Cardiovascular health indicator determination from wearable-based physiological data

Abstract

Methods, systems, and devices for determining cardiovascular health indicators are described. The system may be configured to receive a photoplethysmogram (PPG) signal representing a pulse waveform of a user. The pulse waveform may include a first local maximum, a downward slope following the first local maximum, and a bending feature representing a transition from a systolic phase to a diastolic phase of the cardiac cycle. Further, the system may extract one or more morphological features from the pulse waveform and 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 real foot ages. The system may determine a cardiovascular health indicator indicative of a cardiovascular health condition of the user relative to the user's real foot age, and cause the graphical user interface to display an indication of the cardiovascular health indicator.

Description

[0001] Cross-references

[0002] This patent application claims priority to U.S. patent application No. 17 / 818,105, filed by Rantanen et al. on August 8, 2022, entitled "CARDIOVASCULARHEALTH METRIC DETERMINATION FROM WEARABLE-BASED PHYSIOLOGICAL DATA," which application has been assigned to its assignee and is expressly incorporated herein by reference. Technical Field

[0003] The following content involves wearable devices and data processing, including the determination of cardiovascular health indicators based on wearable-based physiological data. Background Art

[0004] Some wearable devices can be configured to collect data from the user, including photoplethysmogram (PPG) data, heart rate data, etc. For example, some wearable devices can be configured to collect physiological data related to the user's cardiovascular health. However, wearable devices may be insufficient in determining cardiovascular health indicators of the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] Figure 1 An example of a system supporting determination of a cardiovascular health metric based on wearable based physiological data according to aspects of the present disclosure is shown.

[0006] Figure 2 An example of a system supporting determination of a cardiovascular health metric based on wearable based physiological data according to aspects of the present disclosure is shown.

[0007] Figure 3 An example of a timing diagram supporting determination of a cardiovascular health metric based on wearable physiological data according to aspects of the present disclosure is shown.

[0008] Figure 4 An example of a timing diagram supporting determination of a cardiovascular health metric based on wearable physiological data according to aspects of the present disclosure is shown.

[0009] Figure 5 An example of a graphical user interface (GUI) supporting determination of cardiovascular health metrics based on wearable physiological data according to aspects of the present disclosure is shown.

[0010] Figure 6 A block diagram of an apparatus supporting determination of a cardiovascular health metric based on wearable based physiological data according to aspects of the present disclosure is shown.

[0011] Figure 7 A block diagram of a wearable application supporting cardiovascular health metric determination based on wearable physiological data according to aspects of the present disclosure is shown.

[0012] Figure 8 A diagram of a system including a device supporting determination of a cardiovascular health metric based on wearable physiological data according to aspects of the present disclosure is shown.

[0013] Figures 9 to 11 A flow chart illustrating a method for supporting cardiovascular health metric determination based on wearable based physiological data in accordance with aspects of the present disclosure is shown. DETAILED DESCRIPTION

[0014] Some wearable devices can be configured to collect physiological data from users, including photoplethysmogram (PPG) data, temperature data, heart rate, heart rate variability (HRV) data, sleep data, breathing data, blood pressure data, etc. The acquired physiological data can be used to analyze behavioral and physiological characteristics related to the user, such as exercise, etc. Many users want to know more about their physical health, including their activity patterns and overall physical health. Specifically, many users may want to know more about cardiovascular health, including their cardiovascular age, heart health, arterial stiffness, and risk of cardiovascular disease, including coronary heart disease, stroke, heart failure, arrhythmia, etc. However, for a variety of reasons, typical technologies for measuring cardiovascular health and / or health devices and applications lack the ability to provide robust judgments and insights.

[0015] First, devices that record the heart's electrical signals and collect images of the heart and / or blood vessels are available in a single instance and can be combined with other measurement techniques and computations to determine the health of a user's cardiovascular system. Second, even for devices that are wearable or collect physiological data from a user, typical devices and applications lack the ability to collect other physiological, behavioral, or contextual inputs from the user that can be combined with the measured data to more fully understand the full set of physiological factors that affect the user's cardiovascular health.

[0016] Aspects of the present disclosure relate to techniques for determining cardiovascular health indicators based on wearable-based physiological data. Specifically, 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 of the user. Aspects of the present disclosure may identify morphological features of the pulse waveform, including at least a first local maximum, a downward slope after the first local maximum, and a curved feature representing a transition from a systolic phase to a diastolic phase of the cardiac cycle.

[0017] In some examples, aspects of the present disclosure may compare the morphological features of the identified pulse waveform with features of multiple PPG signal morphologies associated with multiple chronological ages. For example, the system may compare the 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 a technique for determining a cardiovascular health index of a user based on the comparison, wherein the cardiovascular health index indicates the cardiovascular health of the user relative to the user's chronological age.

[0018] For purposes of this disclosure, terms such as "cardiovascular age indicator", "cardiovascular health indicator" or "cardiovascular age" may be used to refer to health indicators of a user's cardiovascular system. The cardiovascular system may include the heart, blood vessels and / or blood, wherein the primary function of the cardiovascular system is to transport nutrients and oxygen-rich blood to various parts of the body and return deoxygenated blood to the lungs. Cardiovascular age (e.g., heart age and / or vascular age) is an indicator used to understand a user's risk of cardiovascular disease (including heart attack or stroke). In some cases, cardiovascular (e.g., heart) age can be calculated based on heart disease risk factors (e.g., age, blood pressure, and cholesterol), as well as diet, exercise, and smoking. Vascular age can provide a measurement of the apparent age of a user's arteries.

[0019] In some cases, determining cardiovascular health indicators may reduce the user's health risks in later life, particularly the risk of cardiovascular disease. In this case, it may be necessary to determine cardiovascular health indicators and provide users with recommendations for improving their cardiovascular health indicators to improve quality of life, sleep, and mood, and reduce future health risks. For example, methods and techniques may be needed to help users understand in a personalized way how to optimize lifestyle changes to reduce the risk of cardiovascular disease. In this case, the system may be able to determine cardiovascular health indicators relative to the user's chronological age in order to provide indicators that enable users to understand how behavioral changes (e.g., improvements in sleep, exercise, diet, and mood) can help improve their cardiovascular health indicators and reduce the risk of cardiovascular disease, etc.

[0020] The technology described herein can notify the user of the determined cardiovascular health index in a variety of ways. For example, the system can cause a graphical user interface (GUI) of the user's device to display a message or other notification to notify the user of the determined cardiovascular health index and make recommendations to the user. In one example, the system can generate recommendations for the user to avoid certain foods and / or beverages, intensify the user's training, or increase recovery time based on the cardiovascular health index.

[0021] The GUI may also include graphics / text indicating the data used to make the cardiovascular health index. The system may also send a message to the user to confirm the change in the cardiovascular health index. Based on early warning (e.g., before obvious symptoms appear), the user can take early measures to help reduce the severity of impending symptoms associated with cardiovascular health indicators that are greater than the user's actual age (e.g., symptoms associated with the onset of cardiovascular health problems). The GUI may also include graphics / text that reflect physiological changes related to blood pressure and heart rate, and update suggestions to the user based on physiological changes.

[0022] Aspects of the present disclosure are initially described in the context of a system that supports collecting physiological data from a user via a wearable device. Other aspects of the present disclosure are described in the context of example timing diagrams and example GUIs. Aspects of the present disclosure are further illustrated and described using apparatus diagrams, system diagrams, and flow diagrams related to determining cardiovascular health indicators based on wearable device-based physiological data.

[0023] Figure 1 An example of a system 100 that supports determining cardiovascular health indicators based on wearable device-based physiological data according to various aspects of the present disclosure is shown. The system 100 includes multiple electronic devices (e.g., wearable devices 104, user devices 106), which can be worn and / or operated by one or more users 102. The system 100 also includes a network 108 and one or more servers 110.

[0024] The electronic devices may include any electronic devices known in the art, including wearable devices 104 (e.g., ring wearable devices, watch wearable devices, etc.), 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 measured data, 3) processing data, 4) providing output to the user 102 based on the processed data (e.g., via a GUI), and 5) communicating data with each other and / or other computing devices. Different electronic devices may perform one or more functions.

[0025] Example wearable devices 104 may include wearable computing devices, such as ring computing devices (hereinafter referred to as "rings") configured to be worn on the fingers of user 102, wrist computing devices (e.g., smart watches, fitness bands, or bracelets) configured to be worn on the wrists of user 102, and / or head-mounted computing devices (e.g., glasses / goggles). Wearable devices 104 may also include bands, straps (e.g., flexible or non-flexible bands or straps), adhesive sensors, etc., which may be positioned in other locations, such as bands around the head (e.g., forehead bands), bands around the arms (e.g., forearm bands and / or bicep bands), and / or bands around the legs (e.g., thigh or calf bands), bands behind the ears, bands under the arms, etc. Wearable devices 104 may also be attached to or included in clothing. For example, wearable devices 104 may be included in pockets and / or pouches on clothing. As another example, the wearable device 104 can be clipped and / or secured to clothing, or can otherwise be maintained in the vicinity of the user 102. Example clothing can include, but is not limited to, hats, shirts, gloves, pants, socks, outerwear (e.g., jackets), and underwear. In some implementations, the wearable device 104 can be included with other types of equipment, such as training / exercise equipment used during a sporting activity. For example, the wearable device 104 can be attached to or included in a bicycle, a snowboard, a tennis racket, a golf club, and / or a training weight.

[0026] Much of the disclosure may be described in the context of a ring-shaped wearable device 104. Therefore, unless otherwise specified herein, the terms "ring 104," "wearable device 104," and similar terms may be used interchangeably. However, the use of the term "ring 104" should not be considered limiting, as it is contemplated herein that aspects of the disclosure may be performed using other wearable devices (e.g., a watch wearable device, a necklace wearable device, a bracelet wearable device, an earring wearable device, an anklet wearable device, etc.).

