Biometric measurement application for measuring activity level

Abstract

A computing device includes one or more memories to store one or more instructions and one or more processors. The one or more processors execute the one or more instructions to perform operations including: obtaining, via one or more heart rate sensors, heart rate information associated with a user, obtaining, via one or more motion sensors, motion information associated with the user, and determining an activity level among a plurality of activity levels which is associated with the user based on whether the heart rate information indicates a first threshold heart rate value is exceeded and based on whether the motion information indicates a first threshold motion value is exceeded.

Description

BIOMETRIC MEASUREMENT APPLICATION FOR MEASURING ACTIVITY LEVELFIELD

[0001] The disclosure relates generally to computing devices. More particularly, the disclosure relates to computing devices which are used to measure biometric information of a user.BACKGROUND

[0002] Some methods for measuring the activity level of a user provide information such as the number of steps walked, number of stairs climbed, distance traveled, and the like. Some methods may classify a level of activity of a user based only a single biometric value, such as based on an acceleration signal or a heart rate value.SUMMARY

[0004] Aspects and advantages of embodiments of the 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 example embodiments.

[0005] In an example embodiment, a computing device (e.g., a mobile phone, a smartphone, a biometric computing device, a wearable computing device including a smartwatch or tracker, a server computing device, etc.) is provided. The computing device includes one or more memories configured to store one or more instructions; and one or more processors configured to execute the one or more instructions stored in the one or more memories to perform operations, the operations including: obtaining, via one or more heart rate sensors, heart rate information associated with a user, obtaining, via one or more motion sensors, motion information associated with the user, and determining an activity level among a plurality of activity’ levels which is associated with the user based on whether the heart rate information indicates a first threshold heart rate value is exceeded and based on whether the motion information indicates a first threshold motion value is exceeded.

[0006] In some implementations, the activity level is determined to be the sedentary activitylevel when the heart rate information indicates the first threshold heart rate value is exceeded and the motion information indicates the first threshold motion value is not exceeded.

[0007] In some implementations, the activity- level is determined to be the sedentary’ activity level when the heart rate information indicates the second threshold heart rate value is not exceeded and the motion information indicates the first threshold motion value is not exceeded.

[0008] In some implementations, the plurality of activity levels include at least a sedentary activity level, a light activity level, and a moderate-to-vigorous activity level, the activity level is determined to be the light activity level when the heart rate information indicates the first threshold heart rate value is not exceeded and the motion information indicates a second threshold motion value is exceeded, the activity level is determined to be the moderate-to- vigorous activity level when the heart rate information indicates the first threshold heart rate value is exceeded and is less than a second threshold heart rate value and the motion information indicates the second threshold motion value is exceeded, the activity level is determined to be the moderate-to-vigorous activity level when the heart rate information indicates the second threshold heart rate value is exceeded, the second threshold heart rate value is greater than the first threshold heart rate value, and the second threshold motion value is greater than the first threshold motion value.

[0009] In some implementations, the operations further include determining the activity level at predetermined time intervals to obtain a plurality of activity level determinations.

[0010] In some implementations, the operations further include determining a representative activity level based on the plurality of activity level determinations according to an activity level that is determined more frequently than other activity levels among the plurality of activity levels.

[0011] In some implementations, the operations further include: determining the heart rate information at predetermined time intervals to obtain a plurality of heart rate values, determining whether each of the plurality of heart rate values exceeds the first threshold heart rate value to determine a respective heart rate associated activity level among a plurality of heart rate associated activity levels for each of the heart rate values, and determining a representative heart rate associated activity level according to a heart rate associated activity level that is determined more frequently than other heart rate associated activity levels among the plurality of heart rate associated activity levels.

[0012] In some implementations, the operations further include: when a heart rate value is determined to be less than the first threshold heart rate value, determining a first heart rate associated activity level among the plurality of heart rate associated activity levels, the first heart rate associated activity level corresponding to a sedentary activity' state.

[0013] In some implementations, the operations further include: when the heart rate value is determined to be more than the first threshold heart rate value and less than a second threshold heart rate value, determining a second heart rate associated activity level among the plurality of heart rate associated activity levels, the second heart rate associated activity levelcorresponding to a light activity state, and when the heart rate value is determined to be more than the first threshold heart rate value and more than the second threshold heart rate value, determining a third heart rate associated activity level among the plurality of heart rate associated activity levels, the third heart rate associated activity level corresponding to a moderate-to-vigorous activity state.

[0014] In some implementations, the operations further include: determining the motion information at predetermined time intervals to obtain a plurality of motion values, determining whether each of the plurality of motion values exceeds the first threshold motion value to determine a respective motion associated activity level among a plurality of motion associated activity levels for each of the motion values, and determining a representative motion associated activity level according to a motion associated activity level that is determined more frequently than other motion associated activity levels among the plurality of motion associated activity' levels.

[0015] In some implementations, the operations further include: when a motion value is determined to be less than the first threshold motion value, determining a first motion associated activity level among the plurality of motion associated activity- levels, the first motion associated activity level corresponding to a sedentary activity state.

[0016] In some implementations, the operations further include: when the motion value is determined to be more than the first threshold motion value and less than a second threshold motion value, determining a second motion associated activity level among the plurality of motion associated activity levels, the second motion associated activity level corresponding to a light activity state, and when the motion value is determined to be more than the first threshold motion value and more than the second threshold motion value, determining a third motion associated activity level among the plurality of motion associated activity levels, the third motion associated activity’ level corresponding to a moderate-to- vigorous activity state.

[0017] In some implementations, the heart rate information includes a heart rate value corresponding to a current heart rate of the user or a heart rate metric associated with a heart rate recovery (HRR) value.

[0018] In some implementations, when the heart rate information includes the heart rate metric associated with the HRR value, the operations include: applying a first maximum heart rate value to determine the HRR when an age of the user is less than a specified age, applying a second maximum heart rate value to determine the HRR when the age of the user is more than the specified age, and determining the heart rate metric based on a ratio of a differencebetween a current heart rate of the user and a resting heart rate of the user to the HRR.

[0019] In some implementations, when a confidence level associated with the heart rate information is less than a threshold confidence level, the operations include determining the activity level based on the motion information while ignoring the heart rate information.

[0020] In some implementations, a dataset from which the first threshold heart rate value and the first threshold motion value are derived is based on biometric measurements obtained from a target population having an age range that corresponds to an age of the user associated with the computing device.

[0021] In some implementations, the one or more heart rate sensors include one or more photoplethysmography sensors and / or one or more electrocardiogram sensors, and the one or more motion sensors include one or more accelerometers and / or one or more gyroscopes.

[0022] In some implementations, the computing device is a wearable computing device, and the wearable computing device includes the one or more heart rate sensors and the one or more motion sensors.

[0023] In an example embodiment, a computer-implemented method is provided. The computer-implemented method includes obtaining, via one or more heart rate sensors, heart rate information associated with a user, obtaining, via one or more motion sensors, motion information associated with the user, and determining an activity level among a plurality' of activity levels which is associated with the user based on whether the heart rate information indicates a first threshold heart rate value is exceeded and based on whether the motion information indicates a first threshold motion value is exceeded.

[0024] The computer-implemented method may include further operations to execute other aspects and operations of the computing device as described herein.

[0025] In an example embodiment, a non-transitory computer-readable medium which stores instructions that are executable by one or more processors of a computing device is provided. The non-transitory computer-readable medium stores instructions which are executable by one or more processors of the computing device. The instructions include: instructions to cause the one or more processors to perform operations, the operations including: obtaining, via one or more heart rate sensors, heart rate information associated with a user, obtaining, via one or more motion sensors, motion information associated with the user, and determining an activity' level among a plurality' of activity' levels which is associated with the user based on whether the heart rate information indicates a first threshold heart rate value is exceeded and based on whether the motion information indicates a first threshold motion value is exceeded.

[0026] The non-transitory computer-readable medium may store additional instructions to execute other aspects and operations of the computing device and computer-implemented method as described herein.

[0027] In an example embodiment, a computing device (e.g., a mobile phone, a smartphone, a biometric computing device, a wearable computing device including a smartwatch or tracker, a server computing device, etc.) is provided. The computing device includes one or more memories configured to store one or more instructions; and one or more processors configured to execute the one or more instructions stored in the one or more memories to perform operations, the operations including: obtaining, via one or more heart rate sensors, heart rate information associated with a user, classifying the heart rate information according to a heart rate associated intensity classification among a plurality of heart rate associated intensity classifications, obtaining, via one or more motion sensors, motion information associated with the user, classifying the motion information according to a motion associated intensity classification among a plurality of motion associated intensify classifications, when the heart rate associated intensity classification and the motion associated intensity classification correspond to one another, determining an activity level from among a plurality of activity levels corresponding to the heart rate associated intensity classification and the motion associated intensify classification, and when the heart rate associated intensity classification and the motion associated intensity classification do not correspond with one another, determining the activity level from among the plurality of activity levels based on whether the heart rate information indicates a first threshold heart rate value is exceeded and based on whether the motion information indicates a first threshold motion value is exceeded.

[0028] In an example embodiment, a computer-implemented method is provided. The computer-implemented method includes obtaining, via one or more heart rate sensors, heart rate information associated with a user, classifying the heart rate information according to a heart rate associated intensify classification among a plurality of heart rate associated intensity classifications, obtaining, via one or more motion sensors, motion information associated with the user, classifying the motion information according to a motion associated intensity classification among a plurality of motion associated intensify classifications, when the heart rate associated intensify classification and the motion associated intensify classification correspond to one another, determining an activity level from among a plurality of activity levels corresponding to the heart rate associated intensity classification and the motion associated intensify classification, and when the heart rate associated intensityclassification and the motion associated intensity classification do not correspond with one another, determining the activity level from among the plurality of activity levels based on whether the heart rate information indicates a first threshold heart rate value is exceeded and based on whether the motion information indicates a first threshold motion value is exceeded.

