Body temperature-based body impedance calibration for biometric estimation and determination

Abstract

A computer-implemented method for biometric monitoring is provided. The method includes obtaining impedance sensor data from an impedance sensor that is indicative of a body impedance of a user. The method includes obtaining temperature data associated with the user from one or more temperature sensors, such as body temperature data indicative of a body temperature of the user. The method includes determining a calibrated body impedance of the user based at least in part on the impedance sensor data and the temperature data. The method includes determining a body composition biometric of the user based at least in part on the calibrated body impedance of the user and user data indicative of one or more characteristics of the user. The method includes monitoring a relative change in the body impedance of the user with respect to the body temperature data during an observation period having a plurality of sampling periods.

Description

BODY TEMPERATURE-BASED BODY IMPEDANCE CALIBRATION FOR BIOMETRIC ESTIMATION AND DETERMINATIONFIELD

[0001] Example aspects of the present disclosure relate generally to determining biometrics of a user of a wearable computing device.BACKGROUND

[0002] A wearable computing device can be worn, for instance, on a user’s wrist. The wearable computing device can include a plurality of sensors such as, e.g., biometric sensors. In this manner, the wearable computing device can determine one or more biometrics of the user wearing the wearable computing device based at least in part on the data output by the biometric sensors.SUMMARY

[0003] Aspects and advantages of embodiments of the present disclosure will be set forth in part in the following description, or can be learned from the description, or can be learned through practice of the embodiments.

[0004] In one aspect, a computer-implemented method for biometric monitoring is provided. The method includes obtaining, by a computing system comprising one or more computing devices, impedance sensor data from an impedance sensor of a computing device of the computing system, the impedance sensor data indicative of a body impedance of a user of the computing device. The method further includes obtaining, by the computing system, temperature data associated with the user from one or more temperature sensors of the computing system, the temperature data comprising body temperature data indicative of a body temperature of the user. The method further includes determining, by the computing system, a calibrated body impedance of the user based at least in part on the impedance sensor data and the temperature data. The method further includes determining, by the computing system, a body composition biometric of the user based at least in part on the calibrated body impedance of the user and user data indicative of one or more characteristics of the user.

[0005] In some implementations, the body composition biometric of the user includes one of a body fat percentage of the user, a fat-free mass biometric of the user, a bone mineralcontent of the user, a basal metabolic rate of the user, or a total body water biometric of the user.

[0006] In some implementations, the body temperature data includes at least one of skin temperature data indicative of a skin temperature of the user, tissue temperature data indicative of a temperature of subcutaneous tissue of the user, muscle temperature data indicative of a temperature of muscle tissue of the user, or core temperature data indicative of a core temperature of the user.

[0007] In some implementations, the user data includes demographic data of the user and anthropometric data of the user.

[0008] In some implementations, the temperature data further includes device temperature data obtained from the one or more temperature sensors of the computing system that is indicative of an internal temperature of the computing device and ambient temperature data that is indicative of a temperature of an environment of the user.

[0009] In some implementations, determining the calibrated body impedance of the user includes providing, by the computing system, the impedance sensor data and the temperature data to a bioimpedance calibration model of the computing system, which is configured to calibrate the impedance sensor data based at least in part the temperature data. Determining the calibrated body impedance of the user further includes determining, by the computing system, the calibrated body impedance of the user based at least in part on an output of the bioimpedance calibration model.

[0010] In some implementations, determining the body composition biometric of the user includes providing, by the computing system, the calibrated body impedance of the user and the user data to a biometric estimation model of the computing system and determining, by the computing system, the body composition biometric of the user based at least in part on an output of the biometric estimation model.

[0011] In some implementations, determining the calibrated body impedance of the user includes providing, by the computing system, the impedance sensor data and the temperature data to a biometric estimation model of the computing system, which is configured to calibrate the impedance sensor data based at least in part on the temperature data.

[0012] In some implementations, determining the body composition biometric of the user includes providing, by the computing system, the user data to the biometric estimation model and determining, by the computing system, the body composition biometric of the user based at least in part an output of the biometric estimation model.

[0013] In some implementations, the method further includes monitoring, by the computing system, a relative change in the body impedance of the user with respect to the body temperature data during an observation period comprising a plurality of sampling periods. The computing system is configured provide the relative change as training data to a biometric estimation model of the computing system, which is configured to determine the body composition biometric of the user based at least in part on the calibrated body impedance of the user and the user data indicative of the one or more characteristics of the user.

[0014] In some implementations, monitoring the relative change in the body impedance of the user includes: obtaining, by the computing system, distal sensor data from the impedance sensor during each of the plurality of sampling periods of the observation period when the impedance sensor is contacting the user at a distal location of the user; obtaining, by the computing system, the body temperature data from the one or more temperature sensors during each of the plurality of sampling periods of the observation period; and determining, by the computing system, the relative change in the distal sensor data during the observation period based at least in part on the body temperature data obtained during each of the plurality of sampling periods of the observation period.

[0015] In some implementations, the distal location of the user includes a hand of the user, a wrist of the user, a finger of the user, a foot of the user, or an ankle of the user.

[0016] In some implementations, the relative change in the distal sensor data demonstrates a pseudo-linear relationship with the body temperature of the user.

[0017] In some implementations, monitoring the relative change in the body impedance of the user includes: obtaining, by the computing system, proximal sensor data from the impedance sensor during each of the plurality of sampling periods of the observation period, the impedance sensor contacting the user at a proximal location of the user; obtaining, by the computing system, the body temperature data from the one or more temperature sensors during each of the plurality of sampling periods of the observation period; and determining, by the computing system, the relative change in the proximal sensor data during the observation period based at least in part on the body temperature data obtained during each of the plurality of sampling periods of the observation period.

[0018] In some implementations, the proximal location of the user is a trunk of the user.

[0019] In some implementations, the relative change in the proximal sensor data demonstrates a flat relationship with the body temperature of the user.

[0020] In another aspect, a wearable computing device is provided. The wearable computing device includes a housing, a base plate coupled to the housing that defines a bottom surface of the housing, one or more biometric sensors positioned on the bottom surface of the housing that are configured to obtain biometric data of a user wearing the wearable computing device, and one or more processors. The one or more processors are configured to determine a body impedance of the user based at least in part on impedance data received from the one or more biometric sensors, determine a body temperature of the user based at least in part on body temperature data received from the one or more biometric sensors, calibrate the impedance data based at least in part on the body temperature data to determine a calibrated body impedance of the user, and determine a body composition biometric of the user based at least in part on the calibrated body impedance of the user and user data indicative of one or more characteristics of the user.

[0021] In some implementations, the wearable computing device further includes an internal temperature sensor disposed within the housing and an ambient temperature sensor on or within the housing. The internal temperature sensor is configured to obtain internal temperature data indicative of an internal temperature of the wearable computing device, the ambient temperature sensor is configured to obtain ambient temperature data indicative of an ambient temperature of an environment of the user, and the one or more processors are further configured to calibrate the impedance data based at least in part on the bodytemperature data, the internal temperature data, and the ambient temperature data.

[0022] In some implementations, the body composition biometric of the user includes at least one of a body fat percentage of the user or a fat-free mass biometric of the user. Furthermore, the user data includes demographic data of the user and anthropometric data of the user.