[0027] In some aspects, the user device 106 may include a handheld mobile computing device, such as a smartphone and a tablet computing device. The user device 106 may also include a personal computer, such as a laptop and a desktop computing device. Other example user devices 106 may include a server computing device that can communicate with other electronic devices (e.g., via the Internet). In some implementations, the computing device may include a medical device, such as an external wearable computing device (e.g., a Holter monitor). The medical device may also include an implantable medical device, such as a pacemaker and a cardioverter defibrillator. Other example 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 call displays), hubs (e.g., wireless communication hubs), security systems, smart appliances (e.g., thermostats and refrigerators), and fitness equipment.

[0028] Some electronic devices (e.g., wearable device 104, user device 106) can measure physiological parameters of each user 102, such as photoplethysmography waveform, continuous skin temperature, pulse waveform, respiratory rate, heart rate variability (HRV), actigraphy, galvanic skin response, pulse oximetry, and / or other physiological parameters. Some electronic devices that measure physiological parameters can 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 can process the received physiological data measured by other devices.

[0029] In some implementations, 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, user 102 may have a ring (e.g., wearable device 104) that measures physiological parameters. User 102 may also have or be associated with user device 106 (e.g., mobile device, smart phone), wherein wearable device 104 and user device 106 may be communicatively coupled to each other. In some cases, user device 106 may receive data from wearable device 104 and perform some / all of the calculations described herein. In some implementations, user device 106 may also measure physiological parameters described herein, such as motion / activity parameters.

[0030] For example, Figure 1As shown, a first user 102-a (user 1) can operate a wearable device 104-a (e.g., ring 104a) and a user device 106-a, or can be associated with it, and the wearable device 104-a and the user device 106-a can operate as described herein. In this example, the user device 106-a associated with the user 102-a can process / store physiological parameters measured by the ring 104-a. In contrast, the second user 102-b (user 2) can be associated with the ring 104-b, the watch wearable device 104-c (e.g., watch 104-c) and the user device 106-b, wherein the user device 106-b associated with the user 102-b can process / store physiological parameters measured by the ring 104-b and / or the watch 104-c. In addition, the nth user 102-n (user N) can be associated with the arrangement of electronic devices described herein (e.g., ring 104-n, user device 106-n). In some aspects, wearable devices 104 (eg, ring 104, watch 104) and other electronic devices may be communicatively coupled to user devices 106 of respective users 102 via Bluetooth, Wi-Fi, and other wireless protocols.

[0031] In some implementations, the ring 104 (e.g., wearable device 104) of system 100 can be configured to collect physiological data from each user 102 based on arterial blood flow in the user's finger. Specifically, the ring 104 can collect physiological data based on arterial blood flow in the user's finger using one or more LEDs (e.g., red LED, green LED) that emit light on the palm side of the user's finger. In some cases, the system 100 can be configured to collect physiological data from each user 102 based on blood flow diffused into the microvascular bed of the skin with capillaries and arterioles. For example, the system 100 can collect PPG data based on the measured amount of blood diffused into the microvascular system of capillaries and arterioles. In some implementations, the ring 104 can use a combination of green LEDs and red LEDs to acquire physiological data. The physiological data may include any physiological data known in the art, including but not limited to temperature data, accelerometer data (e.g., movement / motion data), heart rate data, HRV data, blood oxygen level data, or any combination thereof.

[0032] Using green LEDs and red LEDs simultaneously can have multiple advantages over other solutions, because it has been found that red LEDs and green LEDs have their own unique advantages when acquiring physiological data under different conditions (e.g., bright / dark, active / inactive) and via different parts of the body. For example, it has been found that green LEDs perform better during exercise. In addition, it has been found that using multiple LEDs (e.g., green LEDs and red LEDs) distributed around the ring 104 performs better than using wearable devices (e.g., in watch wearable devices) that use LEDs placed close to each other. In addition, blood vessels (e.g., arteries, capillaries) in fingers are easier to access via LEDs than blood vessels in the wrist. Specifically, arteries in the wrist are located at the bottom of the wrist (e.g., the palm side of the wrist), which means that capillaries can only be accessed at the top of the wrist (e.g., the back side of the wrist), where wearable watch devices and similar devices are usually worn. Therefore, it is found that using LEDs and other sensors in the ring 104 performs better than wearable devices worn on the wrist, because the ring 104 can more easily contact the arteries (compared to capillaries), thereby generating stronger signals and more valuable physiological data. In some cases, the system 100 can be configured to collect physiological data from various users 102 based on blood flow diffused into the microvascular bed of the skin having capillaries and arterioles. For example, the system 100 can collect PPG data based on the measured amount of blood diffused into the microvascular system of capillaries and arterioles.

[0033] The electronic devices of the system 100 (eg, user device 106, wearable device 104) may be communicatively coupled to one or more servers 110 via a wired or wireless communication protocol. Figure 1 As shown, an electronic device (e.g., user device 106) can be communicatively coupled to one or more servers 110 via a network 108. The network 108 can implement the Transmission Control Protocol and Internet Protocol (TCP / IP), such as the Internet, or can implement other network 108 protocols. The network connection between the network 108 and the various electronic devices can facilitate the transmission of data via email, web, text messaging, 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 can be communicatively coupled to the user device 106-a, where the user device 106-a is communicatively coupled to the server 110 via the network 108. In addition or alternatively, the wearable device 104 (e.g., ring 104, watch 104) can be directly communicatively coupled to the network 108.

[0034] The system 100 can provide an on-demand database service between a user device 106 and one or more servers 110. In some cases, the server 110 can receive data from the user device 106 via the network 108, and can store and analyze the data. Similarly, the server 110 can provide data to the user device 106 via the network 108. In some cases, the server 110 can be located at one or more data centers. The server 110 can be used for data storage, management and processing. In some implementations, the server 110 can provide a web-based interface to the user device 106 via a web browser.

[0035] In some aspects, the system 100 can detect the time period during which the user 102 sleeps and classify the time period during which the user 102 sleeps into one or more sleep stages (e.g., sleep stage classification). Figure 1 As shown, user 102-a can be associated with wearable device 104-a (e.g., ring 104-a) and user device 106-a. In this example, ring 104-a can collect physiological data associated with user 102-a, including temperature, heart rate, HRV, respiratory rate, etc. In some aspects, the data collected by ring 104-a can be input into a machine learning classifier, wherein the machine learning classifier is configured to determine the time period when user 102-a sleeps (or has slept). In addition, the machine learning classifier can be configured to classify the time period into different sleep stages, including awake sleep stage, rapid eye movement (REM) sleep stage, light sleep stage (non-REM (NREM)) and deep sleep stage (NREM). In some aspects, the classified sleep stage can be displayed to user 102-a via the GUI of user device 106-a. The sleep stage classification can be used to provide feedback to user 102-a about the user's sleep pattern, such as a recommended bedtime, a recommended wake-up time, etc. Furthermore, in some implementations, the sleep stage classification techniques described herein can be used to calculate scores for corresponding users, such as sleep scores, readiness scores, and the like.

[0036] In some aspects, the system 100 can utilize circadian rhythm derived features to further improve physiological data collection, data processing procedures, and other technologies described herein. The term circadian rhythm can refer to a natural internal process that regulates an individual's sleep-wake cycle, which is repeated approximately every 24 hours. In this regard, the technology described herein can utilize a circadian rhythm regulation model to improve physiological data collection, analysis, and data processing. For example, the circadian rhythm regulation model can be input into a machine learning classifier together with physiological data collected from a user 102-a via a wearable device 104-a. In this example, the circadian rhythm regulation model can be configured to "weight" or regulate physiological data collected during the user's natural, approximately 24-hour circadian rhythm. In some implementations, the system can initially start with a "baseline" circadian rhythm regulation model, and the baseline model can be modified using physiological data collected from each user 102 to generate a customized, personalized circadian rhythm regulation model for each respective user 102.

[0037] In certain aspects, the system 100 can utilize other biological rhythms to further improve the collection, analysis, and processing of physiological data that is performed in phase with these other rhythms. For example, if a weekly rhythm is detected in an individual's baseline data, the model can be configured to adjust the "weight" of the data by day of the week. Biological rhythms that may require model adjustment by this method include: 1) ultradian rhythms (faster than daily rhythms), including sleep cycles during sleep states, and periodic oscillations from less than an hour to several hours in physiological variables measured during wakefulness; 2) circadian rhythms; 3) non-endogenous daily rhythms that appear to be imposed on circadian rhythms, such as work schedules; 4) weekly rhythms, or other exogenously imposed artificial time cycles (for example, in a hypothetical culture with a 12-day "week", a 12-day rhythm can be used); 5) multi-day ovarian rhythms in women and spermatogenesis rhythms in men; 6) lunar rhythms (related to people living in environments with low or no artificial light); and 7) seasonal rhythms.

[0038] Biorhythms are not always fixed rhythms. For example, the length of the ovarian cycle varies from cycle to cycle in many women, and ultradian rhythms are not expected to occur at exactly the same time or periodically from day to day, even in the same user. Therefore, the detection of these rhythms can be improved using signal processing techniques sufficient to quantify the frequency content while preserving the temporal resolution of these rhythms in the physiological data, assigning the phase of each rhythm to each moment of measurement, and thereby modifying the tuning models and comparison of time intervals. Biorhythm tuning 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.

[0039] In some aspects, the various devices of system 100 can support techniques for determining cardiovascular health indicators based on wearable physiological data. Specifically, Figure 1 The system 100 shown in FIG. 1 can support techniques for determining a cardiovascular health indicator indicative of the cardiovascular health of a user 102 relative to the chronological age of the user 102 and causing a user device 106 corresponding to the user 102 to display an indication of the cardiovascular health indicator. The indication of the cardiovascular health indicator can be based on a PPG signal received from a wearable device 104 that represents a pulse waveform of the user 102.

