EP4731067A1

EP4731067A1 - Biometric measurement application for measuring heart rate recovery metric value

Info

Publication number: EP4731067A1
Application number: EP23741535.1A
Authority: EP
Inventors: Qian He; Conor Joseph HENEGHAN
Original assignee: Google LLC
Current assignee: Google LLC
Priority date: 2023-06-21
Filing date: 2023-06-21
Publication date: 2026-04-29
Also published as: WO2024263161A1

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 obtain, via one or more optical sensors, first heart rate information associated with a user. When the first heart rate information exceeds the threshold heart rate value, a biometric measurement application associated with measuring a heart rate recovery (HRR) metric is executed. In response to executing the biometric measurement application, second heart rate information associated with the user can be obtained via one or more electrocardiogram (ECG) sensors, for a predetermined duration of time, and a HRR metric value can be determined based on the threshold heart rate value and the second heart rate information.

Description

BIOMETRIC MEASUREMENT APPLICATION FOR MEASURING HEART RATE

RECOVERY METRIC VALUE

FIELD

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

BACKGROUND

[0002] Some methods for determining a heart rate recovery (HRR) metric can require specialized equipment such as treadmills and stationary bikes and may need to be performed in special facilities such as medical settings. The protocols for conducting a HRR assessment can also be quite challenging as they can require a person to reach near their maximum heart rate (which may not be advisable for ill people).

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: obtain, via one or more optical sensors, first heart rate information associated with a user, and in response to detecting the first heart rate information exceeding a threshold heart rate value based on the first heart rate information obtained via the one or more optical sensors: execute a biometric measurement application associated with measuring a heart rate recovery (HRR) metric, in response to executing the biometric measurement application, obtain, via one or more electrocardiogram (ECG) sensors, second heart rate information associated with the user, for a predetermined duration of time, and determine a HRR metric value based on the threshold heart rate value and the second heart rate information associated with the user obtained during the predetermined duration of time.

[0006] In some implementations, the one or more optical sensors include one or more photoplethysmography (PPG) sensors, and the one or more PPG sensors and the one or more ECG sensors are disposed on the computing device.

[0007] In some implementations, the one or more processors are configured to operate with the threshold heart rate value at a predicted maximum heart rate associated with the user. [0008] In some implementations, the one or more processors are configured to operate with the threshold heart rate value at a predetermined percentage of a predicted maximum heart rate associated with the user.

[0009] In some implementations, the computing device further includes a user interface, and the one or more processors are configured to provide via the user interface an indication to the user to measure the HRR metric value.

[0010] In some implementations, the indication to the user to measure the HRR metric value comprises at least one of an audible output indicating to the user to measure the HRR metric value, a haptic output indicating to the user to measure the HRR metric value, or a visual output indicating to the user to measure the HRR metric value.

[0011] In some implementations, the indication to the user to measure the HRR metric value comprises instructions informing the user to remain stationary and standing for obtaining, via the one or more ECG sensors, the second heart rate information associated with the user, for the predetermined duration of time.

[0012] In some implementations, the one or more processors are configured to execute the one or more instructions stored in the one or more memories to, in response to detecting the first heart rate information exceeding the threshold heart rate value based on the first heart rate information obtained via the one or more optical sensors, provide a prompt querying the user whether to execute the biometric measurement application.

[0013] In some implementations, the one or more processors are configured to determine, via one or more motion sensors, motion information associated with the computing device while the second heart rate information associated with the user is obtained during the predetermined duration of time.

[0014] In some implementations, the one or more processors are configured to: for a first duration of time in which the motion information associated with the computing device exceeds a threshold motion value while the second heart rate information associated with the user is obtained during the predetermined duration of time, exclude the second heart rate information corresponding to the first duration of time as second heart rate information for determining the HRR metric value, and for a second duration of time in which the motion information associated with the computing device does not exceed the threshold motion value while the second heart rate information associated with the user is obtained during the predetermined duration of time, include the second heart rate information corresponding to the second duration of time as second heart rate information for determining the HRR metric value.

[0015] In some implementations, the one or more processors are configured to determine a pulse transit time metric value based on first heart rate information and second heart rate information obtained during the predetermined duration of time.

[0016] In some implementations, the second heart rate information include data from an ECG signal output by the one or more ECG sensors, and the one or more processors are configured to determine the HRR metric value based on a time interval between a first peak from the ECG signal and a second peak from the ECG signal measured via the one or more ECG sensors during the predetermined duration of time.

[0017] In some implementations, the first peak from the ECG signal and the second peak from the ECG signal correspond to a R wave component of the ECG signal. In some implementations, the first peak from the ECG signal and the second peak from the ECG signal correspond to a T wave component of the ECG signal.

[0018] In some implementations, the one or more processors are configured to determine the HRR metric value based on a difference between the threshold heart rate value and a heart rate value determined based on the time interval between the first peak and the second peak.

[0019] In some implementations, the predetermined duration of time is at least thirty seconds and no more than five minutes.

[0020] In some implementations, the one or more processors are configured to determine a plurality of HRR metric values based on the threshold heart rate value and the second heart rate information associated with the user obtained during the predetermined duration of time, each of the HRR metric values from the plurality of HRR metric values being determined at specified time points within the predetermined duration of time, and the one or more processors are configured to store the plurality of HRR metric values.

[0021] In an example embodiment, a computer-implemented method is provided. The computer-implemented method includes obtaining, via one or more optical sensors of a computing device, first heart rate information associated with a user; and in response to detecting the first heart rate information exceeding a threshold heart rate value based on the first heart rate information obtained via the one or more optical sensors: executing a biometric measurement application associated with measuring a heart rate recovery (HRR) metric, in response to executing the biometric measurement application, obtaining, via one or more electrocardiogram (ECG) sensors of the computing device, second heart rate information associated with the user, for a predetermined duration of time, and determining a HRR metric value based on the threshold heart rate value and the second heart rate information associated w ith the user obtained during the predetermined duration of time.

[0022] In some implementations, the second heart rate information includes data from an ECG signal output by the one or more ECG sensors, and determining the HRR metric value based on the threshold heart rate value and the second heart rate information associated with the user obtained during the predetermined duration of time comprises: determining a time interval between a first peak from the ECG signal and a second peak from the ECG signal measured via the one or more ECG sensors during the predetermined duration of time, and determining a heart rate value based on the time interval between the first peak and the second peak, and determining the HRR metric value based on a difference between the threshold heart rate value and the heart rate value.

[0023] In some implementations, the computer-implemented method includes determining, via one or more motion sensors of the computing device, motion information associated with the computing device while the second heart rate information associated with the user is obtained during the predetermined duration of time; excluding, as second heart rate information for determining the HRR metric value, second heart rate information obtained during periods of time during the predetermined duration of time in which the motion information associated with the computing device exceeds a threshold motion value; and including, as second heart rate information for determining the HRR metric value, second heart rate information obtained during periods of time during the predetermined duration of time in which the motion information associated with the computing device does not exceed the threshold motion value.

