Patent Application
20210393162
This relates generally to electronic devices, and, more particularly, to electronic devices with health sensor and detection circuitry.
Electronic devices are often worn or carried near a user's body. The devices may include sensors that are capable of detecting health information, such as heart rate, or movement information, such as distance traveled. One standardized test that is used in diagnostic, clinical settings is based on a user's volumetric flow of oxygen within the user's body, which is commonly referred to as the user's VO2 and may be measured in liters of Oxygen per minute (L/min). In particular, the maximum value of VO2 (VO2 max) for a given user may provide an accurate assessment of the user's health and may provide a high indicator of the user's mortality. However, in clinical settings, a user must run at peak exertion and breathe into a mask that will measure the amount of air used. As a result, many users do not get tested in clinical settings.
Portable electronic devices, such as wearable devices, may have heart rate sensors, motion sensors, and other health sensors that may produce health data. Specifically, these devices may use the user's heart rate during very brisk walking and running workouts to estimate their VO2 max. Typically, however, these tests require that the user reach approximately 60-70% of their maximal heart rate and that the user run or walk under fairly specific conditions. Therefore, these tests may be triggered when a user manually begins a workout. However, users that work out less frequently and who may have lower fitness levels than users who work out regularly, may not meet the criteria needed to perform a VO2 max test. Therefore, it may be desirable to estimate a user's VO2 max in anomalous conditions and/or when a user is not working out (e.g., when a user is walking at a slow pace).
Electronic devices such as cellular telephone, wristwatches, and other portable devices are often worn or carried by users. The electronic devices may include motion sensors, such as accelerometers, gyroscopes, and/or global positioning system (GPS) sensors, as examples, that may indicate movement of the electronic device. Additionally, the devices may include health sensors, such as heart rate sensors, electrocardiogram sensors, and/or perspiration sensors, as examples, that may indicate activity information of the user.
To estimate a user's maximum volumetric flow of oxygen, or VO2 max, control circuitry within the electronic devices may rely on both the movement of the electronic device and the activity information of the user. In particular, the control circuitry may determine a user's calorie data, workout data, and body metrics based on the movement of the device and the activity information, such as the heart rate of the user. The control circuitry may filter at least some of this data to ensure data quality. Moreover, the control circuitry may normalize the heart rate data of the user to account for differences between the user's baseline measurements and anticipated heart rate measurements of similar people in society at large.
The control circuitry may then compute clusters of the calorie data, workout data, and body metrics and aggregate clusters from different periods of time to determine a relationship between the user's heart rate and VO2. Additionally, the circuitry may perform a probabilistic prior calculation based on the user's age and other factors to determine a predicted relationship between the user's heart rate and VO2. The two predicted relationships may be combined, and the combined relationship may be projected to the user's estimated maximum heart rate to estimate the user's VO2 max.
Electronic devices are often carried by users as they conduct their daily activities. For example, a user may carry an electronic device while walking, exercising, or climbing stairs. To provide a user with fitness tracking functionality and other functions, it may be desirable to monitor a user's activities. For example, sensors in an electronic device may monitor user movement. In an illustrative configuration, a motion sensor such as an accelerometer, an altimeter, and/or other sensors in an electronic device may be used in determining when a user has climbed a flight of stairs or performed other physical activities. The same sensors and/or other sensors within the device may be used to determine whether a user has been active or exercised, and the device may track the user's workouts.
To provide a VO2 max metric for a user, an electronic device may determine a user's maximum calories burned over a desired interval, workout data from the user, and body metric information using motion sensors, other sensors, and/or manual modes of input. Control circuitry within the device may ensure that the workout and body metric data are of sufficient quality, normalize the user's heart rate data, and then analyze the normalized data with the user's maximum calories burned to estimate the user's VO2 max. To analyze the data, the control circuitry may determine a personalized approximation curve that relates heart rate to VO2 based on the user's holistic health factors. As a result, this method may be used for any user, including users who do not record workout data very often or at all.
