Passive tracking of low-inertia exercises using multimodal sensor inputs of a wearable computing device

Abstract

A method of passively tracking low-inertia exercises of a user of a wearable computing device includes receiving, via a processor communicatively coupled to the wearable computing device, a plurality of time-series data inputs from a plurality of biometric sensor electrodes of the wearable computing device. The plurality of time-series data includes continuous electrodermal activity (cEDA) data of the user and at least one additional biometric parameter of the user. The method also includes processing, via the processor, the plurality of time-series data inputs using at least one computer-implemented model. Further, the method includes determining, via the processor, an indicator of a physiological response of the user using the at least one computer-implemented model. In addition, the method includes passively tracking, via the processor, the physiological response of the user when the indicator of the physiological response indicates a low-inertia exercise activity is being performed by the user.

Description

PASSIVE TRACKING OF LOW-INERTIA EXERCISES USING MULTIMODAL SENSOR INPUTS OF A WEARABLE COMPUTING DEVICEFIELD

[0001] The present disclosure relates generally to wearable computing devices, and more particularly, to passive tracking of low-inertia exercises using multimodal sensor inputs of a wearable computing device.BACKGROUND

[0002] Recent advances in technology, including those available through consumer devices, have provided for corresponding advances in health detection and monitoring. For example, biometric monitoring devices, such as fitness trackers and smart watches, are able to determine information relating to the pulse or motion of a person wearing the device.

[0003] Certain biometric monitoring devices include a variety of sensors for measuring multiple biological parameters that can be beneficial to a user of the device, such as a heart rate sensor, multi-purpose electrical sensors compatible with electrocardiogram (ECG) and electrodermal activity (EDA) applications, infrared sensors, a gyroscope, an altimeter, an accelerometer, a temperature sensor, an ambient light sensor, Wi-Fi, GPS, a vibration sensor, a speaker, and a microphone, among others. As one example, certain biometric monitoring devices measure EDA responses of the user’s skin using a single or multi-path electrical sensor. These responses are observed as sensitive electrical changes to skin conductance and are usually detected on the user’s palm or fingertips using wet or dry electrode systems.

[0004] It is known that users of wearable computing devices can manually log exercises via such devices. However, compliance of such logging is low because users forget or do not bother. As such, users are unable to take advantage of observing their exercise trends over time. Accordingly, passive detection of exercise is a desirable feature for users of most modem wearable devices. Passive detection of exercise generally refers to automatically tracking activity via a wearable device, such as a smart watch or similar, without a user having to manually enter the activity. However, current passive activity recognition only works for high inertial data, whichmakes it easy to classify using an accelerometer or gyroscope. High inertial data, for example, generally includes high cardio activities, such as running, walking, swimming, stair climbing jumping rope, hiking, boxing, rowing, jumping jacks, etc. Thus, low-inertia activities, such as yoga, Pilates, weightlifting, spinning, strength training, etc., are currently not automatically tracked, even though such activities represent an important component of physical activity. For example, currently, getting at least two days of strength training is a key part of the American Heart Association (AHA) physical activity guidelines, though no wearable device currently tracks such activities or assists users with obtaining these goals without manual logging.

[0005] Accordingly, a wearable computing device that includes passive tracking of low-inertia exercises using multimodal sensor inputs of the wearable computing device would be welcomed in the art.SUMMARY

[0006] 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.

[0007] In an aspect, the present disclosure is directed to a method of passively tracking low-inertia exercises of a user of a wearable computing device. The method includes receiving, via a processor communicatively coupled to the wearable computing device, a plurality of time-series data inputs from a plurality of biometric sensor electrodes of the wearable computing device. The plurality of time-series data includes continuous electrodermal activity (cEDA) data of the user and at least one additional biometric parameter of the user. The method also includes processing, via the processor, the plurality of time-series data inputs using at least one computer- implemented model. Further, the method includes determining, via the processor, an indicator of a physiological response of the user using the at least one computer- implemented model. In addition, the method includes passively tracking, via the processor, the physiological response of the user when the indicator of the physiological response indicates a low-inertia exercise activity is being performed by the user.

[0008] In another aspect, the present disclosure is directed to a wearable computing device that includes an electronic display, a plurality of biometric sensor electrodes for sensing a plurality of time-senes data inputs relating to biometrics of a user of the wearable computing device, and at least one processor communicatively coupled to the plurality of biometric sensor electrodes. The processor(s) is configured to perform a plurality of operations, including but not limited to receiving a plurality of time-series data inputs from a plurality of biometric sensor electrodes of the wearable computing device, the plurality of time-series data including continuous electrodermal activity (cEDA) data of the user and at least one additional biometric parameter of the user, processing the plurality of time-series data inputs using at least one computer-implemented model programmed in the at least one processor, determining an indicator of a physiological response of the user using the at least one computer-implemented model, and passively tracking the physiological response of the user when the indicator of the physiological response indicates a low-inertia exercise activity is being performed by the user.

