application associated with the wearable device that is associated with the user. The operations of 1610 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1610 may be performed by a first guidance component 1330 as described with reference to
At 1615, the method may include determining, during a first time interval corresponding to the first operational mode, one or more scores associated with the user using a first algorithm and based at least in part on the received physiological data. The operations of 1615 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1615 may be performed by a user score component 1350 as described with reference to
At 1620, the method may include receiving, via the user device, a user input comprising an indication of the user being pregnant. The operations of 1620 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1620 may be performed by a user input component 1335 as described with reference to
At 1625, the method may include identifying a trigger to transition from the first operational mode to a second operational mode of the application associated with the wearable device that is associated with the user based at least in part on receiving the indication. The operations of 1625 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1625 may be performed by an operational mode component 1340 as described with reference to
At 1630, the method may include providing, to the user device based at least in part on identifying the trigger, a second set of targets associated with the one or more physiological metrics and a second set of messages associated with the one or more physiological metrics that are adjusted for pregnancy and different from the first set of targets and the first set of messages based at least in part on the second operational mode. The operations of 1630 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1630 may be performed by a second guidance component 1345 as described with reference to
At 1635, the method may include determining, during a second time interval corresponding to the second operational mode, the one or more scores associated with the user being pregnant and using a second algorithm different from the first algorithm and based at least in part on the received physiological data. The operations of 1635 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1635 may be performed by a user score component 1350 as described with reference to
At 1705, the method may include receiving physiological data associated with a user from a wearable device. The operations of 1705 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1705 may be performed by a data acquisition component 1325 as described with reference to
At 1710, the method may include providing, to a user device associated with the user, a first set of targets associated with one or more physiological metrics and a first set of messages associated with the one or more physiological metrics based at least in part on the received physiological data, the first set of targets and the first set of messages associated with a first operational mode of an application associated with the wearable device that is associated with the user. The operations of 1710 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1710 may be performed by a first guidance component 1330 as described with reference to
At 1715, the method may include receiving, via the user device, a user input comprising an indication of the user being pregnant. The operations of 1715 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1715 may be performed by a user input component 1335 as described with reference to
At 1720, the method may include identifying that the temperature data satisfies a temperature threshold for the user, wherein identifying the trigger is based at least in part on the temperature data satisfying the temperature threshold for the user. The operations of 1720 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1720 may be performed by a data acquisition component 1325 as described with reference to
At 1725, the method may include identifying a trigger to transition from the first operational mode to a second operational mode of the application associated with the wearable device that is associated with the user based at least in part on receiving the indication. The operations of 1725 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1725 may be performed by an operational mode component 1340 as described with reference to
At 1730, the method may include providing, to the user device based at least in part on identifying the trigger, a second set of targets associated with the one or more physiological metrics and a second set of messages associated with the one or more physiological metrics that are adjusted for pregnancy and different from the first set of targets and the first set of messages based at least in part on the second operational mode. The operations of 1730 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1730 may be performed by a second guidance component 1345 as described with reference to
It should be noted that the methods described above describe possible implementations, and that the operations and the steps may be rearranged or otherwise modified and that other implementations are possible. Furthermore, aspects from two or more of the methods may be combined.
A method is described. The method may include receiving physiological data associated with a user from a wearable device, providing, to a user device associated with the user, a first set of targets associated with one or more physiological metrics and a first set of messages associated with the one or more physiological metrics based at least in part on the received physiological data, the first set of targets and the first set of messages associated with a first operational mode of an application associated with the wearable device that is associated with the user, receiving, via the user device, a user input comprising an indication of the user being pregnant, identifying a trigger to transition from the first operational mode to a second operational mode of the application associated with the wearable device that is associated with the user based at least in part on receiving the indication, and providing, to the user device based at least in part on identifying the trigger, a second set of targets associated with the one or more physiological metrics and a second set of messages associated with the one or more physiological metrics that are adjusted for pregnancy and different from the first set of targets and the first set of associated with the one or more physiological metrics messages based at least in part on the second operational mode.
An apparatus is described. The apparatus may include a processor, memory coupled with the processor, and instructions stored in the memory. The instructions may be executable by the processor to cause the apparatus to receive physiological data associated with a user from a wearable device, provide, to a user device associated with the user, a first set of targets associated with one or more physiological metrics and a first set of messages associated with the one or more physiological metrics based at least in part on the received physiological data, the first set of targets and the first set of messages associated with a first operational mode of an application associated with the wearable device that is associated with the user, receive, via the user device, a user input comprising an indication of the user being pregnant, identify a trigger to transition from the first operational mode to a second operational mode of the application associated with the wearable device that is associated with the user based at least in part on receiving the indication, and provide, to the user device based at least in part on identifying the trigger, a second set of targets associated with the one or more physiological metrics and a second set of messages associated with the one or more physiological metrics that are adjusted for pregnancy and different from the first set of targets and the first set of messages based at least in part on the second operational mode.