In general, any suitable electronic devices may be used in measuring the user's motion and activity. As shown in
Another illustrative device that may be used to measure the user's motion and activity is shown in
Although electronic devices 10 and 20 may be used separately to determine movement and activity of a user, they may also communicate to provide enhanced measurements. As shown in
Similarly, electronic device 20, which is illustrated in
Additionally, system 8 may include any desired number of electronic devices. Although
As shown in
Electronic device 10 may include wired and wireless communications circuitry. For example, electronic device 10 may include radio-frequency transceiver circuitry 114 such as cellular telephone transceiver circuitry, wireless local area network transceiver circuitry (e.g., WiFi® circuitry), short-range radio-frequency transceiver circuitry that communicates over short distances using ultra high frequency radio waves (e.g., Bluetooth® circuitry operating at 2.4 GHz or other short-range transceiver circuitry), millimeter wave transceiver circuitry, and/or other wireless communications circuitry.
Device 10 may include input-output devices 116. Input-output devices 116 may be used to allow a user to provide device 10 with user input. Input-output devices 116 may also be used to gather information on the environment in which device 10 is operating. Output components in devices 116 may allow device 10 to provide a user with output and may be used to communicate with external electrical equipment.
In some embodiments, the sensors in one of electronic device 10 and electronic device 20 may be used to calibrate the other device. For example, if electronic device 10 is a wearable electronic device and electronic device 20 is a cellular telephone, the motion sensors within electronic device 20 may provide motion data to the wristwatch, which may calibrate its motion sensors based on the motion data from the telephone. This may be beneficial, as the cellular telephone may be carried in a user's pocket, closer to their center of mass, than on the wrist of the user. However, this is merely illustrative. In general, any number of electronic devices in system 8 may generate data that may be communicated to other devices within system 8 and used to calibrate sensors within those other devices. In this way, the accuracy of the devices in the system may be improved, even when the devices are used individually at a later time.
As shown in
Input-output circuitry 116 may include sensors 118. Sensors 118 may include, for example, three-dimensional sensors (e.g., three-dimensional image sensors such as structured light sensors that emit beams of light and that use two-dimensional digital image sensors to gather image data for three-dimensional images from light spots that are produced when a target is illuminated by the beams of light, binocular three-dimensional image sensors that gather three-dimensional images using two or more cameras in a binocular imaging arrangement, three-dimensional lidar (light detection and ranging) sensors, three-dimensional radio-frequency sensors, or other sensors that gather three-dimensional image data), cameras (e.g., infrared and/or visible digital image sensors), gaze tracking sensors (e.g., a gaze tracking system based on an image sensor and, if desired, a light source that emits one or more beams of light that are tracked using the image sensor after reflecting from a user's eyes), touch sensors, capacitive proximity sensors, light-based (optical) proximity sensors, other proximity sensors, force sensors, sensors such as contact sensors based on switches, gas sensors, pressure sensors, moisture sensors, magnetic sensors (e.g., a magnetometer), audio sensors (microphones), ambient light sensors, microphones for gathering voice commands and other audio input, sensors that are configured to gather information on motion, position, and/or orientation (e.g., accelerometers, gyroscopes, pressure sensors, compasses, and/or inertial measurement units that include all of these sensors or a subset of one or two of these sensors), health sensors that measure various biometric information (e.g., heartrate sensors, such as a photoplethysmography sensor), electrocardiogram sensors, and perspiration sensors) and/or other sensors.
User input and other information may be gathered using sensors and other input devices in input-output devices 116. If desired, input-output devices 116 may include other devices 122 such as haptic output devices (e.g., vibrating components), light-emitting diodes and other light sources, speakers such as ear speakers for producing audio output, circuits for receiving wireless power, circuits for transmitting power wirelessly to other devices, batteries and other energy storage devices (e.g., capacitors), joysticks, buttons, and/or other components.