[0023] In another aspect, a computing system is provided. The computing system includes an impedance sensor, one or more temperature sensors, and one or more processors. The computing system further includes one or more computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computing system to perform operations. The operations include: obtaining impedance sensor data from the impedance sensor, the impedance sensor data indicative of a body7impedance of a user of the computing system; obtaining temperature data associated with the user from the one or more temperature sensors, the temperature data comprising bodytemperature data indicative of a body temperature of the user: determining a calibrated body impedance of the user based at least in part on the impedance sensor data and the temperaturedata; and determining a body composition biometric of the user based at least in part on the calibrated body impedance of the user and user data indicative of one or more characteristics of the user.

[0024] These and other features, aspects and advantages of various embodiments will become better understood with reference to the following description and appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the related principles.BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Detailed discussion of embodiments directed to one of ordinary skill in the art are set forth in the specification, which makes reference to the appended figures, in which:

[0026] FIG. 1 depicts an embodiment of a wearable computing device according to example embodiments of the present disclosure;

[0027] FIG. 2 depicts a front perspective view of a wearable computing device according to example embodiments of the present disclosure;

[0028] FIG. 3 depicts a rear perspective view of the wearable computing device of FIG. 2 according to example embodiments of the present disclosure;

[0029] FIG. 4 depicts a block diagram of components of a wearable computing device according to example embodiments of the present disclosure;

[0030] FIGS. A-5B depict block diagrams of example models for determining a body composition biometric according to example embodiments of the present disclosure;

[0031] FIG. 6 depicts a flow chart diagram of an example method according to example embodiments of the present disclosure;

[0032] FIG. 7 depicts rates of change of example biometrics according to example embodiments of the present disclosure; and

[0033] FIG. 8 depicts a computing system according to example embodiments of the present disclosure.

[0034] Repeat use of reference characters in the present specification and drawings is intended to represent the same and / or analogous features or elements of the present invention.DETAILED DESCRIPTION

[0035] Reference now will be made in detail to embodiments, one or more examples of which are illustrated in the drawings. Each example is provided by way of explanation of theembodiments, not limitation of the present disclosure. In fact, it will be apparent to those skilled in the art that various modifications and variations can be made to the embodiments without departing from the scope or spirit of the present disclosure. For instance, features illustrated or described as part of one embodiment can be used with another embodiment to yield a still further embodiment. Thus, it is intended that aspects of the present disclosure cover such modifications and variations.

[0036] Example aspects of the present disclosure relate to computer systems and methods for determining and monitoring a body composition biometric of a user. In particular, a computing system according to examples of the present disclosure may include one or more computing devices, such as a wearable computing device, a mobile computing device (e.g., smartphone, tablet, etc.), and the like. In some embodiments, a computing device of the computing system may include one or more biometric sensors configured to obtain biometric data of a user of the computing device. For instance, a wearable computing device may include an impedance sensor configured to obtain data indicative of a body impedance of the user wearing the wearable computing device. It should be understood that the discussions relating to a wearable computing device are for purposes of illustration and discussion.Those having ordinary skill in the art, using the disclosures provided herein, will understand that the disclosures provided herein may be applicable to any suitable computing device.

[0037] As will be discussed in greater detail below, body composition biometrics (e.g.. body fat percentage (BF%), fat-free mass (FFM), bone mineral content (BMC), basal metabolic rate (BMR), total body water (TBW), etc.) may be estimated based on a variety of data associated with the user. For instance, body composition biometrics may be determined based on the user’s demographic information (e.g., age, sex, etc.), the user's anthropometric data (e.g., height, weight, waist circumference, etc.), and the user’s body impedance. However, to ensure the body composition biometrics are accurate, the data associated with the user that is used by the computing system to determine the user’s body composition — namely, the body impedance data — must likewise be accurate.

[0038] As noted above, a user’s body impedance may be determined based on measurements from an impedance sensor (e.g., an electrode) that contacts the user’s skin. However, various confounders, such as measurement posture and / or body conditions (e g., skin conditions) of the user, can affect the body impedance measurements by the impedance sensor, despite the user’s body composition remaining the same. For instance, despite a user’s body composition remaining constant, a user’s body impedance may vary based on, e.g., the angle of the measurement by the impedance sensor and / or the user’s bodytemperature. Thus, because the user’s body impedance may be used by the computing system to determine the user’s body composition, inaccuracies and inconsistencies in the measured body impedance data may result in inaccurate body composition estimations.

[0039] As will be discussed in greater detail below, a user’s body temperature (and measurements thereof) may include a variety7of temperatures and temperature measurements. For instance, by way of non-limiting examples, a user’s body temperature may include a temperature of the user’s skin (e.g., ‘"skin temperature”), a temperature of the user’s subcutaneous tissue (e.g., ‘'tissue temperature” and / or “internal temperature”), a temperature of the user's muscle(s) (e.g., “muscle temperature”), a temperature of the user’s core (e.g., “core temperature”), and the like.

[0040] Some devices, such as wearable computing devices and / or non-wearable computing devices, provide body composition estimations in a similar manner as set forth above. That is, some devices provide body composition estimations based on demographic information, anthropometric information, and body impedance data measured by electrodes contacting skin. Hence, some devices are susceptible to providing inaccurate body composition estimations due, in large part, to the confounders discussed above.

[0041] Example aspects of the present disclosure are directed to computing systems and methods that reduce the adverse effects on body impedance measurements and body composition estimations caused by the confounders discussed above, thereby providing for more accurate and reliable body composition estimations. For example, as will be discussed in greater detail below, example aspects of the present disclosure provide a computing system operable to measure the confounders (e.g., body temperature) and, subsequently, calibrate the measured body impedance data based on the measured confounders. In this manner, as will be discussed in greater detail below, computing systems according to examples of the present disclosure provide more accurate body composition estimations, because the body impedance data used in the body composition estimations is calibrated to reduce the adverse effects caused by the above-described confounders. Furthermore, it should be understood that the present disclosure is discussed with reference to body composition estimations for purposes of illustration and discussion. Those having ordinary skill in the art, using the disclosures provided herein, will appreciate that example aspects of the present disclosure are likewise applicable to other biometric estimations, such as impedance cardiography, impedance pneumography, and the like.

[0042] More particularly, the temperature of a user’s skin and / or tissue under the skin — particularly at peripheral locations (e.g., wrist, hand, finger, ankle, foot, etc.) — are known tovary due to various factors, such as the environment the user is in, the clothing the user is wearing, and / or physiological states of the user. Temperature, however, is not easily controlled, and limiting body composition estimations to situations where the user is in a temperature-controlled environment is not practical, especially given the increase in popularity of consumer health devices (e.g., wearable computing devices).

