Patent Grant
11638561
The present invention relates generally to monitoring devices and, more particularly, to monitoring devices for measuring physiological information.
Photoplethysmography (PPG) is based upon shining light into the human body and measuring how the scattered light intensity changes with each pulse of blood flow. The scattered light intensity will change in time with respect to changes in blood flow or blood opacity associated with heart beats, breaths, blood oxygen level (SpO2), and the like. Such a sensing methodology may require the magnitude of light energy reaching the volume of flesh being interrogated to be steady and consistent so that small changes in the quantity of scattered photons can be attributed to varying blood flow. If the incidental and scattered photon count magnitude changes due to light coupling variation between the source or detector and the skin or other body tissue, then the signal of interest can be difficult to ascertain due to large photon count variability caused by motion artifacts. Changes in the surface area (and volume) of skin or other body tissue being impacted with photons, or varying skin surface curvature reflecting significant portions of the photons may also significantly impact optical coupling efficiency. Physical activity, such a walking, cycling, running, etc., may cause motion artifacts in the optical scatter signal from the body, and time-varying changes in photon intensity due to motion artifacts may swamp-out time-varying changes in photon intensity due to blood flow changes. Each of these changes in optical coupling can dramatically reduce the signal-to-noise ratio (S/N) of biometric PPG information to total time-varying photonic interrogation count. This can result in a much lower accuracy in metrics derived from PPG data, such as heart rate and breathing rate.
An earphone, such as a headset, earbud, etc., may be a good choice for incorporation of a photoplethysmograph device because it is a form factor that individuals are familiar with, it is a device that is commonly worn for long periods of time, and it frequently is used during exercise which is a time when individuals may benefit most from having accurate heart rate data (or other physiological data). Unfortunately, incorporation of a photoplethysmograph device into an earphone poses several challenges. For example, earphones may be uncomfortable to wear for long periods of time, particularly if they deform the ear surface. Moreover, human ear anatomy may vary significantly from person to person, so finding an earbud form that will fit comfortably in many ears may pose significant challenges. In addition, earbuds made for vigorous physical activity typically incorporate an elastomeric surface and/or elastomeric features to function as springs that dampen earbud acceleration within the ear. Although, these features may facilitate retention of an earbud within an ear during high acceleration and impact modalities, they may not adequately address optical skin coupling requirements needed to achieve quality photoplethysmography.
Conventional photoplethysmography devices, as illustrated for example in
A conventional earbud device that performs photoplethysmography in the ear is the MX-D100 player from Perception Digital of Wanchai, Hong Kong (www.perceptiondigital.com). This earbud device, illustrated in
Because PPG used in wearable devices employs an optical technology, requiring the powering of optical emitters and microprocessors via a wearable battery, managing power consumption can be challenging. For example, high-power algorithms may be required to accurately measure heart rate during exercise. Thus, employing a high-power algorithm during exercise may have the benefit of accurately monitoring heart rate during exercise but may also have the unwanted effect of draining the battery of the wearable device such that the device will not have enough power to measure a subject over the course of a day or week during non-exercising periods.
It should be appreciated that this Summary is provided to introduce a selection of concepts in a simplified form, the concepts being further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of this disclosure, nor is it intended to limit the scope of the invention.
According to some embodiments of the present invention, a monitoring device configured to be attached to a body of a subject includes a sensor that is configured to detect and/or measure physiological information from the subject and a processor coupled to the sensor that is configured to receive and analyze signals produced by the sensor. The sensor may be an optical sensor that includes at least one optical emitter and at least one optical detector, although various other types of sensors may be utilized. The processor is configured to change the signal analysis frequency (i.e., the signal sampling rate), sensor algorithm, and/or sensor interrogation power in response to detecting a change in subject activity. For example, in some embodiments, the processor increases signal analysis frequency and/or sensor interrogation power in response to detecting an increase in subject activity, and decreases signal analysis frequency and/or sensor interrogation power in response to detecting a decrease in subject activity. In other embodiments, the processor may change the sensor algorithm in response to a change in subject activity. For example, the processor may implement frequency-domain digital signal processing in response to detecting high subject activity, and implement time-domain digital signal processing in response to detecting low subject activity. The frequency- and time-domain algorithms represent two different signal extraction methods for extracting accurate biometrics from optical sensor signals, where the frequency-domain algorithm may require substantially greater processing power than that of the time-domain algorithm.
In some embodiments, detecting a change in subject activity comprises detecting a change in at least one subject vital sign, such as subject heart rate, subject blood pressure, subject temperature, subject respiration rate, subject perspiration rate, etc. In other embodiments, the sensor includes a motion sensor, such as an accelerometer, gyroscope, etc., and detecting a change in subject activity includes detecting a change in subject motion via the motion sensor. In some embodiments, detecting a change in subject activity may include predicting a type of activity the subject is engaged in.
According to some embodiments of the present invention, a method of monitoring a subject via a monitoring device having a sensor includes changing signal analysis frequency and/or sensor interrogation power in response to detecting a change in subject activity. In some embodiments, detecting a change in subject activity comprises detecting a change in at least one subject vital sign, such as subject heart rate, subject blood pressure, subject temperature, subject respiration rate, and/or subject perspiration rate, etc. In other embodiments, detecting a change in subject activity comprises detecting a change in subject motion via a motion sensor associated with the sensor.
In some embodiments, changing signal analysis frequency and/or sensor interrogation power in response to detecting a change in subject activity includes increasing signal analysis frequency and/or sensor interrogation power in response to detecting an increase in subject activity, and decreasing signal analysis frequency and/or sensor interrogation power in response to detecting a decrease in subject activity. In other embodiments, the processor is configured to implement frequency-domain digital signal processing in response to detecting high subject activity, and to implement time-domain digital signal processing in response to detecting low subject activity.
According to other embodiments of the present invention, a monitoring device configured to be attached to a subject includes a sensor configured to detect and/or measure physiological information from the subject. The monitoring device also includes a processor coupled to the sensor that is configured to receive and analyze signals produced by the sensor. The sensor may be an optical sensor that includes at least one optical emitter and at least one optical detector, although various other types of sensors may be utilized. The processor is configured to change signal analysis frequency and/or sensor interrogation power in response to detecting, via the sensor or another sensor, a change in the at least one environmental condition, such as temperature, humidity, air quality, barometric pressure, radiation, light intensity, and sound. For example, in some embodiments, the processor increases signal analysis frequency and/or sensor interrogation power in response to detecting an increase in the at least one environmental condition, and decreases signal analysis frequency and/or sensor interrogation power in response to detecting a decrease in the at least one environmental condition.
In some embodiments, a method of monitoring a subject via a monitoring device includes changing signal analysis frequency and/or sensor interrogation power in response to detecting a change in at least one environmental condition. For example, in some embodiments, changing signal analysis frequency and/or sensor interrogation power in response to detecting a change in at least one environmental condition includes increasing signal analysis frequency and/or sensor interrogation power in response to detecting an increase in at least one environmental condition, and decreasing signal analysis frequency and/or sensor interrogation power in response to detecting a decrease in at least one environmental condition.
According to other embodiments of the present invention, a monitoring device configured to be attached to a subject includes a clock (e.g., a digital clock, an internal software clock, etc.) or is in communication with a clock, a sensor configured to detect and/or measure physiological information from the subject, and a processor coupled to the clock and the sensor. The sensor may be an optical sensor that includes at least one optical emitter and at least one optical detector, although various other types of sensors may be utilized. The processor is configured to receive and analyze signals produced by the sensor, and is configured to change signal analysis frequency and/or sensor interrogation power at one or more predetermined times. For example, in some embodiments, the processor increases signal analysis frequency and/or sensor interrogation power at a first time, and decreases signal analysis frequency and/or sensor interrogation power at a second time. In other embodiments, the processor adjusts signal analysis frequency and/or sensor interrogation power according to a circadian rhythm of the subject.
According to some embodiments, a method of monitoring a subject via a monitoring device includes changing signal analysis frequency and/or sensor interrogation power at one or more predetermined times. In some embodiments, changing signal analysis frequency and/or sensor interrogation power at one or more predetermined times includes increasing signal analysis frequency and/or sensor interrogation power at a first time, and decreasing signal analysis frequency and/or sensor interrogation power at a second time. In other embodiments, changing signal analysis frequency and/or sensor interrogation power at one or more predetermined times comprises adjusting signal analysis frequency and/or sensor interrogation power according to a circadian rhythm of the subject.
According to other embodiments of the present invention, a monitoring device configured to be attached to a subject includes a location sensor or is in communication with a location sensor, a sensor configured to detect and/or measure physiological information from the subject, and a processor coupled to the location sensor and the sensor. The sensor may be an optical sensor that includes at least one optical emitter and at least one optical detector, although various other types of sensors may be utilized. The processor is configured to receive and analyze signals produced by the sensor and to change signal analysis frequency and/or sensor interrogation power when the subject has changed locations. For example, in some embodiments, the processor increases signal analysis frequency and/or sensor interrogation power when the subject is at a particular location, and decreases signal analysis frequency and/or sensor interrogation power when the subject is no longer at the particular location
According to some embodiments, a method of monitoring a subject via a monitoring device includes changing signal analysis frequency and/or sensor interrogation power when a location sensor associated with the monitoring device indicates the subject has changed locations. For example, in some embodiments, signal analysis frequency and/or sensor interrogation power is increased when the subject is at a particular location, and signal analysis frequency and/or sensor interrogation power is decreased when the subject is no longer at the particular location.
According to other embodiments of the present invention, a monitoring device configured to be attached to a subject includes a sensor configured to detect and/or measure physiological information from the subject, and a processor coupled to the sensor. The sensor includes at least one optical emitter and at least one optical detector. The processor is configured to receive and analyze signals produced by the sensor, and is configured to change the wavelength of light emitted by the at least one optical emitter in response to detecting a change in subject activity. In some embodiments, the processor instructs the at least one optical emitter to emit shorter wavelength light (e.g., a decrease in wavelength by 100 nm or more) in response to detecting an increase in subject activity, and instructs the at least one optical emitter to emit longer wavelength light (e.g., an increase in wavelength by 100 nm or more) in response to detecting an decrease in subject activity.
In some embodiments, detecting a change in subject activity comprises detecting a change in at least one subject vital sign, such as subject heart rate, subject blood pressure, subject temperature, subject respiration rate, subject perspiration rate, etc. In other embodiments, the sensor includes a motion sensor, such as an accelerometer, gyroscope, etc., and detecting a change in subject activity includes detecting a change in subject motion via the motion sensor.
In some embodiments, detecting a change in subject activity may include predicting a type of activity the subject is engaged in.
According to some embodiments of the present invention, a method of monitoring a subject via a monitoring device having a sensor includes changing wavelength of light emitted by at least one optical emitter associated with the sensor in response to detecting a change in subject activity. For example, in some embodiments, changing wavelength of light emitted by the at least one optical emitter may include instructing the at least one optical emitter to emit shorter wavelength light in response to detecting an increase in subject activity, and instructing the at least one optical emitter to emit longer wavelength light in response to detecting an decrease in subject activity.
According to other embodiments of the present invention, a monitoring device configured to be attached to a subject includes a sensor configured to detect and/or measure physiological information from the subject, and a processor coupled to the sensor and configured to receive and analyze signals produced by the sensor. The sensor comprises at least one optical emitter and at least one optical detector, and the processor instructs the at least one optical emitter to emit a different wavelength of light during each of a series of respective time intervals such that a respective different physiological parameter can be measured from the subject during each time interval via the at least one optical detector.
According to some embodiments of the present invention, a method of monitoring a subject via a monitoring device having a sensor with at least one optical emitter and at least one optical detector comprises emitting a different wavelength of light during each of a series of respective time intervals, and measuring a respective different physiological parameter of the subject during each time interval via the at least one optical detector.
According to other embodiments of the present invention, a monitoring device configured to be attached to a subject includes a sensor configured to detect and/or measure physiological information from the subject, and a processor coupled to the sensor. The processor is configured to receive and analyze signals produced by the sensor, and is configured to change signal analysis frequency and/or change sensor interrogation power in response to detecting a change in subject stress level (e.g., by detecting a change in at least one subject vital sign, such as heart rate, blood pressure, temperature, respiration rate, and/or perspiration rate). For example, in some embodiments, the processor increases signal analysis frequency and/or increases sensor interrogation power in response to detecting an increase in subject stress level, and decreases signal analysis frequency and/or decreases sensor interrogation power in response to detecting a decrease in subject stress level.
In some embodiments, the sensor comprises a voice recognition system. The processor is configured to increase processing power for the voice recognition system in response to detecting an increase in subject stress level, and to decrease processing power for the voice recognition system in response to detecting an decrease in subject stress level.
In some embodiments, the sensor is in communication with a user interface. In some embodiments, the processor may be configured to increase user interface brightness and/or font size of alphanumeric characters displayed on the user interface in response to detecting an increase in subject stress level, and is configured to decrease user interface brightness and/or font size of alphanumeric characters displayed on the user interface in response to detecting a decrease in subject stress level. In some embodiments, the processor may be configured to enlarge an image displayed within the user interface and/or make an image displayed within the user interface easier to view/comprehend (e.g., increase the resolution of the image, etc.) in response to detecting an increase in subject stress level. The processor may be configured to decrease an image displayed within the user interface and/or reduce the resolution of an image displayed within the user interface in response to detecting an increase in subject stress level.
According to some embodiments of the present invention, a method of monitoring a subject via a monitoring device having a sensor includes changing signal analysis frequency and/or changing sensor interrogation power via the processor in response to detecting a change in subject stress level. For example, in some embodiments signal analysis frequency and/or sensor interrogation power is increased in response to detecting an increase in subject stress level, and signal analysis frequency and/or sensor interrogation power is decreased in response to detecting a decrease in subject stress level.