Similarly, electronic device 20 may have control circuitry 212, communication circuitry 214, and input-output devices 216. Input-output devices 216 may include sensors 218, optional display 24, and other devices 222. Control circuitry 212, communication circuitry 214, input-output devices 216, sensors 218, display 24, and other devices 222 may function similarly as described above in regards to the corresponding parts of electronic device 10. However, electronic device 20 may have different configurations of control circuitry, different bands of communications circuitry, and different combinations of sensors, if desired.
During operation, the communications circuitry of the devices in system 8 (e.g., communications circuitry 112 and communications circuitry 212), may be used to support communication between the electronic devices. For example, one electronic device may transmit video data, audio data, and/or other data to another electronic device in system 8. Bluetooth circuitry may transmit Bluetooth advertising packets and other Bluetooth packets that are received by Bluetooth receivers in nearby devices. Electronic devices in system 8 may use wired and/or wireless communications circuitry to communicate through one or more communications networks (e.g., the internet, local area networks, etc.). The communications circuitry may be used to allow data to be transmitted to and/or received by device 10 from external equipment (e.g., a tethered computer, a portable device such as a handheld device or laptop computer, online computing equipment such as a remote server or other remote computing equipment, an accessory such as a hands-free audio system in a vehicle or a wireless headset, or other electrical equipment) and/or to provide data to external equipment.
During operation, devices 10 and 20 may transmit wireless signals such as Bluetooth signals or other short-range wireless signals and may monitor for these signals from other devices.
For example, devices 10 may transmit Bluetooth signals such as Bluetooth advertising packets that are received by other devices 10. Transmitting devices 10 may sometimes be referred to as remote devices, whereas receiving devices 10 may sometimes be referred to as local devices. In transmitting Bluetooth advertisements (advertisement packets), each remote device may include information in the transmitted advertisements on the recent movement activity of that remote device and other information about the state of the remote device. Movement activity, which may sometimes be referred to as motion context, user motion information, or motion activity information, reflects the recent activities of the user of the remote device involving movement of the user's body (e.g. activities such as resting by sitting and/or standing or moving by walking, running, and/or cycling), and may be shared over Bluetooth between devices. However, any desired protocol may be used to share movement activity between devices in system 8, if desired.
During operation, devices 10 and/or 20 may use sensors 118, wireless circuitry such as satellite navigation system circuitry, and/or other circuitry in making measurements that are used in determining a device's motion context. For example, motion data from an accelerometer and/or an inertial measurement unit may be used to identify if a user's motions (e.g., repetitive up and down motions and/or other motions with a particular intensity, a particular cadence, or other recognizable pattern) correspond to walking, running, or cycling. If desired, location information from a satellite navigation system receiver may be used in determining a user's velocity and thereby determining whether a user is or is not walking, running, or cycling. In some arrangements, the frequency with which a user's cellular telephone transceiver links to different cellular telephone towers may be analyzed to help determine the user's motion. The user's frequency of linking to or receiving signals from different wireless local area network hotspots may also be analyzed to help determine the user's motion and/or other sensor information (e.g., altimeter readings indicating changes in altitude, etc.) may be gathered and processed to determine a user's activity. These techniques and/or other techniques may be used in determining motion context.
In addition to gathering and processing sensor data and other data indicative of the user's motion context, control circuitry 112 in device 10 may, if desired, monitor whether device 10 is wirelessly linked by a short-range wireless link (e.g., via Bluetooth) to handsfree audio systems in vehicles or other vehicle equipment known to be located in or associated with vehicles. In this way, the in-vehicle status of device 10 can be determined. For example, control circuitry 112 in a given device can determine whether the given device is preset in a vehicle or not based on whether circuitry 12 is or is not wirelessly linked with an in-vehicle hands-free system.