[0029] The computer-implemented method may include further operations to execute other aspects and operations of the computing device as described herein.

[0030] In an example embodiment, a non-transitory computer-readable medium which stores instructions that are executable by one or more processors of a computing device is provided. The non-transitory computer-readable medium stores instructions which are executable by one or more processors of the computing device. The instructions include: instructions to cause the one or more processors to perform operations, the operations including: obtaining, via one or more heart rate sensors, heart rate information associated with a user, classifying the heart rate information according to a heart rate associated intensity classification among a plurality of heart rate associated intensity classifications, obtaining, via one or more motion sensors, motion information associated with the user, classifying the motion information according to a motion associated intensity classification among a plurality of motion associated intensity' classifications, when the heart rate associated intensity classification and the motion associated intensity classification correspond to one another, determining an activity level from among a plurality of activity levels corresponding to the heart rate associated intensity classification and the motion associated intensity classification, and when the heart rate associated intensity classification and the motion associated intensity classification do not correspond with one another, determining the activity' level from among the plurality of activity levels based on whether the heart rate information indicates a first threshold heart rate value is exceeded and based on whether the motion information indicates a first threshold motion value is exceeded.

[0031] The non-transitory computer-readable medium may store additional instructions to execute other aspects and operations of the computing device and computer-implemented method as described herein.

[0032] These and other features, aspects, and advantages of various embodiments of the disclosure will become better understood with reference to the following description, drawings, and appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate examples of the disclosure and, together with the description, serve to explain the related principles.BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Detailed discussion of example embodiments directed to one of ordinary skill in the art is set forth in the specification, which makes reference to the appended drawings, in which:

[0034] FIG. 1 is an example system including block diagrams of a user computing device, a serv er computing system, and an external computing device, according to one or more examples of the disclosure;

[0035] FIG. 2 is an example illustration of a user taking a biometric measurement via the user computing device, according to one or more examples of the disclosure;

[0036] FIG. 3 is an example block diagram of a biometric measurement application, according to one or more examples of the disclosure;

[0037] FIG. 4 is an example table illustrating an example classification scheme based on acceleration information and heart rate information associated with a user, according to one or more examples of the disclosure;

[0038] FIG. 5 is an example graph illustrating ground truth data for activity level classifications with respect to various types of activities, according to one or more examples of the disclosure;

[0039] FIG. 6 is an example graph illustrating experimental output data obtained by implementing aspects of the disclosure described herein, depicting activity level classifications with respect to various types of activities, according to one or more examples of the disclosure;

[0040] FIGS. 7 through 9 are example visual depictions of the performance of the disclosed method compared to the ground truth data, according to one or more examples of the disclosure;

[0041] FIGS. 10 A- 10C are example user interface screens, according to one or more examples of the disclosure; and

[0042] FIG. 11 illustrates an example flow diagram of a non-limiting computer- implemented method for determining biometric information of a user, according to one or more examples of the disclosure.DETAILED DESCRIPTION

[0043] Reference now will be made to embodiments of the disclosure, one or more examples of which are illustrated in the drawings. Each example is provided by way of explanation of the disclosure and is not intended to limit the disclosure. In fact, it will beapparent to those skilled in the art that various modifications and variations can be made to the disclosure without departing from the scope or spirit of the 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 the disclosure covers such modifications and variations as come within the scope of the appended claims and their equivalents.

[0044] Terms used herein are used to describe the example embodiments and are not intended to limit and / or restrict the disclosure. The singular forms "a." “an” and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. In this disclosure, terms such as "including", "having", “comprising”, and the like are used to specify features, numbers, steps, operations, elements, components, or combinations thereof, but do not preclude the presence or addition of one or more of the features, elements, steps, operations, elements, components, or combinations thereof.

[0045] It will be understood that, although the terms first, second, third, etc., may be used herein to describe various elements, the elements are not limited by these terms.Instead, these terms are used to distinguish one element from another element. For example, without departing from the scope of the disclosure, a first element may be termed as a second element, and a second element may be termed as a first element.

[0046] The term "and / or" includes a combination of a plurality of related listed items or any item of the plurality of related listed items. For example, the scope of the expression or phrase "A and / or B" includes the item "A", the item "B", and the combination of items "A and B”.

[0047] In addition, the scope of the expression or phrase "at least one of A or B" is intended to include all of the following: (1) at least one of A, (2) at least one of B, and (3) at least one of A and at least one of B. Likewise, the scope of the expression or phrase "at least one of A. B, or C" is intended to include all of the following: (1) at least one of A, (2) at least one of B, (3) at least one of C, (4) at least one of A and at least one of B, (5) at least one of A and at least one of C, (6) at least one of B and at least one of C, and (7) at least one of A, at least one of B, and at least one of C.

[0048] As mentioned above, some methods for measuring the activity level of a user provide information such as the number of steps w alked, number of stairs climbed, distance traveled, and the like. Some methods may classify a level of activity of a user based only asingle biometric value, such as based on an acceleration signal or a heart rate value. However, some users (e.g., children) engage in erratic, sporadic, volatile, and disorganized movement patterns. Further, classifying a level of activity based on a single biometric value can be inaccurate as some biometric signals may not reflect a user’s true level of activity. For example, using only acceleration signals does not work well for characterizing a level of activity for cycling or biking activities. Also, using only a heart rate value can result in a significant delay in classification, and may not work well with respect to activities that produce excitement in the user compared to activities that are associated with actual exercise.

[0049] According to examples of the disclosure, a computing device can be configured to accurately characterize a level of activity of a user based on both acceleration signals and heart rate signals, by applying a classification method which is based on threshold values and data that are associated with characteristics of the user (e.g., an age of the user).

[0050] In some implementations, the computing device can be configured to classify activity levels of a user at predetermined time intervals (e.g., every second, every five seconds, etc.).

[0051] In some implementations, the computing device can be configured to associate a rating or a certain number of points with a particular activity level, and the rating or certain number of points can be further associated with a duration of time in which the user is engaging at the particular activity level.

[0052] Example aspects of the disclosure provide several technical effects, benefits, and / or improvements in computing technology and the technology of computing devices and health monitoring devices. For example, according to one or more examples of the disclosure, biometric measurements (e.g., the classification of an activity level) can be determined and recorded in an accurate and efficient manner by use of a biometric measurement application that utilizes both acceleration signals and heart rate signals. Therefore, a user’s level of activity can be characterized in an accurate manner and in a timely fashion.

[0053] Furthermore, according to one or more examples of the disclosure, a biometric computing device can obtain accurate HRR metnc values by utilizing an ECG signal to measure heart rate for determining the HRR rather than a PPG signal which may be more susceptible to motion artifacts or other noise. In addition, heart rate information can be extracted from the ECG signal in a more instantaneous fashion than the PPG signal (e.g., fifteen second averaging). Therefore, utilizing the ECG signal may result in measurements having a better resolution and more measurements may be obtained.

[0054] Furthermore, according to one or more examples of the disclosure, a biometric computing device can obtain accurate biometric measurements (e.g., the classification of an activity level) over a wide range of activities based on a method that is derived from real data that is appropriate for the user of the computing device (e.g., appropriate for a user who engages in erratic, sporadic, volatile, and disorganized movement patterns). In contrast, previous methods of classifying a level of activity may be inaccurate with respect to certain activities.

[0055] Referring now to the drawings, FIG. 1 illustrates an example system including block diagrams of a user computing device, a server computing system, and an external computing device, according to one or more examples of the disclosure. FIG. 2 is an example illustration of a user computing device which can be used for obtaining biometric information (e.g., heart rate information, acceleration information, etc.) associated with a user via the biometric measurement application, according to one or more examples of the disclosure. FIG. 3 is an example block diagram of a biometric measurement application which may be provided to the user computing device, according to one or more examples of the disclosure.

[0056] In FIG. 1, the example system 1000 includes a user computing device 100, a server computing system 300, and an external computing device 400. For example, the user computing device 1 0. server computing system 300. and external computing device 400 may be connected with one another over a network 200. Any communications interfaces suitable for communicating via the network 200 (such as a network interface card) may be utilized as appropriate or desired by the user computing device 100, server computing system 300, and external computing device 400.

[0057] The user computing device 100 may include biometric wearable computing devices (e.g.. a biometric smartwatch), a tracker, a smartphone, and the like. In example embodiments described herein, the user computing device 100 may be any computing device that can measure biometric information of a user. The server computing system 300 may include a server, or a combination of servers (e.g.. a web server, application server, etc.) in communication with one another, for example in a distributed fashion. The external computing device 400 may include a personal computer, a smartphone, a laptop, a tablet computer, and the like. In example embodiments described herein, the external computing device 400 may be a computing device that can communicate with the user computing device 100 to receive biometric information that is measured by the user computing device 100. Theuser computing device 100 may be configured to measure various biometrics, including biometrics associated with an ECG, PPG, heart rate, heart rate recovery, pulse information, BMI, heart rate variability, blood pressure, oxygen saturation, body temperature, sleep quality, movement information (e.g., acceleration, velocity, etc.), physical activities (e.g., number of steps walked, number of stairs climbed, distance traveled, etc.), and the like. Further, the user computing device 100 may be configured to generate or display information associated with an electrocardiogram, a photoplethysmogram, heart rate, heart rate recovery, blood pressure, oxygen saturation, respiration rate, body temperature, acceleration, velocity, distance traveled, physical activity level, a sleep metric, electrical conductance, and the like.