[0043] Accordingly, example aspects of the present disclosure are directed to a computing system operable to obtain impedance data indicative of a body impedance of a user and temperature data associated with the user. The computing system may be further operable to calibrate the impedance data based at least in part on the temperature data, thereby generating calibrated body impedance data. Furthermore, the computing system may then use the calibrated body impedance data to determine the body composition of the user. In other words, the computing system may be configured to calibrate (e.g., adjust) the impedance data to take into account the temperature-based confounders that typically adversely affect the body impedance data and body composition estimations, thereby- improving the accuracy of the body impedance data and the body composition estimations. In some implementations, by way of non-limiting example, the determined body composition biometric may, for instance, be used for at least one of the following: displaying biometric characteristics of the user for informing the user about its health and / or fitness level; outputting at least one (e.g., audible, visual, haptic, etc.) alarm and / or notification in the computing system, e.g., at the wearable computing device and / or the mobile computing device; and / or triggering performing a software and / or hardware controlled operation in the computing system, e.g., at the wearable computing device and / or the mobile computing device, such as initiating a call or sending an electronic message (e.g., in case it is detected, based on the determined body composition biometric, that the user experiences and / or experienced a health-related infirmity, such as a cardiovascular disease).

[0044] More particularly, as will be discussed in greater detail below, the computing system according to examples of the present disclosure may obtain impedance sensor data from an impedance sensor of the computing system that is indicative of a body impedance of a user. For instance, the computing system may obtain the impedance sensor data from an impedance sensor of a computing device of the computing system, such as an impedance sensor on a wearable computing device that is being worn by the user.

[0045] The computing system may also obtain temperature data associated with the user from one or more temperature sensors of the computing system. The temperature data may include, for instance, body temperate data indicative of a body temperature of the user, suchas skin temperature data, tissue temperature data, muscle temperature data, core temperature data, and the like. The temperature data may further include device temperature data and ambient temperature data. For instance, the computing system may obtain the device temperature data from a temperature sensor of the wearable computing device that is configured to measure an internal temperature of the wearable computing device. Furthermore, the ambient temperature data may be indicative of a temperature of an environment in which the user and / or wearable computing device is located. In some embodiments, the ambient temperature data may be determined based on athermal model associated with one or more computing devices of the computing system.

[0046] It should be understood that, although discussed as being measured by the wearable computing device, the impedance sensor data and the temperature data may be obtained from any of the computing devices of the computing system without deviating from the scope of the present disclosure. By way of example, in some embodiments, the impedance sensor data, body temperature data, and the device temperature data may be obtained from the wearable computing device, while the ambient temperature data may be obtained from another computing device, such as a mobile computing device (e.g.. smartphone, tablet) of the computing system. Similarly, the impedance sensor data, the body temperature data, the device temperature data, and the ambient temperature data may be obtained from another computing device, such as a mobile computing device of the computing system.

[0047] The computing system may be configured to determine a calibrated body impedance of the user based at least in part on the impedance sensor data and the temperature data. The computing system may then determine a body composition biometric of the user based at least in part on the calibrated body impedance of the user and other user-related data indicative of one or more characteristics of the user, such as demographic data and / or anthropometric data of the user.

[0048] In some embodiments, to determine the calibrated body impedance of the user, the computing system may provide the impedance sensor data and the temperature data to one or more machine-learned models of the computing system, such as a bioimpedance calibration model. As will be discussed in greater detail below, the bioimpedance calibration model may be configured to calibrate the impedance sensor based at least in part on the temperature data. The computing system may then determine the calibrated body impedance of the user based at least in part on an output of the bioimpedance calibration model. More particularly, subsequent to determining the calibrated body impedance of the user, the computing systemmay provide the calibrated body impedance of the user to another machine-learned model of the computing system, such as a biometric estimation model. The computing system may also provide the other user-related data (e.g., demographic data, anthropometric data) to the biometric estimation model. Then, based on an output of the biometric estimation model, the computing system may determine the body composition biometric of the user.

[0049] In other embodiments, the biometric estimation model may be configured to determine the calibrated body impedance of the user itself. As such, the computing system may bypass the bioimpedance calibration model and provide the impedance sensor data and the temperature sensor data directly to the biometric estimation model. Furthermore, the computing system may also provide the other user-related data directly to the biometric estimation model. Subsequently, the computing system may determine the body composition biometric of the user based at least in part on an output of the biometric estimation model.

[0050] Additionally and / or alternatively, the computing system may be configured to monitor a relative change in the body impedance of the user with respect to the body temperature data over the course of an observation period. As will be discussed in greater detail below, the observation period may include a plurality of sampling periods.Furthermore, the computing system may also provide data corresponding to the monitored relative change to the one or more machine-learned models (e.g., bioimpedance calibration model, biometric estimation model) as training data to further refine the associated outputs and, hence, ensure the accuracy of those outputs.

[0051] To monitor the relative change in the body impedance of the user, the computing system may obtain body temperature data and impedance sensor data during the observation period. As will be discussed in greater detail below, the body temperature data and the impedance sensor data may be provided by one or more biometric sensors (e.g., impedance sensor measuring impedance, temperature sensor measuring temperature) at a point of contact with the user’s skin. In some embodiments, the body temperature data and the impedance sensor data may be obtained from the same point of contact with the user’s skin; in other embodiments, the body temperature data and the impedance sensor data may be obtained at different points of contact with the user’s skin.

[0052] In some embodiments, the impedance sensor data may be measured by an impedance sensor that contacts the user at a distal location (e.g., wrist, hand, finger, ankle, foot, etc.) of the user. In such embodiments, the relative change of the impedance sensor data may demonstrate a pseudo-linear relationship with respect to the skin temperature of the user. It should be noted that, as used herein, the “distal location” of the user refers to a location ona limb (e.g., arm, leg) of the user that is remote from the user’s torso, such as the user’s ankle and / or wrist. Furthermore, "distal sensor data” refers to impedance sensor data measured from a distal location of the user.

[0053] In other embodiments, the impedance sensor data may be measured by an impedance sensor that contacts the user at a proximal location (e.g., inner side of knee, inner side of elbow) of the user. In such embodiments, the relative change of the impedance sensor data may demonstrate a substantially flat (e.g., unchanging) relationship with respect to the skin temperature of the user. It should be noted that, as used herein, the “proximal location” of the user refers to a location on the user that is proximate to the user’s trunk, such as the user’s torso, an inner side of the user’s knee, an inner side of the user’s elbow, and the like. Furthermore, “proximal sensor data” refers to impedance sensor data measured from a proximal location of the user.

[0054] Example aspects of the present disclosure provide numerous technical effects and benefits. For instance, example aspects of the present disclosure provide a computing system operable to mitigate error in bioimpedance and body composition measurements. The error in bioimpedance and body composition measurements is reduced by obtaining temperature data associated with the user and using that temperature data to calibrate the impedance sensor signals. In this manner, computing systems of the present disclosure provide more accurate and more reliable bioimpedance and body composition measurements, which is an improvement over conventional commercial devices that base bioimpedance and body composition measurements on raw impedance data and fail to take confounders (e.g., body temperature) into account.

[0055] The present disclosure also enables the refinement of data received from sensors within the computing system by combining and analyzing data from multiple sensors in a w ay that allows the user to observe more than just raw data, but also trends and events inferred from the data. In this way, the present disclosure can also obviate the need for additional sensors within the computing system, thereby expending minimal computing resources by, e g., saving device space and processor usage. The present disclosure also allows for more accurate devices to monitor bioimpedance and body composition biometrics of users in ways that are more efficient, predictable, and useful.

