Determining Cardiovascular Health Metrics from Wearable-Based Physiological Data

Abstract

Methods, systems, and devices for determining a cardiovascular health metric are described. The system may be configured to receive a photoplethysmogram (PPG) signal representing a pulse waveform for a user. The pulse waveform may include a first maximum, a downward slope following the first maximum, and a curved feature representing a transition from the systolic phase to the diastolic phase of the cardiac cycle. Additionally, the system may extract one or more morphological features from the pulse waveform and compare the one or more morphological features with one or more features from multiple baseline PPG signal morphologies associated with multiple chronological ages. The system may determine a cardiovascular health metric indicative of the user's cardiovascular health relative to the user's chronological age and display an indicia of the cardiovascular health metric in a graphical user interface.

Description

[Technical Field]

[0001] [Cross reference] This patent application claims priority to U.S. patent application Ser. No. 17 / 818,105 by Rantanen et al., entitled "CARDIOVASCULAR HEALTH METRIC DETERMINATION FROM WEARABLE-BASED PHYSIOLOGICAL DATA," filed Aug. 8, 2022, which is assigned to the assignee of the present application and expressly incorporated herein by reference.

[0002] [Technical field] The following description relates to wearable devices and data processing, including determining cardiovascular health metrics based on wearable-based physiological data. [Background technology]

[0003] Some wearable devices may be configured to collect data from a user, including photoplethysmogram (PPG) data, heart rate data, etc. For example, some wearable devices may be configured to collect physiological data associated with a user's cardiovascular health. However, wearable devices may be insufficient to determine a user's cardiovascular health metrics. [Brief explanation of the drawings]

[0004] [Figure 1] FIG. 1 illustrates an example of a system for assisting in wearable-based physiological data-based cardiovascular health metric determination according to aspects of the present disclosure.

[0005] [Figure 2] FIG. 1 illustrates an example of a system for assisting in wearable-based physiological data-based cardiovascular health metric determination according to aspects of the present disclosure.

[0006] [Figure 3]FIG. 1 illustrates an example timing diagram for assisting wearable-based physiological data-based cardiovascular health metric determination according to aspects of the present disclosure.

[0007] [Figure 4] FIG. 1 illustrates an example timing diagram for assisting wearable-based physiological data-based cardiovascular health metric determination according to aspects of the present disclosure.

[0008] [Figure 5] FIG. 1 illustrates an example of a graphical user interface (GUI) for assisting wearable-based physiological data-based cardiovascular health metric determination according to aspects of the present disclosure.

[0009] [Figure 6] FIG. 1 illustrates a block diagram of an apparatus for assisting in wearable-based physiological data-based cardiovascular health metric determination according to aspects of the present disclosure.

[0010] [Figure 7] FIG. 1 illustrates a block diagram of a wearable application that assists in wearable-based physiological data-based cardiovascular health metric determination according to aspects of the present disclosure.

[0011] [Figure 8] FIG. 1 illustrates a system including a device for assisting in wearable-based physiological data-based cardiovascular health metric determination according to aspects of the present disclosure.

[0012] [Figure 9] 1 is a flowchart illustrating a method for assisting wearable-based physiological data-based cardiovascular health metric determination according to aspects of the present disclosure. [Figure 10] 1 is a flowchart illustrating a method for assisting wearable-based physiological data-based cardiovascular health metric determination according to aspects of the present disclosure. [Figure 11]1 is a flowchart illustrating a method for assisting wearable-based physiological data-based cardiovascular health metric determination according to aspects of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0013] Some wearable devices may be configured to collect physiological data from a user, including photoplethysmogram (PPG) data, temperature data, heart rate, heart rate variability (HRV) data, sleep data, respiration data, blood pressure data, etc. The acquired physiological data may be used to analyze behavioral and physiological characteristics associated with the user, such as movement. Many users desire more insight into their physical health, such as activity patterns and overall physical health. In particular, many users may desire more insight into their cardiovascular health, including cardiovascular age, heart health, arteriosclerosis, and risk of cardiovascular disease, including coronary heart disease, stroke, heart failure, cardiac arrhythmia, etc. However, typical technologies and / or health devices and applications for measuring cardiovascular health lack the ability to provide robust determinations and insights for several reasons.

[0014] First, devices that record cardiac electrical signals and collect cardiac and / or vascular images may be obtained in a single instance and may be combined with other measurement techniques and calculations to determine a user's cardiovascular health. Second, even for devices that are wearable or that collect a user's physiological data, typical devices and applications lack the ability to collect other physiological, behavioral, or contextual inputs from the user that can be combined with the measurement data to more comprehensively understand the full set of physiological contributors to the user's cardiovascular health.

[0015] Aspects of the present disclosure relate to techniques for determining cardiovascular health metrics from wearable-based physiological data. In particular, a computing device of the present disclosure may receive physiological data from a wearable device associated with a user. The physiological data may include at least a PPG signal representing a pulse waveform for the user. Aspects of the present disclosure may identify morphological features of the pulse waveform, including at least a first maximum, a downward slope following the first maximum, and a curved feature representing a transition from the systole to the diastole of the cardiac cycle.

[0016] In some examples, aspects of the present disclosure may compare the identified morphological features of the pulse waveform with multiple PPG signal morphological features associated with multiple chronological ages. For example, the system may compare an individual pulse waveform with typical pulse waveforms of different age groups to identify which age group's pulse waveform matches the individual pulse waveform. Thus, aspects of the present disclosure may provide techniques for determining a user's cardiovascular health metric based on the comparison, where the cardiovascular health metric indicates the user's cardiovascular health relative to the user's chronological age.

[0017] For purposes of this disclosure, terms such as "cardiovascular age metric," "cardiovascular health metric," or "cardiovascular age" may be used to refer to the health metric of a user's cardiovascular system. The cardiovascular system may include the heart, blood vessels, and / or blood, and the cardiovascular system's primary function is to transport nutrient- and oxygen-rich blood to all parts of the body and return deoxygenated blood to the lungs. Cardiovascular age (e.g., heart age and / or vascular age) is a metric used to understand a user's risk for cardiovascular disease, including heart attack or stroke. In some cases, cardiovascular (e.g., heart) age may be calculated based on age, blood pressure, and cholesterol, as well as heart disease risk factors such as diet, exercise, and smoking. Vascular age may provide a measure of the apparent age of a user's arteries.

[0018] In some cases, determining cardiovascular health metrics may reduce a user's subsequent health risks, particularly the risk of cardiovascular disease. In such cases, a technique for determining cardiovascular health metrics and providing recommendations for improving a user's cardiovascular health metrics may be desirable in order to improve quality of life, sleep, and mood and reduce future health risks. For example, a method and technique for helping a user understand in a personalized way how to optimize lifestyle changes to reduce the risk of cardiovascular disease may be desirable. In such cases, the system may be capable of determining a user's cardiovascular health metrics relative to their chronological age to provide an indication that may enable the user to understand how behavioral changes (e.g., improving sleep, exercise, diet, and mood) can help improve the user's cardiovascular health metrics and reduce the risk of cardiovascular disease.

[0019] The techniques described herein may notify the user of the determined cardiovascular health metric in various ways. For example, the system may cause a graphical user interface (GUI) of the user device to display a message or other notification informing the user of the determined cardiovascular health metric and making recommendations to the user. In one example, the system may generate recommendations to the user about avoiding certain foods and / or beverages, strengthening the user's training, or increasing recovery time based on the cardiovascular health metric.

[0020] The GUI may also include graphics / text that show the data used to create the cardiovascular health metric. The system may also send messages to the user to confirm changes in the cardiovascular health metric. Based on early warnings (e.g., before noticeable symptoms), the user can take early steps that may help reduce the severity of upcoming symptoms associated with a cardiovascular health metric higher than the user's chronological age (e.g., symptoms associated with the onset of a cardiovascular health problem). The GUI may also include graphics / text that reflect physiological changes associated with blood pressure, heart rate, and update recommendations to the user based on physiological changes.

[0021] Aspects of the disclosure are first described in the context of a system that facilitates physiological data collection from a user via a wearable device. Further aspects of the disclosure are described in the context of exemplary timing diagrams and exemplary GUIs. Aspects of the disclosure are further illustrated by and described with reference to apparatus diagrams, system diagrams, and flowcharts related to wearable-based physiological data-based cardiovascular health metric determination.

[0022] 1 illustrates an example of a system 100 for assisting in wearable-based physiological data-based cardiovascular health metric determination according to an embodiment of the present disclosure. System 100 includes multiple electronic devices (e.g., wearable device 104, user device 106) that can be worn and / or operated by one or more users 102. System 100 further includes a network 108 and one or more servers 110.

[0023] The electronic devices may include any electronic devices known in the art, including wearable devices 104 (e.g., ring-type wearable devices, watch-type wearable devices, etc.) and user devices 106 (e.g., smartphones, laptops, tablets). The electronic devices associated with each user 102 may include one or more of the following functions: (1) measuring physiological data, (2) storing the measured data, (3) processing the data, (4) providing output to the user 102 (e.g., via a GUI) based on the processed data, and (5) communicating data with each other and / or other computing devices. Different electronic devices may perform one or more functions.

[0024] Exemplary wearable devices 104 may include wearable computing devices such as a finger-shaped computing device (hereinafter "ring") configured to be worn on the finger of the user 102, a wrist-based computing device (e.g., a smartwatch, fitness band, or bracelet) configured to be worn on the wrist of the user 102, and / or a head-worn computing device (e.g., eyeglasses / goggles). The wearable devices 104 may include bands, straps (e.g., flexible or non-flexible bands or straps), stick-on sensors, etc. that may be positioned elsewhere, such as a band around the head (e.g., a forehead headband), a band around the arm (e.g., a forearm band and / or an upper arm band), and / or a band around the leg (e.g., a thigh or calf band), behind the ear, under the arm, etc. The wearable devices 104 may also be attached to or included in clothing. For example, the wearable devices 104 may be included in a pocket and / or pouch of clothing. As another example, the wearable device 104 may be clipped and / or pinned to clothing or otherwise maintained near the user 102. Exemplary clothing may include, but is not limited to, hats, shirts, gloves, pants, socks, outerwear (e.g., jackets), and underwear. In some implementations, the wearable device 104 may be included with other types of devices, such as training / sports equipment used during physical activity. For example, the wearable device 104 may be attached to or included in a bicycle, skis, tennis racket, golf club, and / or training weights.

[0025] Much of the present disclosure may be described in connection with a ring-type wearable device 104. Accordingly, "ring 104," "wearable device 104," and similar terms may be used interchangeably herein unless otherwise specified. However, use of the term "ring 104" should not be considered limiting, as it is contemplated herein that aspects of the present disclosure may be implemented using other wearable devices (e.g., watch-type wearable devices, necklace wearable devices, bracelet wearable devices, earring wearable devices, anklet wearable devices, etc.).

[0026] In some aspects, the user devices 106 may include handheld mobile computing devices such as smartphones and tablet computing devices. The user devices 106 may include personal computers such as laptops and desktop computing devices. Other exemplary user devices 106 may include server computing devices that may communicate with other electronic devices (e.g., via the Internet). In some implementations, the computing devices may include medical devices such as external wearable computing devices (e.g., Holter monitors). Medical devices may also include implantable medical devices such as pacemakers and defibrillators. Other exemplary user devices 106 may include home computing devices such as Internet of Things (IoT) devices (e.g., IoT devices), smart TVs, smart speakers, smart displays (e.g., video calling displays), hubs (e.g., wireless communication hubs), security systems, smart appliances (e.g., thermostats and refrigerators), and fitness equipment.

[0027] Some electronic devices (e.g., wearable device 104, user device 106) may measure physiological parameters of each user 102, such as photoplethysmography waveforms, continuous skin temperature, pulse waveforms, respiration rate, heart rate, heart rate variability (HRV), actigraphy, galvanic skin response, pulse oximetry, and / or other physiological parameters. Some electronic devices that measure physiological parameters may also perform some / all of the calculations described herein. Some electronic devices may not measure physiological parameters but may perform some / all of the calculations described herein. For example, a ring (e.g., wearable device 104), a mobile device application, or a server computing device may process received physiological data measured by other devices.

[0028] In some implementations, the user 102 may operate or be associated with multiple electronic devices, some of which may measure physiological parameters and some of which may process the measured physiological parameters. In some implementations, the user 102 may have a ring (e.g., a wearable device 104) that measures physiological parameters. The user 102 may have or be associated with a user device 106 (e.g., a mobile device, smartphone), where the wearable device 104 and the user device 106 are communicatively coupled to each other. In some cases, the user device 106 may receive data from the wearable device 104 and perform some / all of the calculations described herein. In some implementations, the user device 106 may also measure physiological parameters described herein, such as movement / activity parameters.

[0029] 1 , a first user 102-a (user 1) may operate or be associated with a wearable device 104-a (e.g., ring 104-a) and a user device 106-a, which may operate as described herein. In this example, the user device 106-a associated with user 102-a may process / store physiological parameters measured by the ring 104-a. In comparison, a second user 102-b (user 2) may be associated with a ring 104-b, a wristwatch-type wearable device 104-c (e.g., watch 104-c), and a user device 106-b, where the user device 106-b associated with user 102-b may process / store physiological parameters measured by the ring 104-b and / or watch 104-c. Additionally, an nth user 102-n (user N) may be associated with a configuration of electronic devices (e.g., ring 104-n, user device 106-n) described herein. In some aspects, wearable devices 104 (e.g., ring 104, watch 104) and other electronic devices may be communicatively coupled to the user device 106 of each user 102 via Bluetooth, Wi-Fi, and other wireless protocols.

[0030] In some implementations, the ring 104 (e.g., wearable device 104) of system 100 may be configured to collect physiological data from each user 102 based on arterial blood flow in the user's finger. In particular, the ring 104 may utilize one or more LEDs (e.g., red LED, green LED) emitting light on the palm side of the user's finger to collect physiological data based on arterial blood flow in the user's finger. In some cases, the system 100 may be configured to collect physiological data from each user 102 based on blood flow diffused into a microvascular bed of the skin having capillaries and arterioles. For example, the system 100 may collect PPG data based on a measured volume of blood diffused into the microvasculature of capillaries and arterioles. In some implementations, the ring 104 may acquire physiological data using a combination of both green and red LEDs. The physiological data may include any physiological data known in the art, including, but not limited to, temperature data, accelerometer data (e.g., movement / exercise data), heart rate data, HRV data, blood oxygen level data, or any combination thereof.