[0058] Referring to FIG. 2, according to some implementations of the disclosure, in the illustrated overview 2000 the user computing device 2100 (e.g., a wearable computing device including a smartwatch, fitness tracker, etc.) may be configured to obtain a heart rate value of a user. For example, in some implementations a heart rate value can be obtained or determined from an electrocardiogram (ECG or EKG) associated with a user. For example, in some implementations a heart rate value can be obtained or determined from photoplethysmography (PPG) signals associated with a user.

[0059] The example of FIG. 2 illustrates an example by which a user contacts a plurality' of electrodes disposed on the user computing device 2100 to obtain an ECG measurement. For example, electrodes may be disposed at first location 2156 and second location 2158, corresponding to sides or edges of the housing or body 2152 of the user computing device 2100 as well as one or more electrodes located on the rear side of the user computing device 2100 which is in contact with the user’s body part (e.g., the wrist). While the electrode located on the rear side of the user computing device 2100 contacts a body part (e.g.. body part 2200) of the user which the rear side faces, a first body part (e.g., a thumb) 2300 may contact a first electrode at a first location 2156 and a second body part (e.g., an index finger) 2400 may contact a second electrode at a second location 2158. Other electrodes may also be implemented to measure the ECG signal and the disclosure is not limited to the example of FIG. 2. For example, electrodes may be disposed at other locations including at the rear of the housing or body 2152 which contacts body part 2200, on the wrist strap 2500, integrated in the display device 2150, etc.

[0060] The one or more ECG electrodes may be configured to obtain heart rate information (e.g., an ECG signal) associated with the user, for a predetermined duration of time. For example, the predetermined duration of time may be selectable or specified by theuser and can include a duration of time of 30 seconds, 1 minute, 2 minutes, 5 minutes, etc. As illustrated in FIG. 2 for example, the display device 2150 may provide a graphical user interface which displays information to guide the user during the biometric measurement. For example, the user may be instructed to remain stationary, to maintain contact with the electrodes during the measurement, and the like. For example, the user may be informed about the time remaining 2154 for the measurement (e.g., via a timer, via a graphic which indicates a time remaining, etc.). In some implementations, the user computing device 2100 may be configured to determine a heart rate value or metric based on a duration of time between peaks of consecutive R waves of the ECG. In some implementations, the user computing device 2100 may be configured to determine a heart rate value or metric based on the number of QRS complexes from the ECG which are present over a predetermined duration of time.

[0061] As another example, the user computing device 2100 may be configured to determine a heart rate value or metric based on a PPG signal which measures or detects a change in blood flow caused by pumping of the heart using optical sensors. For example, peaks from the PPG signal can be identified to calculate a heart rate value or metric (e.g., in beats per minute). In some implementations, the PPG signal may be obtained in a passive manner by providing the optical sensors at a location on the user computing device 2100 which is in contact with a body part of the user. For example, the optical sensors may be disposed on a back side of the user computing device 2100 which faces (and is in contact with) the wrist of the user. For example, the optical sensor may include an emitter (e.g., a light emitting diode) which is configured to emit light into the skin and a receiver (e.g., a photodetector) which is configured to receive light which is reflected back from the user. Variations or fluctuations in the amount of received light correspond to a change in blood volume. Heart rate information can be extracted from the PPG signal to determine the heart rate value or metric (beats per minute), for example, based on the peaks or troughs present in the PPG signal.

[0062] For example, the network 200 may include any type of communications network such as a local area network (LAN), wireless local area network (WLAN), wide area network (WAN), personal area network (PAN), virtual private network (VPN), or the like. For example, wireless communication between elements of the examples described herein may be performed via a wireless LAN, Wi-Fi. Bluetooth, ZigBee, Wi-Fi direct (WFD), ultra wideband (UWB), infrared data association (IrDA), Bluetooth low energy (BLE), near fieldcommunication (NFC), a radio frequency (RF) signal, and the like. For example, w ired communication between elements of the examples described herein may be performed via a pair cable, a coaxial cable, an optical fiber cable, an Ethernet cable, and the like.Communication over the netw ork can use 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).

[0063] The user computing device 100 may include one or more processors 110, one or more memory devices 120, a biometric measurement application 130, an input device 140, a display device 150, an output device 160, one or more cameras 170, and one or more sensors 180. Each of the components of the user computing device 100 may be operatively connected with one another via a system bus. For example, the system bus may be any of several types of bus structures that may further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and / or a local bus using any of a variety of commercially available bus architectures.

[0064] The server computing system 300 may include one or more processors 310, one or more memory devices 320, and a biometric measurement application 330. Each of the features of the server computing system 300 may be operatively connected with one another via a system bus. For example, the system bus may be any of several types of bus structures that may further interconnect to a memory' bus (with or without a memory controller), a peripheral bus, and / or a local bus using any of a variety of commercially available bus architectures.

[0065] The external computing device 400 may include a personal computer, a smartphone, a laptop, a tablet computer, and the like. In example embodiments described herein, the external computing device 400 may be a computing device that can communicate with the user computing device 100 to receive biometric information that is measured by the user computing device 100. The external computing device 400 can include some or all of the components described with respect to the user computing device 100 including the biometric measurement application 130. Therefore, descriptions of these components in the context of the user computing device 100 are also applicable to the external computing device 400 and will not be repeated for the sake of brevity. Each of the features of the external computing device 400 may be operatively connected with one another via a system bus. For example, the system bus may be any of several types of bus structures that may further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and / ora local bus using any of a variety of commercially available bus architectures.

[0066] For example, the one or more processors 110, 310 can be any suitable processing device that can be included in a user computing device 100 or server computing system 300. For example, such a processor 110, 310 may include one or more of a processor, processor cores, a controller and an arithmetic logic unit, a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), an image processor, a microcomputer, a field programmable array, a programmable logic unit, an applicationspecific integrated circuit (ASIC), a microprocessor, a microcontroller, etc., and combinations thereof, including any other device capable of responding to and executing instructions in a defined manner. The one or more processors 110, 310 can be a single processor or a plurality of processors that are operatively connected, for example in parallel.

[0067] The one or more memory devices 120, 320 can include one or more non- transitory computer-readable storage mediums, such as such as a Read Only Memory' (ROM), Programmable Read Only Memory (PROM), Erasable Programmable Read Only Memory (EPROM), and flash memory, a USB drive, a volatile memory device such as a Random Access Memory (RAM), an internal or external hard disk drive (HDD), floppy disks, a blueray disk, or optical media such as CD ROM discs and DVDs, and combinations thereof. However, examples of the one or more memory devices 120, 320 are not limited to the above description, and the one or more memory devices 120, 320 may be realized by other various devices and structures as would be understood by those skilled in the art.

[0068] For example, the one or more memory devices 120 can store instructions, that when executed, cause the one or more processors 110 to: obtain, via one or more heart rate sensors, heart rate information associated with a user; obtain, via one or more motion sensors, motion information associated with the user; and determine an activity level among a plurality of activity levels which is associated with the user based on whether the heart rate information indicates a first threshold heart rate value is exceeded and based on whether the motion information indicates a first threshold motion value is exceeded, as described according to examples of the disclosure.

[0069] For example, the one or more memory' devices 320 can store instructions, that when executed, cause the one or more processors 310 to: obtain, via one or more heart rate sensors, heart rate information associated with a user; obtain, via one or more motion sensors, motion information associated with the user; and determine an activity level among aplurality of activity levels which is associated with the user based on whether the heart rate information indicates a first threshold heart rate value is exceeded and based on whether the motion information indicates a first threshold motion value is exceeded, as described according to examples of the disclosure.

[0070] The one or more memory devices 120 can also include data 122 and instructions 124 that can be retrieved, manipulated, created, or stored by the one or more processors 110. In some examples, such data can be accessed and used as input to obtain and output an activity level associated with a user, as described according to examples of the disclosure. The one or more memory devices 320 can also include data 322 and instructions 324 that can be retrieved, manipulated, created, or stored by the one or more processors 310. In some examples, such data can be accessed and used as input to obtain and output an activity level associated with the user, as described according to examples of the disclosure.

[0071] The biometric measurement application 130 can include any biometric application which allows or is capable of determining biometric information associated with a user (e.g., based on biometric measurements obtained via the one or more sensors 180). As explained with reference to FIG. 3, in some implementations, the biometric measurement application 130 includes aheart rate information determiner 132, a threshold heart rate value comparator 133, a motion information determiner 134, a threshold motion value comparator 135, an activity level determiner 136, a scoring determiner 137. and a user interface generator 138.

[0072] For example, in some implementations a user may execute the biometric measurement application 130 by providing an input to the user computing device 100 via input device 140 to measure, determine, and store a biometric measurement (e.g., a heart rate value, an acceleration value, an activity level, etc.). For example, the user may be prompted to execute the biometric measurement application 130 in response to the threshold heart rate value comparator 133 determining that a heart rate measured via the heart rate information determiner 132 exceeds a threshold heart rate value, in response to the threshold motion value comparator 135 determining that an acceleration value measured via the motion information determiner 134 exceeds a threshold acceleration value, or in response to the threshold heart rate value comparator 133 determining that the heart rate measured via the heart rate information determiner 132 exceeds the threshold heart rate value and the threshold motion value comparator 135 determining that the acceleration value measured via the motion information determiner 134 exceeds the threshold acceleration value.