[0009] In yet another aspect, the present disclosure is directed to a method of passively tracking low-inertia exercises of a user of a wearable computing device. The method includes receiving, via a processor communicatively coupled to the wearable computing device, a plurality of time-series data inputs from a plurality of biometric sensor electrodes of the wearable computing device. The plurality of timeseries data includes continuous electrodermal activity (cEDA) data of the user and at least one additional biometric parameter of the user. The method also includes determining, via the processor, a type of low-inertia exercise activity being performed by the user based, at least in part, on the plurality of time-series data inputs using at least one computer-implemented model. Further, the method includes automatically tracking, via the processor, the low-inertia exercise activity of the user.

[0010] These and other features, aspects, and advantages of various embodiments of the present disclosure 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 example embodiments of the present disclosure and, together with the description, serve to explain the related principles.BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Detailed discussion of embodiments directed to one of ordinary skill in the art is set forth in the specification, which makes reference to the appended figures, in which:

[0012] FIG. 1 provides a graphical representation of electrodermal activity (EDA) amplitude (y-axis) versus time (x-axis) according to one embodiment of the present disclosure;

[0013] FIG. 2 provides a perspective view of a wearable computing device on a wrist of a user according to one embodiment of the present disclosure;

[0014] FIG. 3 provides a front perspective view of a wearable computing device according to one embodiment of the present disclosure;

[0015] FIG. 4 provides a rear perspective view of the wearable computing device of FIG. 3;

[0016] FIG. 5 provides an exploded view of the display of the wearable computing device of FIG. 3;

[0017] FIG. 6 provides a schematic diagram of an example set of devices that are able to communicate according to one embodiment of the present disclosure;

[0018] FIG. 7 illustrates various controller components of an example system that can be utilized according to one embodiment of the present disclosure;

[0019] FIG. 8 illustrates a flow diagram of an embodiment of a method of passively tracking low-inertia exercises of a user of a wearable computing device according to the present disclosure;

[0020] FIG. 9 illustrates a schematic diagram of an embodiment of a system for passively tracking low-mertia exercises of a user of a wearable computing device according to the present disclosure; and

[0021] FIG. 10 illustrates a flow diagram of another embodiment of a method of passively tracking low-inertia exercises of a user of a wearable computing device according to the present disclosure.DETAILED DESCRIPTION

[0022] Reference now will be made in detail to embodiments of the invention,one or more examples of which are illustrated in the drawings. 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 or spirit 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.Overview

[0023] Recent advances in technology, including those available through consumer devices, have provided for corresponding advances in health detection and monitoring. For example, biometric monitoring devices, such as fitness trackers and smart watches, are able to determine information relating to the pulse or motion of a person wearing the device.

[0024] Certain biometric monitoring devices include a variety of sensors for measuring multiple biological parameters that can be beneficial to a user of the device, such as a heart rate sensor, multi-purpose electrical sensors compatible with electrocardiogram (ECG) and electrodermal activity (EDA) applications, infrared sensors, a gyroscope, an altimeter, an accelerometer, a temperature sensor, an ambient light sensor, Wi-Fi, GPS, a vibration sensor, a speaker, and a microphone, among others. As one example, certain biometric monitoring devices measure EDA responses of the user’s skin using a multi-path electrical sensor. These responses are observed as sensitive electrical changes to skin conductance and are usually detected on the user’s palm or fingertips using wet or dry electrode systems.

[0025] Typical EDA responses can be measured using at least two electrodes, wherein skin conductance is calculated using the measured electrical impedance. EDA responses are represented as the phasic component of skin conductance - skin conductance responses (SCRs) - and are detected by identifying momentary spikes to skin conductance in comparison to a background tonic measurement, the skin conductance level (SCL). More particularly, in terms of timing, the primary difference between SCL and SCR is that SCRs occur on the scale of seconds, whereasSCL is evaluated across seconds, minutes, hours, and / or days. As an example, FIG. 1 illustrates a graphical representation 10 of EDA amplitude versus time. As shown, the graph provides a comparison of the phasic skin conductance component (SCRs) 12 represented as peaks 16 to the tonic skin conductance component (SCL) 14. Thus, accurately detecting changes to SCL needs to be continuously measured (over seconds / minutes / hours / days, etc.). Furthermore, by measuring continuous electrodermal activity (cEDA), SCL can be used to observe certain biological events such as the body’s sweat response to certain activities.

[0026] It is known that users of wearable computing devices can manually log exercises via such devices. However, compliance of such logging is low because users forget or do not bother. As such, users are unable to take advantage of observing their exercise trends over time. Accordingly, passive detection of exercise is a desirable feature for users of most modem wearable devices. Passive detection of exercise generally refers to automatically tracking activity via a wearable device, such as a smart watch or similar, without a user having to manually enter the activity.However, current passive activity recognition only works for high inertial data, which makes it easy to classify using an accelerometer or gyroscope. High inertial data, for example, generally includes high cardio activities, such as running, walking, swimming, stair climbing, jumping rope, hiking, boxing, rowing, jumping jacks, etc. Thus, low-inertia activities, such as yoga, Pilates, weightlifting, spinning, etc., are currently not automatically tracked, even though such activities represent an important component of physical activity. For example, currently, getting at least two days of strength training is a key part of the American Heart Association (AHA) physical activity guidelines, though no wearable device currently tracks such activities or assists users with obtaining these goals without manual logging.