In some embodiments, the sensor comprises a voice recognition system, and the method includes increasing processing power for the voice recognition system in response to detecting an increase in subject stress level, and decreasing processing power for the voice recognition system in response to detecting a decrease in subject stress level.
In some embodiments, the sensor is in communication with a user interface, and the method includes increasing user interface brightness and/or font size of alphanumeric characters displayed on the user interface in response to detecting an increase in subject stress level, and decreasing user interface brightness and/or font size of alphanumeric characters displayed on the user interface in response to detecting a decrease in subject stress level.
Monitoring devices in accordance with some embodiments of the present invention may be configured to be positioned at or within an ear of a subject or secured to an appendage or other body location of the subject.
Monitoring devices, according to embodiments of the present invention, are advantageous over conventional monitoring devices because, by changing signal analysis frequency and/or sensor interrogation power, power savings may be incurred. Moreover, increasing sensing power or sampling frequency may allow for finer, more accurate sensor data to be collected during periods of rapid body activity, e.g., during exercising, running, walking, etc. Conversely sensor data changes during periods of inactivity maybe infrequent and require significantly lower power to achieve sufficient data resolution to accurately describe physiological changes.
It is noted that aspects of the invention described with respect to one embodiment may be incorporated in a different embodiment although not specifically described relative thereto. That is, all embodiments and/or features of any embodiment can be combined in any way and/or combination. Applicant reserves the right to change any originally filed claim or file any new claim accordingly, including the right to be able to amend any originally filed claim to depend from and/or incorporate any feature of any other claim although not originally claimed in that manner. These and other objects and/or aspects of the present invention are explained in detail below.
The accompanying drawings, which form a part of the specification, illustrate various embodiments of the present invention. The drawings and description together serve to fully explain embodiments of the present invention.
The present invention will now be described more fully hereinafter with reference to the accompanying figures, in which embodiments of the invention are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Like numbers refer to like elements throughout. In the figures, certain layers, components or features may be exaggerated for clarity, and broken lines illustrate optional features or operations unless specified otherwise. In addition, the sequence of operations (or steps) is not limited to the order presented in the figures and/or claims unless specifically indicated otherwise. Features described with respect to one figure or embodiment can be associated with another embodiment or figure although not specifically described or shown as such.
It will be understood that when a feature or element is referred to as being “on” another feature or element, it can be directly on the other feature or element or intervening features and/or elements may also be present. In contrast, when a feature or element is referred to as being “directly on” another feature or element, there are no intervening features or elements present. It will also be understood that, when a feature or element is referred to as being “secured”, “connected”, “attached” or “coupled” to another feature or element, it can be directly secured, directly connected, attached or coupled to the other feature or element or intervening features or elements may be present. In contrast, when a feature or element is referred to as being “directly secured”, “directly connected”, “directly attached” or “directly coupled” to another feature or element, there are no intervening features or elements present. Although described or shown with respect to one embodiment, the features and elements so described or shown can apply to other embodiments.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise.
As used herein, the terms “comprise”, “comprising”, “comprises”, “include”, “including”, “includes”, “have”, “has”, “having”, or variants thereof are open-ended, and include one or more stated features, integers, elements, steps, components or functions but does not preclude the presence or addition of one or more other features, integers, elements, steps, components, functions or groups thereof. Furthermore, as used herein, the common abbreviation “e.g.”, which derives from the Latin phrase “exempli gratia,” may be used to introduce or specify a general example or examples of a previously mentioned item, and is not intended to be limiting of such item. The common abbreviation “i.e.”, which derives from the Latin phrase “id est,” may be used to specify a particular item from a more general recitation.
As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items and may be abbreviated as “/”.
As used herein, phrases such as “between X and Y” and “between about X and Y” should be interpreted to include X and Y. As used herein, phrases such as “between about X and Y” mean “between about X and about Y.” As used herein, phrases such as “from about X to Y” mean “from about X to about Y.”
Spatially relative terms, such as “under”, “below”, “lower”, “over”, “upper” and the like, may be used herein for ease of description to describe one element or feature's relationship to another element(s) or feature(s) as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. For example, if a device in the figures is inverted, elements described as “under” or “beneath” other elements or features would then be oriented “over” the other elements or features. Thus, the exemplary term “under” can encompass both an orientation of over and under. The device may be otherwise oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly. Similarly, the terms “upwardly”, “downwardly”, “vertical”, “horizontal” and the like are used herein for the purpose of explanation only unless specifically indicated otherwise.
It will be understood that although the terms first and second are used herein to describe various features or elements, these features or elements should not be limited by these terms. These terms are only used to distinguish one feature or element from another feature or element. Thus, a first feature or element discussed below could be termed a second feature or element, and similarly, a second feature or element discussed below could be termed a first feature or element without departing from the teachings of the present invention.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the specification and relevant art and should not be interpreted in an idealized or overly formal sense unless expressly so defined herein. Well-known functions or constructions may not be described in detail for brevity and/or clarity.
The term “about”, as used herein with respect to a value or number, means that the value or number can vary more or less, for example by +/−20%, +/−10%, +/−5%, +/−1%, +1-0.5%, +/−0.1%, etc.
The terms “sensor”, “sensing element”, and “sensor module”, as used herein, are interchangeable and refer to a sensor element or group of sensor elements that may be utilized to sense information, such as information (e.g., physiological information, body motion, etc.) from the body of a subject and/or environmental information in a vicinity of the subject. A sensor/sensing element/sensor module may comprise one or more of the following: a detector element, an emitter element, a processing element, optics, mechanical support, supporting circuitry, and the like. Both a single sensor element and a collection of sensor elements may be considered a sensor, a sensing element, or a sensor module.
The term “optical emitter”, as used herein, may include a single optical emitter and/or a plurality of separate optical emitters that are associated with each other.
The term “optical detector”, as used herein, may include a single optical detector and/or a plurality of separate optical detectors that are associated with each other.
The term “wearable sensor module”, as used herein, refers to a sensor module configured to be worn on or near the body of a subject.
The terms “monitoring device” and “biometric monitoring device”, as used herein, are interchangeable and include any type of device, article, or clothing that may be worn by and/or attached to a subject and that includes at least one sensor/sensing element/sensor module. Exemplary monitoring devices may be embodied in an earpiece, a headpiece, a finger clip, a digit (finger or toe) piece, a limb band (such as an arm band or leg band), an ankle band, a wrist band, a nose piece, a sensor patch, eyewear (such as glasses or shades), apparel (such as a shirt, hat, underwear, etc.), a mouthpiece or tooth piece, contact lenses, or the like.
The term “monitoring” refers to the act of measuring, quantifying, qualifying, estimating, sensing, calculating, interpolating, extrapolating, inferring, deducing, or any combination of these actions. More generally, “monitoring” refers to a way of getting information via one or more sensing elements. For example, “blood health monitoring” includes monitoring blood gas levels, blood hydration, and metabolite/electrolyte levels.
The term “headset”, as used herein, is intended to include any type of device or earpiece that may be attached to or near the ear (or ears) of a user and may have various configurations, without limitation. Headsets incorporating optical sensor modules, as described herein, may include mono headsets (a device having only one earbud, one earpiece, etc.) and stereo headsets (a device having two earbuds, two earpieces, etc.), earbuds, hearing aids, ear jewelry, face masks, headbands, and the like. In some embodiments, the term “headset” may include broadly headset elements that are not located on the head but are associated with the headset. For example, in a “medallion” style wireless headset, where the medallion comprises the wireless electronics and the headphones are plugged into or hard-wired into the medallion, the wearable medallion would be considered part of the headset as a whole. Similarly, in some cases, if a mobile phone or other mobile device is intimately associated with a plugged-in headphone, then the term “headset” may refer to the headphone-mobile device combination. The terms “headset” and “earphone”, as used herein, are interchangeable.
The term “physiological” refers to matter or energy of or from the body of a creature (e.g., humans, animals, etc.). In embodiments of the present invention, the term “physiological” is intended to be used broadly, covering both physical and psychological matter and energy of or from the body of a creature.
The term “body” refers to the body of a subject (human or animal) that may wear a monitoring device, according to embodiments of the present invention.
The term “processor” is used broadly to refer to a signal processor or computing system or processing or computing method which may be localized or distributed. For example, a localized signal processor may comprise one or more signal processors or processing methods localized to a general location, such as to a wearable device. Examples of such wearable devices may comprise an earpiece, a headpiece, a finger clip, a digit (finger or toe) piece, a limb band (such as an arm band or leg band), an ankle band, a wrist band, a nose piece, a sensor patch, eyewear (such as glasses or shades), apparel (such as a shirt, hat, underwear, etc.), a mouthpiece or tooth piece, contact lenses, or the like. Examples of a distributed processor comprise “the cloud”, the internet, a remote database, a remote processor computer, a plurality of remote processors or computers in communication with each other, or the like, or processing methods distributed amongst one or more of these elements. The key difference is that a distributed processor may include delocalized elements, whereas a localized processor may work independently of a distributed processing system. As a specific example, microprocessors, microcontrollers, ASICs (application specific integrated circuits), analog processing circuitry, or digital signal processors are a few non-limiting examples of physical signal processors that may be found in wearable devices.
The term “remote” does not necessarily mean that a remote device is a wireless device or that it is a long distance away from a device in communication therewith. Rather, the term “remote” is intended to reference a device or system that is distinct from another device or system or that is not substantially reliant on another device or system for core functionality. For example, a computer wired to a wearable device may be considered a remote device, as the two devices are distinct and/or not substantially reliant on each other for core functionality. However, any wireless device (such as a portable device, for example) or system (such as a remote database for example) is considered remote to any other wireless device or system.
The terms “signal analysis frequency” and “signal sampling rate”, as used herein, are interchangeable and refer to the number of samples per second (or per other unit) taken from a continuous sensor (i.e., physiological sensor and environmental sensor) signal to ultimately make a discrete signal.
The term “sensor module interrogation power”, as used herein, refers to the amount of electrical power required to operate one or more sensors (i.e., physiological sensors and environmental sensors) of a sensor module and/or any processing electronics or circuitry (such as microprocessors and/or analog processing circuitry) associated therewith. Examples of decreasing the sensor interrogation power may include lowering the voltage or current through a sensor element (such as lowering the voltage or current applied to a pair of electrodes), lowering the polling (or polling rate) of a sensor element (such as lowering the frequency at which an optical emitter is flashed on/off in a PPG sensor), lowering the sampling frequency of a stream of data (such as lowering the sampling frequency of the output of an optical detector in a PPG sensor), selecting a lower-power algorithm (such as selecting a power-efficient time-domain processing method for measuring heart rate vs. a more power-hungry frequency-domain processing method), or the like. Lowering the interrogation power may also include powering only one electrode, or powering less electrodes, in a sensor module or sensor element such that less total interrogation power is exposed to the body of a subject. For example, lowering the interrogation power of a PPG sensor may comprise illuminating only one light-emitting diode rather than a plurality of light-emitting diodes that may be present in the sensor module, and lowering the interrogation power of a bioimpedance sensor may comprise powering only one electrode pair rather than a plurality of electrodes that may be present in the bioimpedance sensor module.
The term “polling” typically refers to controlling the intensity of an energy emitter of a sensor or to the “polling rate” and/or duty cycle of an energy emitter element in a sensor, such as an optical emitter in a PPG sensor or an ultrasonic driver in an ultrasonic sensor. Polling may also refer to the process of collecting and not collecting sensor data at certain periods of time. For example, a PPG sensor may be “polled” by controlling the intensity of one or more optical emitters, i.e. by pulsing the optical emitter over time. Similarly, the detector of a PPG sensor may be polled by reading data from that sensor only at a certain point in time or at certain intervals, i.e., as in collecting data from the detector of a PPG sensor for a brief period during each optical emitter pulse. A sensor may also be polled by turning on or off one or more elements of that sensor in time, such as when a PPG sensor is polled to alternate between multiple LED wavelengths over time or when an ultrasonic sensor is polled to alternate between mechanical vibration frequencies over time.
The terms “sampling frequency”, “signal analysis frequency”, and “signal sampling rate”, as used herein, are interchangeable and refer to the number of samples per second (or per other unit) taken from a continuous sensor or sensing element (for example, the sampling rate of the thermopile output in a tympanic temperature sensor).
It should be noted that processes for managing hysteresis are implied herein. Namely, several embodiments herein for controlling sensors (and other wearable hardware) may involve a processor sending commands to a sensor element depending on the sensor readings. Thus, in some embodiments, a sensor reading (such as a reading from an optical detector or a sensing electrode) above X may result in a processor sending a command to electrically bias another sensor element (such as an optical emitter or a biasing electrode) above Y. Similarly, as soon as the sensor reading drops below X, a processor may send a command to bias another sensor element below Y. However, in borderline situations this may cause unwanted hysteresis in the biasing command, as sensor readings may rapidly toggle above/below X resulting in the toggling of the biasing of another sensor element above/below Y. As such, hysteresis management may be integrated within the algorithm(s) for controlling the execution of a processor. For example, the processor may be configured by the algorithm to delay a biasing command by a period of time Z following the timing of a prior biasing command, thereby preventing or reducing the aforementioned toggling.
In the following figures, various monitoring devices will be illustrated and described for attachment to the ear or an appendage of the human body. However, it is to be understood that embodiments of the present invention are not limited to those worn by humans.
The ear is an ideal location for wearable health and environmental monitors. The ear is a relatively immobile platform that does not obstruct a person's movement or vision. Monitoring devices located at an ear have, for example, access to the inner-ear canal and tympanic membrane (for measuring core body temperature), muscle tissue (for monitoring muscle tension), the pinna, earlobe, and elsewhere (for monitoring blood gas levels), the region behind the ear (for measuring skin temperature and galvanic skin response), and the internal carotid artery (for measuring cardiopulmonary functioning), etc. The ear is also at or near the point of exposure to: environmental breathable toxicants of interest (volatile organic compounds, pollution, etc.); noise pollution experienced by the ear; and lighting conditions for the eye. Furthermore, as the ear canal is naturally designed for transmitting acoustical energy, the ear provides a good location for monitoring internal sounds, such as heartbeat, breathing rate, and mouth motion.