In addition to this presence-in-vehicle state information, control circuitry 112 can determine other information about the location of device 10. As an example, control circuitry 112 can conclude that a device is indoors if the device is linked by a short-range wireless link to in-home equipment (e.g., a set-top box, television, countertop speaker, in-home desktop computer, etc.) and can determine that the device is not indoors (and is therefore outdoors) if the device is not linked to this type of in-home equipment and, if desired, sensors in the device sense one or more additional indicators of presence in an outdoors environment such as bright sunlight, etc. In general, any suitable device status information (e.g. device context such as in-vehicle states, indoor-outdoor states, etc.) may be determined by devices 10 and can potentially be shared between devices, as appropriate.
In some embodiments, devices 10 and/or 20 (and/or other devices within system 8) may determine motion of a user. As shown in
Using data generated by the sensors that collect the motion information, control circuitry, such as control circuitry 112 of device 10, may perform a motion sensor analysis 38 by analyzing the data generated by the one or more sensors. For example, the control circuitry may compare the data generated by each sensor and fuse the data to determine a motion metric value 40. This may be done statistically through weighting, removing outlier measurements from the set, averaging the data, or any other desired method. Motion metric value 40 may be stored within the storage circuitry of the electronic device.
In general, the sensors used to calculate motion metric value 40 may automatically obtain updated motion data at any desired time interval and/or be manually triggered by actions of a user. In either case, the motion metric value 40 may be updated and logged within the storage circuitry when there is enough data to calculate the metric value.
In addition to calculating the motion of the device, sensors with electronic device 10 and/or device 20 may determine activity information of the user. As shown in
Using data generated by the activity information sensors (and the motion information sensors, if desired), control circuitry, such as control circuitry 112 of device 10, may perform an activity sensor analysis 50 by analyzing the data generated by the one or more sensors. For example, the control circuitry may compare the data generated by each sensor and fuse the data to determine an activity metric value 52. This may be done statistically through weighting, removing outlier measurements from the set, averaging the data, or any other desired method. Activity metric value 52 may be stored within the storage circuitry of the electronic device.
In general, the sensors used to calculate activity metric value 52 may automatically obtain updated motion data at any desired time interval and/or be manually triggered by actions of a user. In one example, the electronic device may be placed into an exercise mode, in which the activity information sensors and/or the motion sensors are activated more frequently to determine the user's biometric information more often. In any case, the activity metric value 52 may be updated and logged within the storage circuitry when there is enough data to calculate the metric value.
Based on the motion metric value, the activity metric value, and any other desired values, control circuitry within the electronic device, such as control circuitry 112 of device 10, may estimate a VO2 max value for the user. A flowchart of illustrative steps that may be used to determine the VO2 max, despite the possibility of conditions that may adversely impact the test, is shown in
As shown in
At step 56, the system may ensure that the user is in a state that reflects their maximal ability. In particular, the device may use the motion and activity sensors, such as a heart rate sensor and an accelerometer, to determine whether they recently completed a fatiguing exercise by calculating the user's heart rate, calories burned, and step rate or cadence. Additionally or alternatively, the circuitry may compare the recent heart rate to the user's resting heart rate or to the user's typical walking heart rates (which may be regularly calculated and stored within the device's circuitry, if desired). If the user has recently engaged in a fatiguing workout (e.g., their heart rate is over a threshold with respect to their resting heart rate or their typical walking heart rate), the system may postpone the VO2 max test, as performing the test during this period may lead to erroneous results. On the other hand, if the user has not recently engaged in a fatiguing exercise, the system may proceed to step 58.
At step 58, the circuitry may predict that the user will walk continuously for some period of time. In one example, the system may test for VO2 max when the user has been continuously for a threshold period of time, and may stop the test when the user stops walking. Alternatively or additionally, the circuitry may determine the user's cadence or calories burned using the motion sensors in the device. For example, the user's cadence or calories burned may be compared to their median or typical cadence or calories burned from the prior week (or any other desired time period). If the user's cadence or calories burned are atypically low, this may indicate atypical walking behaviors, such as walking a dog or walking with a slower person, and the system may postpone the VO2 max test. On the other hand, if the user's cadence or calories burned are higher than average, the user may intend to walk faster, and it may be desirable to conduct a VO2 test during that period, and may proceed to step 60.