[0027] Accordingly, the present disclosure is directed to a wearable computing device that includes passive tracking of low-inertia exercises using multimodal sensor inputs of the wearable computing device.

[0028] With reference now to the Figures, example embodiments of the present disclosure will be discussed in further detail.Example Devices and Systems

[0029] Referring now to the drawings, FIGS. 2-5 illustrate perspective views of a wearable computing device 100 according to the present disclosure. In particular, as shown in FIG. 2, the wearable computing device 100 may be worn on a user's forearm 102 like a wristwatch. Thus, as shown, the wearable computing device 100 may include a wristband 103 for securing the w earable computing device 100 to the user's forearm 102. In addition, as shown in FIGS. 2, 3, and 5, the wearable computing device 100 has an outer covering 105 and a housing 104 that contains the electronics associated with the wearable computing device 100. For example, in an embodiment, the outer covering 105 may be constructed of glass, polycarbonate, acrylic, or similar. Further, as shown in FIGS. 2, 3, and 5, the wearable computing device 100 includes an electronic display 106 arranged within the housing 104 and viewable through the outer covering 105. Moreover, as shown, the wearable computing device 100 may also include one or more buttons 108 that may be implemented to provide a mechanism to activate various sensors of the wearing computing device 100 to collect certain health data of the user. Moreover, in an embodiment, the electronic display 106 may cover an electronics package (not shown), which may also be housed within the housing 104.

[0030] Referring particularly to FIG. 4, the housing 104 of the wearable computing device 100 further includes a dorsal wrist-side face 110 configured to sit against a dorsal wrist of a user when being worn by the user and a plurality of sensor electrodes 112 positioned on the dorsal wrist-side face 110 of the housing 104 so as to maintain skin contact with the user when being worn on the wrist by the user. Thus, in such embodiments, each of the sensor electrodes 112 continuously measure, at least, electrical impedance of the user at a location of the skin contact on the dorsal wrist. Accordingly, in one or more embodiments, one or more (or all) of the plurality of sensor electrodes 112 may be cEDA sensor electrodes. In some embodiments, the wearable computing device 100 may also include at least one additional biometric sensor electrode in addition to the cEDA sensor electrodes. In such embodiments, the additional biometric sensor electrode may include one or more temperature sensors (such as an ambient temperature sensor or a skin temperature sensor), a humidity sensor, a light sensor, a pressure sensor, a microphone, an optical sensor, or a photoplethysmography (PPG) sensor.

[0031] Further, the sensor electrodes 112 described herein may be constructed of any suitable material. For example, in an embodiment, the sensor electrodes 112 descnbed herein may be constructed of stainless steel, graphene, or any other matenal having a suitable conductivity and / or corrosion resistance and may have an optional PVD coating, that may be 1 -micrometer thick titanium nitride. In such embodiments, the PVD coating may provide a desired color to the sensor electrodes 112, thereby preventing oxidation beyond what the stainless steel already provides, and also increases durability.

[0032] In additional embodiments, PVD and surface finish can be used to increase / decrease moisture retention, which affects the cEDA signal and user comfort. In particular embodiments, the sensor electrodes 112 may be formed of an alloy of tin and nickel (TiN) with a shiny or mirror surface finish. Moreover, in an embodiment, the sensor electrodes 112 may be constructed of a hydrophobic material or a transparent material.

[0033] Referring now to FIG. 6, components of an example system 200 of the wearable computing device 100 that can be utilized in accordance with various embodiments are illustrated. In particular, as shown, the system 200 may also include at least one controller 202 communicatively coupled to the plurality of sensor electrodes 112. Moreover, in an embodiment, the controller(s) 202 may be a central processing unit (CPU) or graphics processing unit (GPU) for executing instructions that can be stored in a memory device 204, such as flash memory or DRAM, among other such options.

[0034] For example, in an embodiment, the memory device 204 may include RAM, ROM, FEASH memory, or other non-transitory digital data storage, and may include a control program having sequences of instructions which, when loaded from the memory device 204 and executed using the controller(s) 202, cause the controller(s) 202 to perform the functions that are described herein. As would be apparent to one of ordinary' skill in the art, the system 200 can include many types of memory, data storage, or computer-readable media, such as data storage for program instructions for execution by the controller or any suitable processor. The same or separate storage can be used for images or data, a removable memory can be available for sharing information with other devices, and any number of communicationapproaches can be available for sharing with other devices.

[0035] In addition, as shown, the system 200 includes any suitable display 206, such as a touch screen, organic light emitting diode (OLED), or liquid crystal display (LCD), although devices might convey information via other means, such as through audio speakers, projectors, or casting the display or streaming data to another device, such as a mobile phone, wherein an application on the mobile phone displays the data.