Optical coupling into the blood vessels of the ear may vary between individuals. As used herein, the term “coupling” refers to the interaction or communication between excitation energy (such as light) entering a region and the region itself. For example, one form of optical coupling may be the interaction between excitation light generated from within an optical sensor of an earbud (or other device positioned at or within an ear) and the blood vessels of the ear. In one embodiment, this interaction may involve excitation light entering the ear region and scattering from a blood vessel in the ear such that the temporal change in intensity of scattered light is proportional to a temporal change in blood flow within the blood vessel. Another form of optical coupling may be the interaction between excitation light generated by an optical emitter within an earbud and a light-guiding region of the earbud. Thus, an earbud with integrated light-guiding capabilities, wherein light can be guided to multiple and/or select regions along the earbud, can assure that each individual wearing the earbud will generate an optical signal related to blood flow through the blood vessels. Optical coupling of light to a particular ear region of one person may not yield photoplethysmographic signals for each person. Therefore, coupling light to multiple regions may assure that at least one blood-vessel-rich region will be interrogated for each person wearing an earbud. Coupling multiple regions of the ear to light may also be accomplished by diffusing light from a light source within an earbud.
According to some embodiments of the present invention, “smart” monitoring devices including, but not limited to, armbands and earbuds, are provided that change signal analysis frequency and/or sensor module interrogation power in response to detecting a change in subject activity, a change in environmental conditions, a change in time, a change in location of the subject and/or a change in stress level of the subject.
Embodiments of the present invention may be utilized in various devices and articles including, but not limited to, patches, clothing, etc. Embodiments of the present invention can be utilized wherever PPG and blood flow signals can be obtained and at any location on the body of a subject. Embodiments of the present invention are not limited to the illustrated monitoring devices 20, 30 of
The sensor modules 24, 34 for the illustrated monitoring devices 20, 30 of
A sensor module utilized in accordance with embodiments of the present invention may be an optical sensor module that includes at least one optical emitter and at least one optical detector. Exemplary optical emitters include, but are not limited to light-emitting diodes (LEDs), laser diodes (LDs), compact incandescent bulbs, micro-plasma emitters, IR blackbody sources, or the like. In addition, a sensor module may include various types of sensors including and/or in addition to optical sensors. For example, a sensor module may include one or more inertial sensors (e.g., an accelerometer, piezoelectric sensor, vibration sensor, photoreflector sensor, etc.) for detecting changes in motion, one or more thermal sensors (e.g., a thermopile, thermistor, resistor, etc.) for measuring temperature of a part of the body, one or more electrical sensors for measuring changes in electrical conduction, one or more skin humidity sensors, and/or one or more acoustical sensors.
Referring to
The processor 40 is configured to change signal analysis frequency and/or sensor module interrogation power in response to detecting a change in activity of a subject wearing the monitoring device. For example, in some embodiments, the processor 40 increases signal analysis frequency and/or sensor module interrogation power in response to detecting an increase in subject activity, and decreases signal analysis frequency and/or sensor module interrogation power in response to detecting a decrease in subject activity. In other embodiments, the processor 40 implements frequency-domain digital signal processing in response to detecting high subject activity (e.g., the subject starts running, exercising, etc.), and implements time-domain digital signal processing in response to detecting low subject activity. The frequency- and time-domain algorithms represent two different signal extraction methods for extracting accurate biometrics from optical sensor signals, where the frequency-domain algorithm may require substantially greater processing power than that of the time-domain algorithm. The reason that frequency-domain algorithms may require more power is because spectral transforms may be employed, whereas time-domain algorithms may employ lower-power filters and pulse picking.
An analysis platform 50 may be in communication with the processor 40 and a memory storage location 60 for the algorithms. The analysis platform 50 may be within a wearable device (e.g., monitoring devices 20, 30) or may be part of a remote system in wireless or wired communication with the wearable device. The analysis platform 50 may analyze data generated by the processor 40 to generate assessments based on the data. For example, the analysis platform 50 may analyze vital sign data (such as heart rate, respiration rate, RRi, blood pressure, etc.) in context of the user's activity data to assess a health or fitness status of the person, such as a health or fitness score. In a specific example of such an assessment, the analysis platform 50 may assess a subject's VO2max (maximum volume of oxygen consumption) by: 1) identifying data where the subject walked at a speed (as measured by a motion sensor) less than a threshold value (for example, 2.5 mph), 2) selectively analyzing the breathing rate (as measured by a physiological sensor) for this selected data (for example, by taking an average value of the selected breathing rate data and inverting it to get 1/breathing rate), and 3) generating a fitness assessment (such as a VO2max assessment) by multiplying the inverted value by a scalar value. A number of assessments can be made by analyzing physiological and motion (activity) data, and this is only a specific example.
It should be noted that, herein, the steps described wherein the processor 40 is used to make a determination or decision may be interchanged with the analysis platform 50 instead, as the analysis platform may be configured to have the same features as the processor 40 itself. For example, if the processor 40 determines that a subject's VO2max is too high, via an algorithm, the analysis platform 50 may also be configured to assess this determination. Thus, in some embodiments, the analysis platform 50 may be configured such that a processor 40 is not needed, such as the case where a sensor of a sensor module (e.g., sensor module 24, 34) is in wireless communication directly with a remote analysis platform 50.
The analysis platform 50 may be configured to analyze data processed by the processor 40 to assess the efficacy (or confidence value) of the algorithms used by the processor 40 and to autonomously modify the algorithms to improve the acuity of the wearable monitoring device. For example, the processer 40 may be configured to generate a confidence score for a given metric. The confidence score may be an indication of how strongly a processed metric may be trusted. For example, signal-to-noise (S/N) ratio may be processed from a PPG signal by assessing the AC amplitude of the blood flow waveform to a noise value, and a low S/N may represent a low confidence. If the analysis platform 50 determines that confidence value for a given algorithm is low, it may adjust the algorithm for future processing events implemented by the processor 40. For example, the algorithm may be changed such that a threshold may be lowered; as a specific example, the activity threshold for raising the signal analysis frequency and/or sensor module interrogation power may be lowered such that the acuity of the wearable sensor increases during activity. In some embodiments, the analysis platform 50 may determine that an entirely different algorithm must be used for processing, and a replacement algorithm may be selected via command from the analysis platform 50. In some embodiments, this replacement algorithm may be associated with a given confidence value range, and the analysis platform 50 may select the replacement algorithm based on the determined confidence value. For example, if the analysis platform 50 determines that the confidence value of one algorithm is too low for a user, the analysis platform may automatically replace the algorithm with another algorithm that provides higher confidence. However, other methods may be used to select an algorithm for implementation by the processor 40 based on a confidence determination, in accordance with some embodiments of the present invention.
In the case where the sensor module (or modules) comprises PPG sensor functionality, readings from the sensor module (for example, readings from optical sensors or motion sensors) can be used to trigger changes to the optomechanical engine (the optical emitter, detector, and associated optics). For example, the detection of low activity may change the polling of the optomechanical engine. In a specific example, a detection of low activity may change the optical wavelength used for PPG. In this example, if the activity level processed by the processor 40 is deemed to be “low”, the primary wavelength of detection may shift from visible (such as green or yellow) wavelengths to infrared wavelengths. This can be useful for automatically turning off visible emitters when the person is rested, helping to prevent visible light pollution so that the person can sleep better.
For example, in one embodiment, the processor 40 and/or analysis platform may determine that the person is sleeping, and then the action of changing wavelengths may be initiated by the processor 40 (i.e., via a command to the PPG sensor). This may be achieved by the processor and/or analytics engine processing activity and/or physiological data against a threshold criteria (i.e., processing accelerometer data to determine a state of low enough physical activity and that the person is laying flat/parallel to the ground) and/or physiological model (i.e., processing PPG sensor information to determine that the person's breathing, heart rate, and/or HRV is of a pattern associated with sleeping) to determine that the person is sleeping. Alternatively, the processor and/or analytics platform may automatically determine that the person is in a dark environment (i.e., by processing optical sensor data to determine that the person is in a dark enough environment) and then send a command to switch/change the wavelengths of the PPG sensor. In another embodiment, the user may manually initiate a command (i.e., by pressing a button) that the person is going to sleep, which my then be used by the processor and/or analysis platform to change the wavelengths. Also, although the PPG S/N ratio for infrared (IR) wavelengths may be less than that for visible wavelengths, the total electrical power levels (i.e., the bias voltage) required to bias the IR emitter may be lower, thereby saving battery life in conditions of low activity.
This approach may also be used for pulse oximetry via a PPG sensor. For example, the processor 40 may process sensor readings from a sensor module 24, 34 to determine that the subject wearing the wearable device is indoors or outdoors, and the processor 40 may select a different optomechanical polling routine for indoors vs. outdoors. For example, when indoors, a visible and IR emitter may be engaged to facilitate SpO2 determination. But once the user is outdoors, where visible outdoor light may pollute PPG sensor readings with noise signals too intense to remove with physical or digital optical filters, the processor may engage (poll) multiple IR emitters instead of the visible and IR emitter, and SpO2 determination may be executed via two IR wavelength bands rather than a visible+IR wavelength band. For example, the processor 40 may turn off visible emitters when the user is outdoors and may turn on multiple IR emitters, such as a ˜700 nm and ˜940 nm emitter, instead. Because pulse oximetry requires two distinct wavelengths or two different wavelength bands in order to generate an estimate of SpO2, these two IR wavelengths/wavelength bands may be used with efficacy outdoors. The example of these two wavelengths/wavelength bands should not be construed to be limiting, as various wavelength configurations more resilient to outdoor light contamination may be used, such as spectral bands in solar blind regions (wavelengths that are naturally attenuated by the earth's atmosphere, such as ˜763 nm and others). Additionally, it should be noted that monitoring blood oxygen (SpO2) and tissue oxygen may each be achieved via this method, depending on the sensor positioning used. For example, locating a PPG sensor at a leg or arm may facilitate a more accurate determination of muscle oxygenation, whereas locating a PPG sensor at a finger, ear, or forehead may be facilitate a more accurate determination of blood oxygenation. Moreover, the muscle oxygenation signals collected may be used as a proxy for estimating lactic acid and/or lactate threshold (or anaerobic threshold) in the muscle of the subject, as oxygen depletion may be correlated with higher lactic acid build-up in the muscles.
Besides the example just described, autonomously triggering changes in the optomechanical engine of a PPG sensor, in response to activity data sensed by an activity (motion) sensor, can be applied towards a number of useful functions. For example, the detection of low activity may change the type of PPG-based measurement to be executed. This can be useful for cases where the accuracy of a physiological measurement or assessment demands a certain level of physical activity or inactivity. As a specific example, a measurement of blood pressure or RRi (R-R interval, which is the interval from the peak of one QRS complex to the peak of the next as shown on an electrocardiogram) may provide best results during periods of inactivity. The processor 40 may deem that activity is “low enough” to execute one or more of such measurements, and then execute an algorithm to start measuring. This way, blood pressure and/or RRi measurements are only executed at time periods where a reliable measurement can be made, thereby saving system power. Similarly, in some embodiments, a measurement of HRR (heart rate recovery) may be executed only when the processor 40 deems that activity “high enough” to make such a measurement meaningful. For example, the processor 40 may determine that a user's activity level (perhaps as sensed by an activity sensor) or exertion level (perhaps as sensed by a heart rate sensor) has been high enough for a long enough period of time, followed by a resting phase, such that HRR may be accurately assessed. In this case, several data points of activity level and/or heart rate may be stored in memory or buffered, such that the processor 40 may run through the dataset to determine if the user has been in a state of high activity or exertion for a long enough period of time to justify an HRR measurement. This way, HRR measurements are only executed at time periods where a reliable measurement can be made, saving power consumption.
In another example, if the processor 40 determines that subject activity level has been very low, the processor 40 may engage a longer wavelength light, such as IR light, as the wavelength for PPG. But if subject activity is heightened, the processor 40 may switch the wavelength to a shorter wavelength, such as green, blue, or violet light. Such a process may address the problem of low perfusion, which often prevents PPG readings during periods of subject inactivity, especially for wrist-based PPG sensors. Shorter wavelength light for PPG generally yields a higher signal-to-noise ratio (S/N) over longer wavelength, but low perfusion can reduce blood flow at the surface of the skin, pushing blood flow so far below the surface that shorter wavelength light is absorbed by the skin before reaching blood flow. However, during exercise, perfusion may return and shorter wavelength light may be used once again, providing a higher S/N for PPG and thereby reducing system power requirements.
In another example, if the processor 40 determines that subject perfusion is low, for example by processing PPG information to determine that the signal-to-noise level is quite low, the processor 40 may send a command to the sensor module 24, 34 to raise the localized temperature of the neighboring skin, thereby increasing perfusion. This may be achieved by the processor 40 sending a command to turn on a heater element on the sensor module 24, 34 or to increase the electrical bias across an LED such that the LED heats up the skin to encourage blood flow. Once the signal-to-noise level is determined to be high enough for accurate and reliable physiological monitoring by the processor 40, the processor 40 may send a command to terminate heating of the skin.
For the case of PPG sensor modules 24, 34 in the system of
Readings from sensor module(s) can also be used to trigger a change in the algorithm sequence executed by a processor 40. For example if a normal heart rate level and/or heart rate variability (HRV) level is detected by the processor (such as a heart rate and/or HRV within a specified range), then the processor 40 may select an algorithm that has less sequential steps in time, thus saving power on the processor 40. More specifically, once an abnormal heart rate and/or HRV is detected outside the specified range, the processor 40 may select an algorithm that also implements continuous cardiac monitoring, such as monitoring of arrhythmia, atrial fibrillation, blood pressure, cardiac output, irregular heart beats, etc. And when heart rate and/or HRV fall back within the specified range, the processor 40 may return to a lower-power algorithm with less sequential steps in time.