Another apparatus is described. The apparatus may include means for receiving physiological data associated with a user from a wearable device, means for providing, to a user device associated with the user, a first set of targets associated with the one or more physiological metrics and a first set of messages associated with the one or more physiological metrics based at least in part on the received physiological data, the first set of targets and the first set of messages associated with a first operational mode of an application associated with the wearable device that is associated with the user, means for receiving, via the user device, a user input comprising an indication of the user being pregnant, means for identifying a trigger to transition from the first operational mode to a second operational mode of the application associated with the wearable device that is associated with the user based at least in part on receiving the indication, and means for providing, to the user device based at least in part on identifying the trigger, a second set of targets associated with the one or more physiological metrics and a second set of messages associated with the one or more physiological metrics that are adjusted for pregnancy and different from the first set of targets and the first set of messages based at least in part on the second operational mode.
A non-transitory computer-readable medium storing code is described. The code may include instructions executable by a processor to receive physiological data associated with a user from a wearable device, provide, to a user device associated with the user, a first set of targets associated with one or more physiological metrics and a first set of messages associated with the one or more physiological metrics based at least in part on the received physiological data, the first set of targets and the first set of messages associated with a first operational mode of an application associated with the wearable device that is associated with the user, receive, via the user device, a user input comprising an indication of the user being pregnant, identify a trigger to transition from the first operational mode to a second operational mode of the application associated with the wearable device that is associated with the user based at least in part on receiving the indication, and provide, to the user device based at least in part on identifying the trigger, a second set of targets associated with the one or more physiological metrics and a second set of messages associated with the one or more physiological metrics that are adjusted for pregnancy and different from the first set of targets and the first set of messages based at least in part on the second operational mode.
Some examples of the method, apparatuses, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for determining, during a first time interval corresponding to the first operational mode, one or more scores associated with the user using a first algorithm and based at least in part on the received physiological data and determining, during a second time interval corresponding to the second operational mode, the one or more scores associated with the user being pregnant and using a second algorithm different from the first algorithm and based at least in part on the received physiological data.
In some examples of the method, apparatuses, and non-transitory computer-readable medium described herein, the one or more scores comprise a Sleep Score, a Readiness Score, an Activity Score, or any combination thereof.
Some examples of the method, apparatuses, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for identifying a second trigger to transition away from the second operational mode, transitioning from the second operational mode to a third operational mode of the application associated with the wearable device that may be associated with the user based at least in part on the second trigger, wherein the third operational mode comprises an intermediary mode for transitioning from the second operational mode to the first operational mode, and providing, to the user device based at least in part on transitioning to the third operational mode, a third set of targets associated with the one or more physiological metrics and a third set of messages associated with the one or more physiological metrics that may be adjusted for postpartum and different from the second set of targets and the second set of messages based at least in part on the third operational mode.
In some examples of the method, apparatuses, and non-transitory computer-readable medium described herein, identifying the second trigger may include operations, features, means, or instructions for receiving, via the user device, a user input comprising an indication that the user may be in a postpartum state.
Some examples of the method, apparatuses, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for identifying a third trigger to transition from the third operational mode to the first operational mode, transitioning from the third operational mode to the first operational mode based at least in part on the third trigger, and providing, to the user device based at least in part on transitioning to the first operational mode, the first set of targets and the first set of messages based at least in part on the first operational mode.
Some examples of the method, apparatuses, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for identifying the third trigger may be based at least in part on a duration of time spent in the second operational mode, a duration of time spent in the third operational mode, measured physiological parameters included within the received physiological data that indicate the user may be no longer pregnant, or a combination thereof.
In some examples of the method, apparatuses, and non-transitory computer-readable medium described herein, the first operational mode comprises a non-pregnant mode, the second operational mode comprises a pregnancy mode, and the third operational mode comprises a postpartum mode.
Some examples of the method, apparatuses, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for identifying a second trigger to transition away from the second operational mode, transitioning from the second operational mode to the first operational mode based at least in part on the second trigger, and providing, to the user device based at least in part on transitioning to the first operational mode, the first set of targets and the first set of messages based at least in part on the first operational mode.
In some examples of the method, apparatuses, and non-transitory computer-readable medium described herein, the physiological data comprises temperature data and the method, apparatuses, and non-transitory computer-readable medium may include further operations, features, means, or instructions for identifying that the temperature data satisfies a temperature threshold for the user, wherein identifying the trigger may be based at least in part on the temperature data satisfying the temperature threshold for the user.