[0031] The use of both green and red LEDs may offer several advantages over other solutions, as red and green LEDs have been found to have distinct advantages in themselves, such as when acquiring physiological data through different parts of the body under different conditions (e.g., light / dark, active / inactive). For example, green LEDs have been found to perform better during exercise. Furthermore, the use of multiple LEDs (e.g., green and red LEDs) distributed around the circumference of the ring 104 has been found to perform better compared to wearable devices that utilize LEDs placed closely together, such as in a watch wearable device. Furthermore, blood vessels (e.g., arteries, capillaries) in the fingers are more accessible via LEDs compared to blood vessels in the wrist. In particular, arteries in the wrist are located on the lower part of the wrist (e.g., the palm side of the wrist), which means that only capillaries are accessible at the upper part of the wrist (e.g., the back side of the wrist), where wearable watch devices and similar devices are typically worn. In this manner, utilizing LEDs and other sensors within the ring 104 has been found to provide superior performance compared to wrist-worn wearable devices because the ring 104 may have greater access to arteries (as compared to capillaries), thereby providing stronger signals and more valuable physiological data. In some cases, the system 100 may be configured to collect physiological data from each user 102 based on blood flow diffused into the skin's microvascular bed, which includes capillaries and arterioles. For example, the system 100 may collect PPG data based on a measured volume of blood diffused into the microvasculature of capillaries and arterioles.

[0032] The electronic devices (e.g., user device 106, wearable device 104) of system 100 may be communicatively coupled to one or more servers 110 via wired or wireless communication protocols. For example, as shown in FIG. 1 , the electronic devices (e.g., user device 106) may be communicatively coupled to one or more servers 110 via a network 108. The network 108 may implement Transmission Control Protocol and Internet Protocol (TCP / IP), such as the Internet, or may implement other network 108 protocols. The network connection between the network 108 and each electronic device may facilitate the transfer of data via email, web, text message, mail, or any other suitable form of interaction within the computer network 108. For example, in some implementations, a ring 104-a associated with a first user 102-a may be communicatively coupled to a user device 106-a, where the user device 106-a is communicatively coupled to the server 110 via the network 108. Additionally or alternatively, the wearable device 104 (eg, ring 104, watch 104) may be directly communicatively coupled to the network 108.

[0033] The system 100 may provide on-demand database services between the user devices 106 and one or more servers 110. In some cases, the servers 110 may receive data from the user devices 106 via the network 108, store the data, and analyze it. Similarly, the servers 110 may provide data to the user devices 106 via the network 108. In some cases, the servers 110 may be located in one or more data centers. The servers 110 may be used for data storage, management, and processing. In some implementations, the servers 110 may provide a web-based interface to the user devices 106 via a web browser.

[0034] In some aspects, the system 100 may detect periods of time when the user 102 is asleep and classify the periods of time when the user 102 is asleep into one or more sleep stages (e.g., sleep stage classifications). For example, as shown in FIG. 1 , the user 102-a may be associated with a wearable device 104-a (e.g., a ring 104-a) and a user device 106-a. In this example, the ring 104-a may collect physiological data associated with the user 102-a, including body temperature, heart rate, HRV, respiratory rate, etc. In some aspects, the data collected by the ring 104-a may be input into a machine learning classifier, which is configured to determine periods of time when the user 102-a is (or was) asleep. Furthermore, the machine learning classifier may be configured to classify the periods into different sleep stages, including a wake sleep stage, a rapid eye movement (REM) sleep stage, a light sleep stage (non-REM sleep), and a deep sleep stage (NREM). In some aspects, the classified sleep stages may be displayed to the user 102-a via a GUI of the user device 106-a. The sleep stage classification may be used to provide feedback to the user 102-a regarding the user's sleep patterns, such as a recommended bedtime, a recommended wake-up time, etc. Additionally, in some implementations, the sleep stage classification techniques described herein may be used to calculate a score for each user, such as a Sleep Score, a Readiness Score, etc.

[0035] In some embodiments, the system 100 may utilize features derived from circadian rhythms to further improve the physiological data collection, data processing procedures, and other techniques described herein. The term circadian rhythm refers to the natural internal process that regulates an individual's sleep-wake cycle, which repeats approximately every 24 hours. In this regard, the techniques described herein may utilize a circadian rhythm regulation model to improve physiological data collection, analysis, and data processing. For example, the circadian rhythm regulation model may be input into a machine learning classifier along with physiological data collected from the user 102-a via the wearable device 104-a. In this example, the circadian rhythm regulation model may be configured to "weight" or adjust the physiological data collected throughout the user's natural approximately 24-hour circadian rhythm. In some implementations, the system may initially start with a "baseline" circadian rhythm regulation model and then modify the baseline model using physiological data collected from each user 102 to generate a customized, personalized circadian rhythm regulation model specific to each respective user 102.

[0036] In some embodiments, system 100 may utilize other biological rhythms to further improve physiological data collection, analysis, and processing by accounting for the phases of these other rhythms. For example, if a weekly rhythm is detected in an individual's baseline data, the model may be configured to adjust the "weight" of the data by day of the week. Biological rhythms that may require model adjustment in this manner include: (1) ultradian rhythms (rhythms faster than a day, including sleep cycles during sleep states, and oscillations in physiological variables measured during wakefulness from sub-hourly to multi-hourly periodicities); (2) circadian rhythms; (3) non-endogenous daily rhythms that have been shown to be imposed in addition to circadian rhythms, such as work schedules; (4) weekly rhythms or other exogenously imposed artificial time periodicities (e.g., a hypothetical culture with a 12-day "week" might use a 12-day rhythm); (5) multi-day ovarian rhythms in women and spermatogenic rhythms in men; (6) lunar rhythms (associated with individuals living with low or no artificial light); and (7) seasonal rhythms.

[0037] Biological rhythms are not necessarily stationary rhythms. For example, many women experience variability in the length of their ovarian cycles across cycles, and ultradian rhythms are not expected to occur at exactly the same time or periodicity across days, even within a single user. Thus, signal processing techniques sufficient to quantify the frequency composition of these rhythms while maintaining their temporal resolution in physiological data can be used to improve the detection of these rhythms and assign the phase of each rhythm to each instant of measured time, thereby modifying adjustment models and comparisons of time intervals. Biological rhythm adjustment models and parameters can be added in linear or nonlinear combinations as needed to more accurately capture the dynamic physiological baseline of an individual or group of individuals.

[0038] In some embodiments, each device in system 100 may support techniques for determining cardiovascular health metrics from wearable-based physiological data. In particular, system 100 shown in FIG. 1 may support techniques for determining a cardiovascular health metric indicative of the cardiovascular health status of user 102 relative to the chronological age of user 102 and displaying an indication of the cardiovascular health metric on user device 106 corresponding to user 102. The indication of the cardiovascular health metric may be based on a received PPG signal representing a pulse waveform for user 102 from wearable device 104.

[0039] For example, as shown in FIG. 1 , user 1 (user 102-a) may be associated with wearable device 104-a (e.g., ring 104-a) and user device 106-a. In this example, ring 104-a may collect data associated with user 102-a, including a PPG signal, temperature, heart rate, HRV, respiratory rate, etc. In some aspects, the data collected by ring 104-a may be used to determine cardiovascular health metrics of user 102 relative to the chronological age of user 102. Determining the cardiovascular health metrics may be performed by any component of system 100, including ring 104-a, user device 106-a associated with user 1, one or more servers 110, or any combination thereof. Upon determining the cardiovascular health metrics, system 100 may selectively display indicia of the cardiovascular health metrics on a GUI of user device 106. In such a case, the user device 106 may be associated with User 1, User 2, User N, or a combination thereof, where User 2 and User N may be examples of a clinician, a caregiver, a user associated with User 1, or a combination thereof.

[0040] In some implementations, upon receiving physiological data (e.g., including a PPG signal representing a pulse waveform), system 100 may extract one or more morphological features from the pulse waveform. For example, the pulse waveform may include a first maximum, a downward slope following the first maximum, and a curved feature representing a transition from the systolic phase to the diastolic phase of the cardiac cycle. In such a case, system 100 may extract one or more morphological features related to the location of the first maximum, the value of the downward slope, the degree of the curved feature, or a combination thereof. It should be understood that additional or alternative morphological features of the pulse waveform may be used, and the listed examples are for illustrative purposes and should not be considered limiting. In some cases, the morphological features may be identified by a machine learning model and represent a complex combination of features. System 100 may compare the extracted one or more morphological features to one or more features from multiple baseline PPG signal morphologies associated with multiple chronological ages.

[0041] In some implementations, system 100 may generate an alert, message, or recommendation to user 1, user 2, and / or user N (e.g., via ring 104-a, user device 106-a, or both) based on the determined cardiovascular health metric, where the message may provide insight regarding the determined cardiovascular health metric. In some cases, the message may provide insight regarding symptoms associated with the cardiovascular health metric, educational video and / or text (e.g., content) associated with a decline in the cardiovascular health metric, recommendations for improving the cardiovascular health metric, an adjusted set of activity and / or sleep goals, or a combination thereof.

[0042] It should be understood by those skilled in the art that one or more aspects of the present disclosure may be implemented in system 100 to solve additional or alternative problems other than those described above. Furthermore, aspects of the present disclosure may provide technical improvements over the "conventional" systems or processes described herein. However, the description and accompanying drawings include only exemplary technical improvements resulting from implementing aspects of the disclosure and therefore do not represent all of the technical improvements provided in the claims.

[0043] 2 illustrates an example of a system 200 for assisting in wearable-based physiological data-based cardiovascular health metric determination according to an aspect of the present disclosure. System 200 may implement or be implemented by system 100. In particular, system 200 illustrates an example of a ring 104 (e.g., wearable device 104), a user device 106, and a server 110, as described with reference to FIG.

[0044] In some embodiments, ring 104 may be configured to be worn around a user's finger and, when worn around the user's finger, may determine one or more physiological parameters of the user, including, but not limited to, the user's skin temperature, pulse waveform, respiratory rate, heart rate, HRV, blood oxygen level, etc.

[0045] The system 200 further includes a user device 106 (e.g., a smartphone) that communicates with the ring 104. For example, the ring 104 may be in wireless and / or wired communication with the user device 106. In some implementations, the ring 104 may transmit measured and processed data (e.g., temperature data, photoplethysmogram (PPG) data, movement / accelerometer data, ring input data, etc.) to the user device 106. The user device 106 may also transmit data, such as firmware / configuration updates for the ring 104, to the ring 104. The user device 106 may process the data. In some implementations, the user device 106 may transmit the data to the server 110 for processing and / or storage.

[0046] The ring 104 may include a housing 205, which may include an inner housing 205-a and an outer housing 205-b. In some embodiments, the housing 205 of the ring 104 may store or otherwise contain various components of the ring, including, but not limited to, device electronics, a power source (e.g., a battery 210 and / or a capacitor), one or more substrates (e.g., printable circuit boards) interconnecting the device electronics and / or power source, etc. The device electronics may include device modules (e.g., hardware / software) such as a processing module 230-a, a memory 215, a communications module 220, and a power module 225. The device electronics may include one or more sensors. Exemplary sensors may include one or more temperature sensors 240, a PPG sensor assembly (e.g., a PPG system 235), and one or more motion sensors 245.

[0047] The sensors may include an associated module (not shown) configured to communicate with and generate a signal associated with the respective sensor in the ring 104. In some aspects, each of the components / modules in the ring 104 may be communicatively coupled to one another via a wired or wireless connection. Additionally, the ring 104 may include additional and / or alternative sensors or other components configured to collect physiological data from the user, including optical sensors (e.g., LEDs), oximeters, etc.

[0048] The ring 104 shown and described with reference to FIG. 2 is provided for illustrative purposes only. As such, the ring 104 may include additional or alternative components as shown in FIG. 2. Other rings 104 may be manufactured that provide the functionality described herein. For example, rings 104 may be manufactured with fewer components (e.g., sensors). In particular embodiments, rings 104 may be manufactured with a single temperature sensor 240 (or other sensor), a power source, and device electronics configured to read the single temperature sensor 240 (or other sensor). In other specific examples, the temperature sensor 240 (or other sensor) may be attached to a user's finger (e.g., with a clamp, a spring-loaded clamp, etc.). In this case, the sensor may be wired to another computing device, such as a wrist-worn computing device, that reads the temperature sensor 240 (or other sensor). In other examples, rings 104 may be manufactured that include additional sensors and processing capabilities.

[0049] The housing 205 may include one or more housing 205 components. The housing 205 may include an outer housing 205-b component (e.g., a shell) and an inner housing 205-a component (e.g., a molding). The housing 205 may include additional components (e.g., additional layers) not explicitly shown in FIG. 2 . For example, in some implementations, the ring 104 may include one or more insulating layers that electrically insulate the device electronics and other conductive materials (e.g., electrical traces) from the outer housing 205-b (e.g., the metal outer housing 205-b). The housing 205 may provide structural support for the device electronics, battery 210, board, and other components. For example, the housing 205 may protect the device electronics, battery 210, and board from mechanical forces such as pressure and impact. The housing 205 may also protect the device electronics, battery 210, and board from water and / or other chemicals.

[0050] The outer housing 205-b can be made from one or more materials. In some implementations, the outer housing 205-b can include a metal such as titanium, which can provide strength and wear resistance while being relatively lightweight. The outer housing 205-b can also be made from other materials, such as polymers. In some implementations, the outer housing 205-b can be both protective and decorative.

[0051] The inner housing 205-a may be configured to interface with a user's finger. The inner housing 205-a may be formed from a polymer (e.g., a medical-grade polymer) or other material. In some implementations, the inner housing 205-a may be transparent. For example, the inner housing 205-a may be transparent to light emitted by a PPG light-emitting diode (LED). In some implementations, the inner housing 205-a components may be molded onto the outer housing 205-b. For example, the inner housing 205-a may include a polymer that is molded (e.g., injection molded) to fit into the metal shell of the outer housing 205-b.

[0052] The ring 104 may include one or more substrates (not shown). The device electronics and battery 210 may be included on one or more substrates. For example, the device electronics and battery 210 may be mounted on one or more substrates. Exemplary substrates may include one or more printed circuit boards (PCBs), such as flexible PCBs (e.g., polyimide). In some implementations, the electronics / battery 210 may include surface-mounted devices (e.g., surface-mount technology (SMT) devices) on a flexible PCB. In some implementations, the one or more substrates (e.g., one or more flexible PCBs) may include electrical traces that provide electrical communication between the device electronics. The electrical traces may connect the battery 210 to the device electronics.

[0053] The device electronics, battery 210, and substrate may be arranged in a variety of ways within ring 104. In some implementations, one substrate containing the device electronics may be mounted along the bottom (e.g., bottom half) of ring 104 so that sensors (e.g., PPG system 235, temperature sensor 240, motion sensor 245, and other sensors) interface with the underside of a user's finger. In these implementations, battery 210 may be included along the top of ring 104 (e.g., on another substrate).