[0056] As used herein, the terms “first,” “second,” and “third” may be used interchangeably to distinguish one component from another and are not intended to signify location or importance of the individual components. The terms “includes” and “including” are intended to be inclusive in a manner similar to the term “comprising.” Similarly, the term“or” is generally intended to be inclusive (e.g., “A or B” is intended to mean “A or B or both”). The term “at least one of’ in the context of. e.g., “at least one of A. B, and C” refers to only A, only B, only C, or any combination of A, B, and C. In addition, here and throughout the specification and claims, range limitations may be combined and / or interchanged. Such ranges are identified and include all the sub-ranges contained therein unless context or language indicates otherwise. For example, all ranges disclosed herein are inclusive of the endpoints, and the endpoints are independently combinable with each other. The singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.

[0057] Approximating language, as used herein throughout the specification and claims, may be applied to modify any quantitative representation that could permissibly vary without resulting in a change in the basic function to which it is related. Accordingly, a value modified by a term or terms, such as “generally,” “about,” “approximately,” and “substantially,” are not to be limited to the precise value specified. In at least some instances, the approximating language may correspond to the precision of an instrument for measuring the value, or the precision of the methods or machines for constructing or manufactunng the components and / or systems. For example, the approximating language may refer to being within a 10 percent margin, i.e., including values within ten percent greater or less than the stated value. In this regard, for example, when used in the context of an angle or direction, such terms include within ten degrees greater or less than the stated angle or direction, e.g., “generally vertical” includes forming an angle of up to ten degrees in any direction, e g., clockwise or counterclockwise, with the vertical direction V.

[0058] The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” In addition, references to “an embodiment” or “one embodiment” does not necessarily refer to the same embodiment, although it may. Any implementation described herein as “exemplary” or “an embodiment” is not necessarily to be construed as preferred or advantageous over other implementations. Moreover, each example is provided by way of explanation of the invention, not limitation of the invention. In fact, it will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the scope of the invention. For instance, features illustrated or described as part of one embodiment can be used with another embodiment to yield a still further embodiment. Thus, it is intended that the present invention covers such modifications and variations as come within the scope of the appended claims and their equivalents.

[0059] Referring now to FIGS. 1-3, an example wearable computing device 100 according to some implementations of the present disclosure is depicted. As shown, the wearable computing device 100 can be worn on a distal location of a user, such as, an arm 102 (e.g., wrist) of the user. Although not depicted, in some embodiments, the wearable computing device 100 may be worn on another distal location of the user, such as an ankle or a foot of the user, and / or on a proximal location of the user, such as an inner side of a knee of the user or an inner side of an elbow of the user.

[0060] The wearable computing device 100 can include a housing 110. The housing 110 can include a base plate 112 coupled to the housing 110. In this manner, the base plate 112 can define a bottom surface of the housing 110. The housing 110 can define a cavity (e.g., internal volume) (not shown) in which one or more electronic components (e.g., disposed on printed circuit boards) are disposed. For instance, the wearable computing device 100 can include a printed circuit board (e.g., flexible printed circuit board) (not shown) disposed within the cavity. Furthermore, one or more electronic components can be disposed on the printed circuit board. The wearable computing device 100 can further include a battery’ (not shown) that is disposed within the cavity defined by the housing. Even further, the wearable computing device 100 can also include one or more internal temperature sensors (not shown) within the cavity' that are configured to obtain internal temperature data indicative of an internal temperature of the w earable computing device.

[0061] The wearable computing device 100 can further include one or more ambient temperature sensors (not show n) within the cavity' and / or on an exterior side of the housing 110 configured to measure a temperature of an environment of the user (e.g., the air surrounding the wearable computing device). In some embodiments, the one or more ambient temperature sensors may be configured to directly obtain ambient temperature data. Additionally and / or alternatively, in some embodiments, the temperature data obtained by the one or more ambient temperature sensors may be used in conjunction with thermal mapping to estimate the ambient temperature of the user.

[0062] As shown, the wearable computing device 100 can include a first band 120 coupled to the housing 110 at a first location and a second band 122 coupled to the housing 110 at a second location. The first band 120 and the second band 122 can be coupled to one another at a third location (not show n) to secure the housing 110 to the arm 102 of the user. For instance, the first band 120 can include a buckle or clasp (not shown). Additionally, the second band 122 can define a plurality of apertures 124 spaced apart from one another along a length of the second band 122. In such embodiments, a prong of the buckle associated withthe first band 120 can extend through one of the plurality of openings defined by the second band 122 to couple the first band 120 to the second band 122.

[0063] It should be appreciated that the first band 120 can be coupled to the second band 122 using any suitable type of fastener. For instance, in some embodiments, the first band 120 and the second band 122 can include a magnet (not shown). In such embodiments, the first band 120 and the second band 122 can be magnetically coupled to one another to secure the housing 110 to the arm 102 of the user.

[0064] The wearable computing device 100 can include a display 130 configured to display content (e.g., time, date, biometric, notifications, etc.) for viewing by the user. The display 130 can include a plurality of pixels. For instance, in some embodiments, the display 130 can include an organic light-emitting diode (OLED) display. It should be understood, however, that the display 130 can include any suitable type of display.

[0065] In some implementations, the wearable computing device 100 can include a first electrode 140 and a second electrode 142. It should be understood that the wearable computing device 100 can include more or fewer electrodes. As shown, the first electrode 140 and the second electrode 142 are positioned with respective apertures (e.g., cutouts) defined by the housing 110. Furthermore, the first electrode 140 and the second electrode 142 can each contact (e.g., touch) the wrist of the user. In this manner, the first electrode 140 and the second electrode 142 can be used to measure one or more biometrics (e.g., electrodermal activity, electrocardiogram, body impedance, skin temperature) of the user.

[0066] The wearable computing device 100 can include a display cover 150 positioned on the housing 110 such that the display cover 150 is positioned on top of the display 130. In this manner, the display cover 150 can protect the display 130 from being damaged (e.g.. scratched or cracked). In some embodiments, the wearable computing device 100 can include a seal (not shown) positioned between the housing 110 and the display cover 150. For instance, a first surface of the seal can contact the housing 110 and a second surface of the seal can contact the display cover 150. In this manner, the seal betw een the housing 110 and the display cover 150 can prevent a liquid (e.g., water) from entering the cavity defined by the housing 110.

[0067] It should be understood that the display cover 150 can be optically transparent so that the user can view information being displayed on the display 130. For instance, in some embodiments, the display cover 150 can include a glass material. It should be understood, however, that the display cover 150 can include any suitable optically transparent material.

[0068] Referring now to FIG. 4, a block diagram of components of a wearable computing device 100 is provided according to some embodiments of the present disclosure. It should be understood that the wearable computing device 100 can be implemented within the computing system 600 discussed below with reference to FIG. 8.

[0069] As shown, the wearable computing device 100 can include one or more processors 202. The one or more processors 202 can include any suitable processing device (e.g., a processor core, a microprocessor, an application specific integrated circuit (AISC). a field programmable gate array (FPGA), a microcontroller, etc ). The wearable computing device 100 can further include a memory 204. The memory 204 can include one or more non- transitory computer-readable storage media, such as random access memory (RAM), readonly memory (ROM), electronically erasable programmable ready-only memory (EEPROM), erasable programmable read-only memory (EPROM), flash memory devices, and combinations thereof. The memory 204 can store data 206 and instructions 208 that, when executed by the one or more processors 202, cause the one or more processors 202 to perform operations disclosed herein.