[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: obtain, via one or more optical sensors, first heart rate information associated with a user, and in response to detecting the first heart rate information exceeding a threshold heart rate value based on the first heart rate information obtained via the one or more optical sensors: execute a biometric measurement application associated with measuring a heart rate recovery (HRR) metric, in response to executing the biometric measurement application, obtain, via one or more electrocardiogram (ECG) sensors, second heart rate information associated with the user, for a predetermined duration of time, and determine a HRR metric value based on the threshold heart rate value and the second heart rate information associated with the user obtained during the predetermined duration of time.

[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] 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

[0028] 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:

[0029] FIG. 1 is 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;

[0030] 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;

[0031] FIG. 3 is an example block diagram of a biometric measurement application, according to one or more examples of the disclosure;

[0032] FIG. 4 is an example graph illustrating data collected from an electrocardiogram measurement and a photoplethysmogram measurement, according to one or more examples of the disclosure;

[0033] FIG. 5 is an example graph illustrating data collected from an electrocardiogram measurement, according to one or more examples of the disclosure;

[0034] FIG. 6 is another example graph illustrating data collected from an electrocardiogram measurement, according to one or more examples of the disclosure;

[0035] FIG. 7 is an example graph illustrating heart rate data, according to one or more examples of the disclosure; [0036] FIG. 8 is an example graph illustrating a distribution of heart rate recovery metric values, according to one or more examples of the disclosure;

[0037] FIGS. 9A-9F are example user interface screens for guiding a user with respect to measuring a heart rate recovery metric, according to one or more examples of the disclosure; and

[0038] FIG. 10 is a flow diagram of an example, non-limiting computer-implemented method according to one or more examples of the disclosure.

DETAILED DESCRIPTION

[0039] 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 be apparent 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.

[0040] 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.

[0041] 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.

[0042] 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”.

[0043] 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.

[0044] People are interested in understanding more about the health and efficacy of their cardiovascular system. For example, an athlete may wish to understand their overall cardiovascular performance limit over a period of time (e.g., over a month of intense training), or may wish to get a day-to-day assessment of changes in their cardiovascular system performance to avoid over-training, or indeed to push themselves harder. Nonathletes may be interested in using a metric of cardiovascular performance to judge their overall health, or to risk stratify. Doctors or other medical professionals may be interested in using heart rate recovery as a metric for assessing the health of their patients.

[0045] There are several techniques for assessing cardiovascular fitness and performance. For example, VChmax represents the maximum amount of oxygen that a person’s body can consume at peak exercise, and this is measured in mL of O2 consumed per min per kg of body weight. It is a function of cardiac function, pulmonary function, and muscle fitness and may be used to both assess athletic fitness (e.g., values of >50 mL/min/kg would be good markers of high fitness) and in medical settings (e.g., a VChmax of <14 mL/min/kg might be used to stratify heart transplant patients). However, an accurate measurement of VChmax generally requires a treadmill or exercise bike with variable load, a mask and exhaled gas analysis (referred to as a metabolic cart). It is also dependent on the psychological readiness of a person to push to their limit, and subject to risk assessment on whether the person can comfortably reach their maximum heart rate. Finally, VChmax tends to be a slowly moving variable, in which small changes over days or even weeks may not be observed.

[0046] Another method for assessing cardiovascular function is to measure the recovery of the heart rate from a maximum value. For example, if a person reaches a maximum heart rate of 180 beats per minute after vigorous running, and then the person’s heart rate is measured over the following 5 minutes, the rate at which the person’s heart returns to their resting heart rate can be quantified. For example, if the heart rate is at 155 bpm after 1 minute, then a heart rate recovery estimate would be 25 bpm/minute (=180 - 155). This metric is sometimes referred to as the 1 -minute heart rate recovery (HRR-lmin). The total drop in heart rate can also be measured over 2 minutes (HRR-2min), or over 30 seconds. The heart rate recovery value can also be a useful indicator for assessing the impact of an exercise improvement program (e.g., observing an increase of approximately 3 bpm in the HRR lmin metric after a 12-week exercise program), in addition to serving as a useful prognostic indicator for overall cardiovascular risk.

[0047] As another example, a subject may have a maximum heart rate of about 131 bpm during activity, and the decay in heart rate starts after approximately 10 seconds of standing. One minute after the peak heart rate, the subject’s heart rate may be about 105 bpm. Therefore, the estimated HRR-lmin is 26 bpm/min (=131-105).Example aspects of the disclosure are directed to a computing device, for example, a biometric computing device such as a wearable computing device (e.g., a smartwatch), that can be used to capture biometric information associated with a user. The biometric information can include, for example, a heart rate recovery metric that may also be a useful prognostic indicator for overall cardiovascular risk and/or health.

[0048] Previous methods for determining a heart rate recovery (HRR) metric can require specialized equipment such as treadmills and stationary bikes and may be performed in special facilities such as medical settings. The protocols for conducting a HRR assessment can also be quite challenging as they require a person to reach near their maximum heart rate (which may not be advisable for ill people).

[0049] According to example embodiments of the disclosure, a biometric computing device includes a biometric (e.g., heart rate recovery) measurement application which can be activated or executed in response to detecting that a user’s heart rate has exceeded a threshold heart rate level or heart rate value (e.g., 90% of the user’s predicted maximum heart rate, 95% of the user’s predicted maximum heart rate, 100% of the user’s predicted maximum heart rate, etc.). The biometric measurement application can be configured to record biometric information (e.g., in the form of raw data) for a predetermined duration of time (e.g., between 30 seconds to 5 minutes). The biometric information may include an electrocardiogram signal, an optical PPG signal, a raw accelerometer signal, and the like. In some implementations, the electrocardiogram signal may be obtained by the user contacting electrodes which are disposed along sides (e.g., edges) of the biometric computing device. The accelerometer signal may be used by the biometric measurement application to detect excess motion and provide feedback to the user to remain stationary. In some implementations, the biometric measurement application may provide an indication to the user to stand (while remaining stationary) during the recording of the biometric information. [0050] For example, the biometric computing device may be configured to measure biometric information (e.g., a heart rate metric) while the user engages in an activity. In some implementations, the biometric computing device may implement a continuous optical PPG heart rate algorithm to measure the heart rate of the user based on the optical PPG signal. The measured heart rate can be compared to a predicted maximum heart rate value to determine whether to activate or execute the biometric (e.g., heart rate recovery) measurement application to alert the user that the HRR can be measured (e.g., using the electrocardiogram signal).

[0051] The user’s predicted maximum heart rate may be determined according to various known methods. For example, the predicted maximum heart rate may correspond to a value of 220 minus the user’s age. For example, the predicted maximum heart rate may correspond to a value of 208 minus (0.7 x the user’s age). As yet another example, the predicted 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.

[0052] In some implementations, the biometric (e.g., HRR) measurement application can provide a user interface to provide instructions to the user to ensure an accurate electrocardiogram (ECG) measurement. For example, the instructions can include instructions to remain stationary, instructions to maintain contact with the electrodes while the ECG measurement is being recorded, instructions regarding a duration of time pertaining to the ECG measurement, instructions informing the user that the ECG measurement is being suspended due to movement, instructions informing the user that the ECG measurement is being restarted once the user is stationary after the ECG measurement is suspended due to movement, and the like.