[0040] For example, Figure 1 As shown, user 1 (user 102-a) can be associated with a wearable device 104-a (e.g., ring 104-a) and a user device 106-a. In this example, the ring 104-a can collect data associated with the user 102-a, including PPG signals, temperature, heart rate, HRV, breathing rate, etc. In some aspects, the data collected by the ring 104-a can be used to determine a cardiovascular health indicator of the user 102 relative to the chronological age of the user 102. Determining the cardiovascular health indicator can be performed by any component of the system 100, including the ring 104-a, the user device 106-a associated with the user 1, one or more servers 110, or any combination thereof. After determining the cardiovascular health indicator, the system 100 can selectively cause the GUI of the user device 106 to display an indication of the cardiovascular health indicator. In this case, the user device 106 can be associated with user 1, user 2, user N, or a combination thereof, where user 2 and user N can be examples of clinicians, caregivers, users associated with user 1, or a combination thereof.

[0041] In some implementations, upon receiving physiological data (e.g., including a PPG signal representing a pulse waveform), the system 100 may extract one or more morphological features from the pulse waveform. For example, the pulse waveform may include a first local maximum, a downward slope after the first local maximum, and a curved feature representing a transition from the systolic phase to the diastolic phase of the cardiac cycle. In this case, the system 100 may extract one or more morphological features associated with the location of the first local 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 examples listed are for illustrative purposes only and should not be considered limiting. In some cases, morphological features may be identified by a machine learning model and may represent a complex combination of features. The system 100 may compare one or more extracted morphological features with one or more features from multiple baseline PPG signal morphologies associated with multiple chronological ages.

[0042] In some implementations, the system 100 may generate alerts, messages, or recommendations for user 1, user 2, and / or user N based on the determined cardiovascular health indicators (e.g., via ring 104-a, user device 106-a, or both), wherein the messages may provide insights about the determined cardiovascular health indicators. In some cases, the messages may provide insights about symptoms associated with the cardiovascular health indicators, educational videos and / or text (e.g., content) related to loss of cardiovascular health indicators, recommendations for improving cardiovascular health indicators, adjusted activity and / or sleep goal sets, or combinations thereof.

[0043] Those skilled in the art will appreciate that one or more aspects of the present disclosure may be implemented in the system 100 to additionally or alternatively address other issues in addition to the above-mentioned issues. In addition, aspects of the present disclosure may provide technical improvements to the "conventional" systems or processes described herein. However, the description and drawings only include example technical improvements resulting from implementing aspects of the present disclosure, and therefore do not represent all technical improvements provided within the scope of the claims.

[0044] Figure 2 An example of a system 200 that supports determining cardiovascular health indicators from wearable-based physiological data according to aspects of the present disclosure is shown. The system 200 can implement the system 100, or be implemented by the system 100. Specifically, the system 200 shows an example of a ring 104 (e.g., a wearable device 104), a user device 106, and a server 110, as shown in reference Figure 1 described.

[0045] In some aspects, the ring 104 can be configured to be worn on a user's finger and can determine one or more user physiological parameters when worn on the user's finger. Example measurements and determinations can include, but are not limited to, user skin temperature, pulse waveform, respiratory rate, heart rate, HRV, blood oxygen level, etc.

[0046] The system 200 also includes a user device 106 (e.g., a smartphone) in communication with the ring 104. For example, the ring 104 can communicate with the user device 106 wirelessly and / or by wire. In some implementations, the ring 104 can send measured and processed data (e.g., temperature data, photoplethysmogram (PPG) data, motion / accelerometer data, ring input data, etc.) to the user device 106. The user device 106 can also send data to the ring 104, such as ring 104 firmware / configuration updates. The user device 106 can process the data. In some implementations, the user device 106 can transmit the data to the server 110 for processing and / or storage.

[0047] The ring 104 may include a housing 205, which may include an inner housing 205-a and an outer housing 205-b. In certain aspects, the housing 205 of the ring 104 may store or otherwise include 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., a printed circuit board) interconnecting the device electronics and / or the power source, and the like. The device electronics may include device modules (e.g., hardware / software), such as: a processing module 230-a, a memory 215, a communication module 220-a, a power module 225, and the like. The device electronics may also include one or more sensors. Example 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.

[0048] The sensors may include associated modules (not shown) configured to communicate with corresponding components / modules of the ring 104 and to generate signals associated with the corresponding sensors. In some aspects, each component / module of the ring 104 may be communicatively coupled to each other via a wired or wireless connection. Furthermore, the ring 104 may include additional and / or alternative sensors or other components configured to collect physiological data from the user, including light sensors (e.g., LEDs), oximeters, etc.

[0049] refer to Figure 2 The ring 104 shown and described is for illustration purposes only. Figure 2 Additional or alternative components shown. Other rings 104 that provide the functionality described herein can be manufactured. For example, a ring 104 with fewer components (e.g., sensors) can be manufactured. In a specific example, a ring 104 with a single temperature sensor 240 (or other sensor), a power source, and device electronics configured to read a single temperature sensor 240 (or other sensor) can be manufactured. In another specific example, the temperature sensor 240 (or other sensor) can be attached to a user's finger (e.g., using a clamp, a spring-loaded clamp, etc.). In this case, the sensor can be connected to another computing device, such as a wrist-worn computing device that reads the temperature sensor 240 (or other sensor). In other examples, a ring 104 that includes additional sensors and processing functionality can be manufactured.

[0050] The housing 205 may include one or more housing 205 components. The housing 205 may include an outer housing 205-b component (eg, a shell) and an inner housing 205-a component (eg, a molded part). The housing 205 may include Figure 2205-b). For example, in some embodiments, 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., a metal outer housing 205-b). The housing 205 may provide structural support for the device electronics, battery 210, substrate, and other components. For example, the housing 205 may protect the device electronics, battery 210, and substrate from mechanical forces (such as pressure and shock). The housing 205 may also protect the device electronics, battery 210, and substrate from water and / or other chemicals.

[0051] The outer shell 205-b can be made of one or more materials. In some implementations, the outer shell 205-b can include a metal (such as titanium), which can provide strength and wear resistance at a relatively light weight. The outer shell 205-b can also be made of other materials (such as polymers). In some implementations, the outer shell 205-b can play both a protective role and a decorative role.

[0052] The inner housing 205-a can be configured to interact with a user's finger. The inner housing 205-a can be formed of a polymer (e.g., a medical grade polymer) or other material. In some implementations, the inner housing 205-a can be transparent. For example, the inner housing 205-a can be transparent to light emitted by a PPG light emitting diode (LED). In some implementations, the inner housing 205-a assembly can be molded to the outer housing 205-b. For example, the inner housing 205-a can include a polymer that is molded (e.g., injection molded) to fit the metal shell of the outer housing 205-b.

[0053] Ring 104 may include one or more substrates (not shown). Device electronics and battery 210 may be included on one or more substrates. For example, device electronics and battery 210 may be mounted on one or more substrates. Example substrates may include one or more printed circuit boards (PCBs), such as flexible PCBs (e.g., polyimide). In some implementations, electronics / battery 210 may include surface mounted devices (e.g., surface mount technology (SMT) devices) on flexible PCBs. In some implementations, one or more substrates (e.g., one or more flexible PCBs) may include electrical traces that provide electrical communication between device electronics. Electrical traces may also connect battery 210 to device electronics.

[0054] The device electronics, battery 210, and substrate can be arranged in the ring 104 in a variety of ways. In some implementations, one substrate including the device electronics can be mounted along the bottom (e.g., lower half) of the ring 104 so that sensors (e.g., PPG system 235, temperature sensor 240, motion sensor 245, and other sensors) interface with the underside of the user's finger. In these implementations, the battery 210 can be mounted along the top of the ring 104 (e.g., on another substrate).

[0055] The various components / modules of the ring 104 represent functions (e.g., circuits and other components) that may be included in the ring 104. A module may include any discrete and / or integrated electronic circuit components that implement analog and / or digital circuits capable of producing the functions represented by the modules herein. For example, a module may include analog circuits (e.g., amplification circuits, filtering circuits, analog / digital conversion circuits, and / or other signal conditioning circuits). A module may also include digital circuits (e.g., combinational or sequential logic circuits, memory circuits, etc.).

[0056] The memory 215 (memory module) of the ring 104 may include any volatile, non-volatile, magnetic or electrical media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically erasable programmable ROM (EEPROM), flash memory, or any other storage device. The memory 215 may store any data described herein. For example, the memory 215 may be configured to store data collected by various sensors and the PPG system 235 (e.g., motion data, temperature data, PPG data). In addition, the memory 215 may include instructions that, when executed by one or more processing circuits, cause the module to perform the various functions that the modules herein have. The device electronics of the ring 104 described herein are merely example device electronics. Therefore, the types of electronic components used to implement the device electronics may vary based on design considerations.

[0057] The functions of the modules of the ring 104 described herein may be embodied as one or more processors, hardware, firmware, software, or any combination thereof. Describing different features as modules is intended to highlight different functional aspects and does not necessarily mean that these modules must be implemented by separate hardware / software components. Instead, the functions associated with one or more modules may be performed by separate hardware / software components or integrated in a common hardware / software component.

[0058] 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 a chip (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 / receive data to / from the modules and other components (such as sensors) of the ring 104. As described herein, the modules may be implemented by various circuit components. Therefore, the modules may also be referred to as circuits (e.g., communication circuits and power supply circuits).

[0059] The processing module 230-a can communicate with the memory 215. The memory 215 can include computer-readable instructions that, when executed by the processing module 230-a, cause the processing module 230-a to perform the various functions of the processing module 230-a described herein. In some implementations, the processing module 230-a (e.g., a microcontroller) can include additional features associated with other modules (such as communication functions provided by the communication module 220-a (e.g., an integrated low-power Bluetooth transceiver)) and / or additional onboard memory 215.

[0060] The communication module 220-a may include circuitry that provides wireless and / or wired communication with a user device 106 (e.g., a 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-a, the ring 104 and the user device 106 may be configured to communicate with each other. The processing module 230-a of the ring may be configured to send data to / receive data from the user device 106 via the communication module 220-a. Example data may include, but is not limited to, motion data, temperature data, pulse waveform, 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 also be configured to receive updates (eg, software / firmware updates) and data from the user device 106 .