Readings from the sensor(s) of a monitoring device can be used to trigger events. In addition, sensor signals may be processed and algorithms may be selected to control a biometric signal extraction method. For example, elevated subject physical activity sensed by an accelerometer may trigger a change in the signal extraction algorithm for PPG towards one of higher acuity (but higher power usage); then, when subject activity winds down, the algorithm may change to one that is lower acuity (but lower power usage). In this way, battery power may be preserved for use cases where high acuity is not needed (such as sedentary behavior where motion artifacts need not be removed.)
In some embodiments, detecting a change in subject activity comprises detecting a change in at least one subject vital sign, such as subject heart rate, subject blood pressure, subject temperature, subject respiration rate, subject perspiration rate, etc. In other embodiments, the sensor module includes a motion sensor, such as an accelerometer, gyroscope, etc., and detecting a change in subject activity includes detecting a change in subject motion via the motion sensor.
According to some embodiments, the type of activity may be identified or predicted via the processor 40. Changing signal analysis frequency and/or sensor module interrogation power may be based on stored profiles (such as a look-up table) or learned profiles (such as machine learning with human input) of activity identification information, such as: 1) a known accelerometry profile for a given sport or exercising activity and/or 2) a known accelerometry profile for a particular person, for example.
According to other embodiments of the present invention, a monitoring device configured to be attached to a subject, such as monitoring devices 20, 30, includes a sensor module configured to detect and/or measure physiological information from the subject and to detect and/or measure at least one environmental condition in a vicinity of the subject. The sensor module may be an optical sensor module that includes at least one optical emitter and at least one optical detector, although various other types of sensors may be utilized. A processor 40 is coupled to the sensor module and is configured to receive and analyze signals produced by the sensor module. In addition, the processor 40 is configured to change signal analysis frequency and/or sensor module interrogation power in response to detecting a change in the at least one environmental condition. Exemplary changes in environmental conditions include changes in one or more of the following ambient conditions: temperature, humidity, air quality, barometric pressure, radiation, light intensity, and sound. In some embodiments, the processor 40 increases signal analysis frequency and/or sensor module interrogation power in response to detecting an increase in the at least one environmental condition, and decreases signal analysis frequency and/or sensor module interrogation power in response to detecting a decrease in the at least one environmental condition. For example, the signal analysis frequency and/or sensor module interrogation power may be increased when air quality worsens or becomes detrimental to the wearer, and signal analysis frequency and/or sensor module interrogation power may be decreased when air quality improves. The principle behind this process is that extreme or harsh ambient changes in environment (such as extreme hot or cold, extreme humidity or dryness, etc.) may lower the S/N ratio of the processed signals. Thus, higher processing power may be required to actively remove noise.
Referring to
In some embodiments, the processor 40 increases signal analysis frequency and/or sensor module interrogation power at a first time, and decreases signal analysis frequency and/or sensor module interrogation power at a second time. For example, signal analysis frequency and/or sensor module interrogation power may be increased at a particular time of day (e.g., the time of day when the wearer is typically exercising), and may be decreased at another time of day, for example, at a time of day when the wearer is less active (e.g., nighttime, etc.).
In other embodiments, the processor 40 adjusts signal analysis frequency and/or sensor module interrogation power according to a circadian rhythm of the subject. For example, signal analysis frequency and/or sensor module interrogation power may be increased at a particular time of day (e.g., the time of day when the wearer is at peak metabolism), and may be decreased at another time of day (for example, during sleep).
In other embodiments, the processor 40 adjusts signal analysis frequency and/or interrogation power of a sensor module 24, 34 or analysis platform 50 based on the determined stress state of the user. For example, the processor 40 may determine that a user is psychologically stressed based on, for example, an elevated heart rate over a period of time during low (not high) physical activity. The processor 40 may then send a signal to another sensor and/or analysis platform, such as a voice analysis/recognition system 84 that is in communication with the system 90 of
As yet another example, the processor 40 may adjust signal analysis frequency and/or interrogation power of a user interface 70 that is in communication with the system 90 of
According to other embodiments of the present invention, a monitoring device configured to be attached to a subject, such as monitoring devices 20, 30, includes a location sensor 80 (
In some embodiments, the processor 40 increases signal analysis frequency and/or sensor module interrogation power when the location sensor 80 indicates the subject is at a particular location, and decreases signal analysis frequency and/or sensor module interrogation power when the location sensor 80 indicates the subject is no longer at the particular location. For example, signal analysis frequency and/or sensor module interrogation power may be increased when the location sensor 80 indicates the subject is at a particular location (e.g., at the gym, outdoors, at the mall, etc.), and may be decreased when the location sensor 80 indicates the subject is no longer at the particular location (e.g., when the wearer is at work, home, etc.). The locations selected for the increase or decrease in processing power may be personalized for the user and stored in memory. For example, people who are more active at outdoors than at work may see the decision tree described above, but for those who are more active at work, the decision tree may be swapped such that higher power processing is selected for work locations over home locations.
Other factors may be utilized to trigger an increase or decrease in signal analysis frequency and/or sensor module interrogation power. For example, higher body temperature readings detected by a thermal sensor associated with the sensor module 24, 34 may trigger changes in signal analysis frequency and/or sensor module interrogation power. The principle behind this may be that higher body temperatures are associated with higher motion, for example. The detection of higher light levels, the detection of higher changes in light intensity, and/or the detection of particular wavelengths via an optical sensor associated with the sensor module 24, 34 may trigger changes in signal analysis frequency and/or sensor module interrogation power. Lower potential drops detected by an electrical sensor associated with the sensor module 24, 34 may trigger changes in signal analysis frequency and/or sensor module interrogation power. Lower skin humidity readings detected via a humidity sensor associated with the sensor module may trigger changes in signal analysis frequency and/or sensor module interrogation power. Higher acoustic noise levels detected via an acoustical sensor associated with the sensor module 24, 34 may trigger changes in signal analysis frequency and/or sensor module interrogation power.
Referring now to
As illustrated in
In other embodiments as illustrated in
Referring now to
Referring now to
Referring now to
Referring now to
As illustrated in
Referring now to
For example, an algorithm may comprise a list of successive intervals, wherein each interval may comprise: 1) a different polling of the optical emitter and/or detector and/or 2) a different interrogation wavelength or set of interrogation wavelengths. As a specific example, an algorithm may focus on collecting and/or processing information for the measurement of heart rate, RRi, and blood pressure in order. In such case, the following intervals may be executed in series (in no particular order): 1) calculate heart rate, 2) calculate RRi, 3) calculate blood pressure, and 4) calculate breathing rate. Heart rate may be calculated with a processor-intensive calculation to actively remove motion artifacts via a motion (noise) reference, such as footstep and body motion artifacts, as disclosed in U.S. Patent Application Publication No. 2015/0018636, U.S. Patent Application Publication No. 2015/0011898, U.S. Pat. No. 870,011, and U.S. Pat. No. 8,157,730, which are incorporated herein by reference in their entireties.
RRi may be calculated via a time-domain approach, such as applying a processor-efficient peak-finder or by leveraging a heart rate feedback filter to improve RRi tracking, for example as disclosed in U.S. Patent Application Publication No. 2014/0114147, which is incorporated herein by reference in its entirety. Blood pressure may be calculated by processing the photoplethysmogram itself (e.g., via intensity, shape, 1st derivative, 2nd derivative, integral, etc.) via a processor-efficient time-domain algorithm. Breathing rate (respiration rate) may be calculated by running the optical detector signal through a low-pass filter, in some cases by applying a variable feedback loop to align the corner frequency with the heart rate, for example as disclosed in U.S. Patent Application Publication No. 2014/0114147.
In all four cases of this specific example, a different optical wavelength (or a different set of wavelengths) may be used. For example, calculating heart rate may employ a variety of different wavelengths, but calculating breathing rate may employ shorter-wavelength light (such as wavelengths shorter than 600 nm, or preferably shorter than 480 nm) such that heart rate PPG signals do not overpower breathing rate PPG signals during processing of breathing rate. In the example just given, with 4-intervals of optical signal sampling, further power reductions can be realized by an algorithm which selects which intervals to execute depending on the activity state of the user. For example, if the activity state reaches a certain threshold, the algorithm may select that only the first and fourth intervals (the heart rate and breathing rate data collection intervals) are activated. Similarly, if the activity state is below a certain threshold, the algorithm may select that only the second and third intervals (the RRi and blood pressure intervals) are activated. In this manner, only the physiological parameters that are relevant to a particular activity state may be calculated, thereby saving system power and increasing the battery life of the wearable monitoring device.
In some embodiments, the wavelength of the optical emitter and optical detector may stay the same for each interval, but in contrast the sampling and/or polling of the sensor element (i.e., the sampling of the detector(s) and the polling of the emitter(s)) may be changed depending on the measurement goal of each interval. For example, an algorithm may focus on processing at least one photoplethysmogram to measure or estimate 1) blood pressure (highest sampling and/or polling), 2) heart rate variability (2nd-higest sampling and/or polling), and 3) low-motion (“lifestyle”) heart rate monitoring (lowest sampling and/or polling) in sequence. This may be because accurately assessing blood pressure from a photoplethysmogram may require a higher data acuity, whereas accurate heart rate variability may require less acuity, and heart rate under lifestyle (low motion) conditions may require the least acuity. In another embodiment, the polling and/or sampling for blood pressure may be greater than 125 Hz, the polling and/or sampling of HRV may be between 250 Hz and 100 Hz, and the polling and/or sampling of lifestyle heart rate may be less than 75 Hz.
In another embodiment, an algorithm may focus on processing at least one photoplethysmogram to generate a single real-time biometric parameter at different intervals, with each interval having a different polling and/or sampling rate. As an example, an algorithm may process a photoplethysmogram to generate RRi at various different intervals where, for each interval, the polling rate of the optical emitter and the sampling rate of the optical detector may be different. As a specific example, there may be three intervals, each having an increasingly lower polling and/or sampling rate. The optimum sampling rate to maintain measurement accuracy while limiting power consumption has been found by experiment, as shown in
Referring now to
If the system 90 of
As illustrated in
As illustrated in
As illustrated in
Example embodiments are described herein with reference to block diagrams and flowchart illustrations. It is understood that a block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, can be implemented by computer program instructions that are performed by one or more computer circuits. These computer program instructions may be provided to a processor circuit of a general purpose computer circuit, special purpose computer circuit, and/or other programmable data processing circuit to produce a machine, such that the instructions, which execute via the processor of the computer and/or other programmable data processing apparatus, transform and control transistors, values stored in memory locations, and other hardware components within such circuitry to implement the functions/acts specified in the block diagrams and flowchart block or blocks, and thereby create means (functionality) and/or structure for implementing the functions/acts specified in the block diagrams and flowchart blocks.
These computer program instructions may also be stored in a tangible computer-readable medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable medium produce an article of manufacture including instructions which implement the functions/acts specified in the block diagrams and flowchart blocks.
A tangible, non-transitory computer-readable medium may include an electronic, magnetic, optical, electromagnetic, or semiconductor data storage system, apparatus, or device. More specific examples of the computer-readable medium would include the following: a portable computer diskette, a random access memory (RAM) circuit, a read-only memory (ROM) circuit, an erasable programmable read-only memory (EPROM or Flash memory) circuit, a portable compact disc read-only memory (CD-ROM), and a portable digital video disc read-only memory (DVD/BlueRay).
The computer program instructions may also be loaded onto a computer and/or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer and/or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions/acts specified in the block diagrams and flowchart blocks. Accordingly, embodiments of the present invention may be embodied in hardware and/or in software (including firmware, resident software, micro-code, etc.) that runs on a processor such as a digital signal processor, which may collectively be referred to as “circuitry,” “a module” or variants thereof.
It should also be noted that in some alternate implementations, the functions/acts noted in the blocks may occur out of the order noted in the flowcharts. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Moreover, the functionality of a given block of the flowcharts and block diagrams may be separated into multiple blocks and/or the functionality of two or more blocks of the flowcharts and block diagrams may be at least partially integrated. Finally, other blocks may be added/inserted between the blocks that are illustrated. Moreover, although some of the diagrams include arrows on communication paths to show a primary direction of communication, it is to be understood that communication may occur in the opposite direction to the depicted arrows.
The foregoing is illustrative of the present invention and is not to be construed as limiting thereof. Although a few exemplary embodiments of this invention have been described, those skilled in the art will readily appreciate that many modifications are possible in the exemplary embodiments without materially departing from the teachings and advantages of this invention. Accordingly, all such modifications are intended to be included within the scope of this invention as defined in the claims. The invention is defined by the following claims, with equivalents of the claims to be included therein.