[0070] The wearable computing device 100 can include a plurality of sensors 210. For instance, in some embodiments, the plurality of sensors 210 can include an accelerometer 212 (e.g., a multi-axis accelerometer) and a gy roscope 214. In this manner, the accelerometer 212, the gyroscope 214, or both can obtain motion data (e.g., acceleration, angular velocity) indicative of movement of the user.

[0071] In some embodiments, the plurality of sensors 210 can include one or more biometric sensors 216. For instance, the one or more biometric sensors 216 may be positioned on the bottom surface of the housing 110 (FIGS. 1-3) of the w earable computing device 100. In this manner, the one or more biometric sensors 216 can obtain biometric data of the user wearing the wearable computing device 100. Furthermore, in some embodiments, the one or more biometric sensors 216 can include a photoplethy smogram (PPG) sensor. Additionally and / or alternatively, in some embodiments, the one or more biometric sensors 216 can include a temperature sensor configured to detect temperature data associated with the user wearing the wearable computing device 100, such as a body temperature of the user and / or a skin temperature of the user. For instance, in some embodiments, the one or more biometric sensors 216 may include one or more temperature sensors operable to obtain body temperature data, such as, e.g.. skin temperature data indicative of a skin temperature of the user, tissue temperature data indicative of a temperature of subcutaneous tissue of the user, muscle temperature data indicative of a temperature of muscle tissue of the user, and coretemperature data indicative of a core temperature of the user. Additionally and / or alternatively, in some embodiments, the one or more biometric sensors 216 can include an impedance sensor configured to measure a body impedance of the user wearing the wearable computing device 100. It should be understood that the one or more biometric sensors 216 can include any suitable biometric sensor configured to obtain biometric data of the user wearing the wearable computing device 100 without deviating from the scope of the present disclosure.

[0072] In some embodiments, the wearable computing device 100 can include one or more output devices 218. For instance, the one or more output devices 218 can include a display screen (e.g., display 130). In this manner, the wearable computing device 100 can display content (e.g.. notifications) that can be viewed by the user. Alternatively, or additionally, the one or more output devices 218 can include one or more speakers. In this manner, the wearable computing device 100 can emit audible noises (e.g., alarm, voice automated message, etc.) for the user. As will be discussed below, the wearable computing device 100 can be configured to display a body composition biometric (or any suitable biometric) of the user wearing the wearable computing device 100.

[0073] In some embodiments, the one or more processors 202 can be communicatively coupled to the plurality of sensors 210. For instance, the one or more processors 202 can be communicatively coupled to the plurality of sensors 210 via a data interface (e.g., data bus). In this manner, the one or more processors 202 can obtain data from the plurality of sensors 210. In some embodiments, the one or more processors 202 can determine a body impedance of the user based at least in part on impedance data received from the one or more biometric sensors 216. The one or more processors 202 can further determine a body temperature of the user based at least in part on body temperature data received from the one or more biometric sensors 216. As will be discussed in greater detail below, the one or more processors 202 can calibrate the impedance data based at least in part on the body temperature data to determine a calibrated body impedance of the user. In this manner, the one or more processors 202 can determine a body composition biometric of the user based at least in part on the calibrated body impedance of the user and user data indicative of one or more characteristics of the user.

[0074] In some embodiments, biometric data obtained from the one or more biometric sensors 216 of the wearable computing device 100 can indicate whether the wearable computing device 100 is currently being worn by the user. For instance, in some embodiments, the biometric data obtained from the one or more biometric sensors 216 canindicate the wearable computing device 100 is not being worn (e.g., off- wrist) by the user. In such embodiments, the one or more processors 202 can be configured to disable impedance data collection functionality while the biometric data obtained from the one or more biometric sensors 216 indicates the wearable computing device 100 is not being worn by the user. In this manner, erroneous data from the one or more biometric sensors 216 can be ignored. It should be understood that the one or more processors 202 can be configured to enable impedance data collection functionality when the biometric data obtained from the one or more biometric sensors 216 indicates the wearable computing device 100 is being worn (e.g., on-wrist) by the user.

[0075] In some embodiments, the wearable computing device 100 can include one or more machine-learned models 220. For instance, in some embodiments, the one or more machine-learned models 220 can be stored in the memory 204 of the wearable computing device 100. In alternative embodiments, the one or more machine-learned models 220 can be stored in the memory' of one or more devices that are remote relative to the wearable computing device 100. For instance, in some embodiments, the one or more machine-learned models 220 can be stored in memory' of the mobile computing device 610 (FIG. 8) that is communicatively coupled with the wearable computing device 100 via a network 620 (FIG. 8). Alternatively, or additionally, the one or more machine-learned models 220 can be stored on one or more servers (not shown) that are communicatively coupled with the wearable computing device 100 via the network 620. As will now be discussed, the one or more machine-learned models 220 can be configured to calibrate impedance sensor data with temperature data associated with the user (e.g., body temperature, ambient temperature, device temperature) to determine a body composition biometric of the user (e.g., body fat percentage (BF%). fat-free mass (FFM). bone mineral content (BMC), basal metabolic rate (BMR), total body water (TBW), etc.).

[0076] Referring now to FIG. 5 A, in some embodiments, the one or more machine- learned models 220 may include a bioimpedance calibration model 310 and a biometric estimation model 320. The bioimpedance calibration model 310 may be configured to calibrate impedance sensor data 302 measured by the one or more biometric sensors 216 (FIG. 4) based at least in part on temperature data measured by the one or more sensors 210, such as body temperature data 304, device temperature data 306, and ambient temperature data 308. It should be understood that, in some embodiments, the ambient temperature data 308 may be directly measured by the one or more sensors 210. Additionally and / or alternatively, in some embodiments, temperature data may be obtained from one or moredevices, and the ambient temperature data 308 may be determined based on the temperature data and a thermal model of the one or more measuring devices.

[0077] Subsequent to calibrating the impedance sensor data 302, the bioimpedance calibration model 310 may output data indicative of a calibrated body impedance of the user (hereinafter referred to as “calibrated body impedance data 312”). It should be understood that the temperature data may also be measured by one or more temperature sensors of a computing device remote to the mobile computing device 100, such as mobile computing device 610 (FIG. 8) and / or remote computing system 630 (FIG. 8). The calibrated body impedance data 312 may then be provided to the biometric estimation model 320. The biometric estimation model 320 may be configured to determine a body composition biometric 330 of the user based at least in part on the calibrated body impedance data 312.