[0061] The ring 104 may include a battery 210 (e.g., a rechargeable battery 210). Example batteries 210 may include lithium-ion or lithium-polymer type batteries 210, although there may be a variety of battery 210 options. The battery 210 may be wirelessly rechargeable. In one implementation, the ring 104 may include a power source other than the battery 210, such as a capacitor. The power source (e.g., battery 210 or capacitor) may have a curved geometry that matches the curvature of the ring 104. In some aspects, the charger or other power source may include additional sensors that may be used to collect data in addition to or to supplement the data collected by the ring 104 itself. In addition, the charger or other power source of the ring 104 may be used 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 transfer data between the ring 104 and the server 110.

[0062] In some aspects, the ring 104 includes a power module 225 that can control the 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 a datum structure that cooperates with the ring 104 datum structure to form a specified orientation with the ring 104 during 104 charging. The power module 225 can also regulate the voltage of the device electronics, regulate the power output to the device electronics, and monitor the charging state 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 104 charging, and undervoltage during 104 discharge. The power module 225 can also include electrostatic discharge (ESD) protection.

[0063] 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) indicating a temperature read or sensed by the temperature sensor 240. The processing module 230-a may determine the temperature of the user 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 at the user's finger (e.g., skin temperature). 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 thermally insulating portion may isolate portions of the ring 104 (e.g., the temperature sensor 240) from the ambient temperature.

[0064] In some implementations, the temperature sensor 240 can generate a digital signal (e.g., temperature data) that the processing module 230-a can use to determine the temperature. As another example, where the temperature sensor 240 includes a passive sensor, the processing module 230-a (or the temperature sensor 240 module) can measure the current / voltage generated by the temperature sensor 240 and determine the temperature based on the measured current / voltage. Example temperature sensors 240 can 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.

[0065] The processing module 230-a can sample the user's body temperature over time. For example, the processing module 230-a can sample the user's body temperature according to a sampling rate. An example sampling rate can include one sample per second, although the processing module 230-a can be configured to sample the temperature signal at other sampling rates that are higher or lower than one sample per second. In some implementations, the processing module 230-a can continuously sample the user's body temperature throughout the day and throughout the night. Sampling at a sufficient rate (e.g., one sample per second) throughout the day can provide sufficient temperature data to perform the analysis described herein.

[0066] The processing module 230-a can store the sampled temperature data in the memory 215. In some implementations, the processing module 230-a can process the sampled temperature data. For example, the processing module 230-a can determine the average temperature value over a period of time. In one example, the processing module 230-a can determine the average temperature value per minute by summing all temperature values ​​collected within one minute and dividing by the number of samples within one minute. In a specific example where the temperature is sampled at one sample per second, the average temperature can be the sum of all sampled temperatures within one minute divided by sixty seconds. The memory 215 can store the average temperature value over a period of time. In some implementations, the memory 215 can store the average temperature (e.g., one per minute) instead of the sampled temperature to save the memory 215.

[0067] The sampling rate that may be stored in memory 215 is configurable. In some implementations, the sampling rate may be the same during the day and night. In other implementations, the sampling rate may vary during the day / night. In some implementations, ring 104 may filter / reject temperature readings, such as large spikes in temperature that do not represent physiological changes (e.g., temperature spikes caused by a hot bath). In some implementations, ring 104 may filter / reject temperature readings that may be unreliable due to other factors, such as excessive movement during a 104 workout (e.g., as indicated by motion sensor 245).

[0068] Ring 104 (eg, communication module) may transmit the sampled and / or average temperature data to user device 106 for storage and / or further processing. User device 106 may transmit the sampled and / or average temperature data to server 110 for storage and / or further processing.

[0069] Although the ring 104 is shown as including a single temperature sensor 240, the ring 104 can include multiple temperature sensors 240 at 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 can be a stand-alone temperature sensor 240. In addition, or alternatively, one or more temperature sensors 240 can be included with other components (e.g., packaged with other components), such as with an accelerometer and / or a processor.

[0070] The processing module 230-a can acquire and process data from multiple temperature sensors 240 in a manner similar to that described for a single temperature sensor 240. For example, the processing module 230 can individually sample, average, and store temperature data from each of the multiple temperature sensors 240. In other examples, the processing module 230-a can sample the sensors at different rates and average / store different values ​​for different sensors. In some implementations, the processing module 230-a can 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.

[0071] The temperature sensor 240 on the ring 104 can obtain the remote temperature at the user's finger (e.g., any finger). For example, one or more temperature sensors 240 on the ring 104 can obtain the user's temperature from different locations on the underside of the finger or on the finger. In some implementations, the ring 104 can continuously obtain the remote temperature (e.g., at a sampling rate). Although the remote temperature measured by the ring 104 at the finger is described herein, other devices can measure the temperature of the same / different locations. In some cases, the remote temperature measured at the user's finger may be different from the temperature measured at the user's wrist or other external body location. In addition, the remote temperature (e.g., "shell" temperature) measured at the user's finger may be different from the user's core temperature. Therefore, the ring 104 can provide useful temperature signals that may not be obtained at other internal / external locations of the body. In some cases, continuous temperature measurements at the finger can capture temperature fluctuations (e.g., small fluctuations or large fluctuations) that may not be obvious in the core temperature. For example, continuous temperature measurements at the finger can capture temperature fluctuations every minute or every hour, which can provide additional insights that temperature measurements at other parts of the body may not provide.

[0072] The ring 104 may include a PPG system 235. The PPG system 235 may include one or more light emitters that emit light. The PPG system 235 may also include one or more light receivers that receive light emitted by the one or more light emitters. The light receiver may generate a signal indicating the amount of light received by the light receiver (hereinafter referred to as a "PPG" signal). The light emitter 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 a change in blood volume 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 respiratory rate, heart rate, HRV, blood oxygen saturation, and other circulatory parameters.

[0073] In some implementations, the PPG system 235 can be configured as a reflective PPG system 235, in which the light receiver receives transmitted light reflected by an area of ​​the user's finger. In some implementations, the PPG system 235 can be configured as a transmissive PPG system 235, in which the light emitter and the light receiver are arranged relative to each other so that light is transmitted directly through a portion of the user's finger to the light receiver.

[0074] The number and ratio of emitters and receivers included in the PPG system 235 can vary. Example light emitters can include light emitting diodes (LEDs). Light emitters can emit light in the infrared spectrum and / or other spectrums. Example light receivers can include, but are not limited to, light sensors, phototransistors, and photodiodes. Light receivers can be configured to generate a PPG signal in response to wavelengths received from light emitters. The locations of emitters and receivers can vary. In addition, a single device can include a reflective and / or transmissive PPG system 235.

[0075] Figure 2 The PPG system 235 shown in FIG. 1 may include a reflective PPG system 235 in some implementations. In these implementations, the PPG system 235 may include a centrally located light receiver (e.g., at the bottom of the ring 104) and two light transmitters located on either side of the light receiver. In this implementation, the PPG system 235 (e.g., the light receiver) may generate a PPG signal based on light received from one or both light transmitters. In other implementations, other placements, combinations, and / or configurations of one or more light transmitters and / or light receivers are contemplated.

[0076] The processing module 230-a can control one or two light emitters to emit light while sampling the PPG signal generated by the light receiver. In some implementations, the processing module 230-a can cause the light emitter with a stronger received signal to emit light while sampling the PPG signal generated by the light receiver. For example, the selected light emitter can continuously emit light while sampling the PPG signal at a sampling rate (e.g., 250 Hz).

[0077] Sampling the PPG signal generated by the PPG system 235 can produce a pulse waveform that can be referred to as a "PPG". The pulse waveform can indicate the relationship between blood pressure and time for multiple cardiac cycles. The pulse waveform can include peaks indicating cardiac cycles. In addition, the pulse waveform can include changes caused by breathing that can be used to determine breathing rate. In some implementations, the processing module 230-a can store the pulse waveform in the memory 215. The processing module 230-a can process the pulse waveform as it is generated and / or from the memory 215 to determine the user's physiological parameters described herein.

[0078] Processing module 230-a may determine the user's heart rate based on the pulse waveform. For example, processing module 230-a may determine the heart rate (e.g., in units of beats per minute) based on the time between peaks in the pulse waveform. The time between peaks may be referred to as an interbeat interval (IBI). Processing module 230-a may store the determined heart rate value and IBI value in memory 215.

[0079] The processing module 230-a can determine the HRV that changes over time. For example, the processing module 230-a can determine the HRV based on the change of IBI. The processing module 230-a can store the HRV value that changes over time in the memory 215. In addition, the processing module 230-a can determine the user's breathing frequency that changes over time. For example, the processing module 230-a can determine the breathing frequency based on the frequency modulation, amplitude modulation, or baseline modulation of the user's IBI value over a period of time. The breathing frequency can be calculated as the number of breaths per minute or calculated as another breathing frequency (for example, the number of breaths per 30 seconds). The processing module 230-a can store the user's breathing frequency value that changes over time in the memory 215.

[0080] 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 gyros. The motion sensor 245 may generate a motion signal indicating the motion of the sensor. For example, the ring 104 may include one or more accelerometers that generate an acceleration signal indicating the acceleration of the accelerometer. As another example, the ring 104 may include one or more gyro sensors that generate a gyro signal indicating angular motion (e.g., angular velocity) and / or orientation change. The motion sensor 245 may be included in one or more sensor packages. An example accelerometer / gyro sensor is a Bosch BM1160 inertial micro-electromechanical system (MEMS) sensor that can measure angular rate and acceleration on three perpendicular axes.

[0081] The processing module 230-a may sample the motion signal at a sampling rate (e.g., 50 Hz) and determine the motion of the ring 104 based on the sampled motion signal. For example, the processing module 230-a may sample the acceleration signal to determine the acceleration of the ring 104. As another example, the processing module 230-a may sample the gyroscope signal to determine the angular motion. In some implementations, the processing module 230-a may store the motion data in the memory 215. The motion data may include the sampled motion data and the motion data calculated based on the sampled motion signal (e.g., acceleration and angle values).

[0082] 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 value, IBI value, HRV value, and respiratory rate value). The ring 104 may also store motion data, such as sampled motion data indicating linear and angular motion.