This application is a divisional application of U.S. patent application Ser. No. 15/369,946, filed Dec. 6, 2016, which is a divisional application of U.S. patent application Ser. No. 14/807,061, filed Jul. 23, 2015, now U.S. Pat. No. 9,538,921, which claims the benefit of and priority to U.S. Provisional Patent Application No. 62/030,951 filed Jul. 30, 2014, and U.S. Provisional Patent Application No. 62/109,196 filed Jan. 29, 2015, the disclosures of which are incorporated herein by reference as if set forth in their entireties.
| Number | Name | Date | Kind |
|---|---|---|---|
| 3595219 | Friedlander et al. | Jul 1971 | A |
| 4240882 | Ang et al. | Dec 1980 | A |
| 4331154 | Broadwater et al. | May 1982 | A |
| 4438772 | Slavin | Mar 1984 | A |
| 4491760 | Linvill | Jan 1985 | A |
| 4521499 | Switzer | Jun 1985 | A |
| 4541905 | Kuwana et al. | Sep 1985 | A |
| 4592807 | Switzer | Jun 1986 | A |
| 4655225 | Dahne et al. | Apr 1987 | A |
| 4830014 | Goodman et al. | May 1989 | A |
| 4882492 | Schlager | Nov 1989 | A |
| 4896676 | Sasaki | Jan 1990 | A |
| 4928704 | Hardt | May 1990 | A |
| 4957109 | Groeger et al. | Sep 1990 | A |
| 5002060 | Nedivi | Mar 1991 | A |
| 5007423 | Branstetter et al. | Apr 1991 | A |
| 5022970 | Cook et al. | Jun 1991 | A |
| 5025791 | Niwa | Jun 1991 | A |
| 5079421 | Knudson et al. | Jan 1992 | A |
| 5080098 | Willett et al. | Jan 1992 | A |
| 5086229 | Rosenthal et al. | Feb 1992 | A |
| 5143078 | Mather et al. | Sep 1992 | A |
| 5226417 | Swedlow et al. | Jul 1993 | A |
| 5237994 | Goldberger | Aug 1993 | A |
| 5348002 | Caro | Sep 1994 | A |
| 5377100 | Pope et al. | Dec 1994 | A |
| 5482036 | Diab et al. | Jan 1996 | A |
| 5492129 | Greenberger | Feb 1996 | A |
| 5499301 | Sudo et al. | Mar 1996 | A |
| 5581648 | Sahagen | Dec 1996 | A |
| 5596987 | Chance | Jan 1997 | A |
| 5662117 | Bittman | Sep 1997 | A |
| 5673692 | Schulze et al. | Oct 1997 | A |
| 5697374 | Odagiri et al. | Dec 1997 | A |
| 5711308 | Singer | Jan 1998 | A |
| 5725480 | Oosta et al. | Mar 1998 | A |
| 5743260 | Chung et al. | Apr 1998 | A |
| 5779631 | Chance | Jul 1998 | A |
| 5797841 | Delonzor et al. | Aug 1998 | A |
| 5807114 | Hodges et al. | Sep 1998 | A |
| 5807267 | Bryars et al. | Sep 1998 | A |
| 5853005 | Scanlon | Dec 1998 | A |
| 5904654 | Wohltmann et al. | May 1999 | A |
| 5906582 | Kondo et al. | May 1999 | A |
| 5938593 | Ouellette | Aug 1999 | A |
| 5964701 | Asada et al. | Oct 1999 | A |
| 5971931 | Raff | Oct 1999 | A |
| 5974338 | Asano et al. | Oct 1999 | A |
| 5995858 | Kinast | Nov 1999 | A |
| 6004274 | Nolan et al. | Dec 1999 | A |
| 6013007 | Root et al. | Jan 2000 | A |
| 6023541 | Merchant et al. | Feb 2000 | A |
| 6030342 | Amano et al. | Feb 2000 | A |
| 6045511 | Ott et al. | Apr 2000 | A |
| 6067006 | O'Brien | May 2000 | A |
| 6070093 | Oosta et al. | May 2000 | A |
| 6078829 | Uchida et al. | Jun 2000 | A |
| 6080110 | Thorgersen | Jun 2000 | A |
| 6081742 | Amano et al. | Jun 2000 | A |
| 6088607 | Diab et al. | Jul 2000 | A |
| 6186145 | Brown | Feb 2001 | B1 |
| 6198394 | Jacobsen et al. | Mar 2001 | B1 |
| 6198951 | Kosuda et al. | Mar 2001 | B1 |
| 6205354 | Gellermann et al. | Mar 2001 | B1 |
| 6231519 | Blants et al. | May 2001 | B1 |
| 6283915 | Aceti et al. | Sep 2001 | B1 |
| 6285816 | Anderson et al. | Sep 2001 | B1 |
| 6289230 | Chaiken et al. | Sep 2001 | B1 |
| 6298314 | Blackadar et al. | Oct 2001 | B1 |
| 6332868 | Sato et al. | Dec 2001 | B1 |
| 6358216 | Kraus et al. | Mar 2002 | B1 |
| 6361660 | Goldstein | Mar 2002 | B1 |
| 6371925 | Imai et al. | Apr 2002 | B1 |
| 6374129 | Chin et al. | Apr 2002 | B1 |
| 6402690 | Rhee et al. | Jun 2002 | B1 |
| 6415167 | Blank et al. | Jul 2002 | B1 |
| 6443890 | Schulze et al. | Sep 2002 | B1 |
| 6444474 | Thomas et al. | Sep 2002 | B1 |
| 6454718 | Clift | Sep 2002 | B1 |
| 6458080 | Brown et al. | Oct 2002 | B1 |
| 6470893 | Boesen | Oct 2002 | B1 |
| 6513532 | Mault et al. | Feb 2003 | B2 |
| 6514278 | Hibst et al. | Feb 2003 | B1 |
| 6527711 | Stivoric et al. | Mar 2003 | B1 |
| 6527712 | Brown et al. | Mar 2003 | B1 |
| 6529754 | Kondo | Mar 2003 | B2 |
| 6534012 | Hazen et al. | Mar 2003 | B1 |
| 6556852 | Schulze et al. | Apr 2003 | B1 |
| 6569094 | Suzuki et al. | May 2003 | B2 |
| 6571117 | Marbach | May 2003 | B1 |
| 6605038 | Teller et al. | Aug 2003 | B1 |
| 6616613 | Goodman | Sep 2003 | B1 |
| 6631196 | Taenzer et al. | Oct 2003 | B1 |
| 6647378 | Kindo | Nov 2003 | B2 |
| 6656116 | Kim et al. | Dec 2003 | B2 |
| 6694180 | Boesen | Feb 2004 | B1 |
| 6745061 | Hicks et al. | Jun 2004 | B1 |
| 6748254 | O'Neil et al. | Jun 2004 | B2 |
| 6760610 | Tschupp et al. | Jul 2004 | B2 |
| 6783501 | Takahashi et al. | Aug 2004 | B2 |
| 6808473 | Hisano et al. | Oct 2004 | B2 |
| 6859658 | Krug | Feb 2005 | B1 |
| 6893396 | Schulze et al. | May 2005 | B2 |
| 6941239 | Unuma et al. | Sep 2005 | B2 |
| 6953435 | Kondo et al. | Oct 2005 | B2 |
| 6996427 | Ali et al. | Feb 2006 | B2 |
| 6997879 | Turcott | Feb 2006 | B1 |
| 7018338 | Vetter et al. | Mar 2006 | B2 |
| 7024369 | Brown et al. | Apr 2006 | B1 |
| 7030359 | Römhild | Apr 2006 | B2 |
| 7034694 | Yamaguchi et al. | Apr 2006 | B2 |
| 7041062 | Friedrichs et al. | May 2006 | B2 |
| 7043287 | Khalil et al. | May 2006 | B1 |
| 7054674 | Cane et al. | May 2006 | B2 |
| 7088234 | Naito et al. | Aug 2006 | B2 |
| 7107088 | Aceti | Sep 2006 | B2 |
| 7113815 | O'Neil et al. | Sep 2006 | B2 |
| 7117032 | Childre et al. | Oct 2006 | B2 |
| 7163512 | Childre et al. | Jan 2007 | B1 |
| 7175601 | Verjus et al. | Feb 2007 | B2 |
| 7209775 | Bae et al. | Apr 2007 | B2 |
| 7217224 | Thomas | May 2007 | B2 |
| 7252639 | Kimura et al. | Aug 2007 | B2 |
| 7263396 | Chen et al. | Aug 2007 | B2 |
| 7289837 | Mannheimer et al. | Oct 2007 | B2 |
| 7295866 | Al-Ali | Nov 2007 | B2 |
| 7336982 | Yoo et al. | Feb 2008 | B2 |
| 7341559 | Schultz et al. | Mar 2008 | B2 |
| 7376451 | Mahony et al. | May 2008 | B2 |
| 7470234 | Elhag et al. | Dec 2008 | B1 |
| 7483730 | Diab et al. | Jan 2009 | B2 |
| 7486988 | Goodall et al. | Feb 2009 | B2 |
| 7507207 | Sakai et al. | Mar 2009 | B2 |
| 7519327 | White | Apr 2009 | B2 |
| 7526327 | Blondeau et al. | Apr 2009 | B2 |
| 7583994 | Scholz | Sep 2009 | B2 |
| 7620450 | Kim et al. | Nov 2009 | B2 |
| 7625285 | Breving | Dec 2009 | B2 |
| 7652569 | Kiff et al. | Jan 2010 | B2 |
| 7689437 | Teller et al. | Mar 2010 | B1 |
| 7695440 | Kondo et al. | Apr 2010 | B2 |
| 7725147 | Li et al. | May 2010 | B2 |
| 7756559 | Abreu | Jul 2010 | B2 |
| 7843325 | Otto | Nov 2010 | B2 |
| 7894869 | Hoarau | Feb 2011 | B2 |
| 7914468 | Shalon et al. | Mar 2011 | B2 |
| 7991448 | Edgar et al. | Aug 2011 | B2 |
| 7998079 | Nagai et al. | Aug 2011 | B2 |
| 8050728 | Al-Ali et al. | Nov 2011 | B2 |
| 8055319 | Oh et al. | Nov 2011 | B2 |
| 8055330 | Egozi | Nov 2011 | B2 |
| 8059924 | Letant et al. | Nov 2011 | B1 |
| 8130105 | Al-Ali et al. | Mar 2012 | B2 |
| 8137270 | Keenan et al. | Mar 2012 | B2 |
| 8157730 | LeBoeuf et al. | Apr 2012 | B2 |
| 8172459 | Abreu | May 2012 | B2 |
| 8175670 | Baker, Jr. et al. | May 2012 | B2 |
| 8204730 | Liu et al. | Jun 2012 | B2 |
| 8204786 | LeBoeuf et al. | Jun 2012 | B2 |
| 8233955 | Al-Ali et al. | Jul 2012 | B2 |
| 8251903 | LeBoeuf et al. | Aug 2012 | B2 |
| 8255027 | Al-Ali et al. | Aug 2012 | B2 |
| 8255029 | Addison et al. | Aug 2012 | B2 |
| 8303512 | Kosuda et al. | Nov 2012 | B2 |
| 8320982 | LeBoeuf et al. | Nov 2012 | B2 |
| 8323982 | LeBoeuf et al. | Dec 2012 | B2 |
| 8328420 | Abreu | Dec 2012 | B2 |
| 8416959 | Lott et al. | Apr 2013 | B2 |
| 8430817 | Al-Ali et al. | Apr 2013 | B1 |
| 8491492 | Shinar et al. | Jul 2013 | B2 |
| 8504679 | Spire et al. | Aug 2013 | B2 |
| 8506524 | Graskov et al. | Aug 2013 | B2 |
| 8512242 | LeBoeuf et al. | Aug 2013 | B2 |
| 8647270 | LeBoeuf et al. | Feb 2014 | B2 |
| 8652040 | LeBoeuf et al. | Feb 2014 | B2 |
| 8652409 | LeBoeuf et al. | Feb 2014 | B2 |
| 8679008 | Hughes et al. | Mar 2014 | B2 |
| 8700111 | LeBoeuf et al. | Apr 2014 | B2 |
| 8702607 | LeBoeuf et al. | Apr 2014 | B2 |
| 8730048 | Shen et al. | May 2014 | B2 |
| 8788002 | LeBoeuf et al. | Jul 2014 | B2 |
| 8886269 | LeBoeuf et al. | Nov 2014 | B2 |
| 8888701 | LeBoeuf et al. | Nov 2014 | B2 |
| 8923941 | LeBoeuf et al. | Dec 2014 | B2 |
| 8929965 | LeBoeuf et al. | Jan 2015 | B2 |
| 8929966 | LeBoeuf et al. | Jan 2015 | B2 |
| 8934952 | LeBoeuf et al. | Jan 2015 | B2 |
| 8942776 | LeBoeuf et al. | Jan 2015 | B2 |
| 8961415 | LeBoeuf et al. | Feb 2015 | B2 |
| 9005129 | Venkatraman et al. | Apr 2015 | B2 |
| 9538921 | LeBoeuf et al. | Jan 2017 | B2 |
| 9649055 | Ashe et al. | May 2017 | B2 |
| 10524736 | Gross | Jan 2020 | B2 |
| 10893835 | LeBoeuf et al. | Jan 2021 | B2 |
| 11179108 | LeBoeuf et al. | Nov 2021 | B2 |
| 11185290 | LeBoeuf et al. | Nov 2021 | B2 |
| 11337655 | LeBoeuf et al. | May 2022 | B2 |
| 11412988 | LeBoeuf et al. | Aug 2022 | B2 |
| 20010049471 | Suzuki et al. | Dec 2001 | A1 |
| 20020035340 | Fraden et al. | Mar 2002 | A1 |
| 20020143242 | Nemirovski | Oct 2002 | A1 |
| 20020156386 | Dardik et al. | Oct 2002 | A1 |
| 20020156654 | Roe et al. | Oct 2002 | A1 |
| 20020186137 | Skardon | Dec 2002 | A1 |
| 20020188210 | Aizawa | Dec 2002 | A1 |
| 20020194002 | Petrushin | Dec 2002 | A1 |
| 20030002705 | Boesen | Jan 2003 | A1 |
| 20030007631 | Bolognesi et al. | Jan 2003 | A1 |
| 20030045785 | Diab et al. | Mar 2003 | A1 |
| 20030050563 | Suribhotla et al. | Mar 2003 | A1 |
| 20030064712 | Gaston et al. | Apr 2003 | A1 |
| 20030065257 | Mault et al. | Apr 2003 | A1 |
| 20030083583 | Kovtun et al. | May 2003 | A1 |
| 20030109030 | Uchida et al. | Jun 2003 | A1 |
| 20030181795 | Suzuki et al. | Sep 2003 | A1 |
| 20030212336 | Lee et al. | Nov 2003 | A1 |
| 20030220584 | Honeyager et al. | Nov 2003 | A1 |
| 20030222268 | Yocom et al. | Dec 2003 | A1 |
| 20040004547 | Appelt et al. | Jan 2004 | A1 |
| 20040022700 | Kim et al. | Feb 2004 | A1 |
| 20040030581 | Leven | Feb 2004 | A1 |
| 20040034289 | Teller et al. | Feb 2004 | A1 |
| 20040034293 | Kimball | Feb 2004 | A1 |
| 20040054291 | Schulz et al. | Mar 2004 | A1 |
| 20040075677 | Loyall et al. | Apr 2004 | A1 |
| 20040077934 | Massad | Apr 2004 | A1 |
| 20040082842 | Lumba et al. | Apr 2004 | A1 |
| 20040103146 | Park | May 2004 | A1 |
| 20040117204 | Mazar et al. | Jun 2004 | A1 |
| 20040120844 | Tribelsky et al. | Jun 2004 | A1 |
| 20040122294 | Hatlestad et al. | Jun 2004 | A1 |
| 20040122702 | Sabol et al. | Jun 2004 | A1 |
| 20040133123 | Leonhardt et al. | Jul 2004 | A1 |
| 20040135571 | Uutela et al. | Jul 2004 | A1 |
| 20040138578 | Pineda et al. | Jul 2004 | A1 |
| 20040186390 | Ross et al. | Sep 2004 | A1 |
| 20040219056 | Tribelsky et al. | Nov 2004 | A1 |