[0054] The various components / modules of ring 104 represent functions (e.g., circuits and other components) that may be included in ring 104. A module may include any discrete and / or integrated electronic circuit components that implement analog and / or digital circuitry that may produce the functions ascribed to the module herein. For example, a module may include analog circuitry (e.g., amplification circuitry, filtering circuitry, analog-to-digital conversion circuitry, and / or other signal conditioning circuitry). A module may also include digital circuitry (e.g., combinational or sequential logic circuitry, memory circuitry, etc.).

[0055] The memory 215 (memory module) of the ring 104 may include any volatile, nonvolatile, magnetic, or electrical medium, such as random access memory (RAM), read-only memory (ROM), nonvolatile RAM (NVRAM), electrically erasable programmable ROM (EEPROM), flash memory, or any other memory device. The memory 215 may store any of the data described herein. For example, the memory 215 may be configured to store data collected by the respective sensors and the PPG system 235 (e.g., motion data, temperature data, PPG data). Additionally, the memory 215 may include instructions that, when executed by one or more processing circuits, cause the modules to perform the various functions attributed to the modules herein. The device electronics of the ring 104 described herein are merely one example of device electronics. Thus, the types of electronic components used to implement the device electronics may vary based on design considerations.

[0056] The functionality attributed to the modules of ring 104 described herein may be implemented as one or more processors, hardware, firmware, software, or any combination thereof. The depiction of different features as modules is intended to emphasize different functional aspects and does not necessarily imply that such modules must be realized by separate hardware / software components. Rather, the functionality associated with one or more modules may be performed by separate hardware / software components or may be integrated within a common hardware / software component.

[0057] The processing module 230-a of the ring 104 may include one or more processors (e.g., processing units), microcontrollers, digital signal processors, systems-on-chips (SoCs), and / or other processing devices. The processing module 230-a communicates with the modules included in the ring 104. For example, the processing module 230-a may send and receive data to and from other components of the ring 104, such as modules and sensors. As described herein, the modules may be implemented by various circuit components. Thus, the modules may be referred to as circuits (e.g., communication circuits and power circuits).

[0058] The processing module 230-a may be in communication with the memory 215. The memory 215 may produce computer-readable instructions that, when executed by the processing module 230, cause the processing module 230-a to perform the various functions attributed to the processing module 230-a described herein. In some implementations, the processing module 230-a (e.g., a microcontroller) may include additional features associated with other modules, such as communication capabilities provided by the communication module 220 (e.g., an integrated Bluetooth Low Energy transceiver) and / or additional on-board memory 215.

[0059] The communication module 220-a may include circuitry providing wireless and / or wired communication with the user device 106 (e.g., the communication module 220-b of the user device 106). In some implementations, the communication modules 220-a, 220-b may include wireless communication circuitry, such as Bluetooth circuitry and / or Wi-Fi circuitry. In some implementations, the communication modules 220-a, 220-b may include wired communication circuitry, such as Universal Serial Bus (USB) communication circuitry. Using the communication module 220, the ring 104 and the user device 106 may be configured to communicate with each other. The ring's processing module 230-a may be configured to send and receive data to and from the user device 106 via the communication module 220. Examples of data include, but are not limited to, exercise data, temperature data, pulse waveforms, heart rate data, HRV data, PPG data, and status updates (e.g., charging status, battery charge level, and / or ring 104 configuration settings). The ring processing module 230-a may be configured to receive updates (eg, software / firmware updates) and data from the user device 106.

[0060] The ring 104 may include a battery 210 (e.g., a rechargeable battery 210). An exemplary battery 210 may include a lithium-ion or lithium polymer type battery 210, although various battery 210 options are possible. The battery 210 may be wirelessly recharged. In some implementations, the ring 104 may include a power source other than the battery 210, such as a capacitor. The power source (e.g., the battery 210 or capacitor) may have a curved shape that matches the curve of the ring 104. In some aspects, the charger or other power source may be used to collect data in addition to data collected by the ring 104 itself or may include additional sensors that supplement the data collected by the ring. Additionally, the charger or other power source of the ring 104 may function as a user device 106, in which case the charger or other power source of the ring 104 may be configured to receive data from the ring 104, store and / or process data received from the ring 104, and communicate data between the ring 104 and the server 110.

[0061] In some embodiments, the ring 104 includes a power module 225 that can control charging of the battery 210. For example, the power module 225 can interface with an external wireless charger that charges the battery 210 when interfaced with the ring 104. The charger can include datum structures that mate with datum structures on the ring 104 to create a defined orientation for the ring 104 during charging. The power module 225 can also regulate the voltage of the device's electronics, regulate power output to the device's electronics, and monitor the state of charge of the battery 210. In some implementations, the battery 210 can include a protection circuit module (PCM) that protects the battery 210 from high-current discharge, overvoltage during charging, and undervoltage during discharging. The power module 225 can also include electrostatic discharge (ESD) protection.

[0062] One or more temperature sensors 240 may be electrically coupled to the processing module 230-a. The temperature sensor 240 may be configured to generate a temperature signal (e.g., temperature data) indicative of a temperature read or sensed by the temperature sensor 240. The processing module 230-a may determine the user's temperature at the location of the temperature sensor 240. For example, in the ring 104, the temperature data generated by the temperature sensor 240 may indicate the user's temperature (e.g., skin temperature) at the user's finger. In some implementations, the temperature sensor 240 may contact the user's skin. In other implementations, a portion of the housing 205 (e.g., the inner housing 205-a) may form a barrier (e.g., a thin thermally conductive barrier) between the temperature sensor 240 and the user's skin. In some implementations, the portion of the ring 104 configured to contact the user's finger may have a thermally conductive portion and a thermally insulating portion. The thermally conductive portion may conduct heat from the user's finger to the temperature sensor 240. The insulating portion may insulate portions of the ring 104 (eg, the temperature sensor 240) from the ambient temperature.

[0063] In some implementations, the temperature sensor 240 may generate a digital signal (e.g., temperature data) that the processing module 230-a may use to determine the temperature. As another example, if the temperature sensor 240 includes a passive sensor, the processing module 230 (or the temperature sensor 240 module) may measure the current / voltage generated by the temperature sensor 240 and determine the temperature based on the measured current / voltage. Examples of the temperature sensor 240 may include a thermistor, such as a negative temperature coefficient (NTC) thermistor, or other types of sensors including resistors, transistors, diodes, and / or other electrical / electronic components.

[0064] The processing module 230-a may sample the user's temperature over time. For example, the processing module 230-a may sample the user's temperature according to a sampling rate. An exemplary sampling rate may include 1 sample per second, although the processing module 230-a may be configured to sample the temperature signal at other sampling rates higher or lower than 1 sample per second. In some implementations, the processing module 230-a may measure the temperature continuously throughout the day and night. Sampling at a sufficient rate (e.g., 1 sample per second) throughout the day may provide sufficient temperature data for the analyses described herein.

[0065] The processing module 230-a may store the sampled temperature data in the memory 215. In some implementations, the processing module 230-a may process the sampled temperature data. For example, the processing module 230-a may determine an average temperature value for a period of time. In one example, the processing module 230-a may determine the average temperature value for each minute by summing all temperature values ​​collected during that minute and dividing by the number of samples during that minute. In a specific example where temperatures are sampled at one sample per second, the average temperature may be the sum of all sampled temperatures for one minute divided by 60 seconds. The memory 215 may store average temperature values ​​over time. In some implementations, the memory 215 may store average temperatures (e.g., one per minute) instead of sampled temperatures to conserve memory 215.

[0066] The sampling rate, which may be stored in memory 215, may be configurable. In some implementations, the sampling rate may be the same throughout the day and night. In other implementations, the sampling rate may vary throughout the day / night. In some implementations, the ring 104 may filter / eliminate temperature readings, such as large spikes in temperature that do not indicate a physiological change (e.g., a temperature spike from a hot shower). In some implementations, the ring 104 may filter / eliminate temperature measurements that may be unreliable due to other factors, such as excessive movement during exercise (e.g., as indicated by the motion sensor 245).

[0067] The ring 104 (e.g., a communications module) may transmit the sampled and / or average temperature data to the user device 106 for storage and / or further processing. The user device 106 may forward the sampled and / or average temperature data to the server 110 for storage and / or further processing.

[0068] Although the ring 104 is illustrated as including a single temperature sensor 240, the ring 104 may include multiple temperature sensors 240 in one or more locations, such as arranged along the inner housing 205-a near the user's finger. In some implementations, the temperature sensor 240 may be a stand-alone temperature sensor 240. Additionally or alternatively, one or more temperature sensors 240 may be included with (e.g., packaged with) other components, such as an accelerometer and / or a processor.

[0069] The processing module 230-a may obtain and process data from multiple temperature sensors 240 in a manner similar to that described with respect to a single temperature sensor 240. For example, the processing module 230 may separately sample, average, and store temperature data from each of the multiple temperature sensors 240. In other examples, the processing module 230-a may sample the sensors at different rates and average / store different values ​​for the different sensors. In some implementations, the processing module 230-a may be configured to determine a single temperature based on an average of two or more temperatures determined by two or more temperature sensors 240 at different locations on the finger.

[0070] The temperature sensor 240 on the ring 104 may acquire a distal temperature at a user's finger (e.g., any finger). For example, one or more temperature sensors 240 on the ring 104 may acquire the user's temperature from the underside of the finger or at different locations on the finger. In some implementations, the ring 104 may acquire the distal temperature continuously (e.g., at a sampling rate). While distal temperatures measured by the ring 104 at the finger are described herein, other devices may measure temperatures at the same / different locations. In some cases, the distal temperature measured at a user's finger may differ from a temperature measured at the user's wrist or other external body location. Furthermore, the distal temperature measured at a user's finger (e.g., "shell" temperature) may differ from the user's core body temperature. In this manner, the ring 104 may provide a useful temperature signal not acquired at other internal / external body locations. In some cases, continuous temperature measurement at the finger may capture temperature fluctuations (e.g., small or large fluctuations) that may not be apparent in the core body temperature. For example, continuous temperature measurements at the finger may capture minute-to-minute or hour-to-hour temperature fluctuations that provide additional insight that may not be provided by other temperature measurements elsewhere on the body.

[0071] The ring 104 may include a PPG system 235. The PPG system 235 may include one or more optical transmitters that transmit light. The PPG system 235 may also include one or more optical receivers that receive light transmitted by the one or more optical transmitters. The optical receivers may generate a signal (hereinafter, a "PPG" signal) indicating the amount of light received by the optical receivers. The optical transmitters may illuminate an area of ​​the user's finger. The PPG signal generated by the PPG system 235 may indicate blood perfusion in the illuminated area. For example, the PPG signal may indicate blood volume changes in the illuminated area caused by the user's pulse pressure. The processing module 230-a may sample the PPG signal and determine the user's pulse waveform based on the PPG signal. The processing module 230-a may determine various physiological parameters based on the user's pulse waveform, such as the user's respiration rate, heart rate, HRV, oxygen saturation, and other circulatory parameters.

[0072] In some implementations, the PPG system 235 may be configured as a reflective PPG system 235 in which the optical receiver receives transmitted light reflected through a region of the user's finger. In some implementations, the PPG system 235 may be configured as a transmissive PPG system 235 in which the optical transmitter and optical receiver are positioned opposite each other so that light is transmitted directly through a portion of the user's finger to the optical receiver.

[0073] The number and ratio of transmitters and receivers included in the PPG system 235 may vary. An exemplary optical transmitter may include a light-emitting diode (LED). The optical transmitter may transmit light in the infrared spectrum and / or other spectrums. Examples of optical receivers include, but are not limited to, optical sensors, phototransistors, and photodiodes. The optical receiver may be configured to generate a PPG signal in response to wavelengths received from the optical transmitter. The locations of the transmitters and receivers may vary. Furthermore, a single device may include a reflective and / or transmissive PPG system 235.

[0074] 2 may, in some implementations, include a reflective PPG system 235. In these implementations, the PPG system 235 may include a centrally located optical receiver (e.g., at the bottom of the ring 104) and two optical transmitters located on either side of the optical receiver. In this implementation, the PPG system 235 (e.g., the optical receiver) may generate a PPG signal based on light received from one or both of the optical transmitters. In other implementations, other arrangements, combinations, and / or configurations of one or more optical transmitters and / or optical receivers are possible.

[0075] The processing module 230-a may control one or both of the optical transmitters to transmit light while sampling the PPG signal generated by the optical receiver. In some implementations, the processing module 230-a may cause the optical transmitter with the stronger received signal to transmit light while sampling the PPG signal generated by the optical receiver. For example, a selected optical transmitter may emit light continuously while the PPG signal is being sampled at a sampling rate (e.g., 250 Hz).

[0076] Sampling the PPG signal generated by the PPG system 235 may result in a pulse waveform, which may be referred to as a "PPG." The pulse waveform may indicate blood pressure over time for multiple cardiac cycles. The pulse waveform may include peaks indicative of cardiac cycles. Additionally, the pulse waveform may include respiration-induced fluctuations that may be used to determine respiration rate. The processing module 230-a, in some implementations, may store the pulse waveform in memory 215. The processing module 230-a may process the pulse waveform as it is generated and / or from memory 215 to determine the user's physiological parameters as described herein.

[0077] The processing module 230-a may determine the user's heart rate based on the pulse waveform. For example, the processing module 230-a may determine the heart rate (e.g., beats per minute) based on the time between peaks in the pulse waveform. The time between peaks may be referred to as the interbeat interval (IBI). The processing module 230-a may store the determined heart rate and IBI values ​​in the memory 215.

[0078] The processing module 230-a may measure HRV over time. For example, the processing module 230-a may determine HRV based on variations in IBI. The processing module 230-a may store the HRV values ​​over time in the memory 215. Additionally, the processing module 230-a may determine the user's respiration rate over time. For example, the processing module 230-a may determine the respiration rate based on frequency modulation, amplitude modulation, or baseline modulation of the user's IBI value over a period of time. The respiration rate may be calculated as breaths per minute or other respiration rate (e.g., breaths every 30 seconds). The processing module 230-a may store the user's respiration values ​​over time in the memory 215.

[0079] The ring 104 may include one or more motion sensors 245, such as one or more accelerometers (e.g., 6-D accelerometers) and / or one or more gyroscopes (gyros). The motion sensors 245 may generate motion signals indicative of the motion of the sensor. For example, the ring 104 may include one or more accelerometers that generate acceleration signals indicative of the acceleration of the accelerometer. As another example, the ring 104 may include one or more gyro sensors that generate gyro signals indicative of rotational motion (e.g., angular velocity) and / or changes in orientation. The motion sensors 245 may be included in one or more sensor packages. One example of an accelerometer / gyro sensor is a Bosch BM1160 inertial microelectromechanical system (MEMS) sensor that can measure angular velocity and acceleration in three perpendicular axes.