[0083] Ring 104 or other computing device can calculate and store additional values ​​based on sampled / calculated physiological data. For example, processing module 230 can calculate and store various indicators, such as sleep indicators (e.g., sleep scores), activity indicators, and readiness indicators. In some implementations, additional values / indicators can be referred to as "derived values". Ring 104 or other computing / wearable device can calculate various values / indicators related to motion. Example derived values ​​of motion data can include, but are not limited to, motion count values, regularity values, intensity values, metabolic equivalents of task values ​​(METs), and orientation values. Motion counts, regularity values, intensity values, and METs can indicate the amount of user motion (e.g., speed / acceleration) over time. Orientation values ​​can indicate how ring 104 is oriented on a user's finger and whether ring 104 is worn on the left or right hand.

[0084] In some implementations, the movement count and regularity value can be determined by calculating the number of acceleration peaks within one or more time periods (e.g., one or more 30 seconds to 1 minute time periods). The intensity value can indicate the number of movements and the relative intensity of the movement (e.g., acceleration value). The intensity value can be divided into low, medium, and high, depending on the associated threshold acceleration value. METs can be determined based on the intensity of movement within a period of time (e.g., 30 seconds), the regularity / irregularity of movement, and the number of movements associated with different intensities.

[0085] In some implementations, the processing module 230-a can compress the data stored in the memory 215. For example, the processing module 230-a can delete the sampled data after performing calculations based on the sampled data. As another example, the processing module 230-a can average the data over a longer period of time to reduce the number of stored values. In a specific example, if the average temperature of the user over a one-minute period is stored in the memory 215, the processing module 230-a can calculate the average temperature over a five-minute period for storage and then subsequently erase the one-minute average temperature data. The processing module 230-a can compress the data based on a variety of factors, such as the total amount of used / available memory 215 and / or the time that has passed since the ring 104 last transmitted data to the user device 106.

[0086] Although the physiological parameters of the user can be measured by the sensors included on the ring 104, other devices can also measure the physiological parameters of the user. For example, although the body temperature of the user can be measured by the temperature sensor 240 included in the ring 104, other devices can also measure the body temperature of the user. In some examples, other wearable devices (e.g., wrist devices) can include sensors that measure the physiological parameters of the user. In addition, medical devices (such as external medical devices (e.g., wearable medical devices) and / or implantable medical devices) can measure the physiological parameters of the user. One or more sensors on any type of computing device can be used to implement the techniques described herein.

[0087] Physiological measurements can be taken continuously throughout the day and / or night. In some implementations, physiological measurements can be taken during portions of the day and / or portions 104 of the night. In some implementations, physiological measurements can 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 can take physiological measurements in a resting / sleeping state in order to obtain clearer physiological signals. In one example, the ring 104 or other device / system can detect when the user is resting and / or sleeping, and obtain physiological parameters (e.g., temperature) of the detected state. When the user is in other states, the device / system can use resting / sleeping physiological data and / or other data to implement the technology of the present disclosure.

[0088] In some implementations, as previously described herein, the ring 104 may be configured to collect, store, and / or process data, and may transmit any 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., a web browser 280), one or more additional applications, and a GUI 275. The user device 106 may also include other modules and components, including sensors, audio devices, tactile 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.

[0089] 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, the 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, may 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 the data to the server 110 for processing operations that require relatively high processing power and / or operations that allow relatively high latency.

[0090] In some aspects, the ring 104, user device 106, and server 110 of system 200 may be configured to evaluate the sleep pattern of the user. Specifically, the corresponding components of system 200 may be used to collect data from the user via the ring 104, and generate one or more scores (e.g., sleep score, readiness score) for the user based on the collected data. For example, as described earlier in this article, the ring 104 of system 200 may be worn by the 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 falls asleep in order to evaluate the user's sleep situation on a given "sleep day". In some aspects, a score for each corresponding sleep day may be calculated for the user, so that the first sleep day is associated with a first set of scores, and the second sleep day is associated with a second set of scores. The score for each corresponding sleep day may be calculated based on the data collected by the ring 104 during the corresponding sleep day. The score may include, but is not limited to, a sleep score, a readiness score, etc.

[0091] In some cases, a "sleep day" can be consistent with a traditional calendar day, so that a given sleep day is from midnight to midnight on the corresponding calendar day. In other cases, the sleep day can be offset relative to the calendar day. For example, the sleep day can be from 6:00 p.m. (18:00) on a calendar day to 6:00 p.m. (18:00) on the next calendar day. In this example, 6:00 p.m. can be used as a "cutoff time", wherein the data collected from the user before 6:00 p.m. is counted into the current sleep day, and the data collected from the user after 6:00 p.m. is counted into the next sleep day. Since most people sleep the most at night, offsetting the sleep day relative to the calendar day can enable the system 200 to evaluate the user's sleep pattern in a manner consistent with the user's sleep schedule. In some cases, the user can selectively adjust (e.g., via GUI) the timing of the sleep day relative to the calendar day so that the sleep day is consistent with the duration length of the usual sleep of each user.

[0092] In some implementations, each overall score (e.g., sleep score, readiness score) for a user on each respective day may be determined / calculated based on one or more "contributing factors," "factors," or "contributing factors." For example, a total sleep score for a user may be calculated based on a set of contributing factors, including: total sleep time, efficiency, restfulness, REM sleep, deep sleep, delay, schedule, or any combination thereof. A sleep score may include any number of contributing factors. The "total sleep" contribution factor may refer to the sum of all sleep periods of a sleep day. The "efficiency" contribution factor may reflect the percentage of sleep time to awake time in bed, and may be calculated using the efficiency average of the long sleep periods (e.g., main sleep periods) of a sleep day, weighted by the duration of each sleep period. The "restfulness" contribution factor may indicate how restful a user's sleep is, and may be calculated using the average of all sleep periods of a sleep day, weighted by the duration of each sleep period. The restfulness contribution factor can be based on "number of wakeups" (e.g., the sum of all wakeups detected during different sleep periods (when the user wakes up)), excessive movement, and "wake-up count" (e.g., the sum of all wakeups detected during different sleep periods (when the user gets up)).

[0093] The "REM sleep" contribution factor may refer to the sum of the REM sleep durations of all sleep periods (including REM sleep) of a sleep day. Similarly, the "deep sleep" contribution factor may refer to the sum of the deep sleep durations of all sleep periods (including deep sleep) of a sleep day. The "delay" contribution factor may represent the time it takes for a user to fall asleep (e.g., average, median, longest), and may be calculated using the average of the long sleep periods within a sleep day, weighted by the duration of each period and the number of such periods (e.g., a given combination of one or more sleep stages may be its own contribution factor or weighted by other contribution factors). Finally, the "timing" contribution factor may refer to the relative time of sleep periods within a sleep day and / or calendar day, and may be calculated using the average of all sleep periods of a sleep day, weighted by the duration of each period.

[0094] As another example, a user's overall readiness score can be calculated based on a set of contribution factors, including: sleep, sleep balance, heart rate, HRV balance, recovery index, body temperature, activity, activity balance, or any combination thereof. The readiness score can include any number of contribution factors. The "sleep" contribution factor can refer to the combined sleep score of all sleep periods within a sleep day. The "sleep balance" contribution factor can refer to the cumulative duration of all sleep periods within a sleep day. Specifically, sleep balance can indicate to the user whether the sleep obtained by the user over a duration (e.g., the past two weeks) is balanced with the user's needs. Typically, adults need 7-9 hours of sleep per night to stay healthy, alert, and perform at their best mentally and physically. However, it is normal to have a bad night's sleep occasionally, so the sleep balance contribution factor takes into account long-term sleep patterns to determine whether each user's sleep needs are met. The "resting heart rate" contribution factor can represent the lowest heart rate in the longest sleep period (e.g., the main sleep period) in a sleep day and / or the lowest heart rate in a nap that occurs after the main sleep period.

[0095] Continuing with reference to the "contributors" (e.g., factors, contributing factors) of the readiness score, the "HRV balance" contribution factor may represent the highest HRV average across the main sleep period and the naps that occur after the main sleep period. The HRV balance contribution factor may help the user track their recovery status by comparing the user's HRV trend over a first time period (e.g., two weeks) to the average HRV over some second, longer time period (e.g., three months). The "recovery index" contribution factor may be calculated based on the longest sleep period. The recovery index measures the time it takes for the user's resting heart rate to stabilize during the night. A sign of very good recovery is that the user's resting heart rate stabilizes in the first half of the night, at least six hours before the user wakes up, giving the body time to recover for the next day. The "body temperature" contribution factor may be calculated based on the longest sleep period (e.g., the main sleep period) or based on the nap that occurs 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 aspects, the ring may measure the user's body temperature while the user is sleeping, and the system 200 may display the user's average temperature relative to the user's baseline temperature. If the user's body temperature is outside of its normal range (e.g., significantly above or below 0.0), the body temperature contribution factor can be highlighted (e.g., enter a "caution" state) or an alert can be otherwise generated for the user.

[0096] In certain aspects, the system 200 may support techniques for determining a cardiovascular health indicator based on wearable-based physiological data. Specifically, the various components of the system 200 may be used to determine a cardiovascular health indicator that indicates a user's cardiovascular health condition relative to the user's chronological age based on comparing one or more morphological features of the user's pulse waveform to one or more features from a plurality of baseline PPG signal morphologies associated with a plurality of chronological ages. The indication of the user's cardiovascular health indicator may be determined by utilizing a PPG sensor on the ring 104 of the system 200. In some cases, the indication of the cardiovascular health indicator may be determined by identifying one or more morphological features of the PPG signal (e.g., the location of a first local maximum, the value of a downward slope, the degree of a curved feature, or a combination thereof), in addition to other morphological features.