At step 60, the system may initiate measurements with varied intensity profiles. The system may initiate these measurements using the motion sensors, such as the motion sensors 30 of
At step 62, the circuitry may ensure that measurements will occur at randomized times throughout the day. In particular, because users may have different activity profiles and behaviors that vary throughout the day, taking randomized readings may correct for abnormalities in the user behavior. In this way, the circuitry may activate the sensors required to perform the VO2 max test only when certain criteria are met and then ensure that the measurements are conducted using various intensity profiles and at randomized times throughout the day to provide for more accurate VO2 max estimation. To determine the user's VO2 max during these selected periods, the circuitry may use predetermined correlations between heart rate and VO2, along with extrapolating the user's activity to a maximum heart rate, at which point the user's VO2 max may be approximated.
Although the steps described in connection with
As shown in
Workout data 68 and body metrics 70 may undergo quality checks 72. In particular, the data collected from the sensors within the device may be passed through grade filters 74, heart rate confirmation 76, and calorie floor 78. These filters may remove data from workout data 68 and body metrics 70 that does not meet certain criteria. For example, there may be a threshold of data that must be collected prior to being passed through a filter, data collected on graded or abnormal surfaces may be removed from the data set, the heart rate sensor may need to detect an elevated heart rate during the workouts for those workouts to be included in the data, and the user may need to burn a minimum number of calories during a workout or during a certain day for that set of data to be included. However, these quality checks are merely illustrative. In general, workout data 68 and body metrics 70 may be filtered in any desired manner to ensure quality data is used in the VO2 max calculations.
Additionally, the user's detected heart rate may undergo heart rate normalization 80 to avoid undue influence from factors such as caffeine intake, stress, age, medication history, or any other factors. In particular, the heart rate may be normalized relative to other people and normalized relative to the user's individual baseline measurements.
A user's minimum heart rate (MIN HR 82), may be best measured from higher fidelity, more frequent heart rate sensor measurements throughout the day during periods of rest. HR MIN 82 may be selected in this way if a user begins walking from a rested state. However, if the user has an elevated heart rate at the beginning of the walk (e.g., due to stress or caffeine), HR MIN 82 may be determined by measuring the user's heart rate at the beginning of a walking period and using a logarithmic projection back to determine the minimum heart rate. For example, the logarithmic projection back may assume a first order rise in the heart rate in response to exercise. However, other back projections may be used if desired.
For some users however, there may not be sufficient heart rate sensor data to estimate HR MIN 82. Therefore, the user's HR MIN 82 may be approximated based on peak calories burned, which may in turn be estimated by the user's equivalent daily steps. In this way, a user who does not have logged heart rate data may still have a minimum heart rate determined.
The user's maximum heart rate (MAX HR 84) may need to be modified from the heart rate measured by the heart rate sensor. For example, a lower fitness user may be on medications that affect the user's maximal heart rate, such as rate control medication or beta blockers for blood pressure control. An example of an illustrative difference in estimated MAX HR vs. actual MAX HR for a low fitness user is shown in
As shown in
After heart rate normalization 80 has occurred, the corrected workout data 68 and body metrics 70, as well as maximum calories 66, may be used to estimate the user's VO2 max. Although traditional models used in VO2 max calculations may require users to exert themselves such that their heart rate is over 40%, physiological models may be used to approximate user's VO2 max using data that is below the 40% threshold. This 40% threshold may be a measured as 40% of the user's heart rate reserve (i.e., 40% of the difference between the user's maximum heart rate and minimum heart rate). As shown in
To determine a curve that correlates the individual user's heart rate to VO2 such that the user's VO2 max may be estimated, heart rate and physical activity data may be gathered by one or more devices and may be projected along the modeled curve to determine VO2 max. For example, if the relation between normalized heart rate and VO2/heart rate is logarithmic, as shown in
It may also be desired to use multi-session cluster aggregation 88 when making the determination of the user's oxygen pulse to normalized heart rate curve. In particular, by clustering multiple sessions of activity data, the effects of outlier data may be reduced.