[0078] In particular, in addition to the calibrated body impedance data 312, user data associated with one or more characteristics of the user (e.g., demographic data 322 and anthropometric data 324) may also be provided to the biometric estimation model 320. The biometric estimation model 320 may, in turn, generate data indicative of the body composition 330 of the user. Furthermore, as will be discussed in greater detail below, in some embodiments, the demographic data 322 and the anthropometric data 324 may be stored in the memory 204 (FIG. 4) of the wearable computing device. Additionally and / or alternatively, in other embodiments, the demographic data 322 and the anthropometric data 324 may be stored in the memory of a computing device remote to the user computing device 100, such as mobile computing device 610 (FIG. 8) and / or remote computing system 630 (FIG. 8). The

[0079] In some embodiments, such as that depicted in FIG. 5B, the biometric estimation model 320 may be configured to calibrate the impedance sensor data 302. In such embodiments, the impedance sensor data 302 and the temperature data (e.g., body temperature data 304, device temperature data 306, ambient temperature data 308) may be provided directly to the biometric estimation model 320. Furthermore, like the embodiment discussed above with reference to FIG. 5A, user data associated with one or more characteristics of the user (e.g., demographic data 322 and anthropometric data 324) may also be provided to the biometric estimation model 320. The biometric estimation model 320 may, in turn, generate data indicative of the body composition 330 of the user.

[0080] Referring now to FIG. 6, a flow diagram of a computer-implemented method 400 for biometric monitoring of a user of a computing device (e.g., wearable computing device, mobile computing device, tablet computing device, etc.) is provided according to exampleembodiments of the present disclosure. The method 400 may be implemented using, for instance, the wearable computing device 100 discussed above with reference to FIGS. 1-5B. Alternatively, the method 400 may be implemented by a computing device (e.g., server, smartphone, etc.) that is communicatively coupled to the wearable computing device 100. It should be understood that, in some embodiments, some steps of the method 400 may be implemented locally on the wearable computing device 100, whereas other steps of the method 400 may be implemented by a computing device that is remote from the wearable computing device 100 and is communicatively coupled to the wearable computing device 100 via one or more wireless networks. FIG. 6 depicts steps performed in a particular order for purposes of illustration and discussion. Those of ordinary' skill in the art, using the disclosures provided herein, will understand that various steps of any of the methods described herein can be omitted, expanded, performed simultaneously, rearranged, and / or modified in various ways without deviating from the scope of the present disclosure. Furthermore, various steps (not illustrated) can be performed without deviating from the scope of the present disclosure. Additionally, the method 400 is generally discussed with reference to the wearable computing device described above with reference to FIGS. 1-5B, the plurality of sensors 210 described above with reference to FIGS. 4-5B, and the machine- learned models 220 described above with reference to FIGS. 4-5B. However, it should be understood that aspects of the present method 400 can find application with any suitable wearable computing device, sensor, and / or machine-learned model.

[0081] The method 400 may include, at (402), obtaining, by a computing system comprising one or more computing devices, impedance sensor data from an impedance sensor of a computing device of the computing system. More particularly, a computing system, such as computing system 600 discussed below with reference to FIG. 8. may obtain impedance sensor data indicative of a body impedance of a user. In some embodiments, the impedance sensor data may be obtained by the one or more biometric sensors 216 of a computing device, such as the wearable computing device 100. Additionally and / or alternatively, the impedance sensor data may be obtained from a biometric sensor of another computing device in the computing system 600. such as mobile computing device 610.

[0082] The method 400 may include, at (404), obtaining, by the computing system, temperature data associated with the user from one or more temperature sensors of the computing system. More particularly, the computing system 600 may obtain temperature data associated with the user, such as body temperature data 304, device temperature data 306, and / or ambient temperature data 308. In some embodiments, the temperature data maybe obtained from the one or more sensors 210 of a computing device, such as the wearable computing device 100. Additionally and / or alternatively, the temperature data may be obtained from one or more temperature sensors of another computing device in the computing system 600, such as mobile computing device 610. Additionally and / or alternatively, in some embodiments, a portion of the temperature data may be obtained from the one or more sensors 210 of the wearable computing device 100, and another portion of the temperature data may be obtained from the one or more temperature sensors of another computing device in the computing system 600, such as mobile computing device 610. Furthermore, as noted above, the body temperature data 304 may include, for instance, skin temperature data indicative of a skin temperature of the user, tissue temperature data indicative of a temperature of subcutaneous tissue of the user, muscle temperature data indicative of a temperature of muscle tissue of the user, core temperature data indicative of a core temperature of the user, and the like.

[0083] The method 400 may include, at (406), determining, by the computing system, a calibrated body impedance of the user based at least in part on the impedance sensor data and the temperature data. In some implementations, the method 400 may include calibrating a body impedance of the user based at least in part on the impedance sensor data and the temperature data to determine a calibrated body impedance of the user. For instance, by way of non-limiting example, the method 400 may include using the bioimpedance calibration model to calibrate the impedance sensor data based at least in part on the temperature data.

[0084] More particularly, in some embodiments (e.g., FIG. 5A), the computing system 600 may provide the impedance sensor data 302 and the temperature data (body temperature data 304, device temperature data 306, ambient temperature data 308) to a bioimpedance calibration model 310 of the computing system 600, which may be used to calibrate the impedance sensor data 302 based on the temperature data (e.g., body temperature data 304, device temperature data 306, ambient temperature data 308). The computing system 600 may then determine the calibrated body impedance based at least in part on calibrated impedance data 312 output by the bioimpedance calibration model 310. In this way, the computing system 600 may perform a calibration for the body impedance of the user based at least in part on the impedance sensor data 302 and the temperature data (e.g., body temperature data 304, device temperature data 306, ambient temperature data 308) to determine the calibrated body impedance (e.g.. calibrated body impedance data 312).

[0085] The method 400 may include, at (408), determining, by a biometric estimation model of the computing system, a body composition biometric of the user based at least inpart on the calibrated body impedance of the user and user data indicative of one or more characteristics of the user. More particularly, in some embodiments, the computing system 600 may provide the calibrated impedance data 312 output by the bioimpedance calibration model 310 to the biometric estimation model 320. The computing system 600 may also provide the user data indicative of one or more characteristics of the user to the biometric estimation model 320. More particularly, as noted above the user data indicative of one or more characteristics of the user may include the demographic data 322 and the anthropometric data 324. The computing system 600 may then determine the body composition biometric of the user based at least in part on the data indicative of a body composition 330 of the user output by the biometric estimation model 320.

[0086] Additionally and / or alternatively, in some examples, the method 400 may include using the bioimpedance estimation model to calibrate the impedance sensor data based at least in part on the temperature data. For instance, in some embodiments (e.g., FIG. 5B), the computing system 600 may provide the impedance sensor data 302 and the temperature data (body temperature data 304, device temperature data 306. ambient temperature data 308) to a biometric estimation model 320 of the computing system 600. As noted above, in such embodiments, the biometric estimation model 320 may be configured to calibrate the impedance sensor data 302 based at least in part on the temperature data (body temperature data 304, device temperature data 306, ambient temperature data 308). Furthermore, the computing system 600 may also provide the user data (e.g.. demographic data 322. anthropometric data 324) to the biometric estimation model 320. The computing system 600 may then determine the body composition biometric of the user based at least in part on the data indicative of a body composition 330 of the user output by the biometric estimation model 320.