[0073] For example, in some implementations the biometric measurement application 130 may automatically be executed to measure, determine, and store a biometric measurement (e.g., a heart rate value, an acceleration value, an activity level, etc.). For example, in some implementations the biometric measurement application 130 may automatically be executed (activated) to measure, determine, and store a biometric measurement (e.g.. a heart rate value, an acceleration value, an activity level, etc.), in response to determining the user computing device 100 is being worn by the user. For example, in some implementations the biometric measurement application 130 may automatically be deactivated or turned off, in response to determining the user computing device 100 is taken off by the user. For example, the biometric measurement application 130 may automatically be executed in response to the threshold heart rate value comparator 133 determining that a heart rate measured via the heart rate information determiner 132 exceeds a threshold heart rate value, in response to the threshold motion value comparator 135 determining that an acceleration value measured via the motion information determiner 134 exceeds a threshold acceleration value, or in response to the threshold heart rate value comparator 133 determining that the heart rate measured via the heart rate information determiner 132 exceeds the threshold heart rate value and the threshold motion value comparator 135 determining that the acceleration value measured via the motion information determiner 134 exceeds the threshold acceleration value.

[0074] The biometric measurement application 330 of the server computing system 300 can also include similar features as the biometric measurement application 130 (e.g., as shown in FIG. 3) which perform similar functions and operations, and therefore a descnption of those features will not be repeated for the sake of brevity.

[0075] The user computing device 100 may include an input device 140 configured to receive an input from a user and may include, for example, one or more of a keyboard (e.g., a physical keyboard, virtual keyboard, etc.), a mouse, a joystick, a button, a switch, an electronic pen or stylus, a gesture recognition sensor (e.g., to recognize gestures of a user including movements of a body part), an input sound device or voice recognition sensor (e.g., a microphone to receive a voice command), a track ball, a remote controller, a portable (e.g., a cellular or smart) phone, and so on. The input device 140 may also be embodied by a touch-sensitive display device having a touchscreen capability, for example. The input device 140 may be used by the user of the user computing device 100 to provide an input to execute the biometric measurement application 130, to provide information about the user(e.g., biometric information, demographic information, user preferences, etc.). The input device 140 may be used by the user of the user computing device 100 to request a biometric measurement, to transmit biometric information of the user to the server computing system 300, external computing device 400, etc. For example, the input may be a voice input, a touch input, a gesture input, a click via a mouse or remote controller, and so on.

[0076] The user computing device 100 may include a display device 150 which presents information viewable by the user, for example on a user interface (e.g., a graphical user interface). For example, the display device 150 may be a touch sensitive display or a nontouch sensitive display. The display device 150 may include a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, active matrix organic light emitting diode (AMOLED), flexible display, 3D display, a plasma display panel (PDP), a cathode ray tube (CRT) display, and the like, for example. However, the disclosure is not limited to these example display devices and may include other types of display devices.

[0077] The user computing device 100 may include an output device 160 configured to provide an output to the user and may include, for example, one or more of an audio device (e.g., one or more speakers), a haptic device to provide haptic feedback to a user, a light source (e.g., one or more light sources such as LEDs which provide visual feedback to a user), and the like. For example, in some implementations of the disclosure the user may be guided through a process for obtaining a biometric measurement including an ECG measurement, a PPG measurement, and the like. For example, in some implementations of the disclosure the user may be provided with an output (e.g., via alerts, notifications, etc.), in response to the user achieving certain biometric goals, in response to the user meeting certain intermediate biometric goals such as making it halfway to a daily goal, etc. For example, in some implementations of the disclosure the user may be provided with an output (e.g., via alerts, notifications, etc.), in response to determining the user has not met certain biometric goals, in response to determining the user has been sedentary for a predetermined duration of time, etc. For example, in some implementations of the disclosure the user may be provided with an output (e.g., via alerts, notifications, etc.), in response to determining the user has been engaging in a certain level of activity for more than a predetermined duration of time. For example, the user can be provided with a notification to rest if the user's level of activity has been maintained at a high level for a certain duration of time, so that the user does not overexert themself, etc.

[0078] The user computing device 100 may include one or more cameras 170. For example, the one or more cameras 170 may include an imaging sensor (e.g., a complementary metal-oxide-semi conductor (CMOS) or charge-coupled device (CCD)) to capture, detect, or recognize a user's behavior, figure, expression, etc.

[0079] The user computing device 100 may include one or more sensors 180. For example, the one or more sensors 180 may include an inertial measurement unit 182 which includes one or more accelerometers 182a and / or one or more gyroscopes 182b. The one or more accelerometers 182a may be used to capture motion information with respect to the user computing device 100. The one or more gyroscopes 182b may also be used additionally or alternatively to capture motion information with respect to the user computing device 100. For example, the inertial measurement unit 182 may be configured as a six-axis or sixdimensional inertial measurement unit (e.g.. a tri-axial accelerometer and a tri-axial gyroscope). The motion information obtained via the inertial measurement unit 182 may be associated with the user when the user computing device 100 is worn or carried by the user.

[0080] For example, the one or more sensors 180 may include one or more optical sensors 184 (e.g., one or more photoplethysmography (PPG) sensors) which can be used to monitor or detect the heart rate of the user. The one or more optical sensors 184 (e.g., one or more PPG sensors) may include one or more emitters (e.g., light-emitting diodes (LEDs)) and one or more detectors (e.g., photodiodes). For example, the one or more optical sensors 184 may be configured to emit light (e g., green or red), onto the skin of the user and to measure variations in the intensity of the reflected or transmitted light caused by changes in blood flow. For example, the one or more optical sensors 184 may be configured to capture the pulsatile nature of the blood flow which can be used to estimate various physiological parameters related to the cardiovascular system. For example, the heart rate of the user may be determined based on the frequency of the pulsatile signal. Additionally, the one or more optical sensors 184 may be configured to provide information about heart rate variability (HRV), blood oxygen saturation (SpO2) levels, and the like. Furthermore, in some examples described herein a pulse transit time (PTT) may be determined based on a measurement of the time delay between the R-wave peak of the ECG signal (representing the electrical activity of the heart) at a first location (e.g., a finger on the right hand) and the arrival of a corresponding pulse wave at a second location (e.g., a left wrist) where a PPG sensor is located. For example, the one or more optical sensors 184 may be disposed at a side of the user computing device 100 (e.g., a rear side) such that the one or more optical sensors 184 arein contact w ith a body part of the user. For example, the one or more optical sensors 184 may be disposed such that a heart rate of the user may be passively monitored and measured without a user actively or consciously engaging the one or more optical sensors 184.

[0081] For example, the one or more sensors 180 may include one or more ECG sensors 186 which can also be used to monitor the heart rate of the user. The one or more ECG sensors 186 may be configured to measure and record the electrical activity of the heart by capturing the electrical impulses generated by the heart's contractions. For example, an ECG sensor may include a plurality of electrodes that are disposed to contact different areas of a user’s body. The electrodes detect electrical signals or impulses generated by the heart’s contractions which are processed and analyzed to generate an electrocardiogram (ECG). For example, the electrodes may be disposed at various locations that can come into contact with the user’s skin (e.g., a band of a smartwatch, one or more sides of the body of the user computing device 100. integrated as part of a display screen of the display device 150. etc.). When the user places one or more body parts (e g., their finger(s) or thumbs) on specified electrodes, the ECG sensor may be configured to record the electrical signals or impulses produced by the heart's contractions. The electrical signals or impulses cause specific patterns on an ECG graph. These patterns include waves, segments, and intervals, including the P w ave, QRS complex, R wave, and T wave, which represent different phases of the heart's electrical activity. The ECG signal may be used to obtain various biometric information about the user including a heart rate which can be obtained by analyzing the intervals between consecutive R-waves on the ECG waveform, heart rate variability, pulse transit time as discussed above, etc.

[0082] The one or more sensors 180 may also include other sensors such as a magnetometer, GPS sensor, proximity sensor, and the like.

[0083] Referring to FIG. 3, an example block diagram of a biometric measurement application is show n, according to one or more examples of the disclosure. FIG. 3 illustrates that the biometric measurement application 130 includes a heart rate information determiner 132, a threshold heart rate value comparator 133, a motion information determiner 134, a threshold motion value comparator 135. an activity level determiner 136. a scoring determiner 137, and a user interface generator 138. However, the biometric measurement application 130 may include fewer or more features than that shown in FIG. 3. For example, any of the features or operations of the components of biometric measurement application 130 may be provided separately from the biometric measurement application 130. Forexample, some operations (such as the determination of the activity level by the activity level determiner 136) may instead be performed by the server computing system 300 (e.g., via biometric measurement application 330).

[0084] Operations of the biometric measurement application 130 will now be described in more detail with reference to FIGS. 3 through 1 1 .

[0085] FIG. 4 is an example table illustrating an example classification scheme based on acceleration information and heart rate information associated with a user, according to one or more examples of the disclosure. FIG. 5 is an example graph illustrating ground truth data for activity level classifications with respect to various types of activities, according to one or more examples of the disclosure. FIG. 6 is an example graph illustrating experimental output data obtained by implementing aspects of the disclosure described herein, depicting activity level classifications with respect to various types of activities, according to one or more examples of the disclosure. FIGS. 7 through 9 are example visual depictions of the performance of the disclosed method compared to the ground truth data, according to one or more examples of the disclosure. FIGS. 10A-10C are example user interface screens, according to one or more examples of the disclosure. FIG. 11 illustrates an example flow diagram of a non-limiting computer-implemented method for determining biometric information of a user, according to one or more examples of the disclosure.

[0086] The flow diagram of FIG. 11 illustrates a method 1100 for determining biometric information of a user, for example, an activity level associated with a user. Although shown in a particular sequence or order, unless otherwise specified, the order of the processes can be modified. Thus, the illustrated embodiments should be understood only as examples, and the illustrated processes can be performed in a different order, and some processes can be performed in parallel. Additionally, one or more processes can be omitted in various embodiments. Thus, not all processes are required in every embodiment. Other process flows are possible.