[0080] The processing module 230-a may sample the motion signals at a sampling rate (e.g., 50 Hz) and determine the motion of the ring 104 based on the sampled motion signals. For example, the processing module 230-a may sample the acceleration signals to determine the acceleration of the ring 104. As another example, the processing module 230-a may sample the gyro signals to determine the rotational motion. In some implementations, the processing module 230-a may store the motion data in the memory 215. The motion data may include sampled motion data and motion data (e.g., acceleration values ​​and angle values) calculated based on the sampled motion signals.

[0081] The ring 104 may store various data described herein. For example, the ring 104 may store temperature data, such as raw sampled temperature data and calculated temperature data (e.g., average temperature). As another example, the ring 104 may store PPG signal data, such as a pulse waveform, and data calculated based on the pulse waveform (e.g., heart rate values, IBI values, HRV values, and respiration values). The ring 104 may also store motion data, such as sampled motion data indicative of linear and rotational motion.

[0082] The ring 104, or other computing device, may calculate and store additional values ​​based on the sampled / calculated physiological data. For example, the processing module 230 may calculate and store various metrics, such as sleep metrics (e.g., sleep score), activity metrics, and readiness metrics. In some implementations, the additional values / metrics may be referred to as "derived values." The ring 104, or other computing / wearable device, may calculate various values / metrics related to exercise. Examples of derived values ​​for exercise data include, but are not limited to, exercise count values, regularity values, intensity values, metabolic equivalence of task values ​​(METs), and orientation values. The exercise count values, regularity values, intensity values, and METs may indicate the amount of exercise (e.g., speed / acceleration) of the user over time. The orientation value may indicate how the ring 104 is oriented relative to the user's finger and whether the ring 104 is worn on the left or right hand.

[0083] In some implementations, movement counts and regularity values ​​may be determined by counting the number of acceleration peaks within one or more time periods (e.g., one or more 30-second to 1-minute time periods). The intensity value may indicate the number of movements and the associated intensities (e.g., acceleration values) of the movements. The intensity values ​​may be classified as low, medium, or high depending on the associated threshold acceleration value. The MET may be determined based on the intensity of the movements, the regularity / irregularity of the movements, and the number of movements associated with different intensities during a time period (e.g., 30 seconds).

[0084] In some implementations, the processing module 230-a may compress data stored in the memory 215. For example, the processing module 230-a may perform calculations based on the sampled data and then delete the sampled data. As another example, the processing module 230-a may average data over a longer period of time to reduce the number of stored values. In a particular embodiment, if a user's average temperature over one minute is stored in the memory 215, the processing module 230-a may calculate the average temperature over five minutes for storage and then erase the one-minute average temperature data. The processing module 230-a may compress data based on various factors, such as the total amount of used / available memory 215 and / or the amount of time elapsed since the ring 104 last transmitted data to the user device 106.

[0085] The user's physiological parameters may be measured by a sensor included in the ring 104, although other devices may measure the user's physiological parameters. For example, the user's temperature may be measured by the temperature sensor 240 included in the ring 104, although other devices may measure the user's temperature. In some examples, other wearable devices (e.g., wrist devices) may include sensors that measure the user's physiological parameters. Additionally, medical devices, such as external medical devices (e.g., wearable medical devices) and / or implantable medical devices, may measure the user's physiological parameters. One or more sensors on any type of computing device may be used to implement the techniques described herein.

[0086] Physiological measurements may be taken continuously throughout the day and / or night. In some implementations, physiological measurements may be taken during portions of the day 104 and / or portions of the night. In some implementations, physiological measurements may be taken in response to determining that the user is in a particular state, such as an active state, a resting state, and / or a sleeping state. For example, the ring 104 may take physiological measurements in a resting / sleeping state to obtain a cleaner physiological signal. In one example, the ring 104 or other device / system may detect when the user is at rest and / or sleeping and obtain physiological parameters (e.g., temperature) for the detected state. The device / system may use resting / sleeping physiological data and / or other data when the user is in other states to implement the techniques of this disclosure.

[0087] In some implementations, as described previously herein, the ring 104 may be configured to collect, store, and / or process data and may forward any of the data described herein to the user device 106 for storage and / or processing. In some aspects, the user device 106 includes a wearable application 250, an operating system (OS), a web browser application (e.g., web browser 280), one or more additional applications, and a GUI 275. The user device 106 may further include other modules and components including sensors, audio devices, haptic feedback devices, etc. The wearable application 250 may include examples of applications (e.g., “apps”) that may be installed on the user device 106. The wearable application 250 may be configured to acquire data from the ring 104, store the acquired data, and process the acquired data as described herein. For example, the wearable application 250 may include a user interface (UI) module 255, an acquisition module 260, a processing module 230-b, a communication module 220-b, and a storage module (e.g., a database 265) configured to store application data.

[0088] The various data processing operations described herein may be performed by the ring 104, the user device 106, the server 110, or any combination thereof. For example, in some cases, data collected by the ring 104 may be pre-processed and transmitted to the user device 106. In this example, the user device 106 may perform some data processing operations on the received data, transmit the data to the server 110 for data processing, or both. For example, in some cases, the user device 106 may perform processing operations that require relatively low processing power and / or operations that require relatively low latency, while the user device 106 may transmit data to the server 110 for processing operations that require relatively high processing power and / or operations that may allow for relatively high latency.

[0089] In some aspects, the ring 104, user device 106, and server 110 of system 200 may be configured to evaluate a user's sleep patterns. In particular, each component of system 200 may be used to collect data from the user via ring 104 and generate one or more scores (e.g., a sleep score, a readiness score) for the user based on the collected data. For example, as described previously herein, the ring 104 of system 200 may be worn by a user to collect data from the user, including temperature, heart rate, HRV, etc. The data collected by the ring 104 may be used to determine when the user is asleep to evaluate the user's sleep for a given “sleep day.” In some aspects, a score may be calculated for the user for each respective sleep day, such that a first sleep day is associated with a first set of scores, a second sleep day is associated with a second set of scores, and so on. A score may be calculated for each respective sleep day based on the data collected by ring 104 during the respective sleep day. The scores may include, but are not limited to, a sleep score, a readiness score, etc.

[0090] In some cases, "sleep days" may be aligned with traditional calendar days, such that a given sleep day runs from midnight to midnight on each calendar day. In other cases, sleep days may be staggered relative to calendar days. For example, a sleep day may run from 6:00 PM (18:00) on one calendar day to 6:00 PM (18:00) on the next calendar day. In this example, 6:00 PM may serve as a "cutoff time," such that data collected from the user before 6:00 PM is counted toward the current sleep day, and data collected from the user after 6:00 PM is counted toward the next sleep day. Due to the fact that most individuals sleep most at night, staggering sleep days relative to calendar days allows system 200 to evaluate a user's sleep patterns in a manner consistent with the user's sleep schedule. In some cases, a user may selectively adjust the timing of sleep days relative to calendar days (e.g., via a GUI) so that sleep days align with the length of time each user typically sleeps.

[0091] In some implementations, a user's respective overall score (e.g., sleep score, readiness score) for each respective day may be determined / calculated based on one or more “contributors,” “factors,” or “contributing factors.” For example, a user's overall sleep score may be calculated based on a set of contributors including total sleep, efficiency, restfulness, REM sleep, deep sleep, latency, timing, or any combination thereof. A sleep score may include any number of contributors. A “total sleep” contributor may refer to the sum of all sleep time on a sleep day. An “efficiency” contributor may reflect the proportion of time spent sleeping compared to time spent awake while in bed and may be calculated using an efficiency average of long sleep periods (e.g., primary sleep periods) on a sleep day, weighted by the duration of each sleep period. The "restfulness" contributor may indicate how restful a user's sleep is and may be calculated using the average of all sleep periods on a sleep day, weighted by the duration of each period. The restfulness contributor may be based on "wake count" (e.g., the sum of all wakeups (when the user wakes up) detected during different sleep periods), excessive movement, and "wake count" (e.g., the sum of all wakeups (when the user gets out of bed) detected during different sleep periods).

[0092] The "REM sleep" contributor may refer to the sum of REM sleep duration across all sleep periods on a sleep day, including REM sleep. Similarly, the "deep sleep" contributor may refer to the sum of deep sleep time across all sleep periods on a sleep day, including deep sleep. The "latency" contributor may indicate the time it takes a user to fall asleep (e.g., average, median, longest) and may be calculated using an average of long sleep periods across a sleep day, weighted by the duration of each period and the number of such periods (e.g., a given sleep stage or a combination of multiple sleep stages may be its own contributor or may weight other contributors). Finally, the "timing" contributor may refer to the relative timing of sleep periods within a sleep day and / or calendar day and may be calculated using an average of all sleep periods on a sleep day, weighted by the duration of each period.

[0093] As another example, a user's overall readiness score may be calculated based on a collection of contributors, including sleep, sleep balance, heart rate, HRV balance, recovery index, body temperature, activity, activity balance, or any combination thereof. A readiness score may include any number of contributors. A "sleep" contributor may refer to the combined sleep score of all sleep periods in a sleep day. A "sleep balance" contributor may refer to the cumulative duration of all sleep periods in a sleep day. In particular, sleep balance can indicate to a user whether the sleep a user is getting over a period of time (e.g., the past two weeks) is in balance with the user's needs. Generally, adults need 7-9 hours of sleep per night to be healthy, alert, and perform at their best mentally and physically. However, because occasional nights of sleep deprivation are normal, sleep balance contributors take long-term sleep patterns into account to determine whether each user's sleep needs are being met. The "resting heart rate" contributor may indicate the lowest heart rate from the longest sleep period (eg, the primary sleep period) of the sleep day and / or the lowest heart rate from a nap occurring after the primary sleep period.

[0094] Continuing with the "contributors" (e.g., factors) to the readiness score, the "HRV balance" contributor may indicate the highest average HRV from the primary sleep period and naps occurring after the primary sleep period. The HRV balance contributor helps users track their recovery status by comparing HRV trends over a first period (e.g., two weeks) with average HRV over a second, longer period (e.g., three months). The "recovery index" contributor may be calculated based on the longest sleep period. The recovery index measures how long it takes for a user's resting heart rate to stabilize during the night. A sign of very good recovery is when a user's resting heart rate stabilizes during the first half of the night, at least six hours before the user wakes up, leaving the body time to recover for the next day. The "body temperature" contributor may be calculated based on the longest sleep period (e.g., the primary sleep period) or based on naps occurring after the longest sleep period if the user's maximum temperature during the nap is at least 0.5°C higher than the maximum temperature during the longest period. In some embodiments, the ring may measure the user's temperature while the user is asleep, and system 200 may display the user's average temperature relative to the user's baseline temperature. If the user's temperature is outside its normal range (e.g., significantly above or below 0.0), the temperature contributor may be highlighted (e.g., go into a "pay attention" state) or otherwise generate a warning to the user.

[0095] In some embodiments, system 200 may support techniques for determining cardiovascular health metrics from wearable-based physiological data. In particular, components of system 200 may be used to determine a cardiovascular health metric indicative of a user's cardiovascular health relative to the user's chronological age based on comparing one or more morphological features of the user's pulse waveform with one or more features from multiple baseline PPG signal morphologies associated with multiple chronological ages. An indicium of the user's cardiovascular health metric may be determined by utilizing a PPG sensor on ring 104 of system 200. In some cases, the indicium of the cardiovascular health metric may be determined by identifying one or more morphological features of the PPG signal, such as the location of the first maximum, the value of the downward slope, the degree of curvature, or a combination thereof, in addition to other morphological features.

[0096] For example, as described previously herein, the ring 104 of the system 200 may be worn by a user to collect data from the user, including a PPG signal, temperature, heart rate, HRV, respiration data, etc. The ring 104 of the system 200 may collect physiological data from the user based on PPG sensors and measurements extracted from arterial blood flow (e.g., using a PPG signal), capillary blood flow, arteriolar blood flow, or a combination thereof. The physiological data may be collected continuously. In some implementations, the processing module 230-a may sample and / or receive the user's PPG signal continuously throughout the day and / or night. Sampling at a sufficient rate (e.g., one sample per second or one sample per minute) throughout the day and / or night may provide sufficient data for the analysis described herein. In some implementations, the ring 104 may acquire the PPG signal continuously (e.g., at a sampling rate). In some examples, even if PPG signals are collected continuously, system 200 may utilize other collected or otherwise derived information about the user (e.g., sleep stages, activity levels, disease onset, etc.) to select a representative PPG signal for a particular day that is an accurate representation of the underlying physiological phenomena.

[0097] In contrast, systems that require a user to manually acquire a PPG signal daily, and / or systems that continuously acquire PPG signals but lack other contextual information about the user, may select inaccurate or inconsistent PPG signals for determining these cardiovascular health metrics, leading to inaccurate determinations and a poor user experience. In contrast, data collected by ring 104 can be used to accurately determine a user's cardiovascular health metrics. Determining cardiovascular health metrics and related techniques are further shown and described with reference to FIG. 3.

[0098] FIG. 3 illustrates an example timing diagram 300 for supporting wearable-based physiological data-based cardiovascular health metric determination according to an embodiment of the present disclosure. The timing diagram 300 illustrates a pulse waveform 305 versus time. In this regard, the solid curve illustrated in the timing diagram 300 may be understood to represent "pulse waveform 305-a," which is an example of a received pulse waveform of a user. The dashed curves illustrated in the timing diagram 300 may be understood to refer to "pulse waveforms 305-b and 305-c," which may be examples of baseline PPG signal morphologies. For example, the pulse waveform 305-b may be an example of a baseline PPG signal morphology for a user between the ages of 40 and 44. The pulse waveform 305-c may be an example of a baseline PPG signal morphology for a user between the ages of 65 and 70. As described in more detail below, by comparing the received pulse waveform 305-a to a baseline pulse waveform associated with a particular chronological age (e.g., pulse waveform 305-b or 305-c), a cardiovascular health metric may be determined, which may indicate how the user's cardiovascular health at their current chronological age compares to the baseline cardiovascular health of users of different chronological ages. For example, if a user is 60 years old but their pulse waveform most closely matches (e.g., based on a comparison of one or more morphological features) the baseline pulse waveform of a 30-year-old, the user may be assigned a relatively high cardiovascular health metric.