[0036] The system 200 may also include one or more wireless components 212 operable to communicate with one or more electronic devices within a communication range of the particular wireless channel. The wireless channel can be any appropriate channel used to enable devices to communicate wirelessly, such as Bluetooth, cellular, NFC, Ultra-Wideband (UWB), or Wi-Fi channels. It should be understood that the system 200 can have one or more conventional wired communications connections as known in the art.

[0037] The system 200 also includes one or more power components 208, such as may include a battery operable to be recharged through conventional plug-in approaches, or through other approaches such as capacitive charging through proximity with a power mat or other such device. In further embodiments, the system 200 can also include at least one additional I / O device 210 able to receive conventional input from a user. This conventional input can include, for example, a push button, touch pad, touch screen, wheel, joystick, keyboard, mouse, keypad, or any other such device or element whereby a user can input a command to the system 200. In another embodiment, the I / O device(s) 210 may be connected by a wireless infrared or Bluetooth or other link as well in some embodiments. In some embodiments, the system 200 may also include a microphone or other audio capture element that accepts voice or other audio commands. For example, in particular embodiments, the system 200 may not include any buttons at all, but might be controlled only through a combination of visual and audio commands, such that a user can control the wearable computing device 100 without having to be in contact therewith. In certain embodiments, the I / O elements 210 may also include one or more of the sensor electrodes 112 described herein, optical sensors, barometric sensors (e.g., altimeter, etc.), and the like.

[0038] Still referring to FIG. 6, the system 200 may also include a driver 214 andat least some combination of one or more emitters 216 and one or more detectors 218 (referred to herein as an optics package 215) for measuring data for one or more metrics of a human body, such as for a person wearing the wearable computing device 100. In such embodiments, as shown in FIG. 4, for example, the optics package 215 may be arranged within the housing 104 and at least partially exposed through the dorsal wrist-side face 110 of the housing 104. Thus, as shown and further explained herein, the sensor electrodes 112 may be positioned around the optics package 215 on the wrist-side face 110 of the housing 104. In alternative embodiments, the various components of the optics package 215 may be positioned around the sensor electrodes 112 and / or in another other suitable configuration such as adjacent to, interspersed with, surrounded by, or on top of the optics package 215. In certain embodiments, for example, wherein the sensor electrodes 112 are transparent, the sensor electrodes 112 may be arranged atop the optics package 215.

[0039] In some embodiments, the system 200 may include at least one imaging element, such as one or more cameras that are able to capture images of the surrounding environment and that are able to image a user, people, or objects in the vicinity of the device. The imaging element can include any appropriate technology, such as a CCD image capture element having a sufficient resolution, focal range, and viewable area to capture an image of the user when the user is operating the device. Further image capture elements may also include depth sensors. Methods for capturing images using a camera element with a computing device are well known in the art and will not be discussed herein in detail. It should be understood that image capture can be performed using a single image, multiple images, periodic imaging, continuous image capturing, image streaming, etc. Further, the system 200 can include the ability to start and / or stop image capture, such as when receiving a command from a user, application, or other device.

[0040] The emitters 216 and detectors 218 of FIG. 6 may also be capable of being used, in one example, for obtaining optical PPG measurements. Some PPG technologies rely on detecting light at a single spatial location, adding signals taken from two or more spatial locations, or an algorithmic combination thereof. Both of these approaches result in a single spatial measurement from which the heart rate (HR) estimate (or other physiological metrics) can be determined. In someembodiments, a PPG device employs a single light source coupled to a single detector (i. e. , a single light path). Alternatively, a PPG device may employ multiple light sources coupled to a single detector or multiple detectors (i.e. , two or more light paths). In other embodiments, a PPG device employs multiple detectors coupled to a single light source or multiple light sources (i.e., two or more light paths). In some cases, the light source(s) may be configured to emit one or more of green, red, infrared (IR) light, as well as any other suitable wavelengths in the spectrum (such as long IR for metabolic monitoring). For example, a PPG device may employ a single light source and two or more light detectors each configured to detect a specific wavelength or wavelength range. In some cases, each detector is configured to detect a different wavelength or wavelength range from one another. In other cases, two or more detectors are configured to detect the same wavelength or wavelength range. In yet another case, one or more detectors configured to detect a specific wavelength or wavelength range different from one or more other detectors). In embodiments employing multiple light paths, the PPG device may determine an average of the signals resulting from the multiple light paths before determining an HR estimate or other physiological metrics.

[0041] Moreover, in an embodiment, the emitters 216 and detectors 218 may be coupled to the controller 202 directly or indirectly using driver circuitry by which the controller 202 may drive the emitters 216 and obtain signals from the detectors 218. The host computer 222 can communicate with the wireless networking components 212 via the one or more networks 220, which may include one or more local area networks, wide area networks, UWB, and / or internetworks using any of terrestrial or satellite links. In some embodiments, the host computer 222 executes control programs and / or application programs that are configured to perform some of the functions described herein.