| 20040220488 | Vyshedskiy et al. | Nov 2004 | A1 |
| 20040225207 | Bae et al. | Nov 2004 | A1 |
| 20040228494 | Smith | Nov 2004 | A1 |
| 20040242976 | Abreu | Dec 2004 | A1 |
| 20050004458 | Kanayama et al. | Jan 2005 | A1 |
| 20050027216 | Guillemaud et al. | Feb 2005 | A1 |
| 20050030540 | Thornton | Feb 2005 | A1 |
| 20050033200 | Soehren et al. | Feb 2005 | A1 |
| 20050038349 | Choi et al. | Feb 2005 | A1 |
| 20050043600 | Diab et al. | Feb 2005 | A1 |
| 20050043630 | Buchert et al. | Feb 2005 | A1 |
| 20050054908 | Blank et al. | Mar 2005 | A1 |
| 20050058456 | Yoo | Mar 2005 | A1 |
| 20050059870 | Aceti | Mar 2005 | A1 |
| 20050084666 | Pong et al. | Apr 2005 | A1 |
| 20050101845 | Nihtila | May 2005 | A1 |
| 20050101872 | Sattler et al. | May 2005 | A1 |
| 20050113167 | Buchner et al. | May 2005 | A1 |
| 20050113656 | Chance | May 2005 | A1 |
| 20050113703 | Farringdon et al. | May 2005 | A1 |
| 20050116820 | Goldreich | Jun 2005 | A1 |
| 20050119833 | Nanikashvili | Jun 2005 | A1 |
| 20050148883 | Boesen | Jul 2005 | A1 |
| 20050154264 | Lecompte et al. | Jul 2005 | A1 |
| 20050177034 | Beaumont | Aug 2005 | A1 |
| 20050187448 | Petersen et al. | Aug 2005 | A1 |
| 20050187453 | Petersen et al. | Aug 2005 | A1 |
| 20050192515 | Givens et al. | Sep 2005 | A1 |
| 20050196009 | Boesen | Sep 2005 | A1 |
| 20050203349 | Nanikashvili | Sep 2005 | A1 |
| 20050203357 | Debreczeny et al. | Sep 2005 | A1 |
| 20050209516 | Fraden | Sep 2005 | A1 |
| 20050222487 | Miller et al. | Oct 2005 | A1 |
| 20050222903 | Buchheit et al. | Oct 2005 | A1 |
| 20050228244 | Banet | Oct 2005 | A1 |
| 20050228299 | Banet | Oct 2005 | A1 |
| 20050240087 | Keenan et al. | Oct 2005 | A1 |
| 20050258816 | Zen et al. | Nov 2005 | A1 |
| 20050259811 | Kimm et al. | Nov 2005 | A1 |
| 20060009685 | Finarov et al. | Jan 2006 | A1 |
| 20060012567 | Sicklinger | Jan 2006 | A1 |
| 20060063993 | Yu et al. | Mar 2006 | A1 |
| 20060064037 | Shalon et al. | Mar 2006 | A1 |
| 20060084878 | Banet et al. | Apr 2006 | A1 |
| 20060084879 | Nazarian et al. | Apr 2006 | A1 |
| 20060122520 | Banet et al. | Jun 2006 | A1 |
| 20060123885 | Yates et al. | Jun 2006 | A1 |
| 20060140425 | Berg et al. | Jun 2006 | A1 |
| 20060142665 | Garay et al. | Jun 2006 | A1 |
| 20060202816 | Crump et al. | Sep 2006 | A1 |
| 20060205083 | Zhao | Sep 2006 | A1 |
| 20060210058 | Kock et al. | Sep 2006 | A1 |
| 20060211922 | Al-Ali et al. | Sep 2006 | A1 |
| 20060211924 | Dalke et al. | Sep 2006 | A1 |
| 20060217598 | Miyajima et al. | Sep 2006 | A1 |
| 20060217615 | Huiku | Sep 2006 | A1 |
| 20060224059 | Swedlow et al. | Oct 2006 | A1 |
| 20060240558 | Zhao | Oct 2006 | A1 |
| 20060246342 | MacPhee | Nov 2006 | A1 |
| 20060251277 | Cho | Nov 2006 | A1 |
| 20060251334 | Oba et al. | Nov 2006 | A1 |
| 20060252999 | Devaul et al. | Nov 2006 | A1 |
| 20060253010 | Brady et al. | Nov 2006 | A1 |
| 20060264730 | Stivoric et al. | Nov 2006 | A1 |
| 20060292533 | Selod | Dec 2006 | A1 |
| 20060293921 | McCarthy et al. | Dec 2006 | A1 |
| 20070004449 | Sham | Jan 2007 | A1 |
| 20070004969 | Kong et al. | Jan 2007 | A1 |
| 20070015992 | Filkins et al. | Jan 2007 | A1 |
| 20070021206 | Sunnen | Jan 2007 | A1 |
| 20070027367 | Oliver et al. | Feb 2007 | A1 |
| 20070027399 | Chou | Feb 2007 | A1 |
| 20070036383 | Romero | Feb 2007 | A1 |
| 20070050215 | Kil et al. | Mar 2007 | A1 |
| 20070060800 | Drinan et al. | Mar 2007 | A1 |
| 20070060819 | Altshuler et al. | Mar 2007 | A1 |
| 20070063850 | Devaul et al. | Mar 2007 | A1 |
| 20070082789 | Nissila et al. | Apr 2007 | A1 |
| 20070083092 | Rippo et al. | Apr 2007 | A1 |
| 20070083095 | Rippo et al. | Apr 2007 | A1 |
| 20070088221 | Stahmann | Apr 2007 | A1 |
| 20070093702 | Yu et al. | Apr 2007 | A1 |
| 20070106167 | Kinast | May 2007 | A1 |
| 20070112273 | Rogers | May 2007 | A1 |
| 20070112598 | Heckerman et al. | May 2007 | A1 |
| 20070116314 | Grilliot et al. | May 2007 | A1 |
| 20070118043 | Oliver et al. | May 2007 | A1 |
| 20070165872 | Bridger et al. | Jul 2007 | A1 |
| 20070167850 | Russell et al. | Jul 2007 | A1 |
| 20070191718 | Nakamura | Aug 2007 | A1 |
| 20070197878 | Shklarski | Aug 2007 | A1 |
| 20070197881 | Wolf et al. | Aug 2007 | A1 |
| 20070213020 | Novac | Sep 2007 | A1 |
| 20070230714 | Armstrong | Oct 2007 | A1 |
| 20070233403 | Alwan et al. | Oct 2007 | A1 |
| 20070265097 | Havukainen | Nov 2007 | A1 |
| 20070270667 | Coppi et al. | Nov 2007 | A1 |
| 20070270671 | Gal | Nov 2007 | A1 |
| 20070293781 | Sims et al. | Dec 2007 | A1 |
| 20070299330 | Couronne et al. | Dec 2007 | A1 |
| 20080004536 | Baxi et al. | Jan 2008 | A1 |
| 20080015424 | Bernreuter | Jan 2008 | A1 |
| 20080039731 | Mccombie et al. | Feb 2008 | A1 |
| 20080076972 | Dorogusker et al. | Mar 2008 | A1 |
| 20080081963 | Naghavi et al. | Apr 2008 | A1 |
| 20080086533 | Neuhauser et al. | Apr 2008 | A1 |
| 20080096726 | Riley et al. | Apr 2008 | A1 |
| 20080114220 | Banet et al. | May 2008 | A1 |
| 20080132798 | Hong et al. | Jun 2008 | A1 |
| 20080141301 | Azzaro et al. | Jun 2008 | A1 |
| 20080154098 | Morris et al. | Jun 2008 | A1 |
| 20080154105 | Lemay | Jun 2008 | A1 |
| 20080165017 | Schwartz | Jul 2008 | A1 |
| 20080170600 | Sattler et al. | Jul 2008 | A1 |
| 20080171945 | Dotter | Jul 2008 | A1 |
| 20080177162 | Bae et al. | Jul 2008 | A1 |
| 20080200774 | Luo | Aug 2008 | A1 |
| 20080200775 | Lynn | Aug 2008 | A1 |
| 20080203144 | Kim | Aug 2008 | A1 |
| 20080221461 | Zhou et al. | Sep 2008 | A1 |
| 20080249594 | Dietrich | Oct 2008 | A1 |
| 20080269626 | Gallagher et al. | Oct 2008 | A1 |
| 20080287752 | Stroetz et al. | Nov 2008 | A1 |
| 20090005662 | Petersen et al. | Jan 2009 | A1 |
| 20090006457 | Stivoric et al. | Jan 2009 | A1 |
| 20090010461 | Klinghult et al. | Jan 2009 | A1 |
| 20090030350 | Yang et al. | Jan 2009 | A1 |
| 20090054751 | Babashan et al. | Feb 2009 | A1 |
| 20090054752 | Jonnalagadda et al. | Feb 2009 | A1 |
| 20090069645 | Nielsen et al. | Mar 2009 | A1 |
| 20090082994 | Schuler et al. | Mar 2009 | A1 |
| 20090088611 | Buschmann | Apr 2009 | A1 |
| 20090093687 | Telfort et al. | Apr 2009 | A1 |
| 20090105548 | Bart | Apr 2009 | A1 |
| 20090105556 | Fricke et al. | Apr 2009 | A1 |
| 20090131761 | Moroney, III et al. | May 2009 | A1 |
| 20090131764 | Lee et al. | May 2009 | A1 |
| 20090175456 | Johnson | Jul 2009 | A1 |
| 20090177097 | Ma et al. | Jul 2009 | A1 |
| 20090214060 | Chuang et al. | Aug 2009 | A1 |
| 20090221888 | Wijesiriwardana | Sep 2009 | A1 |
| 20090227853 | Wijesiriwardana | Sep 2009 | A1 |
| 20090240125 | Such et al. | Sep 2009 | A1 |
| 20090253992 | Van Der Loo | Oct 2009 | A1 |
| 20090253996 | Lee et al. | Oct 2009 | A1 |
| 20090264711 | Schuler et al. | Oct 2009 | A1 |
| 20090270698 | Shioi et al. | Oct 2009 | A1 |
| 20090287067 | Dorogusker et al. | Nov 2009 | A1 |
| 20090299215 | Zhang | Dec 2009 | A1 |
| 20100004517 | Bryenton et al. | Jan 2010 | A1 |
| 20100022861 | Cinbis et al. | Jan 2010 | A1 |
| 20100045663 | Chen et al. | Feb 2010 | A1 |
| 20100100013 | Hu et al. | Apr 2010 | A1 |
| 20100113948 | Yang et al. | May 2010 | A1 |
| 20100168531 | Shaltis et al. | Jul 2010 | A1 |
| 20100172522 | Mooring et al. | Jul 2010 | A1 |
| 20100179389 | Moroney et al. | Jul 2010 | A1 |
| 20100185105 | Baldinger | Jul 2010 | A1 |
| 20100217099 | Leboeuf et al. | Aug 2010 | A1 |
| 20100217100 | LeBoeuf | Aug 2010 | A1 |
| 20100217103 | Abdul-Hafiz et al. | Aug 2010 | A1 |
| 20100222655 | Starr et al. | Sep 2010 | A1 |
| 20100228315 | Nielsen | Sep 2010 | A1 |
| 20100234714 | Mercier et al. | Sep 2010 | A1 |
| 20100249543 | Sethi | Sep 2010 | A1 |
| 20100268056 | Picard et al. | Oct 2010 | A1 |
| 20100274100 | Behar et al. | Oct 2010 | A1 |
| 20100274109 | Hu et al. | Oct 2010 | A1 |
| 20100292589 | Goodman | Nov 2010 | A1 |
| 20100298653 | McCombie et al. | Nov 2010 | A1 |
| 20100324384 | Moon et al. | Dec 2010 | A1 |
| 20110028810 | Van Slyke et al. | Feb 2011 | A1 |
| 20110028813 | Watson et al. | Feb 2011 | A1 |
| 20110081037 | Oh et al. | Apr 2011 | A1 |
| 20110098112 | LeBoeuf | Apr 2011 | A1 |
| 20110105869 | Wilson et al. | May 2011 | A1 |
| 20110112382 | Li et al. | May 2011 | A1 |
| 20110130638 | Raridan, Jr. | Jun 2011 | A1 |
| 20110142371 | King et al. | Jun 2011 | A1 |
| 20110245637 | McKenna | Oct 2011 | A1 |
| 20110288379 | Wu | Nov 2011 | A1 |
| 20120030547 | Raptis et al. | Feb 2012 | A1 |
| 20120095303 | He | Apr 2012 | A1 |
| 20120156933 | Kreger et al. | Jun 2012 | A1 |
| 20120179011 | Moon et al. | Jul 2012 | A1 |
| 20120203081 | LeBoeuf et al. | Aug 2012 | A1 |
| 20120226111 | LeBoeuf et al. | Sep 2012 | A1 |
| 20120226112 | LeBoeuf et al. | Sep 2012 | A1 |
| 20120277548 | Burton | Nov 2012 | A1 |
| 20120283577 | LeBoeuf et al. | Nov 2012 | A1 |
| 20120296184 | LeBoeuf et al. | Nov 2012 | A1 |
| 20130053661 | Alberth et al. | Feb 2013 | A1 |
| 20130072765 | Kahn et al. | Mar 2013 | A1 |
| 20130173171 | Drysdale et al. | Jul 2013 | A1 |
| 20130217979 | Blackadar et al. | Aug 2013 | A1 |
| 20130245387 | Patel | Sep 2013 | A1 |
| 20130324856 | Lisogurski et al. | Dec 2013 | A1 |
| 20130336495 | Burgett et al. | Dec 2013 | A1 |
| 20140012105 | LeBoeuf et al. | Jan 2014 | A1 |
| 20140051940 | Messerschmidt | Feb 2014 | A1 |
| 20140051948 | LeBoeuf et al. | Feb 2014 | A1 |
| 20140052567 | Bhardwaj et al. | Feb 2014 | A1 |
| 20140058220 | LeBoeuf et al. | Feb 2014 | A1 |
| 20140073486 | Ahmed et al. | Mar 2014 | A1 |
| 20140094663 | LeBoeuf et al. | Apr 2014 | A1 |
| 20140100432 | Golda et al. | Apr 2014 | A1 |
| 20140114147 | Romesburg et al. | Apr 2014 | A1 |
| 20140127996 | Park et al. | May 2014 | A1 |
| 20140128690 | LeBoeuf et al. | May 2014 | A1 |
| 20140135596 | LeBoeuf et al. | May 2014 | A1 |
| 20140140567 | LeBoeuf et al. | May 2014 | A1 |
| 20140197946 | Park et al. | Jul 2014 | A1 |
| 20140206954 | Yuen et al. | Jul 2014 | A1 |
| 20140213912 | Su | Jul 2014 | A1 |
| 20140219467 | Kurtz | Aug 2014 | A1 |
| 20140235967 | LeBoeuf et al. | Aug 2014 | A1 |
| 20140235968 | LeBoeuf et al. | Aug 2014 | A1 |
| 20140236531 | Carter | Aug 2014 | A1 |
| 20140243617 | LeBoeuf et al. | Aug 2014 | A1 |
| 20140243620 | LeBoeuf et al. | Aug 2014 | A1 |
| 20140275852 | Hong et al. | Sep 2014 | A1 |
| 20140275855 | LeBoeuf et al. | Sep 2014 | A1 |
| 20140278139 | Hong et al. | Sep 2014 | A1 |
| 20140287833 | LeBoeuf et al. | Sep 2014 | A1 |
| 20140288396 | LeBoeuf et al. | Sep 2014 | A1 |
| 20140323829 | LeBoeuf et al. | Oct 2014 | A1 |
| 20140323830 | LeBoeuf et al. | Oct 2014 | A1 |
| 20140323880 | Ahmed et al. | Oct 2014 | A1 |
| 20140378844 | Fei | Dec 2014 | A1 |
| 20150031967 | LeBoeuf et al. | Jan 2015 | A1 |
| 20150032009 | LeBoeuf et al. | Jan 2015 | A1 |
| 20160278646 | Hu et al. | Sep 2016 | A1 |
| 20160287108 | Wei et al. | Oct 2016 | A1 |