[0053] In some embodiments, the biometric (e.g., HRR) measurement application may be configured to apply a filter to a raw ECG signal to remove noise from the raw ECG signal. [0054] In some embodiments, the biometric (e.g., HRR) measurement application may be configured to record both the ECG signal and the PPG signal simultaneously. The biometric measurement application may further be configured to determine a pulse transit time (PTT), for example, based on a difference between a peak of the PPG signal and a peak of the ECG signal (e.g., the peak associated with the QRS complex). The PTT may be a useful prognostic indicator for overall cardiovascular risk and/or health independently of the HRR, or in combination with the HRR.

[0055] In some embodiments, the biometric (e.g., HRR) measurement application may be configured to analyze the ECG signal (e.g., the filtered ECG signal) to extract ). For example, the biometric (e.g., HRR) measurement application may be configured to extract the interbeat interval RR, which corresponds to the time between contiguous peaks of the ECG signal. For example, the contiguous peaks of the ECG signal may correspond to contiguous QRS complex peaks, contiguous T-wave peaks, and the like. For example, a RR interval of 550 ms may correspond to a heart rate of about 109 bpm (e.g., 60 / RR interval in seconds). [0056] The ECG heart rate can be extracted from the sequence of RR intervals, by using 60 1 RR interval in seconds as an instantaneous heart rate estimate, and then averaging a plurality of extracted values over a first predetermined duration of time (e.g., 5 seconds) to produce a smoother average heart rate over the first predetermined duration of time.

[0057] According to example embodiments of the disclosure, the biometric (e.g., HRR) measurement application can be configured to record the ECG signal and determine the heart rate of the user for a second predetermined duration of time (e.g., between 30 seconds to 5 minutes). The biometric (e.g., HRR) measurement application can determine a decay in the heart rate over the second predetermined duration of time to determine the HRR. For example, the HRR metric value may be determined or categorized based on a selected or default duration of time after the maximum (or threshold) heart rate value is recorded. Thus, a 1 -minute heart rate recovery value may be determined as the maximum (or threshold) heart rate value minus the heart rate value after one minute has elapsed from the time the maximum (or threshold) heart rate value was recorded. Likewise, a 2-minute heart rate recovery value may be determined as the maximum (or threshold) heart rate value minus the heart rate value after two minutes has elapsed from the time the maximum (or threshold) heart rate value was recorded. And a 30-second heart rate recovery value may be determined as the maximum (or threshold) heart rate value minus the heart rate value after thirty seconds has elapsed from the time the maximum (or threshold) heart rate value was recorded.

[0058] As an example, if a predicted maximum heart rate value is 167 for a 53-year-old person (220-167), then a threshold value or level for executing the biometric (e.g., HRR) measurement application may be a heart rate value of 159 (e g., 95% of the user’s predicted maximum heart rate). If the heart rate value at 1 minute is 137.5, the HRR lmin may be equal to 159-137.5 = 21.5 bpm. Other parameters including HRR_30s and HRR_2min may be determined (e.g., based on a graph of the heart rate values recorded over the second predetermined duration of time, based on interpolation of heart rate values, etc.). [0059] In some implementations, the biometric computing device may be configured to store the HRR parameters (e.g., in a database) which can be accessed by the user or other authorized users (e.g., medical professionals). The recorded data can provide a history of heart rate recovery parameters for the user over time. The biometric computing device (biometric measurement application) may be configured to provide a comparison of the user’s HRR measurements relative to an overall population, relative to an age and gender matched group, etc.

[0060] 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., a HRR metric) can be collected and recorded in an accurate and efficient manner by use of a biometric measurement application which is executed in response to a biometric computing device detecting that a user’s heart rate exceeds a threshold value or level. Therefore, specialized equipment and special facilities are not needed to determine a HRR metric value.

[0061] Furthermore, according to one or more examples of the disclosure, a biometric computing device can obtain accurate HRR metric 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.

[0062] Furthermore, according to one or more examples of the disclosure, a biometric computing device can obtain accurate HRR metric values by implementing threshold motion values which are associated with movement of the user. For example, if the biometric computing device determines (e.g., via data measured by the accelerometer) that motion of the user exceeds a threshold motion value, recording of the ECG signal can be stopped or values (data) obtained during such movement can be marked and subsequently filtered or ignored when determining HRR parameter values. Therefore, the data from the ECG signal that is utilized for determining the HRR metric value may be more reliable by omitting data which is recorded when the user’s motion exceeds the threshold motion value.

[0063] For example, according to one or more examples of the disclosure, a biometric computing device can opportunistically obtain both PPG signal values and ECG signal values at the same time when taking measurements for obtaining HRR metric values. Therefore, beter and more accurate measurements of other biometric information (e.g., PTT values) can be obtained while also measuring the HRR values.

[0064] 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., a heart rate, a heart rate recovery metric, 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.

[0065] 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 100, 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.

[0066] 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. The user 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, physical activities (e.g., number of steps walked), 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, phy sical activity, a sleep metric, electrical conductance, and the like.

[0067] 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 an ECG by a user contacting a plurality of electrodes disposed on the user computing device 2100. For example, electrodes may be disposed at locations 2156, 2158 corresponding to sides or edges of the housing or body 2152 of the user computing device 2100. 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.

[0068] The one or more ECG electrodes may be configured to obtain second 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 the user and can include a duration of time of 30 seconds, 1 minute, 2 minutes, 5 minutes, etc. As explained in more detail herein, in response to first heart rate information (e.g., a heart rate of the user) exceeding a threshold heart rate value, the user computing device 100 may be configured to prompt the user to execute biometric measurement application 130 to measure a heart rate recovery (HRR) metric via the one or more ECG electrodes. 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.). The HRR metric value may be determined based on the threshold heart rate value and the second heart rate information (e.g., heart rate of the user) associated with the user obtained during the predetermined duration of time. Further, the HRR metric value may be stored (e.g., at one or more of the user computing device 100, server computing system 300, external computing device 400, or user biometric information data store 350) [0069] 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 field communication (NFC), a radio frequency (RF) signal, and the like. For example, wired 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 network 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).

[0070] 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.

[0071] 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 sy stem 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.

[0072] 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/or a local bus using any of a variety of commercially available bus architectures.

[0073] 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.

[0074] 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.

[0075] 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 optical sensors, first heart rate information associated with a user. When the first heart rate information does not exceed a threshold heart rate value, the instmctions, when executed, may cause the one or more processors 110 to continue to obtain the first heart rate information. When the first heart rate information exceeds the threshold heart rate value, the instructions, when executed, may cause the one or more processors 110 to execute a biometric measurement application associated with measuring a heart rate recovery (HRR) metric. In response to executing the biometric measurement application, the instructions, when executed, may cause the one or more processors 110 to obtain second heart rate information associated w ith the user via one or more electrocardiogram (ECG) sensors, for a predetermined duration of time, and a HRR metric value can be determined based on the threshold heart rate value and the second heart rate information, as described according to examples of the disclosure.