[0097] For example, as previously described herein, the ring 104 of the system 200 can be worn by a user to collect data from the user, including PPG signals, temperature, heart rate, HRV, respiratory data, etc. The ring 104 of the system 200 can collect physiological data from the user based on a PPG sensor 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 can be collected continuously. In some implementations, the processing module 230-a can continuously sample and / or receive the user's PPG signal throughout the day and night. Sampling at a sufficient rate (e.g., one sample per second or one sample per minute) throughout the day and / or night can provide sufficient data for the analysis described herein. In some implementations, the ring 104 can continuously acquire a PPG signal (e.g., at a sampling rate). In some examples, even if PPG signals are collected continuously, system 200 can leverage other information it collects or otherwise derives about the user (e.g., sleep stages, activity levels, illness onset, etc.) to select a representative PPG signal for a particular day that accurately represents the underlying physiological phenomenon.

[0098] In contrast, systems that require users to manually acquire their PPG signals every day and / or systems that continuously acquire PPG signals but lack any other contextual information about the user may select inaccurate or inconsistent PPG signals for use in their cardiovascular health indicator determination, resulting in inaccurate determinations and a degraded user experience. In contrast, the data collected by the ring 104 can be used to accurately determine the user's cardiovascular health indicators. Figure 3 Further shown and described are techniques for determining cardiovascular health indicators and related techniques.

[0099] Figure 3An example of a timing diagram 300 that supports determining a cardiovascular health indicator based on wearable-based physiological data according to aspects of the present disclosure is shown. The timing diagram 300 shows the relationship between a pulse waveform 305 and time. In this regard, the solid curve shown in the timing diagram 300 can be understood to refer to "pulse waveform 305-a", which can be an example of a received user pulse waveform. The dotted curve shown in the timing diagram 300 can be understood to refer to "pulse waveforms 305-b and 305-c", which can be examples of baseline PPG signal morphology. For example, pulse waveform 305-b can be an example of a baseline PPG signal morphology for a user between the ages of 40 and 44. Pulse waveform 305-c can 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 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 index may be determined that may indicate how the user's cardiovascular health at their current chronological age compares to the baseline cardiovascular health of users of a different chronological age. For example, if a user is 60 years old, but their pulse waveform is closest to the baseline pulse waveform of a 30-year-old user (e.g., based on a comparison of one or more morphological features), then that user may be assigned a relatively high cardiovascular health index.

[0100] The pulse waveform 305-a can be generated and / or identified based on data of a single user extracted from a wearable device. For example, the system (e.g., ring 104, user device 106, server 110) can receive physiological data including at least a PPG signal of the user from a wearable device. The pulse waveform 305-a can be an example of an average pulse waveform of the user over multiple days. The multiple days can be examples of at least twenty days (e.g., including at least twenty nights). In this case, the system can estimate a cardiovascular health indicator after receiving PPG signals for at least twenty nights. The system can average the PPG signals received over multiple days to represent a single pulse waveform of the user (e.g., pulse waveform 305-a). In this case, determining the average pulse waveform 305-a can ignore outliers, such as when the user is experiencing illness, stress, or other factors that affect the PPG signal. In addition, the system can omit or adjust the weighting of data collected on certain days based on other contextual information collected from the wearable device or application (e.g., through tags, activity detection, location information, etc.).

[0101] Pulse waveforms 305-b and 305-c may be generated and / or identified based on data of multiple users extracted from wearable devices in multiple wearable devices. In this case, 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 a PPG signal that may be paired with the chronological ages of multiple users. In this case, multiple users may be classified into different groups corresponding to the ages of the users. For each subject (e.g., user) in the group, an average pulse waveform may be formed. For example, the average pulse waveform of each user in the group may represent PPG samples collected over multiple days (e.g., at least twenty nights). In some cases, average pulse waveforms 305-b and 305-c may be generated from 30 average PPG samples of different users in each age group, respectively, to represent the average pulse morphology of users in 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. A baseline signal PPG morphology may be identified in response to receiving physiological data including at least a PPG signal of a user.

[0102] As described herein, features can be extracted from the template pulses (e.g., pulse waveforms 305-b and 305-c) and used as a user age classifier relative to the user's received pulse waveform 305-a. By comparing features extracted from the user's average pulse (e.g., pulse waveform 305-a) with features from the template pulses (e.g., pulse waveforms 305-b and 305-c), the system can estimate the user's cardiovascular health indicators.

[0103] The system can process the PPG signal to determine cardiovascular health indicators. The PPG signal can be collected continuously by the wearable device. Physiological measurements can be taken continuously throughout the day and / or night. For example, in some implementations, the ring can be configured to continuously acquire physiological data (e.g., PPG signals, etc.) based on one or more measurement periods throughout each day / sleep day. In other words, the ring can continuously acquire physiological data from the user without regard to the "trigger conditions" for performing such measurements.

[0104] The PPG signal can be used to generate a pulse waveform 305. The pulse waveform 305 can be an example of an arterial pulse waveform. In this case, the arterial pulse waveform can represent a rhythmic arterial pressure wave sensed by palpating the artery. In some cases, the arterial pulse waveform may be caused by the increase in blood pressure ejected into the aorta and arteries by the left ventricle of the heart. The pulse waveform 305 may include a systolic portion and a diastolic portion. The transition point between the systolic portion and the diastolic portion may manifest itself as a notch or a curved feature in the waveform, and may be referred to as a dicrotic notch. The pulse waveform 305 may each include a local maximum 310, a downward slope 325 after the local maximum 310, a curved feature 330 representing a transition from the systolic portion to the diastolic portion, or a combination thereof. The local maximum 310 may be an example of a systolic peak of the systolic portion, and the dicrotic notch may be an example of a curved feature 330 representing a transition from the systolic portion to the diastolic portion. Local maximum 310 , downward slope 325 after local maximum 310 , and curved feature 330 may be examples of characteristics (eg, morphological features) of pulse waveform 305 .

[0105] In some cases, the amplitude 315-c of the local maximum 310-c of the pulse waveform 305-c may be lower than the amplitude 315-b of the local maximum 310-b of the pulse waveform 305-b. The position 320-c of the local maximum 310-c of the pulse waveform 305-c may be shifted (e.g., shifted to the right) compared to the position 320-b of the local maximum 310-b of the pulse waveform 305-b. In some examples, there may be no second local maximum 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 a systolic portion to a diastolic portion. The amplitude 315 of the local maximum 310 may decrease with age, the position 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 weaken with age, or a combination of these. For example, the shape of pulse waveform 305 may become more triangular as age increases. In this case, pulse waveform 305-c may correspond to a greater chronological age than pulse waveform 305-b, which may correspond to a greater chronological age than pulse waveform 305-a.

[0106] The system can extract morphological features of pulse waveform 305-a. The morphological features can be examples of the location 320-a of local maximum 310-a, the value of downward slope 325-a, the degree of curvature 330, or a combination thereof. In some cases, the system can extract features from pulse waveforms 305-b and 305-c. The features can be examples of the location 320-b of local maximum 310-b, the location 320-c of local maximum 310-c, the value of downward slope 325-b, the value of downward slope 325-c, the degree of curvature 330, or a combination thereof.

[0107] In some cases, the system may determine or identify a local maximum 310-a of 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 of the pulse waveform 305-a after receiving the PPG signal and before extracting the morphological features associated with the value of the downward slope 325-a. In some cases, the system may identify one or more upward slopes of the pulse waveform 305-a. The downward slope 325-a may be an example of a negative slope, while the upward slope may be an example of a positive slope. In some examples, the system may identify the presence of a second local maximum (e.g., representing a curved feature 330) of the pulse waveform 305-a. In addition to these examples, or alternatively, the system may use a variety of statistical methods including machine learning (e.g., unsupervised learning) techniques to identify other morphological features of the pulse waveform 305.

[0108] The system may compare features of pulse waveform 305-a to features of pulse waveform 305-b, pulse waveform 305-c, or any number of other baseline pulse waveforms 305. 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, position 320-a, or both of local maximum 310-a to the amplitude 315-b, position 320-b, or both of local maximum 310-b. In other examples, the system may compare the amplitude 315-a, position 320-a, or both of local maximum 310-a to the amplitude 315-c, position 320-c, or both of local maximum 310-c. In this case, 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 maximums 310-b and 310-c, respectively. The system may determine that the location 320 - a of the local maximum 310 - a is located to the left of the locations 320 - b and 320 - c of the local maxima 310 - b and 310 - c , respectively.

[0109] In some examples, the system may compare the value of downward slope 325-a to the values ​​of downward slopes 325-b and 325-c. In this case, 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 to 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 this case, 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.

[0110] In response to comparing features of pulse waveform 305-a to features of pulse waveform 305-b, pulse waveform 305-c, or both, the system may determine a cardiovascular health indicator that indicates the cardiovascular health of the user relative to the user's chronological age. In some cases, the system may determine which baseline pulse waveform 305 most closely matches pulse waveform 305-a. For example, the system may determine that pulse waveform 305-a may match a pulse waveform 305 (e.g., baseline PPG signal morphology) for a user whose chronological age is between 20 and 24 years old. In this case, the system may determine that the user's cardiovascular health indicator corresponds to a cardiovascular health indicator (e.g., cardiovascular age) for a user whose chronological age is between 20 and 24 years old.

[0111] The system may determine that the cardiovascular health indicated by the user's cardiovascular health indicator is less than or greater than the user's chronological age. For example, the system may determine based on a comparison that the user's cardiovascular health indicator corresponds to an chronological age between 20 and 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 other examples, the system may determine that the cardiovascular health indicator indicating the user's cardiovascular health is greater than the user's chronological age. For example, the system may determine based on a comparison that the user's cardiovascular health indicator corresponds to an chronological age between 40 and 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 this case, the system may provide suggestions for improving the cardiovascular health indicator, such as reference Figure 5 described.

[0112] Figure 4An example of a timing diagram 400 that supports determining cardiovascular health indicators from wearable-based physiological data according to aspects of the present disclosure is shown. The timing diagram 400 shows the relationship between the second-order derivative pulse waveform 405 and time. In this regard, the solid curve shown in the timing diagram 400 can be understood to refer to the "second-order derivative pulse waveform 405-a", which can be a reference to Figure 3 An example of a second derivative of pulse waveform 305-a is depicted. The dashed curves shown in timing diagram 400 may be understood to refer to "second derivative pulse waveforms 405-b and 405-c," which may be examples of second derivatives of pulse waveforms 305-b and 305-c, respectively.