[0087] At operation 1 110 the method 1100 includes obtaining, via one or more heart rate sensors, heart rate information associated with a user. For example, the one or more heart rate sensors can include the one or more optical sensors 184 (e.g., one or more PPG sensors) and / or one or more ECG sensors 186. For example, the heart rate information determiner 132 can be configured to obtain heart rate information via one or more heart rate sensors. For example, the heart rate information can include a heart rate value which is determined bythe optical signal analyzer 132a based on a signal output via the one or more optical sensors 184 (e.g.. a PPG signal output via one or more PPG sensors). For example, the heart rate information can include a heart rate value which is determined by the ECG signal analyzer 132b based on a signal output via the one or more ECG sensors 186.

[0088] For example, various known peak detection methods (e.g., peak amplitude thresholding) may be implemented by the optical signal analyzer 132a to identify peaks in a PPG signal. The optical signal analyzer 132a may be configured to determine a time interval (e.g., interbeat interval) between consecutive peaks which is inversely related to the heart rate. For example, the optical signal analyzer 132a may be configured to determine the heart rate value by taking the reciprocal of the time interval and multiplying it by 60. In some implementations, the heart rate information can include a current heart rate value that is measured, for example, in real-time.

[0089] In some implementations, the heart rate information can include a resting heart rate value that is measured, for example, when the user is stationary’ or has a movement level below a threshold movement level. For example, the resting heart rate can be measured via the one or more heart rate sensors after the user has been stationary’ (or maintains the movement level below the threshold movement level) for a predetermined duration of time (e.g., after 30 seconds, after one minute, etc.). In some implementations, a resting heart rate value can be associated with a user and used in connection with determining a heart rate recovery value.

[0090] In some implementations, the heart rate information can include a maximum heart rate value that can be measured, for example, under specified conditions (e.g., via a treadmill stress test in a lab). In other implementations, the maximum heart rate value can be estimated according to various methods. For example, the maximum heart rate value may correspond to a value of 220 minus the user's age. For example, the maximum heart rate value may correspond to a value of 207 minus (0.7 x the user's age). As yet another example, the maximum heart rate may correspond to a value of 208.609 minus (0.716 x the user’s age) for males and 209.273 minus (0.804 x the user’s age) for females. As yet a further example, when an age of the user is below a threshold age level (e.g.. less than 13 years) or is between a certain age range (e.g., from 7 years to 12 years), the maximum heart rate may correspond to a value of 195. In some implementations, the user may provide an input via the input device 140 to select or identify a maximum heart rate value to be used in association with the user.

[0091] In some implementations, the heart rate recovery determiner 132c may be configured to determine a heart rate metric associated with a heart rate recovery (HRR) value. For example, the HRR value may correspond to a difference between the maximum heart rate value and the resting heart rate value. For example, the heart rate recovery determiner 132c may be configured to determine the heart rate metric based on a ratio of a difference between a current heart rate of the user and a resting heart rate of the user to the HRR (e.g., (current HR - resting HR) / (maximum HR - resting HR)). For example, the heart rate recovery determiner 132c may be configured to apply a first maximum heart rate value (e.g., 195) to determine the HRR (as well as the heart rate metric) when an age of the user is less than a specified age (e.g., less than 13 years). For example, the heart rate recovery determiner 132c may be configured to apply a second maximum heart rate value (e.g.. a value of 220 minus the user’s age) to determine the HRR (as well as the heart rate metric) when an age of the user is more than the specified age (e.g., more than 13 years).

[0092] At operation 1 120 the method 1100 includes obtaining, via one or more motion sensors, motion information associated with the user. For example, the one or more motion sensors may include one or more accelerometers 182a and / or one or more gyroscopes 182b. For example, the motion information determiner 134 can be configured to obtain motion information via the one or more motion sensors. For example, the motion information may include acceleration information. In some implementations, the one or more motion sensors may be configured to quantify an acceleration experienced by the computing device (and by association the user) in terms of the acceleration due to gravity. In some implementations, the one or more motion sensors may be configured to measure the acceleration along a single direction (e.g., a single vector magnitude) where the magnitude of the acceleration is obtained. In some implementations, the one or more motion sensors may be configured to calculate the average single vector magnitude acceleration measurement in "g," by dividing the magnitude of the acceleration vector by the acceleration due to gravity (9.8 m / s2). In some implementations, the one or more motion sensors may be configured to measure the acceleration at predetermined intervals (e.g., every second).

[0093] At operation 1 130 the method 1100 includes determining an activity level among a plurality of activity levels which is associated with the user based on whether the heart rate information indicates a first threshold heart rate value is exceeded and based on whether the motion information indicates a first threshold motion value is exceeded.

[0094] For example, the threshold heart rate value comparator 133 may be configured todetermine whether the heart rate information obtained via the heart rate information determiner 132 indicates a first threshold heart rate value is exceeded. For example, the first threshold heart rate value may correspond to a first threshold HRR metric value (e.g., 0.15, 0.20, 0.25, etc.). In some implementations, the threshold heart rate value comparator 133 may be configured to determine whether the heart rate information obtained via the heart rate information determiner 132 indicates a second threshold heart rate value is exceeded. For example, the second threshold heart rate value may correspond to a second threshold HRR metric value (e.g., 0.45, 0.50, 0.55, etc.).

[0095] In some implementations, when the threshold heart rate value comparator 133 determines a heart rate value (e.g., a HRR metric value) is less than the first threshold heart rate value, the activity level determiner 136 may be configured to determine a first heart rate associated activity level among a plurality of heart rate associated activity levels. For example, the first heart rate associated activity- level may correspond to a sedentary activity state. That is, the activity level determiner 136 may be configured to classify the heart rate information according to a first heart rate associated intensity classification (e.g., a sedentary- activity state) among a plurality of heart rate associated intensity classifications.

[0096] In some implementations, when the threshold heart rate value comparator 133 determines the heart rate value (e.g.. the HRR metric value) is more than the first threshold heart rate value and less than the second threshold heart rate value, the activity level determiner 136 may be configured to determine a second heart rate associated activity level among the plurality of heart rate associated activity levels. For example, the second heart rate associated activity level may correspond to a light activity state. That is. the activity level determiner 136 may be configured to classify the heart rate information according to a second heart rate associated intensity classification (e.g., a light activity state) among the plurality of heart rate associated intensity classifications.

[0097] In some implementations, when the threshold heart rate value comparator 133 determines the heart rate value (e.g., the HRR metric value) is more than the first threshold heart rate value and more than the second threshold heart rate value, the activity level determiner 136 may be configured to determine a third heart rate associated activity level among the plurality of heart rate associated activity levels. For example, the third heart rate associated activity level may correspond to a moderate-to-vigorous activity state. That is, the activity level determiner 136 may be configured to classify the heart rate information according to a third heart rate associated intensity classification (e.g., a moderate-to-vigorousactivity state) among the plurality of heart rate associated intensity classifications.

[0098] In some implementations, a plurality' of heart rate values may be obtained via the heart rate information determiner 132 over a predetermined duration of time. The plurality of heart rate values may be obtained at predetermined time intervals. For example, five heart rate values may be obtained over the course of thirty seconds, one minute, two minutes, etc. The heart rate values may be obtained every' six seconds, every twelve seconds, every' 24 seconds, etc. For each heart rate value measurement, the heart rate value may be classified according to the heart rate associated intensity classifications (e.g., a sedentary activity state, a light activity state, a moderate-to-vigorous activity' state, etc.). The activity level determiner 136 may be configured to select or identify a representative heart rate associated activity level according to a heart rate associated activity' level that is determined more frequently than other heart rate associated activity levels among the plurality of heart rate associated activity levels (e.g.. during the predetermined duration of time). For example, if three of the heart rate values indicate or correspond to a sedentary' activity state yvhile two of the heart rate values indicate or correspond to a moderate-to-vigorous activity' state, the activity level determiner 136 may be configured to determine a representative heart rate associated activity level that corresponds to the sedentary activity state. For example, if two of the heart rate values indicate or correspond to a sedentary activity state yvhile two of the heart rate values indicate or correspond to a moderate-to-vigorous activity state and one of the heart rate values indicate or correspond to a light activity' state, the activity level determiner 136 may be configured to determine a representative heart rate associated activity’ level that corresponds to the sedentary activity' state. That is, the activity level determiner 136 may be configured to select an activity state yvith a loyver intensity level when tyvo or more activity' states are determined the same number of times over the predetermined duration of time and are the most frequently determined activity states compared to other activity states. In some implementations, the activity level determiner 136 may be configured to determine a representative heart rate associated activity' level based on the plurality of heart rate associated activity' level determinations according to a heart rate associated activity' level that is determined more frequently than other heart rate associated activity levels among the plurality of heart rate associated activity levels (e.g., after a predetermined number of heart rate associated activity levels are determined, such as after five heart rate associated activity levels are determined).

[0099] For example, the threshold motion value comparator 135 may be configured todetermine whether the motion information obtained via the motion information determiner 134 indicates a first threshold motion value is exceeded. For example, the first threshold motion value may correspond to a first threshold acceleration value (e.g., 80 mg, 85 mg, 90 mg, etc.). In some implementations, the threshold motion value comparator 135 may be configured to determine whether the motion information obtained via the motion information determiner 134 indicates a second threshold motion value is exceeded. For example, the second threshold motion value may correspond to a second threshold acceleration value (e.g., 290 mg, 300 mg, 310 mg, etc.).

[0100] In some implementations, when the threshold motion value comparator 135 determines a motion value (e.g., an acceleration value) is less than the first threshold motion value, the activity' level determiner 136 may be configured to determine a first motion associated activity level among a plurality of motion associated activity levels. For example, the first motion associated activity level may correspond to a sedentary activity state. That is, the activity' level determiner 136 may be configured to classify the motion information according to a first motion associated intensity classification (e.g., a sedentary' activity state) among a plurality of motion associated intensity classifications.