[0087] By way of non-limiting example, the determined body composition biometric may be used for at least one of the following: displaying biometric characteristics of the user for informing the user about its health and / or fitness level; outputting at least one (e.g., audible, visual, haptic, etc.) alarm and / or notification in the computing system, e.g., at the wearable computing device 100 and / or the mobile computing device 610 and / or the remote computing system 630; and / or triggering performing a software and / or hardware controlled operation in the computing system, e.g., at the wearable computing device 100 and / or the mobile computing device 610 and / or the remote computing system 630, such as initiating a call or sending an electronic message (e.g., in case it is detected, based on the determined bodycomposition biometric, that the user experiences and / or experienced a health-related infirmity, such as a cardiovascular disease).

[0088] The method 400 may include, at (410), monitoring, by the computing system, a relative change in the body impedance of the user with respect to the body temperature data. More particularly, the computing system 600 may monitor a relative change in the impedance sensor data 302 with respect to the body temperature data 304 during an observation period comprising a plurality of sampling periods. Based on the relative change, the computing system 600 may provide training data to the biometric estimation model 320 and / or the bioimpedance calibration model 310.

[0089] In some embodiments, the computing system 600 may be configured to obtain distal sensor data from the one or more biometric sensors 216 (e.g.. impedance sensor) during each of the plurality of sampling periods of the observation period. In such embodiments, the impedance sensor may be contacting the user at a distal location (e.g., wrist, hand, finger, ankle, foot, etc.) of the user. The computing system 600 may obtain body temperature data 604 from the one or more biometric sensors 216 (e.g.. temperature sensor) during each of the plurality of sampling periods of the observation periods. Subsequently, the computing system 600 may determine the relative change in the distal sensor data during the observation period based at least in part on the body temperature data 304 obtained during each of the plurality of sampling periods. It should be understood that the one or more biometric sensors 216 may sample the distal sensor data and the skin temperature data any number of times during each of the plurality of sampling periods without deviating from the scope of the present disclosure.

[0090] The relative change in the distal sensor data may demonstrate a pseudo-linear relationship with the body temperature of the user. For instance, referring briefly to FIG. 7, plot 500 depicts example data corresponding to a relative change in distal sensor data (depicted on the y-axis) with respect to the measured body temperature of the user (depicted on the x-axis) over an observ ation period. More particularly, plot 500 depicts example data corresponding to the relative change in distal sensor data sampled at five different frequencies, each with respect to the measured body temperature. As shown, the relative change in the distal sensor data at each sampling frequency demonstrates a pseudo-linear relationship with the skin temperature of the user over the observ ation period.

[0091] Referring again to FIG. 6 at (410), in some embodiments, the computing system 600 may be configured to obtain proximal sensor data from the one or more biometric sensors 216 (e.g., impedance sensor) during each of the plurality of sampling periods of theobservation period. In such embodiments, the impedance sensor may be contacting the user at a proximal location (e.g., torso, inner side of thigh, inner side of elbow) of the user. The computing system 600 may obtain body temperature data 304 from the one or more biometric sensors 216 (e.g., temperature sensor) during each of the plurality of sampling periods of the observation periods. Subsequently, the computing system 600 may determine the relative change in the proximal sensor data during the observation period based at least in part on the body temperature data 304 obtained during each of the plurality of sampling periods. It should be understood that the one or more biometric sensors 216 may sample the proximal sensor data and the body temperature data any number of times during each of the plurality of sampling periods without deviating from the scope of the present disclosure.

[0092] The relative change in the proximal sensor data may demonstrate a substantially flat relationship with the body temperature of the user. For instance, referring again to FIG. 7, plot 550 depicts example data corresponding to a relative change in proximal sensor data (depicted on the y-axis) with respect to the measured body temperature of the user (depicted on the x-axis) over an observation period. More particularly, plot 550 depicts example data corresponding to the relative change in proximal sensor data sampled at five different frequencies, each with respect to the measured body temperature. As shown, the relative change in the proximal sensor data at each sampling frequency demonstrates a substantially flat relationship with the body temperature of the user over the observation period.

[0093] FIG. 8 depicts an example computing system 600 according to example embodiments of the present disclosure. The computing system 600 can be used, for instance, to implement the method 400 of FIG. 6 or other aspects of any of the methods described herein. The computing system 600 includes the wearable computing device 100 discussed above with reference to FIGS. 1-7 and a remote computing system 530. The wearable computing device 100 can be communicatively coupled to the remote computing system 630 over a netw ork 620.

[0094] In some embodiments, the wearable computing device 100 can communicate the data to mobile computing device 610. In such embodiments, the wearable computing device 100 can communicate the data to the mobile computing device 610 and then the mobile computing device 610 can communicate the data over the netw ork 620 to the remote computing system 630. In alternative embodiments, the wearable computing device 100 can bypass the mobile computing device 610 and instead communicate the data directly to the remote computing system 630 via the network 620. Furthermore, the mobile computingdevice 610 may include similar components to the wearable computing device 100 discussed above with reference to FIG. 4. In this way. the mobile computing device 610 may be capable of performing the same operations discussed herein with respect to the wearable computing device 100.

[0095] The remote computing system 630 includes one or more processors 632 and a memory 634. The one or more processors 632 can be any suitable processing device (e.g.. a processor core, a microprocessor, an ASIC, an FPGA. a controller, a microcontroller, etc.) and can be one processor or a plurality of processors that are operatively connected. The memory' 634 can include one or more non-transitory computer-readable storage medium(s), such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, etc., and combinations thereof. The memory 634 can store data 636 and instructions 638 which are executed by the processor 632 to cause the remote computing system 630 to perform operations, such as any of the operations described herein. For instance, in some embodiments, the memory 634 of the remote computing system 630 can be configured to store the one or more machine-learned models 220 discussed above with reference to FIGS. 4-6. In this manner, the data obtained from one or more sensors 210 (e.g., accelerometer, gyroscope, biometric, etc.) onboard the wearable computing device 100 can be communicated to the remote computing system 630 and provided as an input to the one or more machine-learned models 220 stored in the memory 634 thereof. The one or more machine-learned models 220 can be configured to process the data and output a data indicative of a body composition biometric of the user. Furthermore, the data indicative of the body composition biometric of the user may be communicated over the network 620 to the wearable computing device 100.

[0096] In some embodiments, the remote computing system 630 includes or is otherwise implemented by one or more computing devices. In instances in which the remote computing system 630 includes plural server computing devices, such server computing devices can operate according to sequential computing architectures, parallel computing architectures, or some combination thereof.

[0097] The network 620 can be any type of communications network, such as a local area network (e.g., intranet), wide area network (e.g., Internet), or some combination thereof and can include any number of wired or wireless links. In general, communication over the network 620 can be carried via any type of wired and / or wireless connection, using a wide variety of communication protocols (e.g., TCP / IP. HTTP, SMTP, FTP), encodings or formats (e.g., HTML, XML), and / or protection schemes (e.g., VPN, secure HTTP, SSL).

[0098] The technology discussed herein refers to sensors and other computer-based systems, as well as actions taken, and information sent to and from such systems. One of ordinary skill in the art will recognize that the inherent flexibility of computer-based systems allows for a great variety of possible configurations, combinations, and divisions of tasks and functionality between and among components. For instance, server processes discussed herein may be implemented using a single server or multiple servers working in combination. Databases and applications may be implemented on a single system or distributed across multiple systems. Distributed components may operate sequentially or in parallel.