[0042] Referring now to FIG. 7, a schematic diagram of an environment 300 in which aspects of various embodiments can be implemented is illustrated. In particular, as shown, a user might have a number of different devices that are able to communicate using at least one wireless communication protocol. For example, as shown, the user might have a smartwatch 302 or fitness tracker (such as wearable computing device 100), which the user would like to be able to communicate with asmartphone 304 and a tablet computer 306. The ability to communicate with multiple devices can enable a user to obtain information from the smartwatch 302. e.g., data captured using a sensor on the smartwatch 302, using an application installed on either the smartphone 304 or the tablet computer 306. The user may also want the smartwatch 302 to be able to communicate with a service provider 308, or other such entity, that is able to obtain and process data from the smartwatch and provide functionality that may not otherwise be available on the smartwatch or the applications installed on the individual devices. In addition, as shown, the smartwatch 302 may be able to communicate with the service provider 308 through at least one network 220, such as the Internet or a cellular network, or may communicate over a wireless connection such as Bluetooth® to one of the individual devices, which can then communicate over the at least one network. There may be a number of other ty pes of, or reasons for, communications in various embodiments.

[0043] In addition to being able to communicate, a user may also want the devices to be able to communicate in a number of ways or with certain aspects. For example, the user may want communications between the devices to be secure, particularly where the data may include personal health data or other such communications. The device or application providers may also be required to secure this information in at least some situations. The user may want the devices to be able to communicate with each other concurrently, rather than sequentially. This may be particularly true where pairing may be required, as the user may prefer that each device be paired at most once, such that no manual pairing is required. The user may also desire the communications to be as standards-based as possible, not only so that little manual intervention is required on the part of the user but also so that the devices can communicate with as many other types of devices as possible, which is often not the case for various proprietary formats. A user may thus desire to be able to walk in a room with one device and have such device automatically communicate with another target device with little to no effort on the part of the user. In various conventional approaches, a device will utilize a communication technology such as Wi-Fi to communicate with other devices using wireless local area networking (WLAN). Smaller or lower capacity devices, such as many Internet of Things (loT) devices, instead utilize a communication technology such as Bluetooth®, and in particularBluetooth Low Energy (BLE) which has very low power consumption.

[0044] In further embodiments, the environment 300 illustrated in FIG. 7 enables data to be captured, processed, and displayed in a number of different ways. For example, data may be captured using sensors on the smartwatch 302, but due to limited resources on the smartwatch 302, the data may be transferred to the smartphone 304 or the service provider 308 (or a cloud resource) for processing, and results of that processing may then be presented back to that user on the smartwatch 302, smartphone 304, and / or another such device associated with that user, such as the tablet computer 306. In at least some embodiments, a user may also be able to provide input such as health data using an interface on any of these devices, which can then be considered when making that determination.

[0045] Referring now to FIG. 8, a flow diagram of one embodiment of a method 400 of passively tracking low-inertia exercises of a user of a wearable computing device is provided. In an embodiment, for example, the wearable computing device may be any suitable wearable computing device, such as the wearable computing device 100 described herein with reference to FIGS. 1-7. Furthermore, in an embodiment, the low-inertia exercise activity may include, for example, yoga, Pilates, weightlifting, spinning, strength training, or similar. In general, the method 400 is described herein with reference to the wearable computing device 100 of FIGS. 1-7. However, it should be appreciated that the disclosed method 400 may be implemented with any other suitable wearable computing device having any other suitable configurations. In addition, although FIG. 8 depicts steps performed in a particular order for purposes of illustration and discussion, the methods discussed herein are not limited to any particular order or arrangement. One skilled in the art, using the disclosures provided herein, will appreciate that vanous steps of the methods disclosed herein can be omitted, rearranged, combined, added, and / or adapted in various ways without deviating from the scope of the present disclosure.

[0046] As shown at (402), the method 400 includes receiving, via a processor communicatively coupled to the wearable computing device 100, a plurality of timeseries data inputs from a plurality of biometric sensor electrodes of the wearable computing device 100. In such embodiments, as an example, the plurality of timeseries data may include cEDA data of the user as well as at least one additionalbiometric parameter of the user, such as heart rate data of the user, skin temperature data of the user, heart rate variability (HRV) data of the user, accelerometer data, gyroscope data, altimeter data, and / or combinations thereof. Moreover, in an embodiment, the cEDA data may include skin conductance response (SCR), skin conductance level (SCL), a slope or change in the SCR or the SCL, an indication of individual sweat glands of the user being activated, or combinations thereof, of similar.

[0047] As shown at (404), the method 400 includes processing, via the processor, the plurality of time-series data inputs using at least one computer-implemented model. For example, as will be described in more detail herein, processing the plurality of time-series data inputs may include filtering the cEDA data of the user, e.g., using a high-pass filter for SCR, a low-pass filter for SCL, a median filter to erase glitches, and / or any other type of filter as needed.