[0099] The pulse waveform 305-a may be generated and / or identified based on data extracted from a wearable device for a single user. For example, the system (e.g., the ring 104, the user device 106, the server 110) may receive physiological data including at least the user's PPG signal from the wearable device. The pulse waveform 305-a is an example of an average pulse waveform of the user acquired over multiple days. The multiple days may be an example of at least 20 days (e.g., including at least 20 nights). In such a case, the system may estimate a cardiovascular health metric after receiving at least 20 nights of PPG signals. The system may average the received PPG signals acquired over the multiple days to represent a single pulse waveform (e.g., pulse waveform 305-a) for the user. In such a case, determining the average pulse waveform 305-a may omit outliers, such as when the user is experiencing illness, stress, or other factors affecting the PPG signal. Additionally, the system may omit or adjust the weighting of certain days of collected data based on other contextual information collected from the wearable device or application through tags, activity detection, location information, etc.

[0100] The pulse waveforms 305-b and 305-c may be generated and / or identified based on data extracted from wearable devices for multiple users from multiple wearable devices. In such cases, the system may identify multiple baseline PPG signal morphologies (e.g., including pulse waveforms 305-b and 305-c) associated with multiple chronological ages. For example, the system may receive PPG signals that can be paired with the chronological ages of multiple users. In this case, the multiple users may be classified into different groups according to the users' ages. An average pulse waveform may be formed for each subject (e.g., user) in the group. For example, the average pulse waveform for each user in the group may represent PPG samples collected over multiple days (e.g., at least 20 nights). In some cases, the average pulse waveforms 305-b and 305-c are each generated from 30 averaged PPG samples of different users in each age group and represent the user's average pulse morphology for the corresponding age group. In some cases, baseline PPG signal morphologies (e.g., pulse waveforms 305-b and 305-c) may be generated for users of different genders. The baseline signal PPG morphologies may be determined in response to receiving physiological data including at least a PPG signal for the user.

[0101] As described herein, features may be extracted from template pulse waves (e.g., pulse waveforms 305-b and 305-c) and used as a user's age classifier for the user's received pulse waveform 305-a. By comparing features extracted from the user's average pulse wave (e.g., pulse waveform 305-a) with features from the template pulse waves (e.g., pulse waveforms 305-b and 305-c), the system may estimate the user's cardiovascular health metrics.

[0102] The system may process the PPG signal to determine cardiovascular health metrics. The PPG signal may be continuously collected by the wearable device. The physiological measurements may be made continuously throughout the day and / or night. For example, in some implementations, the ring may be configured to acquire physiological data (e.g., PPG signals, etc.) continuously according to one or more measurement periods throughout each day / sleep day. In other words, the ring may continuously acquire physiological data from the user regardless of "trigger conditions" for making such measurements.

[0103] The PPG signal may be used to generate a pulse waveform 305. The pulse waveform 305 may be an example of an arterial pulse waveform. In such cases, the arterial pulse waveform may represent a rhythmic wave of arterial pressure felt by palpating an artery. In some cases, the arterial pulse waveform may be caused by an increase in blood pressure ejected by the left ventricle of the heart into the aorta and arteries. The pulse waveform 305 may include a systolic portion and a diastolic portion. The transition between the systolic and diastolic portions may appear in the waveform as a notch or curved feature, sometimes referred to as a dicrotic notch. The pulse waveform 305 may each include a local maximum 310, a downward slope 325 following the local maximum 310, a curved feature 330 representing the transition from the systolic portion to the diastolic portion, or a combination thereof. The local maximum 310 is an example of a systolic peak in the systolic portion, and the dicrotic notch is an example of a curved feature 330 representing the transition from the systolic portion to the diastolic portion. The local maximum 310, the downward slope 325 following the local maximum 310, and the curved feature 330 may be embodiments of features (eg, morphological features) of the pulse waveform 305.

[0104] In some cases, the amplitude 310-c of the local maximum 315-c of the pulse waveform 305-c may be smaller than the amplitude 310-b of the local maximum 315-b of the pulse waveform 305-b. The position 310-c of the local maximum 320-c of the pulse waveform 305-c may be shifted (e.g., to the right) compared to the position 310-b of the local maximum 320-b of the pulse waveform 305-b. In some examples, a second local maximum may not be present in the pulse waveform 305-c and / or the pulse waveform 305-b. The second local maximum may be an example of a curved feature 330 representing a transition from the systolic portion to the diastolic portion. The amplitude 315 of the local maximum 310 may decrease with age, the location 320 of the local maximum 310 may shift to the right with age, the downward slope 325 may increase with age, the curved feature 330 may decrease with age, or a combination thereof. For example, the shape of the pulse waveform 305 may become more triangular over time. In such a case, pulse waveform 305-c may correspond to an older chronological age than pulse waveform 305-b, which may correspond to an older chronological age than pulse waveform 305-a.

[0105] The system may extract morphological features of the pulse waveform 305-a. The morphological features may be, for example, the location 320-a of the local maximum 310-a, the value of the downward slope 325-a, the degree of the curved feature 330, or a combination thereof. In some cases, the system may extract features from the pulse waveforms 305-b and 305-c. The features may be, for example, the location 320-b of the local maximum 310-b, the location 320-c of the local maximum 310-c, the value of the downward slope 325-b, the value of the downward slope 325-c, the degree of the curved feature 330, or a combination thereof.

[0106] In some cases, the system may determine or identify a local maximum 310-a for the pulse waveform 305-a. The system may identify one or more downward slopes 325-a based on determining the local maximum 310-a. For example, the system may identify one or more downward slopes 325-a for the pulse waveform 305-a after receiving a PPG signal and before extracting morphological features associated with the values ​​of the downward slopes 325-a. In some cases, the system may identify one or more upward slopes for the pulse waveform 305-a. Note that a downward slope 325-a is an example of a negative slope, and an upward slope is an example of a positive slope. In some examples, the system may identify the presence of a second local maximum (e.g., representing the curved feature 330) for the pulse waveform 305-a. Additionally or alternatively, the system may identify other morphological features of the pulse waveform 305 using several statistical methods, including machine learning (e.g., unsupervised learning) techniques.

[0107] The system may compare features of pulse waveform 305-a with features of pulse waveform 305-b, pulse waveform 305-c, or any number of other baseline pulse waveform 305 features. In some cases, the system may perform the comparison after extracting features of pulse waveform 305-a. For example, the system may compare the amplitude 315-a, location 320-a, or both of local maximum 310-a with the amplitude 315-b, location 320-b, or both of local maximum 310-b. In other examples, the system may compare the amplitude 315-a, location 320-a, or both of local maximum 310-a with the amplitude 315-c, location 320-c, or both of local maximum 310-c. In such cases, the system may determine that the amplitude 315-a of local maximum 310-a is greater than the amplitudes 315-b and 315-c of local maxima 310-b and 310-c, respectively. The system may determine that location 320-a of local maximum 310-a is to the left of locations 320-b and 320-c of local maxima 310-b and 310-c.

[0108] In some examples, the system may compare the value of downward slope 325-a with the values ​​of downward slopes 325-b and 325-c. In such cases, the system may determine that the value of downward slope 325-a is less than the values ​​of downward slopes 325-b and 325-c of pulse waveforms 305-b and 305-c, respectively. The system may compare the degree of curved feature 330 of pulse waveform 305-a with the degree of curved feature 330 of pulse waveforms 305-b and 305-c. In some cases, curved feature 330 may not be present in pulse waveforms 305-b and 305-c. In such cases, the degree of curved feature 330 of pulse waveform 305-a may be greater than the degree of curved feature 330 of pulse waveforms 305-b and 305-c.

[0109] In response to comparing the features of the pulse waveform 305-a with features from the pulse waveform 305-b, the pulse waveform 305-c, or both, the system may determine a cardiovascular health metric indicative of the user's cardiovascular health status relative to the user's chronological age. In some cases, the system may determine which of the baseline pulse waveforms 305 most closely matches the pulse waveform 305-a. For example, the system may determine that the pulse waveform 305-a may match the pulse waveform 305 (e.g., baseline PPG signal morphology) of a user between the ages of 20 and 24. In such a case, the system may determine that the user's cardiovascular health metric corresponds to the cardiovascular health metric (e.g., cardiovascular age) of a user between the ages of 20 and 24.

[0110] The system may determine that a user's cardiovascular health metric indicates cardiovascular health that is lower or higher than the user's chronological age. For example, the system may determine, based on the comparison, that a user's cardiovascular health metric corresponds to a chronological age of 20 to 24 years old while the user's chronological age is 30 years old, thereby indicating that the user's cardiovascular health is healthy (e.g., within a normal or optimal range). In another example, the system may determine that a cardiovascular health metric indicative of a user's cardiovascular health is higher than the user's chronological age. For example, the system may determine, based on the comparison, that a user's cardiovascular health metric corresponds to a chronological age of 40 to 44 years old while the user's chronological age is 30 years old, thereby indicating that the user's cardiovascular health is unhealthy (e.g., within a suboptimal range). In such cases, the system may provide recommendations for improving the cardiovascular health metric, as described with reference to FIG. 5.

[0111] FIG. 4 illustrates an example timing diagram 400 for supporting wearable-based physiological data-based cardiovascular health metric determination according to an embodiment of the present disclosure. The timing diagram 400 illustrates a second derivative pulse waveform 405 versus time. In this regard, the solid curve shown in the timing diagram 400 may be understood to refer to the "second derivative pulse waveform 405-a," which may be an example of the second derivative of the pulse waveform 305-a as described with reference to FIG. 3. The dashed curve shown in the timing diagram 400 may be understood to refer to the "second derivative pulse waveforms 305-b and 305-c," which may be examples of the second derivative of the pulse waveforms 405-b and 405-c, respectively.

[0112] In some cases, the system may calculate and / or determine the first derivative of the original pulse waveform (e.g., pulse waveform 305 described with reference to FIG. 3). In an example, the system may calculate and / or determine the second derivative of the original pulse waveform. The calculated second derivative of the pulse waveform is an example of a second derivative pulse waveform 405. The system may identify one or more maxima 410, one or more minima 415, or both of the second derivative pulse waveform 405. In some cases, the system may identify one or more maxima, one or more minima 415, or both of the first derivative pulse waveform.

[0113] In some examples, the system may compare the second derivative pulse waveform 405-a (e.g., the second derivative of the received pulse waveform) with the second derivative pulse waveforms 405-b and 405-c (e.g., baseline PPG signal morphology). For example, the system may determine that the maximum 410-a of the second derivative pulse waveform 405-a may be greater than the maximum 410-b and 410-c of the second derivative pulse waveforms 405-b and 405-c, respectively. The system may determine that the minimum 415-a of the second derivative pulse waveform 405-a may be greater than the minimum 415-b and 415-c of the second derivative pulse waveforms 405-b and 405-c, respectively. In such cases, the system may calculate a deviation of the characteristics of the second derivative pulse waveform 405-a from the characteristics of the second derivative pulse waveforms 405-b and 405-c. For example, deviations in the second derivative pulse waveform 405 may indicate deviations in the original pulse waveform.

[0114] 3, a cardiovascular health metric may be determined based on a comparison of features of the second derivative pulse waveform 405-a (e.g., the amplitude 425 and / or location 430 of the maxima 410-a, the amplitude 425 and / or location 430 of the minima 415-a, or a combination thereof) with features of the second derivative pulse waveforms 405-b and 405-c. The features of the second derivative pulse waveforms 405-b and 405-c may be, for example, the amplitude and / or location of the maxima 410-b and 410-c, the amplitude and / or location of the minima 415-b and 415-c, or a combination thereof.

[0115] The system may determine the amplitude 420 of the local maximum 410-a of the second derivative pulse waveform 405-a. The amplitude 420 of the local maximum 410-a may be an example of a positive amplitude. In some cases, the amplitude 420 of the second derivative pulse waveform 405-a may indicate a cardiovascular health metric. In such cases, the system may determine the cardiovascular health metric based on identifying the local maximum 410-a and / or determining the amplitude 420 of the local maximum 410-a. For example, the system may determine the cardiovascular health metric based on calculating the first derivative of the pulse waveform, calculating the second derivative pulse waveform 405-a, or both.

[0116] In some cases, the system may determine the amplitude 425 of the local minimum 415-a of the second derivative pulse waveform 405-a. The amplitude 425 of the local minimum 415-a may be an example of a negative amplitude. In some cases, the amplitude 425 of the local minimum 415-a may be indicative of a cardiovascular health metric. In such cases, the system may determine the cardiovascular health metric based on identifying the local minimum 415-a and / or determining the amplitude 425 of the local minimum 415-a. In some cases, the system may determine the position 430 (e.g., location) of the local minimum 415-a. In some cases, the position 430 of the local minimum 415-a may be indicative of a cardiovascular health metric. In such cases, the system may determine the cardiovascular health metric based on determining the position 430 of the local minimum 415-a.

[0117] The second derivative pulse waveform 405 may include features that correlate with chronological age. For example, the amplitude 420 of the local maximum 410-a decreases with chronological age, the amplitude 425 of the local minimum 415-a decreases with chronological age, and the position of the local minimum 415-a increases (e.g., shifts to the right) with chronological age. In some cases, each age group may include variations in cardiovascular health metrics. For example, the 30-34 age group may include a cardiovascular health metric indicative of cardiovascular ages below 30-34 and / or above 30-34.

[0118] In some cases, the system may determine a cardiovascular health index in response to determining a cardiovascular health metric. In such cases, the cardiovascular health index may include the cardiovascular health metric as a component, as well as other inputs. The system may determine arterial stiffness in response to determining the cardiovascular health metric. In such cases, the cardiovascular health index may be determined in response to determining the arterial stiffness. In some cases, the arterial stiffness may be based on the user's blood pressure. In some examples, the system may determine the cardiovascular health index based on the cardiovascular health metric, arterial stiffness, blood pressure, resting heart rate, HRV, or a combination thereof.

[0119] 5 illustrates an example of a GUI 500 that supports wearable-based physiological data-based cardiovascular health metric determination according to aspects of the present disclosure. GUI 500 may implement or be implemented by aspects of system 100, system 200, timing diagram 300, timing diagram 400, or any combination thereof. For example, GUI 500 may be an example of GUI 275 of user device 106 (e.g., user devices 106-a, 106-b, 106-c) corresponding to user 102. In some examples, GUI 500 illustrates a series of application pages 505 that may be displayed to a user via GUI 500 (e.g., GUI 275 shown in FIG. 2).

[0120] A server of the system may generate a message 520 for display on a GUI 500 on a user device that indicates an indicia of the cardiovascular health metric. For example, the server of the system may cause the GUI 500 of a user device (e.g., a mobile device) to display (e.g., via an application page 505) a message 520 associated with the indicia of the cardiovascular health metric. In such a case, the system may output the indicia of the cardiovascular health metric on the GUI 500 of the user device to indicate the user's cardiovascular health relative to the user's chronological age.