[0099] While the present subject matter has been described in detail with respect to specific example embodiments thereof, it will be appreciated that those skilled in the art, upon attaining an understanding of the foregoing can readily produce alterations to, variations of, and equivalents to such embodiments. Accordingly, the scope of the present disclosure is by way of example rather than by way of limitation, and the subject disclosure does not preclude inclusion of such modifications, variations and / or additions to the present subject matter as would be readily apparent to one of ordinary skill in the art.

Claims

WHAT IS CLAIMED IS:

1. A computer-implemented method for biometric monitoring, the method comprising: obtaining, by a computing system comprising one or more computing devices, impedance sensor data from an impedance sensor of a computing device of the computing system, the impedance sensor data indicative of a body impedance of a user of the computing device; obtaining, by the computing system, temperature data associated with the user from one or more temperature sensors of the computing system, the temperature data comprising body temperature data indicative of a body temperature of the user; determining, by the computing system, a calibrated body impedance of the user based at least in part on the impedance sensor data and the temperature data; and determining, by the computing system, a body composition biometric of the user based at least in part on the calibrated body impedance of the user and user data indicative of one or more characteristics of the user.

2. The method of claim 1, wherein the body composition biometric of the user comprises one of a body fat percentage of the user, a fat-free mass biometric of the user, a bone mineral content of the user, a basal metabolic rate of the user, or a total body water biometric of the user.

3. The method of claim 1 , wherein the body temperature data comprises at least one of skin temperature data indicative of a skin temperature of the user, tissue temperature data indicative of a temperature of subcutaneous tissue of the user, muscle temperature data indicative of a temperature of muscle tissue of the user, or core temperature data indicative of a core temperature of the user.

4. The method of claim 1, wherein the user data comprises demographic data of the user and anthropometric data of the user.

5. The method of claim 1, wherein the temperature data further comprises: device temperature data obtained from the one or more temperature sensors of the computing system, the device temperature data indicative of an internal temperature of the computing device; andambient temperature data indicative of a temperature of an environment of the user.

6. The method of claim 1, wherein determining the calibrated body impedance of the user comprises: providing, by the computing system, the impedance sensor data and the temperature data to a bioimpedance calibration model of the computing system, wherein the bioimpedance calibration model is configured to calibrate the impedance sensor data based at least in part the temperature data; and determining, by the computing system, the calibrated body impedance of the user based at least in part on an output of the bioimpedance calibration model.

7. The method of claim 6, wherein determining the body composition biometric of the user comprises: providing, by the computing system, the calibrated body impedance of the user and the user data to a biometric estimation model of the computing system; and determining, by the computing system, the body composition biometric of the user based at least in part on an output of the biometric estimation model.

8. The method of claim 1, wherein determining the calibrated body impedance of the user comprises: providing, by the computing system, the impedance sensor data and the temperature data to a biometric estimation model of the computing system, wherein the biometric estimation model is configured to calibrate the impedance sensor data based at least in part on the temperature data.

9. The method of claim 8, wherein determining the body composition biometric of the user comprises: providing, by the computing system, the user data to the biometric estimation model; and determining, by the computing system, the body composition biometric of the user based at least in part an output of the biometric estimation model.

10. The method of claim 1, further comprising: monitoring, by the computing system, a relative change in the body impedance of the user with respect to the body temperature data during an observation period comprising aplurality of sampling periods, wherein the computing system is configured provide the relative change as training data to a biometric estimation model of the computing system, the biometric estimation model configured to determine the body composition biometric of the user based at least in part on the calibrated body impedance of the user and the user data indicative of the one or more characteristics of the user.

11. The method of claim 10, wherein monitoring the relative change in the body impedance of the user comprises: obtaining, by the computing system, distal sensor data from the impedance sensor during each of the plurality of sampling periods of the observation period, the impedance sensor contacting the user at a distal location of the user; obtaining, by the computing system, the body temperature data from the one or more temperature sensors during each of the plurality of sampling periods of the observation period; and determining, by the computing system, the relative change in the distal sensor data during the observation period based at least in part on the body temperature data obtained during each of the plurality of sampling periods of the observation period.

12. The method of claim 11, wherein the distal location of the user comprises a hand of the user, a wrist of the user, a finger of the user, a foot of the user, or an ankle of the user.

13. The method of claim 11, wherein the relative change in the distal sensor data demonstrates a pseudo-linear relationship with the body temperature of the user.

14. The method of claim 10, wherein monitoring the relative change in the body impedance of the user comprises: obtaining, by the computing system, proximal sensor data from the impedance sensor during each of the plurality of sampling periods of the observation period, the impedance sensor contacting the user at a proximal location of the user; obtaining, by the computing system, the body temperature data from the one or more temperature sensors during each of the plurality of sampling periods of the observation period; and determining, by the computing system, the relative change in the proximal sensor data during the observation period based at least in part on the body temperature data obtained during each of the plurality of sampling periods of the observation period.

15. The method of claim 14, wherein the proximal location of the user is a trunk of the user.

16. The method of claim 14, wherein the relative change in the proximal sensor data demonstrates a flat relationship with the body temperature of the user.

17. A wearable computing device, comprising: a housing; a base plate coupled to the housing, the base plate defining a bottom surface of the housing; one or more biometric sensors positioned on the bottom surface of the housing, the one or more biometric sensors configured to obtain biometric data of a user wearing the wearable computing device; and one or more processors configured to: determine a body impedance of the user based at least in part on impedance data received from the one or more biometric sensors; determine a body temperature of the user based at least in part on body temperature data received from the one or more biometric sensors; calibrate the impedance data based at least in part on the body temperature data to determine a calibrated body impedance of the user; and determine a body composition biometric of the user based at least in part on the calibrated body impedance of the user and user data indicative of one or more characteristics of the user.

18. The wearable computing device of claim 17, further comprising: an internal temperature sensor disposed within the housing, the internal temperature sensor configured to obtain internal temperature data indicative of an internal temperature of the wearable computing device; and an ambient temperature sensor on or within the housing, the ambient temperature sensor configured to obtain ambient temperature data indicative of an ambient temperature of an environment of the user, wherein the one or more processors are further configured to calibrate the impedance data based at least in part on the body temperature data, the internal temperature data, and the ambient temperature data.

19. The wearable computing device of claim 17. wherein the body composition biometric of the user comprises at least one of a body fat percentage of the user or a fat-free mass biometric of the user, and wherein the user data comprises demographic data of the user and anthropometric data of the user.

20. A computing system, comprising: an impedance sensor; one or more temperature sensors; one or more processors; and one or more computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computing system to perform operations, the operations comprising: obtaining impedance sensor data from the impedance sensor, the impedance sensor data indicative of a body impedance of a user of the computing system; obtaining temperature data associated with the user from the one or more temperature sensors, the temperature data comprising body temperature data indicative of a body temperature of the user; determining a calibrated body impedance of the user based at least in part on the impedance sensor data and the temperature data; and determining a body composition biometric of the user based at least in part on the calibrated body impedance of the user and user data indicative of one or more characteristics of the user.

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