[0048] Further, in an embodiment, processing the plurality of time-series data inputs may include updating a certain time frame cache with a plurality of data inputs (i.e., updating a cache storing data inputs relating to time frame / time window of predetermined length), determining whether a time-series data input from the plurality of time-series data inputs indicate one of a plurality of modes of the wearable computing device relating to undesirable motion (e.g., from stress or any other nonexercise related activity) and, if so, eliminating the time-series data input from the plurality of time-series data inputs. Moreover, in an embodiment, processing the plurality of time-series data inputs may include imputing one or more data points into the plurality of time-series data inputs if a minimum number of data points are missing from the plurality of time-series data inputs or dropping the plurality of timesenes data inputs if a certain number of data points are missing from the plurality of time-series data inputs, or normalizing the plurality of time-series data inputs using one or more normalization factors. In such embodiments, for example, the normalization factor(s) may include a mean, a median, a mode, or a standard deviation, and / or transforming each of the plurality of time-series data inputs into a single value.

[0049] Still referring to FIG. 8, as shown at (406), the method 400 includes determining, via the processor, an indicator of a physiological response of the userusing the computer-implemented model(s). In an embodiment, the method 400 may also include post-processing the indicator of the physiological response. For example, in an embodiment, post-processing the indicator of the physiological response may include ensuring that the physiological response includes a duration above a certain threshold, grouping multiple physiological responses together if the multiple physiological responses occur within a certain time frame of each other, and / or any other post-processing to improve accuracy of the data.

[0050] Accordingly, as shown at (408), the method 400 includes passively tracking, via the processor, the physiological response of the user when the indicator of the physiological response indicates a low-inertia exercise activity is being performed by the user. Thus, the method 400 described herein can be implemented by the wearable computing device 100 (and / or any linked smart device) to passively track low-inertia exercise activity of the user without the user having to manually log the activity. Moreover, in an embodiment, the method 400 may also include identifying one or more repetitions of the low-inertia exercise activity and passively tracking, via the processor, the one or more repetitions of the low-inertia exercise activity. For example, in an embodiment, cEDA data generally responds to changes in contact area (i.e., the contact area between a wrist side face of the wearable computing device 100 and skin of the user), which happens naturally as the user participates in low-inertia exercise activities (such as lifting weights), which in addition to bulk sweat (e.g., skin conductance level) may also assist with recognizing the type of activity being performed by the user and potentially contribute to repetition counting.

[0051] As shown at (410), the method 400 includes controlling a display of the wearable computing device and providing the indicator of the physiological response to the user via the display when the indicator of the physiological response indicates the low-inertia exercise activity is being performed by the user. More specifically, in an embodiment, the method 400 may include sending a notification to the user indicating at least one of an occurrence of the indicator of the physiological response exceeding a threshold, a graphical representation of physiological responses over time, and a summary of physiological responses over time (such as daily, weekly, monthly, etc.). Moreover, in an embodiment, the method 400 may also includeprobing the user to respond to the notification via the display (such as the display 206). For example, the user may be prompted to confirm the physiological response, record participation in a presenbed low-inertia exercise activity, and / or any other suitable response.

[0052] Generally, a function of the wearable computing device may be controlled when the indicator of the low-inertia exercise activity exceeds a threshold. As outlined above, such a function of the wearable computing device may be a function of the display 206 of wearable computing device 100, for example, resulting in that the indicator of the low-inertia exercise activity is displayed at the display 206 when the indicator of the low-inertia exercise activity exceeds the threshold. Alternatively or additionally, the function of a wearable computing device 100 controlled by the calculated indicator of the low-inertia exercise activity exceeding the threshold may include generating and sending a notification, e.g., to a user of the wearable computing device, and / or triggering a user interaction process via the wearable computing device 100 in which the user of the wearable computing device 100 has to actively confirm notification of the low-inertia exercise activity. Thereby, a technique and a wearable computing device 100 may be provided for passively tracking low- inertia exercise activity of a user and providing such tracking to the user.

[0053] The method 400 of FIG. 8 can be better understood with reference to FIG. 9. In particular, as shown, a system 500 for passively (e.g., automatically) tracking low-inertia exercises of a user of a wearable computing device is provided. More specifically, as shown, the system 500 includes a processor 502 having at least one computer-implemented model 504 programmed therein. In an embodiment, the processor 502 may be part of the wearable computing device 100 or a separate mobile device. Further, as shown in FIG. 9, the computer-implemented model 504 may be a machine learning model configured to implement one or more machine learning algorithms.

[0054] Accordingly, various machine learning algorithms may be employed in the systems and methods of the present disclosure to iteratively refine the logic and / or the model-based simulations or estimators, virtual representations or simulations, models, sub-models, and / or estimators of the system 500 described herein, thereby increasing accuracy in the predictions that are based on such estimates. As such, incertain embodiments, the machine learning algorithm(s) may receive feedback from the processor(s) 502 and train the feedback. Further, the machine learning algorithm(s) may be a trained neural network, a simple linear regression model, a random forest regression model, a support vector machine, or any suitable type of a supervised learning model based on the quality and quantity of the data received. More specifically, in an embodiment, the system 500 may include an embedded reinforcement learning technique in the machine learning algorithm.