[0076] 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 optical sensors of the user computing device 100, first heart rate information associated with a user. When the first heart rate information does not exceed a threshold heart rate value, the instructions, when executed, may cause the one or more processors 310 to continue to obtain the first heart rate information via the one or more optical sensors of the user computing device 100. When the first heart rate information exceeds the threshold heart rate value, the instructions, when executed, may cause the one or more processors 310 to execute a biometric measurement application associated with measuring a heart rate recovery (HRR) metric. In response to executing the biometric measurement application, the instructions, when executed, may cause the one or more processors 310 to obtain second heart rate information associated w ith the user via one or more electrocardiogram (ECG) sensors of the user computing device 100, for a predetermined duration of time (e.g., by instructing the user computing device 100 to record an ECG measurement and receiving the ECG data from the user computing device 100 via network 200), and a HRR metric value can be determined based on the threshold heart rate value and the second heart rate information, as described according to examples of the disclosure.

[0077] 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 the HRR metric value 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 the HRR metric value associated with the user, as described according to examples of the disclosure.

[0078] 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 a first heart rate information determination application 132, a threshold heart rate value comparator 134, a second heart rate information determination application 136, a HRR metric value determiner 137, a motion determiner 138, and a user interface generator 139.

[0079] 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 recovery metric that is obtained based on data associated with an ECG signal output via the one or more ECG sensors 186). For example, the user may be prompted to execute the biometric measurement application 130 in response to the threshold heart rate value comparator 134 determining that a heart rate measured via the first heart rate information determination application 132 exceeds a threshold heart rate value.

[0080] 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 recovery metric that is obtained based on data associated with an ECG signal output via the one or more ECG sensors 186). For example, the biometric measurement application 130 may automatically be executed in response to the threshold heart rate value comparator 134 determining that the heart rate measured via the first heart rate information determination application 132 exceeds the threshold heart rate value.

[0081] 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 description of those features will not be repeated for the sake of brevity.

[0082] 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, user biometric information data store 350, 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.

[0083] 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.

[0084] 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 (e.g., see FIGS. 9A-9F).

[0085] 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-semiconductor (CMOS) or charge-coupled device (CCD)) to capture, detect, or recognize a user's behavior, figure, expression, etc.

[0086] 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 atri-axial gyroscope).

[0087] 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 also be used to monitor 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 are in contact with 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.

[0088] 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. [0089] 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 contracts 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 wave, 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.

[0090] The one or more sensors 180 may also include other sensors such as a magnetometer, GPS sensor, proximity sensor, and the like.

[0091] Example system 1000 may include a user biometric information data store 350. In some implementations, user biometric information data store 350 can represent a single database. In some implementations, the user biometric information data store 350 represents a plurality of different databases accessible to the user computing device 100, server computing system 300, and external computing device 400. In some examples, the user biometric information data store 350 can include biometric information of a user or a plurality of users (e.g., a hospital or medical facility database). In some examples, the user biometric information data store 350 can include information regarding one or more user profiles, including a variety of user data such as user preference data, user demographic data, user calendar data, user social network data, user historical health data, and the like. For example, the user biometric information data store 350 can include any biometric information or information associated with the biometric information (e.g., time information associated with the collection of the biometric information, location information associated with the collection of the biometric information, etc.). The user biometric information and associated information may be associated with a user account. In some implementations described herein, heart rate information of a user may be stored in user biometric information data store 350, including a heart rate recovery metric value.

[0092] The user biometric information data store 350 is provided to illustrate potential data that could be analyzed or stored, in some embodiments, by the user computing device 100 and/or server computing system 300 to maintain a record of biometric information associated with a user, for example. However, such user data may not be collected, used, or analyzed unless the user has consented after being informed of what data is collected and how such data is used. Further, in some embodiments, the user can be provided with a tool (e.g., in a biometric measurement application or via a user account) to revoke or modify the scope of permissions. In addition, certain information or data can be treated in one or more ways before it is stored or used, so that personally identifiable information is removed or stored in an encrypted fashion. Thus, particular user information stored in the user biometric information data store 350 may or may not be accessible to the user computing device 100 and/or server computing system 300 based on permissions given by the user, or such data may not be stored in the user biometric information data store 350 at all.

[0093] 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 first heart rate information determination application 132, a threshold heart rate value comparator 134, a second heart rate information determination application 136, a HRR metric value determiner 137, a motion determiner 138, and a user interface generator 139. 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. For example, some operations (such as the determination of the heart rate recovery metric value by the HRR metric value determiner 137) may instead be performed by the server computing system 300 (e.g., via biometric measurement application 330).

[0094] Operations of the biometric measurement application 130 will now be described in more detail with reference to FIGS. 3 through 10.

[0095] FIG. 4 is an example graph illustrating data collected from an electrocardiogram measurement and a photoplethysmogram measurement, according to one or more examples of the disclosure. FIG. 5 is an example graph illustrating data collected from an electrocardiogram measurement, according to one or more examples of the disclosure. FIG. 6 is another example graph illustrating data collected from an electrocardiogram measurement, according to one or more examples of the disclosure. FIG. 7 is an example graph illustrating heart rate data, according to one or more examples of the disclosure. FIG. 8 is an example graph illustrating a distribution of heart rate recovery metric values, according to one or more examples of the disclosure. FIGS. 9A-9F are example user interface screens for guiding a user with respect to measuring a heart rate recovery metric, according to one or more examples of the disclosure. FIG. 10 illustrates an example flow diagram of a nonlimiting computer-implemented method for determining and storing biometric information of a user, according to one or more examples of the disclosure.

[0096] The flow diagram of FIG. 10 illustrates a method 1100 for determining and storing biometric information of a user. Although show n 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.

[0097] At operation 1110 the method 1100 includes obtaining, via one or more optical sensors of a computing device, first heart rate information associated with a user. For example, the first heart rate information determination application 132 can obtain first heart rate information via the one or more optical sensors 184 (e.g., one or more PPG sensors). For example, the first heart rate information can include a heart rate which is determined by the PPG signal analyzer 132a when the one or more optical sensors 184 include one or more PPG sensors and a PPG signal is obtained. FIG. 4 is an example graph illustrating data collected from an electrocardiogram measurement and a photoplethysmogram measurement, according to one or more examples of the disclosure. As shown in FIG. 4, the graph 4000 includes original or raw data and filtered data from the one or more ECG sensors 186 as shown in the upper part of the graph 4000, and a scaled PPG value obtained from a signal generated by the one or more optical sensors 184 (one or more PPG sensors). For example, various known peak detection methods (e.g., peak amplitude thresholding) may be implemented by the PPG signal analyzer 132a to identify peaks (e.g., peaks 4200, 4300) in the PPG signal 4100. The PPG signal analyzer 132a may be configured to determine a time interval (e.g., interbeat interval - IBI int) between consecutive peaks which is inversely related to the heart rate. For example, the PPG signal analyzer 132a may be configured to determine the heart rate by taking the reciprocal of the time interval and multiplying it by 60.