[0121] Upon determining an indication of the user's cardiovascular health metrics, the user may be presented with an application page 505 upon opening the wearable application. As shown in FIG. 5 , the application page 505 may display an indication via message 520 that the cardiovascular health metrics have been determined and / or identified. In such a case, the application page 505 may include the message 520 on a home page. As described herein, when the user's cardiovascular health metrics are determined and / or identified, the server may send the message 520 to the user, the message 520 being associated with the cardiovascular health metrics. In some cases, the server may send the message 520 to a clinician, a caregiver, the user's partner, or a combination thereof. In such a case, the system may present the application page 505 on a user device associated with the clinician, caregiver, partner, or a combination thereof.

[0122] For example, the user may receive messages 520 indicating trends associated with the cardiovascular health metric, educational content associated with the cardiovascular health metric, a tailored set of sleep goals, a tailored set of activity goals, recommendations for improving the cardiovascular health metric, etc. The messages 520 may be configurable / customizable, as previously described herein, such that the user may receive different messages 520 based on the determination of the cardiovascular health metric.

[0123] In some cases, message 520 may include a weekly or monthly report associated with the determined cardiovascular health metric. The report may indicate trends associated with the cardiovascular health metric. For example, the trend may indicate whether the cardiovascular health metric is changing (e.g., increasing or decreasing) relative to a previously determined cardiovascular health metric. In some cases, the system may provide personalized recommendations for improving or maintaining the cardiovascular health metric. For example, message 520 may indicate, "Did you know that exercising four times a week impacts your cardiovascular health metric? Try adding some exercise this week."

[0124] In such cases, the message 520 may include insights, recommendations, etc. associated with the determined cardiovascular health metric. The system's server may cause the user device's GUI 500 to display the message 520 associated with the cardiovascular health metric. The user device may display recommendations and / or information associated with the cardiovascular health metric via the message 520. As previously discussed herein, accurately determined cardiovascular health metrics may be beneficial to the user's overall health.

[0125] Further, in some implementations, application page 505 may display one or more scores of the user for each day (e.g., sleep score, readiness score, activity score, etc.). Furthermore, in some cases, the determined cardiovascular health metric may be used to update (e.g., modify) one or more scores associated with the user (e.g., sleep score, readiness score, etc.). That is, data associated with the cardiovascular health metric may be used to update the user's score for the next calendar day. In such cases, the system may notify the user of the score update via alert 510.

[0126] In some cases, the readiness score may be updated based on the cardiovascular health metric. In such cases, the readiness score may indicate to the user to "exercise caution" based on the determined cardiovascular health metric. If the readiness score changes for a user, the system may implement a recovery mode for the user whose symptoms associated with cardiovascular health may be severe and who may benefit from tailored activity and readiness guidance over a period of days, weeks, or months.

[0127] In other examples, the system may determine that a user's determined cardiovascular health metric (e.g., cardiovascular age) is less than or equal to the user's chronological age and adjust the readiness score, sleep score, and / or activity score to match the equal (e.g., expected) or lower cardiovascular health metric. In other cases, the system may determine that a user's determined cardiovascular health metric (e.g., cardiovascular age) is greater than the user's chronological age and adjust the readiness score, sleep score, and / or activity score to offset the impact of the higher cardiovascular health metric. In some cases, the system may provide insights for maintaining the user's cardiovascular age (e.g., cardiovascular health metric) lower than or equal to the user's chronological age. For example, the system may display recommendations and / or motivations for healthy habits via message 520 and provide behavioral insights to the user.

[0128] In some cases, a message 520 displayed to the user via the GUI 500 of the user device may indicate how the determined cardiovascular health metric affected the overall score (e.g., overall readiness score) and / or individual contributing factors. For example, the message 520 may indicate, "Your cardiovascular health metric appears to be greater than your chronological age, but if you feel OK, engaging in light or moderate intensity exercise may improve your cardiovascular health metric" or "From your cardiovascular health metric, it looks like you're heading in the right direction for your chronological age. Keep it up!" If a cardiovascular health metric is determined, the message 520 may provide suggestions for the user to improve their overall health (e.g., including the user's cardiovascular health metric). In such cases, the message 520 displayed to the user may provide targeted insights to help the user adjust their lifestyle.

[0129] The application page 505 may show one or more parameters including a pulse waveform (e.g., a portion of a PPG signal), temperature, heart rate, HRV, respiration rate, sleep data, etc. via a graphical display 515. The graphical display 515 may be an example of the timing diagram 300 or timing diagram 400 described with reference to Figures 3 and 4. In such a case, the system may cause the user device's GUI 500 to display a message 520, warning 510, or graphical display 515 associated with the cardiovascular health metric.

[0130] In some cases, a user may log symptoms or events via user input 525. For example, the system may receive user input (e.g., tags) to log symptoms and / or events associated with illness, stress, pregnancy, etc. For example, the system may receive indicia of data related to the user's health record via user input 525. Data related to the user's health record may include indicia of illness, stress, pregnancy, alcohol use, exercise history, sleep habits, current medications, previous surgeries, etc. In other examples, the system may receive indicia of data related to the user's health record from a wearable device, physiological data from the wearable device, or both. Physiological data from the wearable device may be examples of temperature, heart rate, HRV, respiration rate, sleep data, blood pressure, etc.

[0131] In such cases, the system may adjust the cardiovascular health metric in response to receiving the indicia. For example, the cardiovascular health metric may be adjusted based on the patient's medical history, physiological data obtained from the wearable device, or both. The system may cause the GUI 500 to display an indicia (via an alert 510, a graphical representation 515, and / or a message 520) based on the adjustment of the cardiovascular health metric. In such cases, the system may adjust insights, recommendations, etc. based on the adjusted cardiovascular health metric. For example, the system may indicate, "It looks like you have a cold. Your cardiovascular health metric is higher than usual, but this will all balance out after you get over your cold. Get some rest." In some examples, the system may indicate, "Based on your healthy lifestyle, your cardiovascular health metric is lower than your chronological age. Keep it up!"

[0132] 5, application page 505 may display an indication of the cardiovascular health metric via alert 510. In some cases, application page 505 may display an indication of the adjusted cardiovascular health metric via alert 510. The user may receive alert 510, and application page 505 may prompt the user to confirm or reject the determined or adjusted cardiovascular health metric. For example, the system may receive, via the user device, a confirmation of the cardiovascular health metric in response to adjusting the cardiovascular health metric.

[0133] In some implementations, the system may provide additional insights regarding the user's determined cardiovascular health metric. For example, application page 505 may indicate one or more physiological parameters (e.g., contributing factors) that contribute to the user's determined cardiovascular health metric, such as deviations of one or more morphological features from baseline PPG signal morphology, exercise habits, sleep habits, etc. In other words, the system may be configured to provide some information or other insights regarding the determined cardiovascular health metric. The personalized insights may indicate aspects of the collected physiological data (e.g., contributing factors within the physiological data) that were used to generate the determined cardiovascular health metric.

[0134] In some implementations, the system may be configured to receive user inputs regarding the determined cardiovascular health metric to train a classifier (e.g., supervised learning for a machine learning classifier) ​​and improve the cardiovascular health metric determination technique. For example, a user device may receive user inputs 525, which may then be input to the classifier to train the classifier. In some cases, a PPG signal may be input to the machine learning classifier. In such cases, the system may determine the cardiovascular health metric in response to inputting the PPG signal to the machine learning classifier.

[0135] 6 illustrates a block diagram 600 of a device 605 that assists in wearable-based physiological data-based cardiovascular health metric determination according to an embodiment of the present disclosure. The device 605 may include an input module 610, an output module 615, and a wearable application 620. The device 605 may include a processor. Each of these components may communicate with each other (e.g., via one or more buses).

[0136] The input module 610 may provide a means for receiving information such as packets, user data, control information, or any combination thereof associated with various information channels (e.g., control channels, data channels, information channels related to disease detection techniques). The information may be passed to other components of the device 605. The input module 610 may utilize a single antenna or a collection of multiple antennas.

[0137] The output module 615 may provide a means for transmitting signals generated by other components of the device 605. For example, the output module 615 may transmit information such as packets, user data, control information, or any combination thereof associated with various information channels (e.g., a control channel, a data channel, an information channel related to disease detection techniques). In some examples, the output module 615 may be co-located with the input module 610 within the transceiver module. The output module 615 may utilize a single antenna or a collection of multiple antennas.

[0138] For example, wearable application 620 may include a data acquisition component 625, a morphological feature component 630, a comparison component 635, a cardiovascular metric component 640, a user interface component 645, or any combination thereof. In some examples, wearable application 620, or its various components, may be configured to perform various operations (e.g., receive, monitor, transmit) using or in cooperation with input module 610, output module 615, or both. For example, wearable application 620 may receive information from input module 610, transmit information to output module 615, or be integrated in combination with input module 610, output module 615, or both to receive information, transmit information, or perform various other operations as described herein.

[0139] The data acquisition component 625 may be configured as, or may otherwise assist in, receiving from the wearable device a photoplethysmogram (PPG) signal representing a pulse waveform for the user, the pulse waveform including a first maximum, a downward slope following the first maximum, and a curvature characteristic representing a transition from the systolic phase to the diastolic phase of the cardiac cycle. The morphological feature component 630 may be configured as, or may otherwise assist in, extracting one or more morphological features related to the location of the first maximum, the value of the downward slope, the degree of the curvature characteristic, or a combination thereof. The comparison component 635 may be configured as, or may otherwise assist in, comparing the one or more morphological features with one or more features from multiple baseline PPG signal morphologies associated with multiple chronological ages, based at least in part on the extraction of the one or more morphological features. The cardiovascular metric component 640 may be configured as, or may otherwise assist in, determining a cardiovascular health metric indicative of the user's cardiovascular health relative to the user's chronological age, based at least in part on the comparison. The user interface component 645 may be configured as or otherwise assist in causing a graphical user interface to display indicia of cardiovascular health metrics.

[0140] FIG. 7 illustrates a block diagram 700 of a wearable application 720 that facilitates wearable-based physiological data-based cardiovascular health metric determination according to an embodiment of the present disclosure. The wearable application 720 may be an example of an embodiment of a wearable application and / or wearable application 620, as described herein. The wearable application 720, or various components thereof, may be an example of a means for performing various aspects of wearable-based physiological data-based cardiovascular health metric determination as described herein. For example, the wearable application 720 may include a data acquisition component 725, a morphological characteristics component 730, a comparison component 735, a cardiovascular metric component 740, a user interface component 745, or any combination thereof. Each of these components may communicate with each other directly or indirectly (e.g., via one or more buses).

[0141] The data acquisition component 725 may be configured as, or may otherwise assist in, receiving a photoplethysmogram (PPG) signal representing a pulse waveform for a user from a wearable device, the pulse waveform including a first maximum, a downward slope following the first maximum, and a curvature characteristic representing a transition from the systolic phase to the diastolic phase of the cardiac cycle. The morphological feature component 730 may be configured as, or may otherwise assist in, extracting one or more morphological features related to the location of the first maximum, the value of the downward slope, the degree of the curvature characteristic, or a combination thereof. The comparison component 735 may be configured as, or may otherwise assist in, comparing the one or more morphological features with one or more features from multiple baseline PPG signal morphologies associated with multiple chronological ages, based at least in part on the extraction of the one or more morphological features. The cardiovascular metric component 740 may be configured as, or may otherwise assist in, determining a cardiovascular health metric indicative of the user's cardiovascular health relative to the user's chronological age, based at least in part on the comparison. The user interface component 745 may be configured as or otherwise assist in causing a graphical user interface to display indicia of cardiovascular health metrics.

[0142] In some examples, to aid in the extraction of one or more morphological features, morphological feature component 730 may be configured as or may otherwise assist in calculating a first derivative of the pulse waveform, a second derivative of the pulse waveform, or both. In some examples, to aid in the extraction of one or more morphological features, morphological feature component 730 may be configured as or may otherwise assist in identifying one or more maxima or one or more minima in the first derivative of the pulse waveform, the second derivative of the pulse waveform, or both, where the one or more morphological features are associated with one or more maxima or one or more minima in the first derivative of the pulse waveform, the second derivative of the pulse waveform, or both.

[0143] In some examples, to aid in the extraction of one or more morphological features, the morphological features component 730 may be configured as or may otherwise aid in the means for determining the amplitude, location, or both of the first local maximum, wherein the one or more morphological features are associated with the amplitude, location, or both of the first local maximum.

[0144] In some examples, to aid in the extraction of one or more morphological features, the morphological features component 730 may be configured as or otherwise assist in identifying the presence of a second maximum in the pulse waveform, where a curved feature representing the transition from systole to diastole of the cardiac cycle is associated with the second maximum.

[0145] In some examples, to aid in the extraction of one or more morphological features, the morphological features component 730 may be configured as or otherwise assist in identifying one or more positive slopes or one or more negative slopes of the pulse waveform, wherein a downward slope following a first local maximum is associated with one or more negative slopes of the pulse waveform.

[0146] In some examples, the comparison component 735 may be configured as, or may otherwise assist in, a means for determining, based at least in part on the comparison, which of a plurality of baseline PPG signal morphologies matches one or more morphological features, and a determination of a cardiovascular health metric is based at least in part on the determination.

[0147] In some examples, the comparison component 735 may be configured as or otherwise assist in calculating deviations of one or more morphological features for one or more morphological features from a plurality of baseline PPG signal morphologies based at least in part on the comparison, and determining the cardiovascular health metric is based at least in part on calculating the deviations.

[0148] In some examples, the data acquisition component 725 may be configured as or otherwise assist in receiving, via the user device, indicia of data related to the user's health record from the wearable device, physiological data from the wearable device, or both. In some examples, the cardiovascular metric component 740 may be configured as or otherwise assist in adjusting a cardiovascular health metric based at least in part on receiving the indicia, and causing the indicia to be displayed in the graphical user interface may be based at least in part on the adjustment of the cardiovascular health metric.

[0149] In some examples, the user interface component 745 may be configured as or otherwise assist in causing a graphical user interface of a user device associated with a user to display a message associated with a cardiovascular health metric.

[0150] In some examples, the message further includes recommendations for improving the cardiovascular health metric, trends associated with the cardiovascular health metric, educational content associated with the cardiovascular health metric, an adjusted set of activity goals, an adjusted set of sleep goals, or a combination thereof.

[0151] In some examples, the data acquisition component 725 may be configured as or otherwise assist in identifying, based at least in part on receiving a PPG signal, a plurality of baseline PPG signal morphologies associated with a plurality of chronological ages, and the comparison may be based at least in part on the identification of the plurality of baseline PPG signal morphologies.