[0101] In some implementations, when the threshold motion value comparator 135 determines the motion value (e.g., the acceleration value) is more than the first threshold motion value and less than the second threshold motion value, the activity level determiner 136 may be configured to determine a second motion associated activity level among the plurality of motion associated activity levels. For example, the second motion associated activity level may correspond to a light activity state. That is, the activity level determiner 136 may be configured to classify the motion information according to a second motion associated intensity classification (e.g., a light activity' state) among the plurality of motion associated intensity classifications.

[0102] In some implementations, when the threshold motion value comparator 135 determines the motion value (e.g., the acceleration value) is more than the first threshold motion value and more than the second threshold motion value, the activity level determiner 136 may be configured to determine a third motion associated activity level among the plurality of motion associated activity levels. For example, the third motion associated activity level may correspond to a moderate-to-vigorous activity state. That is, the activity level determiner 136 may be configured to classify' the motion information according to a third motion associated intensity classification (e.g., a moderate-to-vigorous activity state)among the plurality of motion associated intensity classifications.

[0103] In some implementations, a plurality of motion values may be obtained via the motion information determiner 134 over a predetermined duration of time. The plurality of motion values may be obtained at predetermined time intervals. For example, five motion values may be obtained over the course of five seconds, ten seconds, thirty seconds, etc. The motion values may be obtained every one second, every' two seconds, every' six seconds, etc. For each motion value measurement, the motion value may be classified according to the motion associated intensity classifications (e.g., a sedentary activity state, a light activity state, a moderate-to-vigorous activity state, etc.). The activity level determiner 136 may be configured to select or identity' a representative motion associated activity level according to a motion associated activity' level that is determined more frequently than other motion associated activity levels among the plurality of motion associated activity levels (e.g., during the predetermined duration of time). For example, if three of the motion values indicate or correspond to a sedentary activity state while two of the motion values indicate or correspond to a moderate-to-vigorous activity' state, the activity' level determiner 136 may be configured to determine a representative motion associated activity level that corresponds to the sedentary activity state. For example, if two of the motion values indicate or correspond to a sedentary’ activity state while two of the motion values indicate or correspond to a moderate- to-vigorous activity state and one of the motion values indicate or correspond to a light activity state, the activity' level determiner 136 may be configured to determine a representative motion associated activity’ level that corresponds to the sedentary activity state. That is, the activity level determiner 136 may be configured to select an activity state with a lower intensity' level when two or more activity states are determined the same number of times over the predetermined duration of time and are the most frequently determined activity states compared to other activity states. In some implementations, the activity level determiner 136 may be configured to determine a representative motion associated activity level based on the plurality of motion associated activity level determinations according to a motion associated activity' level that is determined more frequently than other motion associated activity levels among the plurality' of motion associated activity levels (e.g., after a predetermined number of motion associated activity levels are determined, such as after five motion associated activity levels are determined).

[0104] The activity level determiner 136 can further determine an activity level (e.g., an overall activity level) among a plurality of activity' levels which is associated with the user.based on whether the heart rate information indicates the first threshold heart rate value is exceeded and based on whether the motion information indicates the first threshold motion value is exceeded. Determining the activity level as disclosed herein can accurately characterize how active a user is - whether they are in a sedentary activity state, a light activity state, or a moderate-to-vigorous activity state, for example. Tracking the activity' levels of the user over time via the disclosed methods described herein can assist the user in determining whether they are getting enough exercise, meeting certain biometric goals, or satisfying other health metrics. Further, according to examples disclosed herein the method for determining an activity level can be implemented accurately according to the age of the user by implementing a method that is based on source (training) data that is associated with an age (or age group) that is similar to the age of the user. For example, certain users (e.g., children) may behave (exercise) differently than other users (e.g., adults). For example, the disclosed method for determining an activity level can accurately classify the user’s activity level including for users (e.g., children) whose movement may be erratic, sporadic, or volatile, resulting in disorganized movement patterns.

[0105] In some implementations, when a confidence level associated with the heart rate information is less than a threshold confidence level, the activity level determiner 136 may be configured to determine the activity level based on the motion information alone while ignoring the heart rate information. For example, heart rate information may be unreliable due to excessive movement of the user, due to noise interference, etc.

[0106] Referring now to FIG. 4, a table 4000 depicts an example method for determining an activity level (overall activity level) associated with a user. For example, when the heart rate associated intensity classification and the motion associated intensity classification correspond to one another, the activity level determiner 136 may be configured to determine an activity level from among a plurality of activity levels which corresponds to the heart rate associated intensity classification and the motion associated intensity classification. For example, if the heart rate associated intensity classification and the motion associated intensity classification are the same (e.g., both corresponding to a light activity state), the activity' level determiner 136 may be configured to determine the activity' level which corresponds to the heart rate associated intensity' classification and the motion associated intensity classification (e.g., the light activity state).

[0107] For example, when the heart rate associated intensity classification and the motion associated intensity classification do not correspond with one another, the activitylevel determiner 136 may be configured to determine an activity level from among the plurality of activity levels based on whether the heart rate information indicates the first threshold heart rate value is exceeded and based on whether the motion information indicates the first threshold motion value is exceeded.

[0108] For example, as illustrated in table 4000, if the heart rate associated intensity classification indicates a light state (e.g., based on the heart rate information indicating the first threshold heart rate value is above the first threshold heart rate value and below a second threshold heart rate value) and the motion associated intensity classification indicates a sedentary state (e.g., based on the motion information indicating the first threshold motion value is below the first threshold motion value), the activity level determiner 136 is configured to determine an overall sedentary' activity level (e.g., a sedentary activity state).

[0109] For example, as illustrated in table 4000, if the heart rate associated intensity classification indicates a moderate-to-vigorous state (e.g., based on the heart rate information indicating the first threshold heart rate value is above the first threshold heart rate value and above the second threshold heart rate value) and the motion associated intensity classification indicates a sedentary- state (e.g., based on the motion information indicating the first threshold motion value is below the first threshold motion value), the activity level determiner 136 is configured to determine an overall moderate-to-vigorous state (e.g., a moderate-to-vigorous state activity state).

[0110] For example, as illustrated in table 4000, if the heart rate associated intensity classification indicates a sedentary state (e.g., based on the heart rate information indicating the first threshold heart rate value is below the first threshold heart rate value) and the motion associated intensity classification indicates a light state (e.g., based on the motion information indicating the first threshold motion value is above the first threshold motion value and less than a second threshold motion value), the activity level determiner 136 is configured to determine an overall light activity level (e.g., a light activity state).

[0111] For example, as illustrated in table 4000, if the heart rate associated intensity classification indicates a moderate-to-vigorous state (e.g., based on the heart rate information indicating the first threshold heart rate value is above the first threshold heart rate value and above the second threshold heart rate value) and the motion associated intensity classification indicates a light state (e.g., based on the motion information indicating the first threshold motion value is above the first threshold motion value and less than the second thresholdmotion value), the activity level determiner 136 is configured to determine an overall moderate-to-vigorous activity level (e.g., a moderate-to- vigorous activity state).

[0112] For example, as illustrated in table 4000, if the heart rate associated intensity classification indicates a sedentary state (e.g., based on the heart rate information indicating the first threshold heart rate value is below the first threshold heart rate value) and the motion associated intensity classification indicates a moderate-to-vigorous state (e.g., based on the motion information indicating the first threshold motion value is above the first threshold motion value and above the second threshold motion value), the activity level determiner 136 is configured to determine an overall light activity level (e.g., a light activity state).

[0113] For example, as illustrated in table 4000, if the heart rate associated intensity classification indicates a light state (e.g., based on the heart rate information indicating the first threshold heart rate value is above the first threshold heart rate value and below the second threshold heart rate value) and the motion associated intensity classification indicates a moderate-to-vigorous state (e.g.. based on the motion information indicating the first threshold motion value is above the first threshold motion value and above the second threshold motion value), the activity level determiner 136 is configured to determine an overall moderate-to-vigorous activity level (e.g., a moderate-to-vigorous activity state).

[0114] In some implementations, the activity level determiner 136 may be configured to determine an overall activity level a plurality' of times over a predetermined duration of time (e.g., over 30 seconds, over one minute, over two minutes, etc.), to obtain a plurality of activity level determinations. The activity level determiner 136 may be configured to determine a representative activity level based on the plurality of activity level determinations according to an activity level that is determined more frequently than other activity levels among the plurality of activity levels (e.g., during the predetermined duration of time). For example, the activity level determiner 136 may be configured to determine a representative activity' level every second (e.g., based on a current classification which can be obtained every second, and based on previous classifications if such prior classifications are utilized in an aggregated manner).

[0115] As an example, if five activity levels are determined over the predetermined duration of time and three of the activity levels indicate or correspond to a sedentary activity state while two of the activity levels indicate or correspond to a moderate-to-vigorous activity state, the activity level determiner 136 may be configured to determine a representative(overall) activity level that corresponds to the sedentary activity state. For example, if two of the activity levels indicate or correspond to a light activity state while two of the activity levels indicate or correspond to a moderate-to-vigorous activity state and one of the activity levels indicate or correspond to a sedentary activity state, the activity level determiner 136 may be configured to determine a representative (overall) activity level that corresponds to the light activity state. That is, the activity level determiner 136 may be configured to select an activity state with a lower intensity level when two or more activity states are determined the same number of times over the predetermined duration of time and are the most frequently determined activity states compared to other activity states. In some implementations, the activity level determiner 136 may be configured to determine a representative activity level based on the plurality of activity level determinations according to an activity level that is determined more frequently than other activity levels among the plurality of activity7levels (e.g., after a predetermined number of activity levels are determined, such as after five activity levels are determined).