[0098] At operation 1120 the method 1100 includes determining whether the first heart rate information is greater than a threshold heart rate value. For example, the threshold heart rate value comparator 134 may receive the first heart rate information (e.g., heart rate of the user) from the first heart rate information determination application 132 and compare the first heart rate information with a threshold heart rate value.

[0099] In some implementations, the threshold heart rate value is associated with a predicted maximum heart rate associated with the user. In some implementations, the threshold heart rate value corresponds to a predetermined percentage value of the predicted maximum heart rate associated with the user. For example, the threshold heart rate value may correspond to 90% of the user’s predicted maximum heart rate, 95% of the user’s predicted maximum heart rate, 100% of the user’s predicted maximum heart rate, etc. The user’s predicted maximum heart rate may be detennined according to various known methods. For example, the predicted maximum heart rate may correspond to a value of 220 minus the user’s age. For example, the predicted maximum heart rate may correspond to a value of 208 minus (0.7 x the user’s age). As yet another example, the predicted 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. For example, a predicted maximum heart rate associated with a user may be stored at the one or more memory devices 120 of the user computing device 100, user biometric information data store 350, etc. In some implementations, the user may provide an input via the input device 140 to select or identify the threshold heart rate value to be used by the threshold heart rate value comparator 134. [0100] The threshold heart rate value may be set to a value for the purpose of determining a heart rate recovery metric value. As discussed herein, the biometric measurement application 130 may be configured to prompt a user to take a biometric measurement when the threshold heart rate value, or automatically begin a process for taking the biometric measurement. The heart rate recover}' metric value is based on a difference between the threshold heart value and a heart rate value that is obtained via the second heart rate information determination application 136 for a predetermined duration of time (e.g., 30 seconds, 1 minute, 2 minutes, 5 minutes, etc.). A plurality of heart rate recovery metric values can be obtained over the course of the predetermined duration of time.

[0101] For example, in some implementations in response to the threshold heart rate value comparator 134 determining that the first heart rate information (e.g., heart rate of the user) measured via the first heart rate information determination application 132 does not exceed the threshold heart rate value, the method may return to operation 1110 and first heart rate information associated with the user may continue to be obtained.

[01021 For example, in some implementations in response to the threshold heart rate value comparator 134 determining that the first heart rate information (e.g., heart rate of the user) measured via the first heart rate information determination application 132 does exceed the threshold heart rate value, the method may proceed to operation 1130 and a biometric measurement application associated with measuring a heart rate recovery (HRR) metric may be executed.

[0103] For example, in some implementations at operation 1130 the user may be prompted to execute the biometric measurement application 130 (a particular aspect of the biometric measurement application 130 such as second heart rate information determination application 136) in response to the threshold heart rate value comparator 134 detemiining that the first heart rate information (e.g., heart rate of the user) measured via the first heart rate information determination application 132 exceeds the threshold heart rate value. For example, the user may be presented with a query regarding whether the user wants to measure a specified biometric (e.g., a heart rate recovery metric value). The prompt or query may be presented via any method (e.g., via one or more of a user interface, haptic feedback, audio, etc.). For example, FIG. 9A illustrates a first graphical user interface 9100 which may be presented in response to the threshold heart rate value comparator 134 determining that the first heart rate information (e.g., heart rate of the user) measured via the first heart rate information determination application 132 exceeds the threshold heart rate value. In the example of FIG. 9A, the first graphical user interface 9100 indicates an ECG and PPG measurement can be taken and provides first and second user interface elements 9110, 9120 that enable the user to select from between two possible durations of time (30 seconds and 5 minutes) for taking the measurement. However, FIG. 9A is only an example and other graphical user interfaces may be presented. For example, the graphical user interface generated by user interface generator 139 may provide for other durations of time to take a biometric measurement, the PPG measurement need not be taken, a user may specify a particular duration of time, etc. For example, FIG. 9B illustrates a second graphical user interface 9200 which may also be presented in response to the threshold heart rate value comparator 134 determining that the first heart rate information (e.g., heart rate of the user) measured via the first heart rate information determination application 132 exceeds the threshold heart rate value. As illustrated in FIG. 9B, the second graphical user interface 9200 requests the user to confirm which body part is in contact with the user computing device 100, for example by asking which wrist the watch is on in the case where the user computing device 100 is a smartwatch. In the example of FIG. 9B, the second graphical user interface 9200 includes first and second user interface elements 9210, 9220 that enable the user to select from between the left and right wrist. Such information may be used by the biometric measurement application and/or the second heart rate information determination application 136 for providing subsequent instructions to the user, for classification purposes of the data which is obtained, for training machine-learning models, for purposes of analyzing the obtained data, for noise filtering, etc.

[0104] For example, in some implementations at operation 1130 the biometric measurement application 130 (or a particular aspect of the biometric measurement application 130 such as second heart rate information determination application 136) may automatically be executed to measure, determine, and store a biometric measurement (e.g., a heart rate recovery metric that is obtained based on data associated with an ECG signal output via the one or more ECG sensors 186). For example, the biometric measurement application 130 (e.g., second heart rate information determination application 136) may automatically be executed in response to the threshold heart rate value comparator 134 determining that the first heart rate information (e.g., heart rate of the user) measured via the first heart rate information determination application 132 exceeds the threshold heart rate value. For example, the second heart rate information determination application 136 may automatically begin to obtain measurements from the one or more ECG sensors 186 and/or provide a prompt or instructions to the user to contact one or more electrodes for the one or more ECG sensors 186 to obtain the measurements. For example, FIG. 9B may be presented as discussed above to confirm the location of the user computing device 100 with respect to a body part of the user. For example, FIG. 9C illustrates a third graphical user interface 9300 which may be presented in response to the threshold heart rate value comparator 134 determining that the first heart rate information (e.g., heart rate of the user) measured via the first heart rate information determination application 132 exceeds the threshold heart rate value. As illustrated in FIG. 9C, the third graphical user interface 9300 instructs the user to contact one or more electrodes by placing their index finger and thumb on metal comers of a wearable computing device, for example, in the manner as shown in FIG. 2. For example, FIG. 9D illustrates a fourth graphical user interface 9400 which may also be presented in response to the threshold heart rate value comparator 134 determining that the first heart rate information (e.g., heart rate of the user) measured via the first heart rate information determination application 132 exceeds the threshold heart rate value. As illustrated in FIG. 9D, the fourth graphical user interface 9400 instructs the user to remain standing and stationary and to maintain contact with the metal comers. In the example of FIG. 9D, the fourth graphical user interface 9400 provides a user interface element 9410 which enables the user to confirm they are standing such that the biometric measurements can begin to be taken or can be classified as being taken (e g., if already being taken) while standing for purposes of analyzing the obtained data, for noise filtering, for classifying the data for training a machined earned model, etc.