[0152] In some examples, the data acquisition component 725 may be configured as or otherwise assist in the means for inputting the PPG signal into a machine learning classifier, and the determination of the cardiovascular health metric is based at least in part on inputting the PPG signal into the machine learning classifier.

[0153] In some examples, multiple baseline PPG signal morphologies associated with multiple chronological ages are extracted from physiological data associated with multiple users.

[0154] In some examples, the wearable device includes a wearable device ring device.

[0155] In some examples, the wearable device collects physiological data from the user based on arterial blood flow, capillary blood flow, arteriolar blood flow, or a combination thereof.

[0156] 8 illustrates a system 800 including a device 805 for assisting in wearable-based physiological data-based cardiovascular health metric determination according to an embodiment of the present disclosure. The device 805 may be an example of the device 605 described herein or may include components of the device 120. The device 805 may include an example of the user device 106, as previously described herein. The device 805 may include components for bidirectional communication, including components for sending and receiving communications with the wearable device 104 and the server 110, such as a wearable application 820, a communication module 810, an antenna 815, a user interface component 825, a database (application data) 830, a memory 835, and a processor 840. These components may be in electronic communication or may be otherwise coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more buses (e.g., bus 845).

[0157] The communications module 810 may manage input and output signals for the device 805 via the antenna 815. The communications module 810 may include an example of the communications module 220-b of the user device 106 shown and described in FIG. 2. In this regard, the communications module 810 may manage communications with the ring 104 and the server 110, as shown in FIG. 2. The communications module 810 may also manage peripheral devices not integrated into the device 805. In some cases, the communications module 810 may represent a physical connection or port to an external peripheral device. In some cases, the communications module 810 may utilize an operating system such as iOS®, ANDROID®, MS-DOS®, MS-WINDOWS®, OS / 2®, UNIX®, LINUX®, or other known operating systems. In other cases, the communications module 810 may represent or interact with a wearable device (e.g., the ring 104), a modem, a keyboard, a mouse, a touchscreen, or similar devices. In some cases, the communications module 810 may be implemented as part of the processor 840. In some examples, a user may interact with the device 805 through the communications module 810, the user interface component 825, or a hardware component controlled by the communications module 810.

[0158] In some cases, the device 805 may include a single antenna 815. However, in some other cases, the device 805 may have two or more antennas 815 that may simultaneously transmit or receive multiple wireless transmissions. The communications module 810 may communicate bidirectionally via one or more antennas 815, wired or wireless links, as described herein. For example, the communications module 810 may represent a wireless transceiver and may communicate bidirectionally with another wireless transceiver. The communications module 810 may also include a modem that modulates packets, provides the modulated packets to one or more antennas 815 for transmission, and demodulates packets received from the one or more antennas 815.

[0159] The user interface component 825 may manage data storage and processing in the database 830. In some cases, a user may interact with the user interface component 825. In other cases, the user interface component 825 may operate automatically without user interaction. The database 830 may be an example of a single database, a distributed database, multiple distributed databases, a data store, a data lake, or an emergency backup database.

[0160] The memory 835 may include RAM and ROM. The memory 835 may store computer-readable, computer-executable software containing instructions that, when executed, cause the processor 840 to perform various functions described herein. In some cases, the memory 835 may include a BIOS, which may control basic hardware or software operations, such as interaction with peripheral components or devices, among other things.

[0161] The processor 840 may include an intelligent hardware device (e.g., a general-purpose processor, a DSP, a CPU, a microcontroller, an ASIC, an FPGA, a programmable logic device, discrete gate or transistor logic components, discrete hardware components, or any combination thereof). In some cases, the processor 840 may be configured to operate a memory array using a memory controller. In other cases, the memory controller may be integrated into the processor 840. The processor 840 may be configured to execute computer-readable instructions stored in the memory 835 to perform various functions (e.g., functions or tasks that support the methods and systems for sleep staging algorithms).

[0162] For example, the wearable application 820 may be configured or otherwise assisting means for receiving from a wearable device a photoplethysmogram (PPG) signal representing a pulse waveform for a user, the pulse waveform including a first maximum, a downward slope following the first maximum, and a curvature characteristic representing a transition from the systolic phase to the diastolic phase of a cardiac cycle. The wearable application 820 may be configured or otherwise assisting means for extracting one or more morphological features related to the location of the first maximum, the value of the downward slope, the degree of the curvature characteristic, or a combination thereof. The wearable application 820 may be configured or otherwise assisting means for comparing the one or more morphological features to one or more features from a plurality of baseline PPG signal morphologies associated with a plurality of chronological ages, based at least in part on extracting the one or more morphological features. The wearable application 820 may be configured as or otherwise assist in determining a cardiovascular health metric indicative of the user's cardiovascular health relative to the user's chronological age based at least in part on the comparison. The wearable application 820 may be configured as or otherwise assist in causing an indicia of the cardiovascular health metric to be displayed in a graphical user interface.

[0163] By including or configuring a wearable application 820 in accordance with embodiments described herein, the device 805 may facilitate techniques for improved communication reliability, reduced latency, an improved user experience associated with reduced processing, reduced power consumption, more efficient utilization of communication resources, improved coordination between devices, longer battery life, improved utilization of processing power, or a combination thereof.

[0164] The wearable applications 820 may include applications (e.g., “apps”), programs, software, or other components configured to facilitate communication with the ring 104, the server 110, other user devices 106, etc. For example, the wearable applications 820 may include applications executable on the user device 106 configured to receive data (e.g., physiological data) from the ring 104, perform processing operations on the received data, send data to and receive data from the server 110, and present the data to the user 102.

[0165] FIG. 9 is a flowchart illustrating a method 900 for assisting in wearable-based physiological data-based cardiovascular health metric determination according to an embodiment of the present disclosure. The operations of method 900 may be implemented by a user device or components thereof as described herein. For example, the operations of method 900 may be performed by a user device as described with reference to FIGS. 1 through 8. In some examples, the user device may execute a set of instructions to control functional elements of the user device to perform the described functions. Additionally or alternatively, the user device may perform aspects of the described functions using dedicated hardware.

[0166] At 905, the method may include receiving a photoplethysmogram (PPG) signal from a wearable device representing a pulse waveform for a user, the pulse waveform including a first maximum, a downward slope following the first maximum, and a curved feature representing a transition from systole to diastole of a cardiac cycle. The operations of 905 may be performed in accordance with embodiments disclosed herein. In some examples, aspects of the operations of 905 may be performed by data acquisition component 725, as described with reference to FIG. 7.

[0167] At 910, the method may include extracting one or more morphological features related to the location of the first maximum, the value of the downward slope, the degree of curvature, or a combination thereof. The operations of 910 may be performed according to embodiments disclosed herein. In some examples, aspects of the operations of 910 may be performed by morphological features component 730, as described with reference to FIG. 7.

[0168] At 915, the method may include comparing, based at least in part on extracting the one or more morphological features, the one or more morphological features to one or more features from a plurality of baseline PPG signal morphologies associated with a plurality of chronological ages. The operations of 915 may be performed in accordance with embodiments disclosed herein. In some examples, aspects of the operations of 915 may be performed by comparison component 735, as described with reference to FIG. 7.

[0169] At 920, the method may include determining a cardiovascular health metric indicative of the user's cardiovascular health relative to the user's chronological age based at least in part on the comparison. The operations of 920 may be performed in accordance with embodiments disclosed herein. In some examples, aspects of the operations of 920 may be performed by cardiovascular metric component 740, as described with reference to FIG. 7.

[0170] At 925, the method may include causing a graphical user interface to display an indicia of the cardiovascular health metric. The operations of 925 may be performed according to embodiments disclosed herein. In some examples, aspects of the operations of 925 may be performed by user interface component 745 as described with reference to FIG. 7.

[0171] FIG. 10 is a flowchart illustrating a method 1000 for assisting in wearable-based physiological data-based cardiovascular health metric determination according to an embodiment of the present disclosure. The operations of method 1000 may be implemented by a user device or components thereof as described herein. For example, the operations of method 1000 may be performed by a user device as described with reference to FIGS. 1 through 8. In some examples, the user device may execute a set of instructions to control functional elements of the user device to perform the described functions. Additionally or alternatively, the user device may perform aspects of the described functions using dedicated hardware.

[0172] At 1005, the method may include receiving a photoplethysmogram (PPG) signal from a wearable device representing a pulse waveform for a user, the pulse waveform including a first maximum, a downward slope following the first maximum, and a curved feature representing a transition from systole to diastole of a cardiac cycle. The operations of 1005 may be performed in accordance with embodiments disclosed herein. In some examples, aspects of the operations of 1005 may be performed by data acquisition component 725, as described with reference to FIG. 7.

[0173] At 1010, the method may include extracting one or more morphological features related to the location of the first maximum, the value of the downward slope, the degree of curvature, or a combination thereof. The operations of 1010 may be performed according to embodiments disclosed herein. In some examples, aspects of the operations of 1010 may be performed by morphological features component 730, as described with reference to FIG. 7.

[0174] At 1015, the method may include calculating a first derivative of the pulse waveform, a second derivative of the pulse waveform, or both. The operations of 1015 may be performed in accordance with embodiments disclosed herein. In some examples, aspects of the operations of 1015 may be performed by morphological features component 730, as described with reference to FIG. 7.

[0175] At 1020, the method may include identifying one or more maxima or one or more minima in a first derivative of the pulse waveform, a second derivative of the pulse waveform, or both, and one or more morphological features are associated with the one or more maxima or one or more minima in the first derivative of the pulse waveform, a second derivative of the pulse waveform, or both. The operations of 1020 may be performed in accordance with embodiments disclosed herein. In some examples, aspects of the operations of 1020 may be performed by morphological feature component 730, as described with reference to FIG. 7.

[0176] At 1025, the method may include comparing, based at least in part on extracting the one or more morphological features, the one or more morphological features to one or more features from a plurality of baseline PPG signal morphologies associated with a plurality of chronological ages. The operations of 1025 may be performed in accordance with embodiments disclosed herein. In some examples, aspects of the operations of 1025 may be performed by comparison component 735, as described with reference to FIG. 7.

[0177] At 1030, the method may include determining a cardiovascular health metric indicative of the user's cardiovascular health relative to the user's chronological age based at least in part on the comparison. The operations of 1030 may be performed in accordance with embodiments disclosed herein. In some examples, aspects of the operations of 1030 may be performed by cardiovascular metric component 740, as described with reference to FIG. 7.

[0178] At 1035, the method may include causing a graphical user interface to display an indicia of the cardiovascular health metric. The operations of 1035 may be performed according to embodiments disclosed herein. In some examples, aspects of the operations of 1035 may be performed by user interface component 745 as described with reference to FIG. 7.

[0179] FIG. 11 is a flowchart illustrating a method 1100 for assisting in wearable-based physiological data-based cardiovascular health metric determination according to an embodiment of the present disclosure. The operations of method 1100 may be implemented by a user device or components thereof as described herein. For example, the operations of method 1100 may be performed by a user device as described with reference to FIGS. 1 through 8. In some examples, the user device may execute a set of instructions to control functional elements of the user device to perform the described functions. Additionally or alternatively, the user device may perform aspects of the described functions using dedicated hardware.

[0180] At 1105, the method may include receiving a photoplethysmogram (PPG) signal from a wearable device representing a pulse waveform for a user, the pulse waveform including a first maximum, a downward slope following the first maximum, and a curved feature representing a transition from systole to diastole of a cardiac cycle. The operations of 1105 may be performed in accordance with embodiments disclosed herein. In some examples, aspects of the operations of 1105 may be performed by data acquisition component 725, as described with reference to FIG. 7.

[0181] At 1110, the method may include extracting one or more morphological features related to the location of the first maximum, the value of the downward slope, the degree of curvature, or a combination thereof. The operations of 1110 may be performed according to embodiments disclosed herein. In some examples, aspects of the operations of 1110 may be performed by morphological features component 730, as described with reference to FIG. 7.

[0182] At 1115, the method may include comparing, based at least in part on extracting the one or more morphological features, the one or more morphological features to one or more features from a plurality of baseline PPG signal morphologies associated with a plurality of chronological ages. The operations of 1115 may be performed in accordance with embodiments disclosed herein. In some examples, aspects of the operations of 1115 may be performed by comparison component 735, as described with reference to FIG. 7.

[0183] At 1120, the method may include determining, based at least in part on the comparison, which of the plurality of baseline PPG signal morphologies matches the one or more morphological features, and determining a cardiovascular health metric based at least in part on the determination. The operations of 1120 may be performed in accordance with embodiments disclosed herein. In some examples, aspects of the operations of 1120 may be performed by comparison component 735, as described with reference to FIG. 7.

[0184] At 1125, the method may include determining a cardiovascular health metric indicative of the user's cardiovascular health relative to the user's chronological age based at least in part on the comparison. The operations of 1125 may be performed in accordance with embodiments disclosed herein. In some examples, aspects of the operations of 1125 may be performed by cardiovascular metric component 740, as described with reference to FIG. 7.

[0185] At 1130, the method may include causing a graphical user interface to display an indicia of the cardiovascular health metric. The operations of 1130 may be performed according to embodiments disclosed herein. In some examples, aspects of the operations of 1130 may be performed by user interface component 745 as described with reference to FIG. 7.

[0186] It should be noted that the above-described methods describe possible implementations, and that the operations and steps may be rearranged or otherwise modified, and that other implementations are possible. Additionally, aspects from two or more methods may be combined.

[0187] A method is described that may include receiving, from a wearable device, a photoplethysmogram (PPG) signal representing a pulse waveform for a user, the pulse waveform including a first maximum, a downward slope following the first maximum, and a curved feature representing a transition from systole to diastole of a cardiac cycle, extracting one or more morphological features related to the location of the first maximum, the value of the downward slope, the degree of the curved feature, or a combination thereof, comparing the one or more morphological features to one or more features from a plurality of baseline PPG signal morphologies associated with a plurality of chronological ages based at least in part on the extracting the one or more morphological features, determining a cardiovascular health metric indicative of the user's cardiovascular health relative to the user's chronological age based at least in part on the comparison, and causing a graphical user interface to display an indicia of the cardiovascular health metric.

[0188] An apparatus is described. The apparatus may include a processor, a memory coupled to the processor, and instructions stored in the memory. The instructions may be executable by the processor to cause the apparatus to: receive from a wearable device a photoplethysmogram (PPG) signal representing a pulse waveform for a user, the pulse waveform including a first maximum, a downward slope following the first maximum, and a curvature feature representing a transition from the systole to the diastole of a cardiac cycle; extract one or more morphological features related to the location of the first maximum, the value of the downward slope, the degree of the curvature feature, or a combination thereof; compare the one or more morphological features with one or more features from a plurality of baseline PPG signal morphologies associated with a plurality of chronological ages based at least in part on the extracting the one or more morphological features; determine a cardiovascular health metric indicative of the user's cardiovascular health relative to the user's chronological age based at least in part on the comparison; and display an indicia of the cardiovascular health metric on a graphical user interface.