[0055] In addition, as shown, the computer-implemented model 504 receives time-series data 506, such as cEDA data, heart rate data, skin temperature data, HRV data, accelerometer data, gyroscope data, altimeter data, and / or combinations thereof. Thus, as shown, the computer-implemented model 504 is configured to determine when a user is participating in a low-inertia exercise activity 508 based on, at least, the cEDA data and one or more of the additional biometric parameters. For example, in an embodiment, if a user is participating in yoga, the cEDA data will include changes in a skin conductance level of the user that are indicative of sweat production, and the additional biometric parameters (such as a low heart rate and / or increase in skin temperature) can distinguish the low-inertia exercise activity 508 from other activities that cause increases in cEDA data (such as stress, as an example). Thus, as shown, the system 500 can passively track 510 the low-inertia exercise activity 508 without the user having to manually log the activity.

[0056] In another embodiment, as shown, the time-series data 506 may also include hydration data. Thus, in an embodiment, the computer-implemented model 504 is configured to receive the hydration data and relate a magnitude of the cEDA data to the hydration data. Accordingly, the processor 502 is configured to notify a user should the user become dehydrated.

[0057] Referring now to FIG. 10, a flow diagram of one embodiment of a method 600 of passively tracking low-inertia exercises of a user of a wearable computing device is provided. In an embodiment, for example, the wearable computing device may be any suitable wearable computing device, such as the wearable computing device 100 described herein with reference to FIGS. 1-7. Thus, in general, the method 600 is described herein with reference to the wearable computing device 100 of FIGS. 1-7. However, it should be appreciated that the disclosed method 600 maybe implemented with any other suitable wearable computing device having any other suitable configurations. In addition, although FIG. 10 depicts steps performed in a particular order for purposes of illustration and discussion, the methods discussed herein are not limited to any particular order or arrangement. One skilled in the art, using the disclosures provided herein, will appreciate that various steps of the methods disclosed herein can be omitted, rearranged, combined, added, and / or adapted in various ways without deviating from the scope of the present disclosure.

[0058] As shown at (602), the method 600 includes receiving, via a processor communicatively coupled to the wearable computing device, a plurality of time-series data inputs from a plurality of biometric sensor electrodes of the wearable computing device. As mentioned, the plurality of time-series data may include cEDA data of the user as well as at least one additional biometric parameter of the user, such as heart rate data of the user, skin temperature data of the user, heart rate variability (HRV) data of the user, accelerometer data, gyroscope data, altimeter data, and / or combinations thereof.

[0059] As shown at (604), the method 600 includes determining, via the processor, a type of low-inertia exercise activity being performed by the user based, at least in part, on the plurality of time-series data inputs using at least one computer- implemented model. As shown at (606), the method 600 includes automatically tracking, via the processor, the low-inertia exercise activity of the user. In further embodiments, the method 600 may also include identifying one or more repetitions of the low-inertia exercise activity and passively tracking, via the processor, the one or more repetitions of the low-inertia exercise activity.Additional Disclosure

[0060] Further to the descriptions above, a user may be provided with controls allowing the user to make an election as to both if and when systems, programs, or features described herein may enable collection of user information (e.g., information about a user’s social network, social actions, or activities, profession, a user’s preferences, or a user’s current location), and if the user is sent content or communications from a server. In addition, certain data may be treated in one or more ways before it is stored or used, so that personally identifiable information isremoved. For example, a user’s identity may be treated so that no personally identifiable information can be determined for the user, or a user’s geographic location may be generalized where location information is obtained (such as to a city, ZIP code, or state level), so that a particular location of a user cannot be determined. Thus, the user may have control over what information is collected about the user, how that information is used, and what information is provided to the user. To that end, any information collected as described herein relating to the user will be kept private and confidential and will not be improperly used or published.

[0061] The technology discussed herein makes reference to servers, databases, software applications, and other computer-based systems, as well as actions taken and information sent to and from such systems. 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, processes discussed herein can be implemented using a single device or component or multiple devices or components working in combination. Databases and applications can be implemented on a single system or distributed across multiple systems. Distributed components can operate sequentially or in parallel.

[0062] While the present subject matter has been described in detail with respect to various specific example embodiments thereof, each example is provided by way of explanation, not limitation of the disclosure. 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 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. 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 disclosure covers such alterations, variations, and equivalents.

Claims

WHAT IS CLAIMED IS:

1. A method of passively tracking low-inertia exercises of a user of a wearable computing device, the method comprising: receiving, via a processor communicatively coupled to the wearable computing device, a plurality of time-series data inputs from a plurality of biometric sensor electrodes of the wearable computing device, the plurality of time-series data comprising continuous electrodermal activity (cEDA) data of the user and at least one additional biometric parameter of the user; processing, via the processor, the plurality of time-series data inputs using at least one computer-implemented model; determining, via the processor, an indicator of a physiological response of the user using the at least one computer-implemented model; and passively tracking, via the processor, the physiological response of the user when the indicator of the physiological response indicates a low-inertia exercise activity is being performed by the user.