[0105] At operation 1140 the method 1100 includes obtaining, via one or more electrocardiogram (ECG) sensors of the computing device, second heart rate information associated with the user, for a predetermined duration of time. For example, in response to executing the biometric measurement application 130, the one or more electrocardiogram (ECG) sensors 186 of the user computing device 100 may obtain second heart rate information (e.g., a heart rate) associated with the user, for a predetermined duration of time (e.g., 30 seconds, one minute, two minutes, five minutes, etc.).

[0106] For example, the second heart rate information determination application 136 can obtain second heart rate information via the one or more ECG sensors 186. For example, the second heart rate information can include a heart rate which is determined by the ECG signal analyzer 136a. FIG. 5 is an example graph illustrating data collected from an electrocardiogram measurement, according to one or more examples of the disclosure. As shown in FIG. 5, the graph 5000 includes original or raw data from the one or more ECG sensors 186 and filtered data. For example, the ECG signal analyzer 136a may be configured to apply a median filter to the original or raw data to obtain the filtered data, for example, to remove noise from the original or raw data. For example, the duration of the median filter may be about 300 ms.

[0107] In some implementations, the first heart rate information determination application 132 can obtain first heart rate information via the one or more optical sensors 184 simultaneously while the second heart rate information determination application 136 obtains second heart rate information via the one or more ECG sensors 186 for the predetermined duration of time. The biometric measurement application 130 may be configured to determine a pulse transit time (PTT) metric value based on first heart rate information and second heart rate information which are (simultaneously) obtained during the predetermined duration of time. The biometric measurement application 130 may be configured to determine the PTT metric value, for example, based on a difference between a peak of the PPG signal and a peak of the ECG signal (e.g., the peak associated with the QRS complex). The PTT metric value may be a useful prognostic indicator for overall cardiovascular risk and/or health independently of the HRR, or in combination with the HRR.

[0108] FIG. 6 is another example graph illustrating data collected from an electrocardiogram measurement, according to one or more examples of the disclosure. As shown in FIG. 6, the graph 6000 includes filtered ECG data represented by ECG signal 6100. For example, various known peak detection methods (e.g., peak amplitude thresholding, slope-based detection, wavelet-based detection, etc.) may be implemented by the ECG signal analyzer 136a to identify peaks (e.g., peaks 6200, 6300, 6400, 6500) in the ECG signal 6100. For example, the ECG signal analyzer 136a may be configured to determine a time interval (e.g., a RR-interval - RR int) between consecutive peaks associated with the QRS complex which is inversely related to the heart rate. In some implementations, the ECG signal analyzer 136a may be configured to implement known techniques including a single scan algorithm for QRS-detection and feature extraction to extract an RR-interval. For example, the ECG signal analyzer 136a may be configured to determine the heart rate by taking the reciprocal of the RR-interval and multiplying it by 60. For example, in FIG. 6 the RR- interval between peaks 6200, 6300 is about 550 ms which corresponds to a heart rate value of about 109 bpm (60 x (1/0.550)). In some implementations, the ECG signal analyzer 136a may also be configured to utilize a time interval between peaks associated with a T wave (e.g., peaks 6400, 6500) to determine a heart rate.

[0109] In some implementations, the ECG signal analyzer 136a may be configured to determine an instantaneous heart rate value from a sequence of RR-intervals based on the method described above. In some implementations a plurality of instantaneous heart rate values obtained over a predetermined period of time (e.g., 2 seconds, 3 seconds, 5 seconds) can be averaged to produce a smoother average heart rate value over the predetermined period of time. The ECG signal analyzer 136a may be configured to determine RR-interval values (and corresponding heart rate values) over the predetermined duration of time (e.g., 30 seconds 1 minute, 2 minutes, 5 minutes, etc.) based on the instantaneous heart rate values or the averaged heart rate values.

[0110] At operation 1150 the method 1100 includes determining a HRR metric value based on the threshold heart rate value and the second heart rate information associated with the user obtained during the predetermined duration of time. For example, the HRR metric value determiner 137 may be configured to determine a heart rate recovery metric value based on n a difference between the threshold heart rate value and a heart rate value determined based on a time interval between a first peak and a second peak of the ECG signal measured via the one or more ECG sensors 186 during the predetermined duration of time. For example, the time interval may correspond to a duration of time between adjacent peaks corresponding to a R wave component of the ECG signal or the time interval may correspond to a duration of time between adjacent peaks corresponding to a T wave component of the ECG signal. For example, the predetermined duration of time may be at least thirty seconds and no more than five minutes. The HRR metric value determiner 137 may be configured to determine a plurality of HRR metric values based on the threshold heart rate value and the second heart rate information (e.g., heart rate value) associated with the user obtained during the predetermined duration of time. Each of the HRR metric values from the plurality of HRR metric values may be determined at specified time points (e.g., at 30 seconds, at 1 minute, at 2 minutes, etc.) within the predetermined duration of time. The HRR metric value determiner 137 may be configured to store the plurality of HRR metric values (e.g., in the one or more memory devices 120, at the server computing system 300, at external computing device 400, at user biometric information data store 350, etc.).

[0111] FIG. 7 is an example graph illustrating heart rate data, according to one or more examples of the disclosure. In FIG. 7, the graph 7000 depicts heart rate data 7100 obtained over a period of time. For example, FIG. 7 illustrates an example of heart rate decay for a 5- minute recording by the one or more ECG sensors 186 after physical activity which brought the user’s heart rate near to their predicted maximum heart rate value (e.g., 167 bpm). In the graph 7000 of FIG. 7, the threshold heart rate value at point 7200 corresponds to a value of about 160 bpm and occurs at time zero. At point 7300 1 minute has elapsed after the threshold heart rate value of 1 0 bpm. The heart rate value at 1 min is 137.5 bpm, hence the HRR metric value determiner 137 may be configured to determine the heart rate recovery metric value HR_lmin as 22.5 bpm (160-137.5=22.5 bpm). From the heart rate fall-off data, the HRR metric value determiner 137 may be configured to determine various heart rate recovery metric values, including HRR_30s, HRR lmin, HRR_2min, etc. For the example of FIG. 7, the HRR metric value determiner 137 may be configured to determine heart rate recovery metric values including HRR_30s as 9 bpm (160-151=9 bpm), HRR lmin as 22.5 (160-137.5), and HRR_2min as 43.5 bpm (160-116.5=43.5 bpm).

[0112] The HRR metric value determiner 137 may also be configured to determine parameters of a single parameter exponential model which fits the time course of the heart rate decay, or alternatively to determine parameters of a two parameter dual exponential decay model for the time series.

[0113] The HRR metric value determiner 137 may be configured to store HRR metric values and any other determined parameters for each recording. For example, the HRR metric value determiner 137 may be configured to store HRR metric values and any other determined parameters locally in the one or more memory' devices 120, at the server computing system 300, at external computing device 400, at user biometric information data store 350, etc. Storage of this biometric information may provide a history of heart rate recovery parameters for an individual over time.