[0189] Another apparatus is described that may include: means for receiving, from a wearable device, a photoplethysmogram (PPG) signal representing a pulse waveform for a user, the pulse waveform including a first maximum, a downward slope following the first maximum, and a curvature feature representing a transition from the systole to the diastole of a cardiac cycle; means for extracting one or more morphological features related to the location of the first maximum, the value of the downward slope, the degree of the curvature feature, or a combination thereof; means for comparing the one or more morphological features with one or more features from a plurality of baseline PPG signal morphologies associated with a plurality of chronological ages based at least in part on the extracting the one or more morphological features; means for determining, based at least in part on the comparison, a cardiovascular health metric indicative of the user's cardiovascular health relative to the user's chronological age; and means for displaying an indicia of the cardiovascular health metric on a graphical user interface.

[0190] A non-transitory computer-readable medium storing code is described, which may include instructions executable by a processor to: receive from a wearable device a photoplethysmogram (PPG) signal representing a pulse waveform for a user, the pulse waveform including a first maximum, a downward slope following the first maximum, and a curvature feature representing a transition from the systole to the diastole of a cardiac cycle; extract one or more morphological features related to the location of the first maximum, the value of the downward slope, the degree of the curvature feature, or a combination thereof; compare the one or more morphological features to one or more features from a plurality of baseline PPG signal morphologies associated with a plurality of chronological ages based at least in part on the extracting the one or more morphological features; determine a cardiovascular health metric indicative of the user's cardiovascular health relative to the user's chronological age based at least in part on the comparison; and cause a graphical user interface to display an indicia of the cardiovascular health metric.

[0191] In some example methods, apparatus, and non-transitory computer-readable media described herein, extracting one or more morphological features may include operations, features, means, or instructions for calculating a first derivative of the pulse waveform, a second derivative of the pulse waveform, or both, and identifying one or more maxima or minima in the first derivative of the pulse waveform or the second derivative of the pulse waveform, or both, and the one or more morphological features may be associated with one or more maxima or minima in the first derivative of the pulse waveform or the second derivative of the pulse waveform, or both.

[0192] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, extracting one or more morphological features may include operations, features, means, or instructions for determining an amplitude, a location, or both, of the first local maximum, and the one or more morphological features may be associated with the amplitude, the location, or both, of the first local maximum.

[0193] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, extracting one or more morphological features may include acts, features, means, or instructions for identifying the presence of a second maximum in the pulse waveform, wherein a curved feature representing a transition from systole to diastole of the cardiac cycle may be associated with the second maximum.

[0194] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, extracting one or more morphological features may include operations, features, means, or instructions for identifying one or more positive slopes or one or more negative slopes of the pulse waveform, and a downward slope following a first local maximum may be associated with one or more negative slopes of the pulse waveform.

[0195] Some example methods, apparatus, and non-transitory computer-readable media described herein may further include operations, features, means, or instructions for determining which of the plurality of baseline PPG signal morphologies match one or more morphological features based at least in part on the comparison, and determining a cardiovascular health metric may be based at least in part on the determination.

[0196] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may further include operations, features, means, or instructions for calculating a deviation of one or more morphological features for the one or more morphological features from a plurality of baseline PPG signal morphologies based at least in part on the comparison, and determining a cardiovascular health metric may be based at least in part on calculating the deviation.

[0197] Some examples of methods, apparatus, and non-transitory computer-readable media described herein may further include operations, features, means, or instructions for receiving, via the user device, an indicia of data related to the user's health record from the wearable device, physiological data from the wearable device, or both, and adjusting the cardiovascular health metric based at least in part on receiving the indicia, and causing the graphical user interface to display the indicia may be based at least in part on adjusting the cardiovascular health metric.

[0198] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may further include operations, features, means, or instructions for causing a graphical user interface of a user device associated with the user to display a message associated with the cardiovascular health metric.

[0199] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the message further includes recommendations for improving the cardiovascular health metric, trends associated with the cardiovascular health metric, educational content associated with the cardiovascular health metric, an adjusted set of activity goals, an adjusted set of sleep goals, or a combination thereof.

[0200] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may further include operations, features, means, or instructions for identifying multiple baseline PPG signal morphologies associated with multiple chronological ages based at least in part on receiving a PPG signal, and the comparison may be based at least in part on identifying the multiple baseline PPG signal morphologies.

[0201] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may further include operations, features, means, or instructions for inputting the PPG signal into a machine learning classifier, and determining the cardiovascular health metric may be based at least in part on inputting the PPG signal into the machine learning classifier.

[0202] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, multiple baseline PPG signal morphologies associated with multiple chronological ages may be extracted from physiological data associated with multiple users.

[0203] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the wearable device includes a wearable ring device.

[0204] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the wearable device collects physiological data from the user based on arterial blood flow, capillary blood flow, arteriolar blood flow, or a combination thereof.

[0205] The description set forth herein with reference to the accompanying drawings describes exemplary configurations and does not represent all embodiments that may be implemented or fall within the scope of the claims. Here, "exemplary" means "serving as an example, example, or illustration," and not "preferred" or "advantageous over other examples." The detailed description includes specific details for the purpose of providing an understanding of the described technology. However, these technologies may be practiced without these specific details. In some instances, well-known structures and devices are shown in block diagram form in order to avoid obscuring the concepts of the described embodiments.

[0206] In the accompanying drawings, similar components or features may have the same reference numeral. Additionally, various components of the same type may be distinguished by following the reference label by a dash and a second label that distinguishes between the similar components. When only a first reference label is used, the description may apply to any of the similar components having the same first reference label, regardless of the second reference label.

[0207] The information and signals described herein may be represented using any of a variety of different technologies and techniques. For example, the data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0208] The various example blocks and modules described in connection with the disclosure herein may be implemented or performed using a general-purpose processor, a DSP, an ASIC, an FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof, designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but alternatively, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices (e.g., a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration).

[0209] The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored on or transmitted as one or more instructions or code on a computer-readable medium. Other embodiments and implementations are within the scope of this disclosure and the appended claims. For example, due to the nature of the software, the functions described above may be implemented using software executed by a processor, hardware, firmware, hardwiring, or any combination thereof. Features implementing the functions may be physically located in various locations, including being distributed such that portions of the functions are implemented in different physical locations. Also, as used herein, including in the claims, "or" used in lists of items (e.g., lists of items preceded by phrases such as "at least one" or "one or more") indicates an inclusive list, such as, for example, a list of at least one of A, B, or C means A or B or C or AB or AC or BC or ABC (i.e., A and B and C). Also, as used herein, the phrase "based on" should not be construed as referring to a closed set of conditions. For example, an example step described as "based on condition A" can be based on both condition A and condition B without departing from the scope of the present disclosure. In other words, as used herein, the phrase "based on" is to be interpreted in the same manner as the phrase "based at least in part on."

[0210] Computer-readable media includes both non-transitory computer storage media and communication media, including any medium that facilitates transfer of a computer program from one place to another. Non-transitory storage media may be any available medium that can be accessed by a general-purpose or special-purpose computer. By way of example, and not limitation, non-transitory computer-readable media may include RAM, ROM, Electrically Erasable Programmable Read Only Memory (EEPROM), Compact Disc (CD) ROM or other optical disk storage, magnetic disk storage or other magnetic storage, or any other non-transitory medium that can be used to carry or store desired program code means in the form of instructions or data structures and that can be accessed by a general-purpose or special-purpose computer or a general-purpose or special-purpose processor. Also, any connection is properly termed a computer-readable medium. For example, if software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. As used herein, disk and disc include CDs, laser discs, optical discs, digital versatile discs (DVDs), floppy disks, and Blu-ray discs, where disks typically reproduce data magnetically while disks reproduce data optically using lasers. Combinations of the above are also included within the scope of computer-readable media.

[0211] The description herein is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to the present disclosure will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the scope of the present disclosure. Thus, the present disclosure is not limited to the embodiments and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. receiving a photoplethysmogram (PPG) signal from a wearable device representing a pulse waveform for a user, the pulse waveform including a first maximum, a downward slope following the first maximum, and a curved feature representing a transition from systole to diastole of a cardiac cycle; extracting one or more morphological features related to the location of the first maximum, the value of the downward slope, the degree of the curved feature, or a combination thereof; comparing the one or more morphological features to one or more features from a plurality of baseline PPG signal morphologies associated with a plurality of chronological ages based at least in part on extracting the one or more morphological features; determining a cardiovascular health metric indicative of the user's cardiovascular health relative to the user's chronological age based at least in part on the comparison; and causing a graphical user interface to display an indicia of the cardiovascular health metric; A method comprising:

2. Extracting the one or more morphological features includes: calculating a first derivative of the pulse waveform, a second derivative of the pulse waveform, or both; and identifying one or more maxima or one or more minima in the first derivative of the pulse waveform, the second derivative of the pulse waveform, or both, wherein the one or more morphological features are associated with the one or more maxima or one or more minima in the first derivative of the pulse waveform, the second derivative of the pulse waveform, or both. The method of claim 1.

3. Extracting the one or more morphological features includes:

2. The method of claim 1, further comprising determining an amplitude, the location, or both, of the first local maximum, wherein the one or more morphological features are associated with the amplitude, the location, or both, of the first local maximum.

4. Extracting the one or more morphological features includes:

2. The method of claim 1, further comprising identifying the presence of a second maximum in the pulse waveform, wherein the curved feature representing the transition from the systole to the diastole of the cardiac cycle is associated with the second maximum.

5. Extracting the one or more morphological features includes:

2. The method of claim 1, further comprising identifying one or more positive slopes or one or more negative slopes of the pulse waveform, wherein the downward slopes following the first local maximum are associated with the one or more negative slopes of the pulse waveform.

6. 2. The method of claim 1, further comprising determining, based at least in part on the comparison, which of the plurality of baseline PPG signal morphologies matches the one or more morphological features, and determining the cardiovascular health metric is based at least in part on the determination.

7. 2. The method of claim 1, further comprising calculating a deviation of the one or more morphological features for the one or more morphological features from a plurality of baseline PPG signal morphologies based at least in part on the comparison, and determining the cardiovascular health metric is based at least in part on calculating the deviation.

8. receiving, via a user device, an indicia of data related to the user's health record from the wearable device, physiological data from the wearable device, or both; 10. The method of claim 1, further comprising: adjusting the cardiovascular health metric based at least in part on receiving the indicia; and causing the graphical user interface to display the indicia based at least in part on adjusting the cardiovascular health metric.

9. The method of claim 1 , further comprising causing a graphical user interface of a user device associated with the user to display a message associated with the cardiovascular health metric.

10. 10. The method of claim 9, wherein the message further includes a recommendation for improving the cardiovascular health metric, a trend associated with the cardiovascular health metric, educational content associated with the cardiovascular health metric, an adjusted set of activity goals, an adjusted set of sleep goals, or a combination thereof.

11. 2. The method of claim 1, further comprising: determining, based at least in part on receiving the PPG signal, the plurality of baseline PPG signal morphologies associated with the plurality of chronological ages; and wherein the comparison is based at least in part on determining the plurality of baseline PPG signal morphologies.

12. 10. The method of claim 1, further comprising inputting the PPG signal to a machine learning classifier, wherein determining the cardiovascular health metric is based at least in part on inputting the PPG signal to the machine learning classifier.

13. The method of claim 1 , wherein the plurality of baseline PPG signal morphologies associated with the plurality of chronological ages are extracted from physiological data associated with a plurality of users.

14. The method of claim 1 , wherein the wearable device comprises a wearable ring device.

15. The method of claim 1 , wherein the wearable device collects physiological data from the user based on arterial blood flow, capillary blood flow, arteriolar blood flow, or a combination thereof.

16. 1. An apparatus comprising: a processor; a memory coupled to the processor; Instructions stored in the memory and executable by the processor, causing the device to: receiving a photoplethysmogram (PPG) signal from a wearable device representing a pulse waveform for a user, the pulse waveform including a first maximum, a downward slope following the first maximum, and a curved feature representing a transition from systole to diastole of a cardiac cycle; extracting one or more morphological features related to the location of the first maximum, the value of the downward slope, the degree of the curved feature, or a combination thereof; comparing the one or more morphological features to one or more features from a plurality of baseline PPG signal morphologies associated with a plurality of chronological ages based at least in part on extracting the one or more morphological features; determining a cardiovascular health metric indicative of the user's cardiovascular health relative to the user's chronological age based at least in part on the comparison; and causing a graphical user interface to display an indicia of the cardiovascular health metric; and An apparatus comprising:

17. The instructions for extracting the one or more morphological features may include: calculating a first derivative of the pulse waveform, a second derivative of the pulse waveform, or both; and identifying one or more maxima or one or more minima in the first derivative of the pulse waveform, the second derivative of the pulse waveform, or both, wherein the one or more morphological features are associated with the one or more maxima or one or more minima in the first derivative of the pulse waveform, the second derivative of the pulse waveform, or both. The apparatus of claim 16 , further executable by the processor to:

18. The instructions to extract the one or more morphological features may include: determining an amplitude, the location, or both, of the first maximum, wherein the one or more morphological features are associated with the amplitude, the location, or both, of the first maximum. The apparatus of claim 16 , further executable by the processor to:

19. A non-transitory computer readable medium having stored thereon code, said code comprising: receiving a photoplethysmogram (PPG) signal from a wearable device representing a pulse waveform for a user, the pulse waveform including a first maximum, a downward slope following the first maximum, and a curved feature representing a transition from systole to diastole of a cardiac cycle; extracting one or more morphological features related to the location of the first maximum, the value of the downward slope, the degree of the curved feature, or a combination thereof; comparing the one or more morphological features to one or more features from a plurality of baseline PPG signal morphologies associated with a plurality of chronological ages based at least in part on extracting the one or more morphological features; determining a cardiovascular health metric indicative of the user's cardiovascular health relative to the user's chronological age based at least in part on the comparison; and causing a graphical user interface to display an indicia of the cardiovascular health metric; comprising instructions executable by a processor to perform Non-transitory computer-readable medium.

20. The instructions for extracting the one or more morphological features include: calculating a first derivative of the pulse waveform, a second derivative of the pulse waveform, or both; and identifying one or more maxima or one or more minima in the first derivative of the pulse waveform, the second derivative of the pulse waveform, or both, wherein the one or more morphological features are associated with the one or more maxima or one or more minima in the first derivative of the pulse waveform, the second derivative of the pulse waveform, or both. and executable by a processor to perform 20. The non-transitory computer-readable medium of claim 19.

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