In some examples of the method, apparatuses, and non-transitory computer-readable medium described herein, the first operational mode comprises a non-pregnant mode and the second operational mode comprises a pregnancy mode, the first set of targets comprise targets associated with the user when the user may be in a non-pregnant state, the second set of targets comprise a set of adjusted targets associated with the user when the user may be pregnant, and the second set of messages may be configured to promote the set of adjusted targets.
Some examples of the method, apparatuses, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for receiving, via the user device, a user input comprising a date of a user's last menstrual cycle, an estimated date of conception, a date of a first positive pregnancy test, an estimated due date, an actual birth date, or a combination thereof, wherein identifying the trigger may be based at least in part on receiving the user input.
Some examples of the method, apparatuses, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for causing a graphical user interface of the user device to display pregnancy symptom tags based at least in part on the second operational mode.
Some examples of the method, apparatuses, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for causing a graphical user interface of the user device to display a message associated with the second set of targets and the second set of messages that may be adjusted for pregnancy based at least in part on the second operational mode.
In some examples of the method, apparatuses, and non-transitory computer-readable medium described herein, the message further comprises trimester-specific physiological insights associated with pregnancy, a recommended wake time during which the user wakes up, a recommended bedtime during which the user goes to sleep, a recommended sleep duration, a recommended time of day during with the user rests, a request to input symptoms associated with pregnancy, educational content associated with pregnancy, or a combination thereof.
Some examples of the method, apparatuses, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for inputting the physiological data into a machine learning classifier, wherein providing, to the user device, the second set of targets and the second set of messages may be based at least in part on inputting the physiological data into the machine learning classifier.
In some examples of the method, apparatuses, and non-transitory computer-readable medium described herein, the one or more physiological metrics comprise one or more sleep metrics.
In some examples of the method, apparatuses, and non-transitory computer-readable medium described herein, the wearable device comprises a wearable ring device.
In some examples of the method, apparatuses, and non-transitory computer-readable medium described herein, the wearable device collects the physiological data from the user based on arterial blood flow.
The description set forth herein, in connection with the appended drawings, describes example configurations and does not represent all the examples that may be implemented or that are within the scope of the claims. The term “exemplary” used herein means “serving as an example, instance, or illustration,” and not “preferred” or “advantageous over other examples.” The detailed description includes specific details for the purpose of providing an understanding of the described techniques. These techniques, however, may be practiced without these specific details. In some instances, well-known structures and devices are shown in block diagram form in order to avoid obscuring the concepts of the described examples.
In the appended figures, similar components or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label by a dash and a second label that distinguishes among the similar components. If just the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label.
Information and signals described herein may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.
The various illustrative blocks and modules described in connection with the disclosure herein may be implemented or performed with a general-purpose processor, a DSP, an ASIC, an FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices (e.g., a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration).
The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Other examples and implementations are within the scope of the disclosure and appended claims. For example, due to the nature of software, functions described above can be implemented using software executed by a processor, hardware, firmware, hardwiring, or combinations of any of these. Features implementing functions may also be physically located at various positions, including being distributed such that portions of functions are implemented at different physical locations. Also, as used herein, including in the claims, “or” as used in a list of items (for example, a list of items prefaced by a phrase such as “at least one of” or “one or more of”) indicates an inclusive list such that, for example, a list of at least one of A, B, or C means A or B or C or AB or AC or BC or ABC (i.e., A and B and C). Also, as used herein, the phrase “based on” shall not be construed as a reference to a closed set of conditions. For example, an exemplary step that is described as “based on condition A” may be based on both a condition A and a condition B without departing from the scope of the present disclosure. In other words, as used herein, the phrase “based on” shall be construed in the same manner as the phrase “based at least in part on.”
Computer-readable media includes both non-transitory computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A non-transitory storage medium may be any available medium that can be accessed by a general purpose or special purpose computer. By way of example, and not limitation, non-transitory computer-readable media can comprise RAM, ROM, electrically erasable programmable ROM (EEPROM), compact disk (CD) ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to carry or store desired program code means in the form of instructions or data structures and that can be accessed by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. Also, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk and disc, as used herein, include CD, laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above are also included within the scope of computer-readable media.
The description herein is provided to enable a person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other variations without departing from the scope of the disclosure. Thus, the disclosure is not limited to the examples and designs described herein, but is to be accorded the broadest scope consistent with the principles and novel features disclosed herein.
The present application for patent claims the benefit of U.S. Provisional Patent Application No. 63/169,314 by Aschbacher et al., entitled “WOMEN'S HEALTH TRACKING,” filed Apr. 1, 2021, assigned to the assignee hereof, and expressly incorporated by reference herein.
| Number | Date | Country | |
|---|---|---|---|
| 63169314 | Apr 2021 | US |