For example, walking or running on a grade may introduce volatility into the user's heart rate and/or speed. However, using clustering techniques, steady-state portions of the user's workout may be extracted, and the data may still be used with less volatility.
In another example, many users may have insufficient data for a reliable prediction after a single walk or run workout, and user's often move at a fairly constant rate over a single session, making predictions based on a single session unreliable. However, clustering across multiple sessions may enable a single, globally optimal estimate of a user's VO2 max. Additionally, sensors may be used outside of workouts (as described previously) to capture different ranges of walking speeds than is present in recorded workouts alone.
In another example, users may walk in undesirable terrain (such as mud, sand or snow), at altitude, carrying a heavy load, or with some other condition that may increase heart rate or reduce the user's activity output, all of which may be unobservable to the heart rate sensor and/or the motion sensors on the device. However, these scenarios are largely outlier scenarios that may be disregarded in a cluster analysis compared to other clusters from other sessions.
In another example, users may pause frequently, causing a change in heart rate, which may occur more frequently for lower fitness users. However, the cluster analysis may be at least partially removed if this is an outlier scenario (e.g., if the user is a higher fitness user), or may be included if it is not an outlier scenario. If desired, the cluster analysis may be performed with clusters of dynamic size, or the system may require that new clusters are created shortly after a pause.
In another example, large amounts of data may be excluded due to quality checks 72. However, using cluster analysis, clusters may be associated with respective confidences and weighted appropriately. Therefore, in a shortage of data, the system may keep more data in the analysis, and weight more accurate data greater than less accurate/significant data. Additionally or alternatively, more data may be gathered outside of user initiated workouts, providing more accurate data than workout-only data.
By computing clusters 86 and performing multi-session cluster aggregation 88, more accurate correlations between a user's heart rate and VO2 may be obtained, thereby resulting in a more accurate VO2 max estimation.
However, while the determined logarithmic relationship based on computed clusters 86 and multi-session cluster aggregation 88 may provide an accurate VO2 max for an active user with a significant amount of workout data at high heart rates, the relationship may be different for lower fitness users. An example of this is shown in
As shown in
Probabilistic prior calculation 90 may be based on probabilistic distributions of age, biological sex, activity (as measured by maximum calories burned over a selected time period), and fitness level (using activity information from the electronic device or calculated based on motion and activity sensor data). In particular, these factors may be used to select a predicted oxygen pulse curve shape for the user. For example, with increased age or reduced activity/fitness, a user's heart response typically becomes more blunted (e.g. the curves for those users may have a smaller slope, as illustrated by curves 93 of
After the cluster analysis 86/88 and personalized curve selection 92, the cluster analysis may be used to fit the personalized curve based on the aggregated cluster data. Once this correction has been made, a projection may be made to the user's maximum heart rate 94. For example, the corrected curve may be extrapolated/projected, and a maximum heart rate predicted. At this maximum heart rate, the user's VO2 max 96 may be determined (e.g., because of the relation between the user's oxygen pulse and VO2). By using the system shown in
A flowchart illustrating the steps performed in connection with the diagram of
At step 100, the circuitry may perform quality checks and heart rate normalization of the workout data and the body metric data. In particular, the data collected from the sensors within the device may be passed through filters, heart rate confirmation, and calorie floor checks. These filters may remove data from workout data and body metric data that does not meet certain criteria. For example, there may be a threshold of data that must be collected prior to being passed through a filter, data collected on graded or abnormal surfaces may be removed from the data set, the heart rate sensor may need to detect an elevated heart rate during the workouts for those workouts to be included in the data, and the user may need to burn a minimum number of calories during a workout or during a certain day for that set of data to be included. However, these quality checks are merely illustrative. In general, the workout data and body metric data may be filtered in any desired manner to ensure quality data is used in the VO2 max calculations.