| 20170034615 | Mankodi et al. | Feb 2017 | A1 |
| 20170251295 | Pergament et al. | Aug 2017 | A1 |
| 20200000412 | LeBoeuf et al. | Jan 2020 | A1 |
| 20200214641 | LeBoeuf et al. | Jul 2020 | A1 |
| 20220257198 | LeBoeuf et al. | Aug 2022 | A1 |
| 20220313168 | LeBoeuf et al. | Oct 2022 | A1 |
| Number | Date | Country |
|---|---|---|
| 101212927 | Jul 2008 | CN |
| 201438747 | Apr 2010 | CN |
| 3910749 | Oct 1990 | DE |
| 1 297 784 | Apr 2003 | EP |
| 1 480 278 | Nov 2004 | EP |
| 2 077 091 | Jul 2009 | EP |
| 2 182 839 | Oct 2011 | EP |
| 2 408 209 | May 2005 | GB |
| 2 411 719 | Sep 2005 | GB |
| 7-241279 | Sep 1995 | JP |
| 9-253062 | Sep 1997 | JP |
| 9-299342 | Nov 1997 | JP |
| 2000-116611 | Apr 2000 | JP |
| 2001-025462 | Jan 2001 | JP |
| 2003-159221 | Jun 2003 | JP |
| 2004-513750 | May 2004 | JP |
| 2004-283523 | Oct 2004 | JP |
| 2007-044203 | Feb 2007 | JP |
| 2007-185348 | Jul 2007 | JP |
| 2008-136556 | Jun 2008 | JP |
| 2008-279061 | Nov 2008 | JP |
| 2009-153664 | Jul 2009 | JP |
| 2010-526646 | Aug 2010 | JP |
| 2014-068733 | Apr 2014 | JP |
| 20-0204510 | Nov 2000 | KR |
| WO 0024064 | Apr 2000 | WO |
| WO 00047108 | Aug 2000 | WO |
| WO 0108552 | Feb 2001 | WO |
| WO 2002017782 | Mar 2002 | WO |
| WO 2005010568 | Feb 2005 | WO |
| WO 2005020121 | Mar 2005 | WO |
| WO 2005110238 | Nov 2005 | WO |
| WO 2006009830 | Jan 2006 | WO |
| WO 2006067690 | Jun 2006 | WO |
| WO 2007012931 | Feb 2007 | WO |
| WO 2007053146 | May 2007 | WO |
| WO 2008141306 | Nov 2008 | WO |
| 2009032074 | Mar 2009 | WO |
| WO 2011127063 | Oct 2011 | WO |
| WO 2013019494 | Feb 2013 | WO |
| WO 2013038296 | Mar 2013 | WO |
| WO 2013109389 | Jul 2013 | WO |
| WO 2013109390 | Jul 2013 | WO |
| WO 2014092932 | Jun 2014 | WO |
| Entry |
|---|
| “Communication with European Search Report”, EP Application No. 21151461.7, dated Apr. 30, 2021, 8 pp. |
| Extended European Search Report corresponding to European Application No. 15830336.2 dated Jun. 7, 2017 (8 pages). |
| “U.S. Army Fitness Training Handbook” by the Department of the Army, 2003, The Lyons Press, p. 17. |
| Warfighter Physiological and Environmental Monitoring: A Study for the U.S. Army Research Institute in Environmental Medicine and the Soldier Systems Center, Massachusetts Institute of Technology Lincoln Laboratory, Final Report, Nov. 1, 2004, prepared for the U.S. Army under Air Force Contract F19628-00-C-0002; approved for public release. |
| Anpo et al. “Photocatalytic Reduction of Co2 With H2O on Titanium Oxides Anchored within Micropores of Zeolites: Effects of the Structure of the Active Sites and the Addition of Pt” J. Phys. Chem. B, 101:2632-2636 (1997). |
| Bârsan et al. “Understanding the fundamental principles of metal oxide based gas sensors; the example of CO sensing with SnO2 sensors in the presence of humidity” Journal of Physics: Condensed Matter 15:R813-R839 (2003). |
| Bott “Electrochemistry of Semiconductors” Current Separations 17(3):87-91 (1998). |
| Colligan, M. J. et al. in “The psychological effects of indoor air pollution”, Bulletin ofthe New York Academy of Medicine, vol. 57, No. 10, Dec. 1981, p. 1014-1026. |
| De Paula Santos, U. et al, in “Effects of air pollution on blood pressure and heart rate variability: a panel study of vehicular traffic controllers in the city of Sao Paulo, Brazil”, European Heart Journal (2005) 26, 193-200. |
| Ebert, T et al., “Influence of Hydration Status on Thermoregulation and Cycling Hill Climbing,” Med. Sci. Sport Exerc. vol. 39, No. 2, pp. 323-329, 2007. |
| Edmison et al., “E-Textile Based Automatic Activity Diary for Medical Annotation and Analysis,” Proc. BSN 2006 Int. Workshop Wearable Implantable Body Sensor Netw. (2006), pp. 131-145, Apr. 3-5, 2006. |
| European Search Report corresponding to European Application No. 07862660.3 dated Apr. 25, 2012; 7 pages. |
| Falkner et al, “Cardiovascular response to mental stress in normal adolescents with hypertensive parents. Hemodynamicsand mental stress in adolescents.” Hypertension 1979. 1:23-30. |
| Fitrainer “The Only Trainer You Need”; http://itami.com; Downloaded Feb. 26, 2010; ©2008 FiTriainer™; 2 pages. |
| Fleming et al., “A Comparison of Signal Processing Techniques for the Extraction ofBreathing Rate from the Photopethysmorgram,” World Academy of Science, Engineering and Technology, vol. 30, Oct. 2007, pp. 276-280. |
| Geladas et al., “Effect of cold air inhalation on core temperature in exercising subjects under stress,” The American Physiological Society, pp. 2381-2387, 1988. |
| Gibbs et al., “Reducing Motion Artifact Reduction for Wearable Sensors Using MEMS Accelerometers for Active Noise Cancellation,” 2005 American Control Conference, Jun. 8-10, 2005, Portland, OR, USA, pp. 1581-1586. |
| Gold, D.R. et al. in “Ambient Pollution and Heart Rate Variability”, Circulation 2000, 101:1267-1273. |
| International Search Report corresponding to International Patent Application No. PCT/US2012/046446, dated Jan. 14, 2013, 3 pages. |
| International Search Report and Written Opinion ofthe International Searching Authority, corresponding to PCT/US2012/0948079, dated Oct. 9, 2012. |
| International Search Report and Written Opinion ofthe International Searching Authority, corresponding to PCT/US2007/025114, dated May 13, 2008. |
| International Search Report Corresponding to International Application No. PCT/US2012/022634, dated Aug. 22, 2012, 9 pages. |
| Maomao et al., “Mobile Context-Aware Game for the Next Generation,” 2nd International Conference on Application and Development of Computer Games ADCOG 2003, p. 78-81. |
| Martins et al. “Zinc oxide as an ozone sensor” Journal of Applied Physics 96(3):1398-1408 (2004). |
| Maughan, R.J., “Impact of mild dehydration on wellness and on exercise performance,” European Journal of Clinical Nutrition, 57, Suppl. 2, pp. S19-S23, 2003. |
| Maughan et al., “Exercise, Heat, Hydration and the Brain,” Journal of the American College of Nutrition, vol. 26, No. 5, pp. 604S-612S, 2007. |
| Mostardi, R., et al., “The effect of increased body temperature due to exercise on the heart rate and the maximal aerobic power,” Europ. J. Appl. Physiol, 33, pp. 237-245, 1974. |
| Nakajima et al., “Monitoring of heart and respiratory rates by photoplethyusmography using a digital filtering technique,” Med. Eng. Phys., vol. 18, No. 5, Jul. 1996, pp. 365-372. |
| Notification Concerning Transmittal of International Preliminary Report on Patentability, PCT/US2014/012909, dated Jul. 28, 2015. |
| Notification of Transmittal of the International Search Report and Written Opinion ofthe International Search Authority dated Jul. 30, 2010 by the Korean Intellectual Property Office for corresponding International Application No. PCT/US2010/021936. |
| Notification of Transmittal of the International Search Report and Written Opinion ofthe International Search Authority dated Aug. 26, 2010 by the Korean Intellectual Property Office for corresponding International Application No. PCT/US2010/021629. |
| Notification of Transmittal of the International Search Report and the Written Opinion ofthe International Search Authority dated Sep. 16, 2010 by the Korean Intellectual Property Office for corresponding International Application No. PCT/US2010/024922. |
| Notification of Transmittal of the International Search Report and the Written Opinion ofthe International Search Authority dated Sep. 27, 2010 by the Korean Intellectual Property Office for corresponding International Application No. PCT/US2010/025216. |
| Notification of Transmittal of The International Search Report and The Written Opinion ofthe International Searching Authority, or the Declaration corresponding to International Application No. PCT/US2013/070271; dated Feb. 26, 2014; 13 pages. |
| Saladin et al. “Photosynthesis of CH4 at a TiO2 Surface from Gaseous H2O and CO2” J. Chem. Soc., Chem. Commun. 533-534 (1995). |
| Shorten et al., “Acute effect of environmental temperature during exercise on subsequent energy intake in active men,” Am. J Clin. Nutr. 90, pp. 1215-1221, 2009. |
| Skubal et al. “Detection and identification of gaseous organics using a TiO2 sensor” Journal of Photochemistry and Photobiology A: Chemistry 148:103-108 (2002). |
| Skubal et al. “Monitoring the Electrical Response of Photoinduced Organic Oxideation on TiO2 Surfaces” Manuscript submitted Oct. 2000 to SPIE Intl. Symposium on Environments Industrial Sensing, Boston, MA, Nov. 5-8, 2000, sponsored by SPIE, 10 pp. |
| Thompson, M.W., “Cardiovascular drift and critical core temperature: factors limiting endurance performance in the heat?” J. Exerc. Sci. Fit, vol. 4, No. 1, pp. 15-24, 2006. |
| Wood et al., “Active Motion Artifact Reduction for Wearable Sensors Using Laguerre Expansion and Signal Separator,” Proceedings of the 2005 IEEE Engineering in Medicine and Biology, 27th Annual Conference, Shanghai, China, Sep. 1-4, 2005, pp. 3571-3574. |
| Zhang et al. “Development of Chemical Oxygen Demand On-Line Monitoring System Based on a Photoelectrochemical Degradation Principle” Environ. Sci. Technol., 40(7):2363-2368 (2006). |
| European Search Report, EP Application No. 13863449.8, dated Oct. 19, 2015, 3 pages. |
| European Search Report, EP Application No. 14743615.8, dated Oct. 12, 2015, 3 pages. |
| European Search Report, EP Application No. 14743839.4, dated Oct. 12, 2015, 3 pages. |
| Gibbs et al., “Reducing Motion Artifact in Wearable Bio-Sensors Using MEMS Accelerometers for Active Noise Cancellation,” 2005 American Control Conference, Jun. 8-10, 2005, Portland, OR, USA, pp. 1581-1586. |
| International Preliminary Report on Patentability, PCT/US2014/012940, dated Jun. 17, 2015, 23 pages. |
| International Search Report and Written Opinion ofthe International Searching Authority, corresponding to International Patent Application No. PCT/US2014/012940, dated Oct. 16, 2014, 13 pages. |
| International Search Report corresponding to International Patent Application No. PCT/US2014/012909, dated May 13, 2014, 3 pages. |
| Notification of Transmittal of the International Search Report and the Written Opinion ofthe International Searching Authority, or the Declaration, PCT/US2015/014562, dated Oct. 28, 2015. |
| Notification of Transmittal of the International Search Report and the Written Opinion ofthe International Searching Authority, or the Declaration, PCT/US2015/042636, dated Oct. 29, 2015. |
| Notification of Transmittal of the International Search Report and the Written Opinion ofthe International Searching Authority, or the Declaration, PCT/US2015/042015, dated Oct. 29, 2015. |
| Notification of Transmittal of the International Search Report and the Written Opinion ofthe International Searching Authority, or the Declaration, PCT/US2015/042035, dated Oct. 29, 2015. |