2. The method of claim 1, wherein the at least one additional biometric parameter of the user comprises at least one of heart rate data of the user, skin temperature data of the user, accelerometer data, gyroscope data, or heart rate variability (HRV) data of the user.

3. The method of any preceding claim, further comprising: receiving, via the processor, hydration data of the user; and relating, via the processor, a magnitude of the cEDA data to the hydration data.

4. The method of any preceding claim, wherein the cEDA data comprises at least one of skm conductance response (SCR), skm conductance level (SCL), a slope or change in the SCR or the SCL, or an indication of individual sweat glands of the user being activated.

5. The method of any preceding claim, wherein the low-inertia exercise activity comprises at least one of yoga, Pilates, weightlifting, spinning, or strength training.

6. The method of any preceding claim, wherein the at least one computer- implemented model is a machine learning model.

7. The method of any preceding claim, further comprising postprocessing the indicator of the physiological response, wherein post-processing the indicator of the physiological response further comprises at least one of ensunng that the physiological response comprises a duration above a certain threshold and grouping multiple physiological responses together if the multiple physiological responses occur within a certain time frame of each other.

8. The method of any preceding claim, further comprising controlling a display of the wearable computing device and providing the indicator of the physiological response to the user via the display when the indicator of the physiological response indicates the low-inertia exercise activity is being performed by the user.

9. The method of any preceding claim, further comprising sending a notification to the user via the display indicating at least one of an occurrence of the indicator of the physiological response exceeding a threshold, a graphical representation of physiological responses over time, and a summary of physiological responses over time.

10. The method of any one of the preceding claims, further comprising identifying one or more repetitions of the low-inertia exercise activity and passively tracking, via the processor, the one or more repetitions of the low-inertia exercise activity.

11. A wearable computing device, comprising: an electronic display; a plurality of biometric sensor electrodes for sensing a plurality of time-series data inputs relating to biometrics of a user of the wearable computing device; and at least one processor communicatively coupled to the plurality of biometric sensor electrodes, the at least one processor configured to perform a plurality of operations, the plurality of operations comprising: receiving a plurality of time-series data inputs from a plurality of biometric sensor electrodes of the wearable computing device, the plurality of time-series data comprising continuous electrodermal activity (cEDA) data of the user and at least one additional biometric parameter of the user; processing the plurality of time-series data inputs using at least onecomputer-implemented model programmed in the at least one processor; determining an indicator of a physiological response of the user using the at least one computer-implemented model; and passively tracking the physiological response of the user when the indicator of the physiological response indicates a low-inertia exercise activity is being performed by the user.

12. The wearable computing device of claim 11, wherein the at least one additional biometric parameter of the user comprises at least one of heart rate data of the user, skin temperature data of the user, accelerometer data, gyroscope data, or heart rate variability (HRV) data of the user.

13. The wearable computing device of claims 11-12, further comprising: receiving, via the processor, hydration data of the user; and relating, via the processor, a magnitude of the cEDA data to the hydration data.

14. The wearable computing device of claims 11-13, wherein the cEDA data comprises at least one of skm conductance response (SCR), skm conductance level (SCL), a slope or change in the SCR or the SCL, or an indication of individual sweat glands of the user being activated.

15. The wearable computing device of claims 11-14, wherein the low- inertia exercise activity comprises at least one of yoga, Pilates, weightlifting, spinning, or strength training.

16. The wearable computing device of claims 11-15, wherein the at least one computer-implemented model is a machine learning model.

17. The wearable computing device of claims 11-16, further comprising post-processing the indicator of the physiological response, wherein post-processing the indicator of the physiological response further comprises at least one of ensuring that the physiological response comprises a duration above a certain threshold and grouping multiple physiological responses together if the multiple physiological responses occur within a certain time frame of each other.

18. The wearable computing device of claims 11-17, further comprising controlling a display of the wearable computing device and providing the indicator of the physiological response to the user via the display when the indicator of thephysiological response indicates the low-inertia exercise activity is being performed by the user.

19. The wearable computing device of claims 11-18, further comprising sending a notification to the user via the display indicating at least one of an occurrence of the indicator of the physiological response exceeding a threshold, a graphical representation of physiological responses over time, and a summary of physiological responses over time.

20. A method of passively tracking low-inertia exercises of a user of a wearable computing device, the method comprising: receiving, via a processor communicatively coupled to the wearable computing device, a plurality of time-series data inputs from a plurality of biometric sensor electrodes of the wearable computing device, the plurality of time-series data comprising continuous electrodermal activity (cEDA) data of the user and at least one additional biometric parameter of the user; determining, via the processor, a type of low-inertia exercise activity being performed by the user based, at least in part, on the plurality' of time-series data inputs using at least one computer-implemented model; and automatically tracking, via the processor, the low-inertia exercise activity of the user.

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