Additionally, the user's detected heart rate may undergo heart rate normalization 80 to avoid undue influence from factors such as caffeine intake, stress, age, medication history, or any other factors. In particular, the heart rate may be normalized relative to other people and normalized relative to the user's individual baseline measurements. As described previously in connection with
At step 102, the workout data, body metric data, and calorie data may be used to compute clusters of data and aggregate the data over multiple periods. In particular, clusters of the user's workout, calorie data, and body metrics may be analyzed to determine a relationship between the user's oxygen pulse and heart rate. As a result, a curve (such a logarithmic curve) may be fit to that user's relationship between oxygen pulse and heart rate, and the user's VO2 max may be determined from the resulting individualized oxygen pulse curve. By clustering multiple sessions of activity data, the effects of outlier data may be reduced.
At step 104, which is in parallel with step 102, probabilistic calculations may be performed, and a personalized curve may be selected for the user to estimate the relationship between the user's normalized heart rate and oxygen pulse. Probabilistic prior calculation may be based on probabilistic distributions of age, biological sex, activity (as measured by maximum calories burned over a selected time period), and fitness level (using activity information from the electronic device or calculated based on motion and activity sensor data). In particular, these factors may be used to select a predicted oxygen pulse curve shape for the user.
At step 106, the personalized curve shape determined in step 104 may be projected and/or fitted based on the cluster analysis of step 102. In this way, the personalized curve may provide a better model of the user's oxygen pulse vs. normalized heart rate.
At step 108, the projected/fitted personalized curve may be used to estimate the user's maximum heart rate and thereby estimate the user's VO2 max (e.g., because of the relation between the user's oxygen pulse and VO2).
After the user's VO2 max has been estimated, it may be stored as health information. This VO2 max value may be stored with previous estimations of VO2 max, may be presented to a user in histograms of data, may be sent to a doctor's office, may trigger an alert to the user or to a physician, may be used by other applications on electronic device 10, electronic device 20, and/or any other desired device, or may be used in any other desired fashion.
To obtain accurate VO2 max estimates while reducing battery drain, it may be desirable to use some sensors all of the time while device 10 is in use, while only activating other sensors when needed to estimate a user's VO2 max. An example of this is shown in
As shown in
At step 112, control circuitry may determine whether a speed and duration threshold has been met by a user of device 10. For example, the threshold may be at least 2 minutes, at least 1 minute, or any other desired duration at least 1.5 mph, at least 1.8 mph, at least 2.0 mph, or any other desired speed. If the speed and duration threshold is not met (i.e., the user has not gone a requisite speed for a minimum amount of time), the process may proceed along line 114 and continue collecting only step and speed information.
If the speed and duration threshold is met, the process may proceed to step 116, in which the control circuitry may determine whether the device has sufficient battery remaining to activate additional sensors. For example, the control circuitry may determine whether there is over 20% battery remaining, over 10% battery remaining, or any other desired battery threshold. If there is insufficient battery remaining, the process may proceed along line 118 to continue collecting step and speed information without activating additional sensors that may drain the device battery.
If there is sufficient battery, the process may proceed to step 120, in which the control circuitry may activate additional sensors that may be used to determine a user's VO2 max. For example, the control circuitry may activate a GPS sensor, such as GPS sensor 36 of
At step 122, the control circuitry may initialized the VO2 max estimation process. This process may be the same or substantially the same as the VO2 max estimation process described in connection with
At step 124, quality checks may be performed, which may be the same as the quality checks discussed above at step 100 of
At step 128, the user's VO2 max may be estimated. This estimation may be done in the same way or substantially the same way as discussed above in connection with
As described above, one aspect of the present technology is the gathering and use of information such as information from input-output devices. The present disclosure contemplates that in some instances, data may be gathered that includes personal information data that uniquely identifies or can be used to contact or locate a specific person. Such personal information data can include demographic data, location-based data, telephone numbers, email addresses, twitter ID's, home addresses, data or records relating to a user's health or level of fitness (e.g., vital signs measurements, medication information, exercise information), date of birth, username, password, biometric information, or any other identifying or personal information.