[0113] In some cases, the system can calculate and / or determine the raw pulse waveform (e.g., reference Figure 3 405). In an example, the system may calculate and / or determine a second-order derivative of the original pulse waveform. The calculated second-order derivative of the pulse waveform may be an example of the second-order derivative pulse waveform 405. The system may identify one or more local maxima 410, one or more local minima 415, or both in the second-order derivative pulse waveform 405. In some cases, the system may identify one or more local maxima, one or more local minima 415, or both in the first-order derivative pulse waveform.

[0114] In some examples, the system may compare second-order derivative pulse waveform 405-a (e.g., the second-order derivative of the received pulse waveform) to second-order derivative pulse waveforms 405-b and 405-c (e.g., baseline PPG signal morphology). For example, the system may determine that local maximum 410-a of second-order derivative pulse waveform 405-a may be greater than local maximums 410-b and 410-c of second-order derivative pulse waveforms 405-b and 405-c, respectively. The system may determine that local minimum 415-a of second-order derivative pulse waveform 405-a may be greater than local minimums 415-b and 415-c of second-order derivative pulse waveforms 405-b and 405-c, respectively. In this case, the system may calculate the deviation of the features of second-order derivative pulse waveform 405-a relative to the features of second-order 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.

[0115] As reference Figure 3As described, the cardiovascular health index may be determined based on a comparison of features of the second-order derivative pulse waveform 405-a (e.g., the amplitude 425 and / or position of the local maximum 410-a, the amplitude 425 and / or position 430 of the local minimum 415-a, or a combination thereof) with features of the second-order derivative pulse waveforms 405-b and 405-c. Features of the second-order derivative pulse waveforms 405-b and 405-c may be examples of the amplitude and / or position of the local maximums 410-b and 410-c, the amplitude and / or position of the local minimums 415-b and 415-c, or a combination thereof.

[0116] The system can 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 can be an example of a positive amplitude. In some cases, the amplitude 420 of the second derivative pulse waveform 405-a can indicate a cardiovascular health indicator. In this case, the system can determine the cardiovascular health indicator based on identifying the local maximum 410-a and / or determining the amplitude 420 of the local maximum 410-a. For example, the system can determine the cardiovascular health indicator in response to calculating the first derivative of the pulse waveform, calculating the second derivative pulse waveform 405-a, or both.

[0117] 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 indicate a cardiovascular health indicator. In this case, the system may determine the cardiovascular health indicator 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 location 430 (e.g., position) of the local minimum 415-a. In some cases, the location 430 of the local minimum 415-a may indicate a cardiovascular health indicator. In this case, the system may determine the cardiovascular health indicator based on determining the location 430 of the local minimum 415-a.

[0118] Second derivative pulse waveform 405 may include features associated with chronological age. For example, amplitude 420 of local maximum 410-a may decrease with chronological age, amplitude 425 of local minimum 415-a may decrease with chronological age, and the position of local minimum 415-a may increase (e.g., shift to the right) with chronological age. In some cases, each age group may include changes in cardiovascular health indicators. For example, an age group between 30 and 34 years old may include cardiovascular health indicators indicating a cardiovascular age less than 30 and 34 years old and / or greater than 30 and 34 years old.

[0119] In some cases, the system may determine a cardiovascular health index in response to determining a cardiovascular health indicator. In this case, the cardiovascular health index may include the cardiovascular health indicator as a component as well as other inputs. The system may determine arterial stiffness in response to determining the cardiovascular health indicator. In this case, the cardiovascular health index may be determined in response to determining arterial stiffness. In some cases, 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 indicator, arterial stiffness, blood pressure, resting heart rate, HRV, or a combination thereof.

[0120] Figure 5 An example of a GUI 500 that supports determining a cardiovascular health metric based on wearable-based physiological data according to aspects of the present disclosure is shown. The GUI 500 can implement aspects of the system 100, the system 200, the timing diagram 300, the timing diagram 400, or any combination thereof, or be implemented by these aspects. For example, the GUI 500 can be an example of a GUI 275 corresponding to a user device 106 (e.g., user devices 106-a, 106-b, 106-c) of the user 102. In some examples, the GUI 500 shows a series of application pages 505 that can be accessed via the GUI 500 (e.g., Figure 2 GUI275 shown in is displayed to the user.

[0121] The server of the system may generate a message 520 for display on the GUI 500 on the user device indicating an indication of the cardiovascular health indicator. For example, the server of the system may cause the GUI 500 of the user device (e.g., a mobile device) to display a message 520 related to an indication of the cardiovascular health indicator (e.g., via the application page 505). In this case, the system may output an indication of the cardiovascular health indicator on the GUI 500 of the user device to indicate the cardiovascular health status of the user relative to the user's chronological age.

[0122] After determining an indication of the user's cardiovascular health metric, the user may be presented with an application page 505 when opening the wearable application. Figure 5As shown, application page 505 can display an indication that a cardiovascular health indicator is determined and / or identified via message 520. In this case, application page 505 can include message 520 on the home page. In the event that a cardiovascular health indicator of a user is determined and / or identified, as described herein, the server can send message 520 to the user, where message 520 is associated with the cardiovascular health indicator. In some cases, the server can send message 520 to a clinician, a caregiver, a partner of the user, or a combination thereof. In this case, the system can present application page 505 on a user device associated with a clinician, a caregiver, a partner, or a combination thereof.

[0123] For example, a user may receive a message 520 that may indicate a trend related to a cardiovascular health metric, educational content related to a cardiovascular health metric, an adjusted set of sleep goals, an adjusted set of activity goals, suggestions for improving cardiovascular health metrics, etc. The message 520 may be configurable / customizable such that a user may receive different messages 520 based on a determination of a cardiovascular health metric, as previously described herein.

[0124] In some cases, message 520 may include a weekly or monthly report related to the determined cardiovascular health indicator. The report may indicate a trend related to the cardiovascular health indicator. For example, the trend may indicate whether the cardiovascular health indicator is changing (e.g., increasing or decreasing) relative to a previously determined cardiovascular health indicator. In some cases, the system may provide personalized suggestions to improve or maintain the cardiovascular health indicator. For example, message 520 may indicate "Did you know that exercising four times a week affects your cardiovascular health indicator? Try adding some exercise this week."

[0125] In this case, the message 520 may include insights, suggestions, etc. related to the determined cardiovascular health indicator. The server of the system may cause the GUI 500 of the user device to display the message 520 related to the cardiovascular health indicator. The user device may display the suggestions and / or information related to the cardiovascular health indicator via the message 520. As previously described herein, an accurately determined cardiovascular health indicator may be beneficial to the overall health of the user.

[0126] In addition, in some implementations, the application page 505 can display one or more scores (e.g., sleep score, readiness score, activity score, etc.) of the user on the corresponding day. In addition, in some cases, the determined cardiovascular health indicator can 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 related to the cardiovascular health indicator can be used to update the user's score on the next calendar day. In this case, the system can notify the user of the score update via an alert 510.

[0127] In some cases, the readiness score may be updated based on the cardiovascular health indicator. In such cases, the readiness score may provide an indication of "attention" to the user based on the determined cardiovascular health indicator. If the user's readiness score changes, the system may implement a recovery mode for users whose cardiovascular health-related symptoms may be severe and may benefit from adjusted activities and readiness guidance over a period of days, weeks, or months.

[0128] In other examples, the system may determine that the user's determined cardiovascular health indicator (e.g., cardiovascular age) is less than or equal to the user's chronological age, and may adjust the readiness score, sleep score, and / or activity score to accommodate an equal (e.g., expected) or lower cardiovascular health indicator. In other cases, the system may determine that the user's determined cardiovascular health indicator (e.g., cardiovascular age) is greater than the user's chronological age, and may adjust the readiness score, sleep score, and / or activity score to offset the impact of a higher cardiovascular health indicator. In some cases, the system may provide insights to keep the user's cardiovascular age (e.g., cardiovascular health indicator) below or at the same age as the user's chronological age. For example, the system may display suggestions and / or motivations for healthy habits via message 520 and provide behavioral insights to the user.

[0129] In some cases, a message 520 displayed to a user via the GUI 500 of the user device may indicate how the determined cardiovascular health metric affects the overall score (e.g., the overall readiness score) and / or individual contributing factors. For example, message 520 may indicate "It looks like your cardiovascular health metric is greater than your chronological age, but if you feel well, doing light or moderate intensity exercise can improve your cardiovascular health metric" or "Looking at your cardiovascular health metric, your chronological age seems to be right on track. Keep working on it!" In the event that a cardiovascular health metric is determined, message 520 may provide suggestions to the user to improve their general health (e.g., including their cardiovascular health metric). In this case, message 520 displayed to the user may provide targeted insights to help the user adjust their lifestyle.

[0130] The application page 505 may indicate one or more parameters via a graphical representation 515, including a pulse waveform (e.g., a portion of a PPG signal), temperature, heart rate, HRV, respiratory rate, sleep data, etc. The graphical representation 515 may be as shown in FIG. Figure 3 and Figure 4 An example of the timing diagram 300 or the timing diagram 400. In this case, the system can cause the GUI 500 of the user device to display a message 520, an alert 510, or a graphical representation 515 related to the cardiovascular health indicator.

[0131] In some cases, a user may record symptoms or events via user input 525. For example, the system may receive user input (e.g., tags) to record symptoms and / or events related to illness, stress, pregnancy, etc. For example, the system may receive an indication of data related to the user's health record via user input 525. The data related to the user's health record may include indications of illness, stress, pregnancy, drinking, exercise history, sleep habits, current medications, previous surgeries, etc. In other examples, the system may receive an indication of data related to the user's health record from a wearable device, physiological data from the wearable device, or both. The physiological data from the wearable device may be examples of temperature, heart rate, HRV, respiratory rate, sleep data, blood pressure, etc.