[0116] As described herein, threshold values for determining different classifications of the heart rate information and the motion information, may be determined based on ground truth data. For example, a dataset from which the first threshold heart rate value and the first threshold motion value are derived may be based on biometric measurements obtained from a target population having an age range that corresponds to an age of the user associated with the computing device. In some implementations, the age of the user may be determined by the biometric measurement application 130 based on a user input, based on user data (e.g., provided in a user profile), or other methods, with permission of the user (or of someone on behalf of the user such as a parent, guardian, etc.).

[0117] FIG. 5 is an example graph illustrating ground truth data for activity level classifications with respect to various types of activities, according to one or more examples of the disclosure. For example, FIG. 5 illustrates data 5000 corresponding to users of a particular age range. On the horizontal axis different activities are displayed (e.g., resting, watching videos, walking normally, jogging, etc.). On the vertical axis a classification percentage is illustrated in a bar graph form, with respect to sedentary, light, and moderate- to-vigorous activity levels. For example, in a sample size of over 100 users, 100% of the time that users were sitting or resting, they were classified as being in a sedentary7activity state, according to a standard biometric measurement method (e.g.. biometric measurements taken while a user is wearing a polar strap). For example, in a sample size of over 100 users.about 91% of the time users were walking normally, they were classified as being in a light activity state, according to the standard biometric measurement method (e.g.. biometric measurements taken while the user is wearing the polar strap). For example, in a sample size of over 100 users, about 12% of the time users were playing ping-pong, they were classified as being in a sedentary state, about 48% of the time the users were classified as being in a light activity state, and about 40% of the time the users were classified as being in a moderate-to-vigorous activity state, according to the standard biometric measurement method (e.g., biometric measurements taken while the user is wearing the polar strap). For example, in a sample size of over 100 users, about 97% of the time that users were jogging, they were classified as being in a moderate-to-vigorous activity state, according to the standard biometric measurement method (e.g., biometric measurements taken while the user is wearing the polar strap).

[0118] FIG. 6 is an example graph illustrating experimental output data obtained by implementing aspects of the disclosure described herein, depicting activity level classifications with respect to various types of activities, according to one or more examples of the disclosure. For example, FIG. 6 illustrates data 6000 corresponding to the users of the particular age range. On the horizontal axis the same activities as depicted in FIG. 5 are displayed (e.g., resting, watching videos, walking normally, jogging, etc.). On the vertical axis a classification percentage is illustrated in a bar graph form, with respect to sedentary, light, and moderate-to-vigorous activity levels. For example, in a sample size of over 100 users, over 99% of the time that users were sitting and resting, they were classified as being in a sedentary activity state, according to the method for determining an activity level of a user as described herein. For example, in a sample size of over 100 users, about 87% of the time that users were walking normally, they were classified as being in a light activity state, according to the method for determining an activity level of a user as described herein. For example, in a sample size of over 100 users, about 12% of the time that users were playing ping-pong, they were classified as being in a sedentary state, about 33% of the time the users were classified as being in a light activity state, and about 55% of the time the users were classified as being in a moderate-to-vigorous activity state, according to method for determining an activity level of a user as described herein. For example, in a sample size of over 100 users, about 94% of the time that users were jogging, they were classified as being in a moderate-to-vigorous activity state, according to the method for determining an activity level of a user as described herein.

[0119] FIGS. 7 through 9 are example visual depictions of the performance of the disclosed method compared to the ground truth data, according to one or more examples of the disclosure. For example, FIG. 7 illustrates data 7000 that characterizes the differences between the classifications of FIG. 5 and FIG. 6. As can be seen from FIG. 7, the disclosed method achieves similar results in classifying most activities, and in particular sedentary activities and moderate-to-vigorous activities. The larger differences in performance are experienced with light activities, and in particular for arm shaking activities, ping pong, and a warm up exercise. However, overall, the disclosed method is quite comparable in performance to the ground truth method.

[0120] FIG. 8 illustrates data 8000 that includes various performance metrics with respect to the differences between the classifications of FIG. 5 and FIG. 6. For example, FIG. 8 depicts performance metrics including accuracy, precision, recall, and Fl values for each of the different activity levels. As can be seen from FIG. 8, the disclosed method achieves similar results in classifying most activities, and in particular sedentary activities and moderate-to-vigorous activities. The larger differences in performance are experienced with light activities, similar to as discussed with respect to FIG. 7. However, overall, the disclosed method is quite comparable in performance to the ground truth method.

[0121] FIG. 9 illustrates a confusion matrix 9000 that represents the accuracy of the classification method described herein. The confusion matrix 9000 displays the number of true positives, true negatives, false positives, and false negatives. As can be seen from FIG.9, the disclosed method achieves best results in classify ing sedentary7activities and moderate- to-vigorous activities, while also performing well with respect to classifying light activities.

[0122] Another aspect of the disclosure relates to providing users a scoring summary7with respect to achieving biometric goals based on the user's activity over time (and the corresponding characterization of the user's activity level). Scoring determiner 137 may be configured to determine an activity metric for a user based on a plurality of activity7levels which are determined over a predetermined duration of time (e.g., over one or more hours, over one day, etc.) In some implementations, the scoring determiner 137 may be configured to implement a scoring system that is based on a user’s activity state (or different activitystates) over the predetermined duration of time. For example, each activity7state may be assigned a certain number of points or a multiplier (e.g., a sedentary activity state may be assigned zero points or a multiplier of zero, a light activity7state may be assigned two points or a multiplier of 0.008, a moderate-to-vigorous activity state may be assigned five points ora multiplier of 0.05, etc.). For example, the points assigned to an activity state may be multiplied by a duration of time (e.g., in seconds) in which the user was in the activity state. As another example, the points assigned to the activity state may be multiplied by a multiplier that is associated with the duration of time in which the user was in the activity state (e.g., 2 points for less than one minute, 5 points for between one minute and five minutes, 10 points for more than five minutes etc ). Other scoring systems may be implemented, and the above examples are merely examples.

[0123] User interface generator 138 may be configured to generate various graphical user interfaces to provide guidance, instructions, or other indications to the user regarding biometric measurements including information relating to a heart rate, activity levels of the user, a scoring summary, etc. User interface generator 138 may also be configured to provide graphical user interfaces with respect to other biometric measurements, including biometric measurements obtained via the inertial measurement unit 182, the one or more optical sensors 184, the one or more ECG sensors 186, etc. User interface generator 138 may be configured to generate various graphical user interfaces (e.g., as shown in FIGS. 10A-10C) to provide information about the status of a user reaching a biometric goal and a number of points earned based on the user's activity level (e.g., over the course of a day).

[0124] Referring to FIG. 10 A, a first user interface screen 1010 depicts an animation 1014 (e.g.. a dog) and a bar 1016 where placement of the animation along the bar represents a user’s progress toward a biometric goal. Further, the first user interface screen 1010 indicates the number of points 1012 accumulated by the user (e.g., over the course of the day). In FIG.IOA, the user has accumulated 22 points.

[0125] Referring to FIG. 10B, a second user interface screen 1020 depicts a summaryregarding a movement goal for the day. For example, the second user interface screen 1020 indicates the number of points 1022 accumulated by the user (e.g., over the course of the day) out of a total number of points that is associated with a movement goal for the day. In FIG.IOB, the user has accumulated 22 points w ith respect to a movement goal for the day that corresponds to 90 points. The second user interface screen 1020 can also indicate information about an activity state 1026 of the user (e.g., an ‘"Active” activity state which may correspond to the moderate-to-vigorous activity level).

[0126] Referring to FIG. 10C, a third user interface screen 1030 depicts another summary regarding activity levels of the user for the day. For example, the third userinterface screen 1030 indicates the number of points 1032 accumulated by the user (e.g., over the course of the day) out of a total number of points that is associated with a movement goal for the day. In FIG. 10C, the user has accumulated 22 points with respect to a movement goal for the day that corresponds to 90 points. The third user interface screen 1030 can also indicate information about a first activity' state 1034 of the user (e.g., an “Active” activity' state which may correspond to the moderate-to-vigorous activity level) and a second activity state 1036 of the user (e.g., a “Light” activity state which may correspond to the light activity level). Further, the third user interface screen 1030 also indicates a duration of time spent in the first activity state (e.g., 2 minutes) and a duration of time spent in the second activity' state (e.g., 33 minutes). For example, the scoring determiner 137 may be configured to determine the activity metric for the user based on the plurality of activity levels which are determined over the predetermined duration of time and with respect to a duration of time in which activity' level is engaged by the user. As an example, the scoring determiner 137 may be configured to determine the activity metric of 22 points by summing a product of a first multiplier for the first activity level and the duration of time spent engaging in the first activity level (e.g., (e.g., a multiplier of 0.05 multiplied by 120 seconds) with a product of a second multiplier for the second activity' level and the duration of time spent engaging in the second activity level (e.g., (e.g., a multiplier of 0.008 multiplied by 1980 seconds). However, this is merely an example, and other multipliers and other scoring methods may be implemented by the scoring determiner 137.

[0127] For example, in some implementations of the disclosure the output device 160 may be configured to generate or provide an output (e.g., via alerts, haptic feedback, notifications, etc.), in response to the user achieving certain biometric goals as determined by the scoring determiner 137, or in response to the user meeting certain intermediate biometric goals such as making it halfway to a daily goal, etc., as determined by the scoring determiner 137. For example, in some implementations of the disclosure the output device 160 may be configured to generate or provide an output (e.g., via alerts, haptic feedback, notifications, etc.), in response to the scoring determiner 137 determining the user has not met certain biometric goals, in response to the activity level determiner 136 determining the user has been sedentary for a predetermined duration of time, etc. (e.g., so that the user can be encouraged or reminded to maintain a health or wellness plan). For example, in some implementations of the disclosure the output device 160 may be configured to generate or provide an output (e.g., via alerts, haptic feedback, notifications, etc.), in response to theactivity level determiner 136 determining the user has been engaging in a certain level of activity for more than a predetermined duration of time. For example, the user can be provided with a notification to rest if the user’s level of activity has been maintained at a high level for a certain duration of time, so that the user does not overexert themself, etc.