[0114] The stored biometric information may also be utilized by the biometric measurement application 130 to provide a comparison of HRR metric values associated with a user relative to the overall population, relative to an age and/or gender matched group, etc. FIG. 8 is an example graph illustrating a distribution of heart rate recovery metric values, according to one or more examples of the disclosure. In FIG. 8, the graph 8000 illustrates a distribution 8100 of HRR lmin values for a particular group and a user’s value 8200 of HRR_lmin is overlaid on the distribution 8100 for purposes of comparison. For example, the biometric measurement application 130 may be configured to provide a comparison of HRR metric values by generating a graph similar to that shown in FIG. 8 for presentation on the display device 150 such that the user can be informed of their HRR metric value in relation to others. For example, in FIG. 8 the user lies at about the 65^th percentile for HRR_lmin for their age group. Data pertaining to a distribution of HRR metric values for particular HRR time frames and/or demographics may be stored locally or obtained from an external source (e.g., server computing system 300 or a particular database which stores such biometric data).

[0115] In the context of performing the operations of method 1100, the biometric measurement application 130 may also be configured to consider motion information associated with a user. For example, biometric measurements may be negatively impacted by excessive motion of a user while a biometric measurement is being recorded by the one or more optical sensors 184 and/or the one or more ECG sensors 186. Motion determiner 138 may be configured to provide information about motion information associated with a user to any of the first heart rate information determination application 132, threshold heart rate value comparator 134, second heart rate information determination application 136, HRR metric value determiner 137, and user interface generator 139.

[0116] In some implementations, the motion determiner 138 may be configured to obtain motion information of the user computing device 100 (which is associated with the user) based on data output by the inertial measurement unit 182 including the one or more accelerometers 182a and one or more gy roscopes 182b. The motion information may be obtained by the motion determiner 138 while the first heart rate information is obtained by the first heart rate information determination application 132 via the one or more optical sensors 184 and/or while the second heart rate information is obtained by the second heart rate information determination application 136 via the one or more ECG sensors 186. The motion determiner 138 may be configured to determine whether the obtained motion information exceeds a threshold motion value. For example, for a first duration of time in which the motion information associated with the user computing device 100 exceeds a threshold motion value (as determined by the motion determiner 138) while the second heart rate information associated with the user is obtained during the predetermined duration of time, the HRR metric value determiner 137 may be configured to exclude the second heart rate information corresponding to the first duration of time as second heart rate information for determining the HRR metric value. For example, the second heart rate information determination application 136, HRR metric value determiner 137, or motion determiner 138 may mark second heart rate information associated with the first duration of time as being unacceptable or to be omitted when determining a heart rate value or other parameter based on the ECG signal obtained via the one or more ECG sensors 186. For example, for a second duration of time in which the motion information (as determined by the motion determiner 138) associated with the user computing device 100 does not exceed the threshold motion value while the second heart rate information associated with the user is obtained during the predetermined duration of time, the HRR metric value determiner 137 may be configured to include the second heart rate information corresponding to the second duration of time as second heart rate information for detennining the HRR metric value.

[0117] Likewise, for a first duration of time in which the motion information associated with the user computing device 100 exceeds a threshold motion value (as determined by the motion determiner 138) while the first heart rate information associated with the user is obtained, the PPG signal analyzer 132a may be configured to exclude the first heart rate information corresponding to the first duration of time as first heart rate information for determining a heart rate value to be compared by the threshold heart rate value comparator 134 with the threshold heart rate value. For example, the first heart rate information determination application 132, threshold heart rate value comparator 134, or motion determiner 138 may mark first heart rate information associated with the first duration of time as being unacceptable or to be omitted when determining a heart rate value or other parameter based on the PPG signal obtained via the one or more optical sensors 184 (one or more PPG sensors). For example, for a second duration of time in which the motion information (as determined by the motion determiner 138) associated with the user computing device 100 does not exceed the threshold motion value while the first heart rate information associated with the user is obtained, the PPG signal analyzer 132a may be configured to include the first heart rate information corresponding to the second duration of time as first heart rate information for determining a heart rate value to be compared by the threshold heart rate value comparator 134 with the threshold heart rate value.

[0118] In some implementations, the user may be alerted or notified via the output device 160 to remain stationary in response to the motion determiner 138 determining that the motion information associated with the user computing device 100 exceeds the threshold motion value. For example, FIG. 9E illustrates an example fifth graphical user interface 9500 which provides instructions to a user to remain stationary and/or to maintain contact with an electrode for taking an ECG measurement (e.g., a message such as “Keep your fingers still and on the rim”). If the user remains stationary, the biometric measurement application 130 may be configured to continue measuring the ECG signal obtained via the one or more ECG sensors 186 and a countdown of the predetermined duration of time may continue, as illustrated in FIG. 9F where sixth graphical user interface 9600 indicates the timer has counted down to 291 seconds from the timer of FIG. 9E which illustrates a time remaining of 294 seconds. In some implementations, in response to the motion determiner 138 determining that the motion information associated with the user computing device 100 exceeds the threshold motion value for longer than a threshold period of time (e.g., 3 seconds, 5 seconds, etc.), the biometric measurement application 130 may discontinue or cancel the measurement of the ECG signal and provide an output to the user indicating the ECG measurement was discontinued. In some implementations, in response to the motion determiner 138 determining that the motion information associated with the user computing device 100 exceeds the threshold motion value for longer than the threshold period of time (e.g., 3 seconds, 5 seconds, etc.), the biometric measurement application 130 may discontinue or cancel the measurement of the ECG signal and restart the timer to the original value associated with the predetermined duration of time (e.g., 300 seconds in the examples of FIGS. 9E and 9F) and have the ECG measurement begin anew. If the motion information associated with the user computing device 100 exceeds the threshold motion value for less than the threshold period of time, the biometric measurement application 130 may continue with the ECG measurements but may disregard biometric information collected during the corresponding period of time as described earlier.

[0119] User interface generator 139 may be configured to generate various graphical user interfaces to provide guidance, instructions, or other indications to the user regarding biometric measurements including measuring a HRR metric value (e.g., as shown in FIGS.

9A through 9F). For example, execution of the biometric measurement application 130 (or of second heart rate information determination application 136) may cause the user interface generator 139 to provide an indication to the user to measure the HRR metric value (e.g., by providing for presentation on the display device 150 the first graphical user interface 9100 of FIG. 9A). In some implementations, output device 160 may provide other indications to the user to measure the HRR metric value via an audible output, a haptic output, a visual output, or combinations thereof. User interface generator 139 may be configured to generate various graphical user interfaces (e.g., as show n in FIG. 9C) to obtain information from the user which may be used by the biometric measurement application 130 for providing subsequent instructions to the user, for classification purposes of the data which is obtained, for training machine-learning models, for purposes of analyzing the obtained data, for noise filtering, etc. User interface generator 139 may be configured to generate a graphical user interface to provide instructions to the user to inform the user how to contact (or to maintain contact with) the one or more ECG sensors 186 for obtaining, via the one or more ECG sensors 186, the second heart rate information associated with the user, for the predetermined duration of time (e g., as shown in FIG. 9C). User interface generator 139 may be configured to generate a graphical user interface to provide instructions to the user to inform the user to remain stationary and/or standing for obtaining, via the one or more ECG sensors 186, the second heart rate information associated with the user, for the predetermined duration of time (e.g., as shown in FIG. 9D). User interface generator 139 may be configured to generate graphical user interfaces to provide information to the user during the predetermined duration of time while the second heart rate information associated with the user is obtained via the one or more ECG sensors 186 (e.g., as shown in FIGS. 9E and 9F). The information may include time information regarding an amount of time remaining for a biometric measurement, status information (e.g., a message indicating a measurement is being taken), guidance information (e.g., a message with instructions to maintain contact with the electrodes while the ECG measurement is being taken), and the like. User interface generator 139 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.