The present disclosure recognizes that the use of such personal information, in the present technology, can be used to the benefit of users. For example, the personal information data can be used to deliver targeted content that is of greater interest to the user. Accordingly, use of such personal information data enables users to calculated control of the delivered content. Further, other uses for personal information data that benefit the user are also contemplated by the present disclosure. For instance, health and fitness data may be used to provide insights into a user's general wellness, or may be used as positive feedback to individuals using technology to pursue wellness goals.
The present disclosure contemplates that the entities responsible for the collection, analysis, disclosure, transfer, storage, or other use of such personal information data will comply with well-established privacy policies and/or privacy practices. In particular, such entities should implement and consistently use privacy policies and practices that are generally recognized as meeting or exceeding industry or governmental requirements for maintaining personal information data private and secure. Such policies should be easily accessible by users, and should be updated as the collection and/or use of data changes. Personal information from users should be collected for legitimate and reasonable uses of the entity and not shared or sold outside of those legitimate uses. Further, such collection/sharing should occur after receiving the informed consent of the users. Additionally, such entities should consider taking any needed steps for safeguarding and securing access to such personal information data and ensuring that others with access to the personal information data adhere to their privacy policies and procedures. Further, such entities can subject themselves to evaluation by third parties to certify their adherence to widely accepted privacy policies and practices. In addition, policies and practices should be adapted for the particular types of personal information data being collected and/or accessed and adapted to applicable laws and standards, including jurisdiction-specific considerations. For instance, in the United States, collection of or access to certain health data may be governed by federal and/or state laws, such as the Health Insurance Portability and Accountability Act (HIPAA), whereas health data in other countries may be subject to other regulations and policies and should be handled accordingly. Hence different privacy practices should be maintained for different personal data types in each country.
Despite the foregoing, the present disclosure also contemplates embodiments in which users selectively block the use of, or access to, personal information data. That is, the present disclosure contemplates that hardware and/or software elements can be provided to prevent or block access to such personal information data. For example, the present technology can be configured to allow users to select to “opt in” or “opt out” of participation in the collection of personal information data during registration for services or anytime thereafter. In another example, users can select not to provide certain types of user data. In yet another example, users can select to limit the length of time user-specific data is maintained. In addition to providing “opt in” and “opt out” options, the present disclosure contemplates providing notifications relating to the access or use of personal information. For instance, a user may be notified upon downloading an application (“app”) that their personal information data will be accessed and then reminded again just before personal information data is accessed by the app.
Moreover, it is the intent of the present disclosure that personal information data should be managed and handled in a way to minimize risks of unintentional or unauthorized access or use. Risk can be minimized by limiting the collection of data and deleting data once it is no longer needed. In addition, and when applicable, including in certain health related applications, data de-identification can be used to protect a user's privacy. De-identification may be facilitated, when appropriate, by removing specific identifiers (e.g., date of birth, etc.), controlling the amount or specificity of data stored (e.g., collecting location data at a city level rather than at an address level), controlling how data is stored (e.g., aggregating data across users), and/or other methods.
Therefore, although the present disclosure broadly covers use of information that may include personal information data to implement one or more various disclosed embodiments, the present disclosure also contemplates that the various embodiments can also be implemented without the need for accessing personal information data. That is, the various embodiments of the present technology are not rendered inoperable due to the lack of all or a portion of such personal information data.
The foregoing is illustrative and various modifications can be made to the described embodiments. The foregoing embodiments may be implemented individually or in any combination.
This application claims the benefit of provisional patent application No. 63/041,735, filed on Jun. 19, 2020, which is hereby incorporated by reference herein in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 63041735 | Jun 2020 | US |