[0128] As mentioned above, aspects of the disclosure have been described in view of the biometric measurement application 130 provided in the user computing device 100 with respect to FIGS. 3 through 11. However, each of those aspects can also be applied to the biometric measurement application 330 provided in the server computing system 300, and thus some or all of the functions and operations of the biometric measurement application 130 may also be applied and carried out by the biometric measurement application 330 in a similar fashion, but will not be described again for the sake of brevity .

[0129] Aspects of the above-described example embodiments may be recorded in non- transitory computer-readable media including program instructions to implement various operations embodied by a computer. The media may also include, alone or in combination with the program instructions, data files, data structures, and the like. Examples of non- transitory computer-readable media include magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD ROM disks, Blue-Ray disks, and DVDs; magneto-optical media such as optical discs; and other hardware devices that are specially configured to store and perform program instructions, such as semiconductor memory, readonly memon (ROM), random access memory (RAM), flash memory. USB memory, and the like. Examples of program instructions include both machine code, such as produced by a compiler, and files containing higher level code that may be executed by the computer using an interpreter. The program instructions may be executed by one or more processors. The described hardware devices may be configured to act as one or more software modules in order to perform the operations of the above-described embodiments, or vice versa. In addition, a non- transitory computer-readable storage medium may be distributed among computer systems connected through a network and computer-readable codes or program instructions may be stored and executed in a decentralized manner. In addition, the non- transitory computer-readable storage media may also be embodied in at least one application specific integrated circuit (ASIC) or Field Programmable Gate Array (FPGA).

[0130] Each block of the flow chart illustrations may represent a unit, module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternativeimplementations, the functions noted in the blocks may occur out of order. For example, two blocks shown in succession may in fact be executed substantially concurrently (simultaneously) or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.

[0131] While the disclosure has been described with respect to various example embodiments, 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 disclosure does not preclude inclusion of such modifications, variations and / or additions to the disclosed subject matter as would be readily apparent to one of ordinary7skill in the art. For example, 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 disclosure covers such alterations, variations, and equivalents.

Claims

WHAT IS CLAIMED IS:

1. A computing device, comprising: one or more memories configured to store one or more instructions; and one or more processors configured to execute the one or more instructions stored in the one or more memories to perform operations, the operations including: obtaining, via one or more heart rate sensors, heart rate information associated with a user, obtaining, via one or more motion sensors, motion information associated with the user, and determining an activity level among a plurality of activity levels which is associated with the user based on whether the heart rate information indicates a first threshold heart rate value is exceeded and based on whether the motion information indicates a first threshold motion value is exceeded.

2. The computing device of claim 1, wherein the plurality of activity7levels include at least a sedentary activity level and a light activity level, the activity level is determined to be the light activity level when the heart rate information indicates the first threshold heart rate value is not exceeded and the motion information indicates the first threshold motion value is exceeded.

3. The computing device of claim 2, wherein the activity level is determined to be the sedentary activity7level when the heart rate information indicates the first threshold heart rate value is exceeded and the motion information indicates the first threshold motion value is not exceeded.

4. The computing device of claim 1, wherein the plurality of activity levels include at least a sedentary activity level, a light activity level, and a moderate-to-vigorous activity level, the activity7level is determined to be the light activity7level when the heart rate information indicates the first threshold heart rate value is not exceeded and the motion information indicates a second threshold motion value is exceeded.the activity level is determined to be the moderate-to-vigorous activity' level when the heart rate information indicates the first threshold heart rate value is exceeded and is less than a second threshold heart rate value and the motion information indicates the second threshold motion value is exceeded, the activity level is determined to be the moderate-to-vigorous activity7level when the heart rate information indicates the second threshold heart rate value is exceeded, the second threshold heart rate value is greater than the first threshold heart rate value, and the second threshold motion value is greater than the first threshold motion value.

5. The computing device of claim 1, wherein the operations further include determining the activity level at predetermined time intervals to obtain a plurality' of activity level determinations.

6. The computing device of claim 5, wherein the operations further include determining a representative activity level based on the plurality of activity level determinations according to an activity level that is determined more frequently than other activity7levels among the plurality' of activity' levels.

7. The computing device of claim 1. wherein the operations further include: determining the heart rate information at predetermined time intervals to obtain a plurality' of heart rate values, determining whether each of the plurality' of heart rate values exceeds the first threshold heart rate value to determine a respective heart rate associated activity level among a plurality of heart rate associated activity levels for each of the heart rate values, and determining a representative heart rate associated activity level according to a heart rate associated activity level that is determined more frequently than other heart rate associated activity levels among the plurality of heart rate associated activity levels.

8. The computing device of claim 7, wherein the operations further include: when a heart rate value is determined to be less than the first threshold heart rate value, determining a first heart rate associated activity level among the plurality' of heart rate associated activity levels, the first heart rate associated activity level corresponding to a sedentary activity state.

9. The computing device of claim 8, wherein the operations further include: when the heart rate value is determined to be more than the first threshold heart rate value and less than a second threshold heart rate value, determining a second heart rate associated activity level among the plurality of heart rate associated activity' levels, the second heart rate associated activity level corresponding to a light activity state, and when the heart rate value is determined to be more than the first threshold heart rate value and more than the second threshold heart rate value, determining a third heart rate associated activity level among the plurality of heart rate associated activity levels, the third heart rate associated activity level corresponding to a moderate-to-vigorous activity state.

10. The computing device of claim 1, wherein the operations further include: determining the motion information at predetermined time intervals to obtain a plurality' of motion values, determining whether each of the plurality of motion values exceeds the first threshold motion value to determine a respective motion associated activity level among a plurality of motion associated activity levels for each of the motion values, and determining a representative motion associated activity' level according to a motion associated activity level that is determined more frequently than other motion associated activity levels among the plurality of motion associated activity levels.

11. The computing device of claim 10, wherein the operations further include: when a motion value is determined to be less than the first threshold motion value, determining a first motion associated activity level among the plurality of motion associated activity levels, the first motion associated activity level corresponding to a sedentary activity state.

12. The computing device of claim 11, wherein the operations further include: when the motion value is determined to be more than the first threshold motion value and less than a second threshold motion value, determining a second motion associated activity' level among the plurality of motion associated activity levels, the second motion associated activity level corresponding to a light activity state, and when the motion value is determined to be more than the first threshold motion value and more than the second threshold motion value, determining a third motion associatedactivity level among the plurality' of motion associated activity levels, the third motion associated activity level corresponding to a moderate-to-vigorous activity state.

13. The computing device of claim 1, wherein the heart rate information includes a heart rate value corresponding to a current heart rate of the user or a heart rate metric associated with a heart rate recovery (HRR) value.

14. The computing device of claim 13, wherein when the heart rate information includes the heart rate metric associated with the HRR value, the operations include: applying a first maximum heart rate value to determine the HRR when an age of the user is less than a specified age, applying a second maximum heart rate value to determine the HRR when the age of the user is more than the specified age, and determining the heart rate metric based on a ratio of a difference between a current heart rate of the user and a resting heart rate of the user to the HRR.

15. The computing device of claim 1, wherein when a confidence level associated with the heart rate information is less than a threshold confidence level, the operations include determining the activity level based on the motion information while ignoring the heart rate information.

16. The computing device of claim 1, wherein a dataset from which the first threshold heart rate value and the first threshold motion value are derived is based on biometric measurements obtained from a target population having an age range that corresponds to an age of the user associated with the computing device.

17. The computing device of claim 1, wherein the one or more heart rate sensors include one or more photoplethysmography sensors and / or one or more electrocardiogram sensors, and the one or more motion sensors include one or more accelerometers and / or one or more gyroscopes.

18. The computing device of claim 17. wherein the computing device is a wearable computing device, and the wearable computing device includes the one or more heart rate sensors and the one or more motion sensors.

19. A computing device, comprising: one or more memories configured to store one or more instructions; and one or more processors configured to execute the one or more instructions stored in the one or more memories to perform operations, the operations including: obtaining, via one or more heart rate sensors, heart rate information associated with a user, classifying the heart rate information according to a heart rate associated intensity classification among a plurality of heart rate associated intensify classifications, obtaining, via one or more motion sensors, motion information associated with the user, classifying the motion information according to a motion associated intensify classification among a plurality of motion associated intensify' classifications, when the heart rate associated intensify classification and the motion associated intensity classification correspond with one another, determining an activity level from among a plurality of activity levels corresponding to the heart rate associated intensify classification and the motion associated intensify classification, and when the heart rate associated intensify’ classification and the motion associated intensify classification do not correspond with one another, determining the activity level from among the plurality of activity levels based on whether the heart rate information indicates a first threshold heart rate value is exceeded and based on whether the motion information indicates a first threshold motion value is exceeded.

20. A non-transitory computer-readable medium which stores instructions that are executable by one or more processors of a computing device, the instructions comprising instructions to cause the one or more processors to perform operations, the operations comprising: obtaining, via one or more heart rate sensors, heart rate information associated with a user,obtaining, via one or more motion sensors, motion information associated with the user, and determining an activity level among a plurality of activity levels which is associated with the user based on whether the heart rate information indicates a first threshold heart rate value is exceeded and based on whether the motion information indicates a first threshold motion value is exceeded.

Images