[0120] 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 10. 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 biometnc 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.

[0121] 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 memory (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).

[0122] Each block of the flowchart 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 alternative implementations, 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.

[0123] 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 ordinary skill 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: obtain, via one or more optical sensors, first heart rate information associated with a user, and in response to detecting the first heart rate information exceeding a threshold heart rate value based on the first heart rate information obtained via the one or more optical sensors: execute a biometric measurement application associated with measuring a heart rate recovery (HRR) metric, in response to executing the biometric measurement application, obtain, via one or more electrocardiogram (ECG) sensors, second heart rate information associated with the user, for a predetermined duration of time, and determine a HRR metric value based on the threshold heart rate value and the second heart rate information associated with the user obtained during the predetermined duration of time.

2. The computing device of claim 1, the one or more optical sensors including one or more photoplethysmography (PPG) sensors, the one or more PPG sensors and the one or more ECG sensors disposed on the computing device.

3. The computing device of claim 1, the one or more processors further configured to operate with the threshold heart rate value at a predicted maximum heart rate associated with the user.

4. The computing device of claim 1, the one or more processors further configured to operate with the threshold heart rate value at a predetermined percentage of a predicted maximum heart rate associated with the user.

5. The computing device of claim 1, the computing device further including a user interface, and the one or more processors configured to provide via the user interface an indication to the user to measure the HRR metric value.

6. The computing device of claim 5, the indication to the user to measure the HRR metric value comprising at least one of an audible output indicating to the user to measure the HRR metric value, a haptic output indicating to the user to measure the HRR metric value, or a visual output indicating to the user to measure the HRR metric value.

7. The computing device of claim 5, the indication to the user to measure the HRR metric value comprising instructions informing the user to remain stationary and standing for obtaining, via the one or more ECG sensors, the second heart rate information associated with the user, for the predetermined duration of time.

8. The computing device of claim 1, the one or more processors being configured to execute the one or more instructions stored in the one or more memories to, in response to detecting the first heart rate information exceeding the threshold heart rate value based on the first heart rate information obtained via the one or more optical sensors, provide a prompt query ing the user whether to execute the biometric measurement application.

9. The computing device of claim 1 , the one or more processors being configured to determine, via one or more motion sensors, motion information associated with the computing device while the second heart rate information associated with the user is obtained during the predetermined duration of time.

10. The computing device of claim 9, the one or more processors being configured to: for a first duration of time in which the motion information associated with the computing device exceeds a threshold motion value while the second heart rate information associated with the user is obtained during the predetermined duration of time, exclude the second heart rate information corresponding to the first duration of time as second heart rate information for determining the HRR metric value, and for a second duration of time in which the motion information associated with the computing device does not exceed the threshold motion value while the second heart rate information associated with the user is obtained during the predetermined duration of time, include the second heart rate information corresponding to the second duration of time as second heart rate information for determining the HRR metric value.

11. The computing device of claim 1 , the one or more processors being configured to determine a pulse transit time metric value based on first heart rate information and second heart rate information obtained during the predetermined duration of time.

12. The computing device of claim 1, the second heart rate information including data from an ECG signal output by the one or more ECG sensors, and the one or more processors being configured to determine the HRR metric value based on a time interval between a first peak from the ECG signal and a second peak from the ECG signal measured via the one or more ECG sensors during the predetermined duration of time.

13. The computing device of claim 12, the first peak from the ECG signal and the second peak from the ECG signal corresponding to a R wave component of the ECG signal.

14. The computing device of claim 12, the one or more processors being configured to determine the HRR metric value based on a difference between the threshold heart rate value and a heart rate value determined based on the time interval between the first peak and the second peak.

15. The computing device of claim 14, the predetermined duration of time being at least thirty seconds and no more than five minutes.

16. The computing device of claim 15, the one or more processors being configured to determine a plurality of HRR metric values based on the threshold heart rate value and the second heart rate information associated with the user obtained during the predetermined duration of time, each of the HRR metric values from the plurality of HRR metric values being determined at specified time points within the predetermined duration of time, and the one or more processors being configured to store the plurality of HRR metric values.

17. A computer-implemented method, comprising: obtaining, via one or more optical sensors of a computing device, first heart rate information associated with a user; and in response to detecting the first heart rate information exceeding a threshold heart rate value based on the first heart rate information obtained via the one or more optical sensors: executing a biometric measurement application associated with measuring a heart rate recovery (HRR) metric, in response to executing the biometric measurement application, obtaining, via one or more electrocardiogram (ECG) sensors of the computing device, second heart rate information associated with the user, for a predetermined duration of time, and determining a HRR metric value based on the threshold heart rate value and the second heart rate information associated with the user obtained during the predetermined duration of time.

18. The computer-implemented method of claim 17, the second heart rate information including data from an ECG signal output by the one or more ECG sensors, and determining the HRR metric value based on the threshold heart rate value and the second heart rate information associated with the user obtained during the predetermined duration of time comprises: determining a time interval between a first peak from the ECG signal and a second peak from the ECG signal measured via the one or more ECG sensors during the predetermined duration of time, and determining a heart rate value based on the time interval between the first peak and the second peak, and determining the HRR metric value based on a difference between the threshold heart rate value and the heart rate value.

19. The computer-implemented method of claim 18, further comprising: determining, via one or more motion sensors of the computing device, motion information associated w ith the computing device while the second heart rate infomiation associated with the user is obtained during the predetermined duration of time; excluding, as second heart rate information for determining the HRR metric value, second heart rate information obtained during periods of time during the predetermined duration of time in which the motion information associated with the computing device exceeds a threshold motion value; and including, as second heart rate information for determining the HRR metric value, second heart rate information obtained during periods of time during the predetermined duration of time in which the motion information associated with the computing device does not exceed the threshold motion value.

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: obtain, via one or more optical sensors, first heart rate information associated with a user, and in response to detecting the first heart rate information exceeding a threshold heart rate value based on the first heart rate information obtained via the one or more optical sensors: execute a biometric measurement application associated with measuring a heart rate recovery (HRR) metric, in response to executing the biometric measurement application, obtain, via one or more electrocardiogram (ECG) sensors, second heart rate information associated with the user, for a predetermined duration of time, and determine a HRR metric value based on the threshold heart rate value and the second heart rate information associated with the user obtained during the predetermined duration of time.