| Wood et al., “Active Motion Artifact Reduction for Wearable Sensors Using Laguerre Expansion and Signal Separation,” Proceedings ofthe 2005 IEEE Engineering in Medicine and Biology, 27th Annual Conference, Shanghai, China, Sep. 1-4, 2005, pp. 3571-3574. |
| Notification of Transmittal of the International Search Report and the Written Opinion ofthe International Searching Authority, or the Declaration, PCT/US2015/046079, dated Dec. 29, 2015. |
| Communication pursuant to Article 94(3) EPC, European Patent Application No. 13863449.8, dated Nov. 5, 2015, 7 pages. |
| Communication pursuant to Article 94(3) EPC, European Patent Application No. 14743615.8, dated Dec. 23, 2015, 7 pages. |
| Communication pursuant to Article 94(3) EPC, European Patent Application No. 14743839.4, dated Dec. 23, 2015, 6 pages. |
| Communication pursuant to Article 94(3) EPC, European Patent Application No. 12820308.0, dated Feb. 3, 2016, 5 pages. |
| Notification of Transmittal of the International Search Report and Written Opinion ofthe International Search Authority dated May 26, 2016 by the Korean Intellectual Property Office for corresponding International Application No. PCT/US2016/019126. |
| Notification of Transmittal of the International Search Report and Written Opinion ofthe International Search Authority dated May 26, 2016 by the Korean Intellectual Property Office for corresponding International Application No. PCT/US2016/019132. |
| Asada, et al., “Mobile Monitoring with Wearable Photoplethysmographic Biosensors,” IEEE Engineering in Medicine and Biology Magazine, May/Jun. 2003, pp. 28-40. |
| Bifulco et al., “Bluetooth Portable Device for Continuous ECG and Patient Motion Monitoring During Daily Life,” Medicon 2007, IFMBE Proceedings 16, 2007, pp. 369-372. |
| Brodersen et al., “In-Ear Acquisition of Vital Signs Discloses New Chances for Preventive Continuous Cardiovascular Monitoring,” 4th International Workshop on Wearable and Implantable Body Sensor Networks (BSN 2007), vol. 13 of the series IFMBE Proceedings, pp. 189-194. |
| Celka et al, “Motion Resistant Earphone Located Infrared based Heart Rate Measurement Device,” Proceedings of the Second IASTED International Conference on Biomedical Engineering, Feb. 16-18, 2004, Innsbruck, Austria, pp. 582-585. |
| Communication Pursuant to Article 94(3) EPC, EP 12 739 502.8, dated Jul. 19, 2016, 7 pages. |
| Communication Pursuant to Article 94(3) EPC, EP 14 743 615.8, dated Jul. 19, 2016, 7 pages. |
| Communication Pursuant to Article 94(3) EPC, EP 14 743 839.4, dated Jul. 20, 2016, 5 pages. |
| Comtois et al., “A Wearable Wireless Reflectance Pulse Oximeter for Remote Triage Applications,” 2006 IEEE, pp. 53-54. |
| Comtois, Gary, W., “Implementation of Accelerometer-Based Adaptive Noise Cancellation in a Wireless Wearable Pulse Oximeter Platform for Remote Physiological Monitoring and Triage,” Thesis, Worcester Polytechnic Institute, Aug. 31, 2007, 149 pages. |
| Duun et al., “A Novel Ring Shaped Photodiode for Reflectance Pulse Oximetry in Wireless Applications,” IEEE Sensors 2007 Conference, pp. 596-599. |
| Geun et al., “Measurement Site and Applied Pressure Consideration in Wrist Photoplethysmography,” The 23rd International Technical Conference on Circuits/Systems, Computers and Communications, 2008, pp. 1129-1132. |
| Gibbs et al., “Active motion artifact cancellation for wearable health monitoring sensors using collocated MEMS accelerometers,” Smart Structures and Materials, 2005: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, Proc. of SPIE, vol. 5765, pp. 811-819. |
| Haahr et al., “A Wearable “Electronic Patch” for Wireless Continuous Monitoring of Chronically Diseased Patients,” Proceedings of the 5th International Workshop on Wearable and Implantable Body Sensor Networks, in conjunction with The 5th International Summer School and Symposium on Medical Devices and Biosensors, The Chinese University of Hong Kong, HKSAR, China, Jun. 1-3, 2008, pp. 66-70. |
| Jiang, Honghui, “Motion-Artifact Resistant Design of Photoplethysmograph Ring Sensor for Driver Monitoring,” Thesis, Massachusetts Institute of Technology, Feb. 2004, 62 pages. |
| Kuzmina et al., “Compact multi-functional skin spectrometry set-up,” Advanced Optical Materials, Technologies, and Devices, Proc. of SPIE, vol. 6596, 2007, pp. 65960T-1 to 65960T-6. |
| Lee et al, “Respiratory Rate Detection Algorithms by Photoplethysmography Signal Processing,” 30th Annual International IEEE EMBS Conference, Vancouver, British Columbia, Canada, Aug. 20-24, 2008, pp. 1140-1143. |
| Lindberg et al., “Monitoring of respiratory and heart rates using a fibre-optic sensor,” Med Biol Eng Comput, Sep. 1992, vol. 30, No. 5, pp. 533-537. |
| Luprano, Jean, “Sensors and Parameter Extraction by Wearable Systems: Present Situation and Future,” pHealth 2008, May 21, 2008, 29 pages. |
| Lygouras et al., “Optical-Fiber Finger Photo-Plethysmograph Using Digital Techniques,” IEEE Sensors Journal, vol. 2, No. 1, Feb. 2002, pp. 20-25. |
| Maguire et al., “The Design and Clinical Use of a Reflective Brachial Photoplethysmograph,” Technical Report NUIM/SS/--/2002/04, Submitted Apr. 2002, Signals and Systems Research Group, National University of Ireland, Maynooth, Co. Kildare, Ireland, 13 pages. |
| Mendelson et al., “Measurement Site and Photodetector Size Considerations in Optimizing Power Consumption of a Wearable Reflectance Pulse Oximeter,” Proceedings of the 25th Annual International Conference of the IEEE EMBS, Cancun, Mexico, Sep. 17-21, 2003, pp. 3016-3019. |
| Mendelson et al., “Noninvasive Pulse Oximetry Utilizing Skin Reflectance Photoplethysmography,” IEEE Transactions on Biomedical Engineering, vol. 35, No. 10, Oct. 1988, pp. 798-805. |
| Poh et al., “Motion Tolerant Magnetic Earring Sensor and Wireless Earpiece for Wearable Photoplethysmography,” IEEE Transactions on Information Technology in Biomedicine, vol. 14, No. 3, May 2010, pp. 786-794. |
| Renevey et al., “Wrist-Located Pulse Detection Using IR Signals, Activity and Nonlinear Artifact Cancellation,” IEEE EMBS, 2001, 4 pages. |
| Rhee et al., “Artifact-Resistant Power-Efficient Design of Finger-Ring Plethysmographic Sensors,” IEEE Transactions on Biomedical Engineering, vol. 48, No. 7, Jul. 2001, pp. 795-805. |
| Shaltis, Phillip Andrew, Analysis and Validation of an Artifact Resistant Design for Oxygen Saturation Measurement Using Photo Plethysmographic Ring Sensors, Thesis, Massachusetts Institute of Technology, Jun. 2004, 103 pages. |
| Shin et al., “A Novel Headset with a Transmissive PPG Sensor for Heart Rate Measurement,” ICBME 2008, Proceedings 23, 2009, pp. 519-522. |
| Spigulis et al, “Wearable wireless photoplethysmography sensors,” Proc. of SPIE, vol. 6991, 2008, pp. 69912O-1 to 69912O-7. |
| Takatani et al., “Optical Oximetry Sensors for Whole Blood and Tissue,” IEEE Engineering in Medicine and Biology, Jun./Jul. 1994, pp. 347-357. |
| Vogel et al., “A System for Assessing Motion Artifacts in the Signal of a Micro-Optic In-Ear Vital Signs Sensor,” 30th Annual International IEEE EMBS Conference, Vancouver, British Columbia, Canada, Aug. 20-24, 2008. |
| Vogel et al., “In-Ear Heart Rate Monitoring Using a Micro-Optic Reflective Sensor,” Proceedings of the 29th Annual International Conference of the IEEE EMBS Cite Internationale, Lyon, France, Aug. 23-26, 2007, pp. 1375-1378. |
| Wang et al., “Multichannel Reflective PPG Earpiece Sensor With Passive Motion Cancellation,” IEEE Transactions on Biomedical Circuits and Systems, vol. 1, No. 4, Dec. 2007, pp. 235-241. |
| Wang et al., “Reflective Photoplethysmograph Earpiece Sensor for Ubiquitous Heart Rate Monitoring,” 4th International Workshop on Wearable and Implantable Body Sensor Networks, 2007, vol. 13 of the series IFMBE Proceedings, pp. 179-183. |
| Wei et al. “A New Wristband Wearable Sensor Using Adaptive Reduction Filter to Reduce Motion Artifact,” Proceedings of the 5th International Conference on Information Technology and Application in Biomedicine, in conjunction with The 2nd International Symposium & Summer School on Biomedical and Health Engineering, Shenzhen, China, May 30-31, 2008, pp. 278-281. |
| Wood, Levi Benjamin, “Motion Artifact Reduction for Wearable Photoplethysmogram Sensors Using Micro Accelerometers and Laguerre Series Adaptive Filters,” Thesis, Massachusetts Institute of Technology, Jun. 2008, 74 pages. |
| Han et al., “Artifacts in wearable photoplethysmographs during daily life motions and their reduction with least mean square based active noise cancellation method,” Computers in Biology and Medicine, 42, 2012, pp. 387-393. |
| Extended European Search Report, EP Application No. 16164775.5 dated Sep. 13, 2016, 7 pages. |
| Notification of Transmittal of the International Search Report and the Written Opinion of the International Searching Authority, or the Declaration, PCT/US2016/041842, dated Oct. 21, 2016, 5 pages. |
| Notification of Transmittal of International Preliminary Report on Patentability, PCT/US2015/041562, dated Oct. 20, 2016, 14 pages. |
| Notification of Transmittal of International Preliminary Report on Patentability, PCT/US2015/042636, dated Oct. 20, 2016, 7 pages. |
| Notification of Transmittal of International Preliminary Report on Patentability, PCT/US2015/042015, dated Oct. 20, 2016, 10 pages. |
| Notification of Transmittal of International Preliminary Report on Patentability, PCT/US2015/042035, dated Oct. 20, 2016, 8 pages. |
| Notification of Transmittal of International Preliminary Report on Patentability, PCT/US2015/046079, dated Oct. 20, 2016, 10 pages. |
| Communication with Supplementary European Search Report, EP Application No. 15826541.3, dated Jul. 10, 2017, 8 pp. |
| Lee et al., “Comparison Between Red, Green and Blue Light Reflection Photoplethysmography for Heart Rate Monitoring During Motion”, 35th Annual International Conference ofthe IEEE EMBS, Osaka, Japan, Jul. 3-7, 2013, pp. 1724-1727. |
| Maeda et al., “The Advantages of Wearable Green Reflected Photoplethysmography”, J. Med. Syst, vol. 35, No. 5, Oct. 2011, pp. 829-834. |
| Tamura et al., “Wearable Photoplethysmographic Sensors—Past and Present”, Electronics, vol. 3, No. 2, Apr. 2014, pp. 282-302. |
| “Communication with European Search Report”, EP Application No. 20185366.0, dated Oct. 1, 2020, 8 pp. |
| Number | Date | Country | |
|---|---|---|---|
| 20210045693 A1 | Feb 2021 | US |
| Number | Date | Country | |
|---|---|---|---|
| 62109196 | Jan 2015 | US | |
| 62030951 | Jul 2014 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 15369946 | Dec 2016 | US |
| Child | 17085545 | US | |
| Parent | 14807061 | Jul 2015 | US |
| Child | 15369946 | US |