[0132] In this case, the system may adjust the cardiovascular health metric in response to receiving the indication. For example, the cardiovascular health metric may be adjusted based on the patient's medical history, physiological data obtained from a wearable device, or both. The system may cause the GUI 500 to display an indication (via an alert 510, a graphical representation 515, and / or a message 520) based on adjusting the cardiovascular health metric. In this case, the system may adjust insights, recommendations, etc. based on the adjusted cardiovascular health metric. For example, the system may indicate "It looks like you may be catching a cold. Your cardiovascular health metric is higher than usual, but it will all balance out after you recover from your cold. Take some time to rest." In some examples, the system may indicate "Based on your healthy lifestyle, your cardiovascular health metric is below your chronological age. Keep up the good work!"

[0133] like Figure 5 As shown, application page 505 can display an indication of the cardiovascular health indicator via alert 510. In some cases, application page 505 can display an indication of the adjusted cardiovascular health indicator via alert 510. The user can receive alert 510, and application page 505 can prompt the user to confirm or ignore the determined cardiovascular health indicator or the adjusted cardiovascular health indicator. For example, the system can receive confirmation of the cardiovascular health indicator via the user device in response to adjusting the cardiovascular health indicator.

[0134] In some implementations, the system may provide additional insights about the user's determined cardiovascular health indicator. For example, the application page 505 may indicate one or more physiological parameters (e.g., contributing factors) that led to the user's determined cardiovascular health indicator, such as deviations of one or more morphological features relative to one or more features from a baseline PPG signal morphology, exercise habits, sleep habits, etc. In other words, the system may be configured to provide some information or other insights about the determined cardiovascular health indicator. The personalized insights may indicate aspects of the collected physiological data used to generate the determined cardiovascular health indicator (e.g., contributing factors within the physiological data).

[0135] In some implementations, the system can be configured to receive user input regarding the determined cardiovascular health indicator in order to train the classifier (e.g., supervised learning for a machine learning classifier) ​​and improve the cardiovascular health indicator determination technique. For example, the user device can receive user input 525, which can then be input into the classifier to train the classifier. In some cases, the PPG signal can be input into the machine learning classifier. In this case, the system can determine the cardiovascular health indicator in response to inputting the PPG signal into the machine learning classifier.

[0136] Figure 6 A block diagram 600 of a device 605 that supports determining cardiovascular health indicators based on wearable-based physiological data according to aspects of the present disclosure is shown. The device 605 may include an input module 610, an output module 615, and a wearable device application 620. The device 605 may also include a processor. Each of these components can communicate with each other (e.g., via one or more buses).

[0137] The input module 610 may provide a means for receiving information such as data packets associated with various information channels (e.g., control channels, data channels, information channels associated with disease detection techniques), user data, control information, or any combination thereof. The information may be passed to other components of the device 605. The input module 610 may use a single antenna or a group of multiple antennas.

[0138] The output module 615 can provide a means for transmitting signals generated by other components of the device 605. For example, the output module 615 can transmit information related to various information channels (e.g., control channels, data channels, information channels related to disease detection technology), such as data packets, user data, control information, or any combination thereof. In some examples, the output module 615 can be co-located with the input module 610 in the transceiver module. The output module 615 can use a single antenna or a group of multiple antennas.

[0139] For example, the wearable application 620 may include a data acquisition component 625, a morphological feature component 630, a comparison component 635, a cardiovascular indicator component 640, a user interface component 645, or any combination thereof. In some examples, the wearable application 620 or its various components may be configured to use or otherwise cooperate with the input module 610, the output module 615, or both to perform various operations (e.g., receive, monitor, send). For example, the wearable application 620 may receive information from the input module 610, send information to the output module 615, or be integrated with the input module 610, the output module 615, or both to receive information, send information, or perform various other operations described herein.

[0140] The data acquisition component 625 may be configured as or otherwise support means for receiving a photoplethysmogram (PPG) signal representing a pulse waveform of a user from a wearable device, the pulse waveform comprising a first local maximum, a downward slope after the first local maximum, and a curved feature representing a transition from a systolic phase to a diastolic phase of a cardiac cycle. The morphological feature component 630 may be configured as or otherwise support means for extracting one or more morphological features associated with the location of the first local maximum, the value of the downward slope, the degree of the curved feature, or a combination thereof. The comparison component 635 may be configured as or otherwise support means for comparing 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 extracting the one or more morphological features. The cardiovascular indicator component 640 may be configured as or otherwise support means for determining a cardiovascular health indicator indicating a cardiovascular health condition of the user relative to the chronological age of the user based at least in part on the comparison. The user interface component 645 may be configured as or otherwise support means for causing a graphical user interface to display an indication of the cardiovascular health indicator.

[0141] Figure 7 A block diagram 700 of a wearable application 720 supporting determination of cardiovascular health indicators based on wearable-based physiological data in accordance with various aspects of the present disclosure is shown. The wearable application 720 can be an example of aspects of the wearable application or the wearable application 620 described herein, or both. The wearable application 720 or its various components can be examples of devices for performing various aspects of determining cardiovascular health indicators based on wearable-based physiological data as described herein. For example, the wearable application 720 can include a data acquisition component 725, a morphological feature component 730, a comparison component 735, a cardiovascular indicator component 740, a user interface component 745, or any combination thereof. Each of these components can communicate with each other directly or indirectly (e.g., via one or more buses).

[0142] The data acquisition component 725 may be configured as or otherwise support means for receiving a photoplethysmogram (PPG) signal representing a pulse waveform of a user from a wearable device, the pulse waveform comprising a first local maximum, a downward slope after the first local maximum, and a curved feature representing a transition from a systolic phase to a diastolic phase of a cardiac cycle. The morphological feature component 730 may be configured as or otherwise support means for extracting one or more morphological features associated with the location of the first local maximum, the value of the downward slope, the degree of the curved feature, or a combination thereof. The comparison component 735 may be configured as or otherwise support means for comparing 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 extracting the one or more morphological features. The cardiovascular indicator component 740 may be configured as or otherwise support means for determining a cardiovascular health indicator indicating a cardiovascular health condition of the user relative to the chronological age of the user based at least in part on the comparison. The user interface component 745 may be configured as or otherwise support means for causing a graphical user interface to display an indication of the cardiovascular health indicator.

[0143] In some examples, to support extraction of one or more morphological features, morphological features component 730 can be configured as or otherwise support means for calculating a first derivative of a pulse waveform, a second derivative of a pulse waveform, or both. In some examples, to support extraction of one or more morphological features, morphological features component 730 can be configured as or otherwise support means for identifying one or more local maxima or one or more local minima of a first derivative of a pulse waveform or a second derivative of a pulse waveform, or both, wherein one or more morphological features are associated with one or more local maxima or one or more local minima of a first derivative of a pulse waveform or a second derivative of a pulse waveform, or both.

[0144] In some examples, to support extraction of one or more morphological features, morphological feature component 730 can be configured as or otherwise support a device for determining the magnitude, location, or both of a first local maximum, where one or more morphological features are associated with the magnitude, location, or both of the first local maximum.

[0145] In some examples, to support extraction of one or more morphological features, morphological feature component 730 can be configured as or otherwise support means for identifying the presence of a second local maximum of the pulse waveform, wherein a curvature feature representing a transition from systole to diastole of the cardiac cycle is associated with the second local maximum.

[0146] In some examples, to support extraction of one or more morphological features, morphological feature component 730 can be configured as or otherwise support means for identifying one or more positive slopes or one or more negative slopes of the pulse waveform, wherein a downward slope after a first local maximum is associated with the one or more negative slopes of the pulse waveform.

[0147] In some examples, comparison component 735 can be configured as or otherwise support means for determining which of a plurality of baseline PPG signal morphologies matches one or more morphology features based at least in part on the comparison, wherein determining a cardiovascular health indicator is based at least in part on the determination.

[0148] In some examples, comparison component 735 can be configured as or otherwise support means for calculating a deviation of one or more morphology features relative to one or more features from a plurality of baseline PPG signal morphologies based at least in part on the comparison, wherein determining a cardiovascular health indicator is based at least in part on calculating the deviation.

[0149] In some examples, data collection component 725 can be configured as or otherwise support means for receiving, via the user device, data indications from the wearable device related to the user's health record, physiological data from the wearable device, or both. In some examples, cardiovascular indicator component 740 can be configured as or otherwise support means for adjusting a cardiovascular health indicator based at least in part on receiving the indication, wherein causing the graphical user interface to display the indication is based at least in part on adjusting the cardiovascular health indicator.

[0150] In some examples, user interface component 745 may be configured as or otherwise support means for causing a graphical user interface of a user device associated with a user to display a message associated with a cardiovascular health metric.

[0151] In some examples, the message also includes suggestions for improving a cardiovascular health metric, trends related to a cardiovascular health metric, educational content related to a cardiovascular health metric, an adjusted set of activity goals, an adjusted set of sleep goals, or a combination thereof.

[0152] In some examples, data collection component 725 can be configured as or otherwise support means for identifying a plurality of baseline PPG signal morphologies associated with a plurality of chronological ages based at least in part on receiving the PPG signal, wherein the comparison is based at least in part on identifying the plurality of baseline PPG signal morphologies.

[0153] In some examples, data collection component 725 can be configured as or otherwise support means for inputting the PPG signal into a machine learning classifier, wherein determining the cardiovascular health indicator is based at least in part on inputting the PPG signal into the machine learning classifier.

[0154] In some examples, a plurality of baseline PPG signal morphologies associated with a plurality of chronological ages are extracted from physiological data associated with a plurality of users.

[0155] In some examples, the wearable device includes a wearable ring device.

[0156] 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.

[0157] Figure 8 A diagram of a system 800 including a device 805 that supports determining a cardiovascular health metric based on wearable-based physiological data according to aspects of the present disclosure is shown. The device 805 can be an example of the device 605 described herein or include components thereof. The device 805 can include an example of a user device 106, as described previously herein. The device 805 can include components for two-way 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 can be in electronic communication or otherwise coupled (e.g., operationally, communicatively, functionally, electronically, electrically) via one or more buses (e.g., bus 845).

[0158] The communication module 810 can manage input and output signals for the device 805 via the antenna 815. The communication module 810 may include Figure 2 An example of a communication module 220-b of a user device 106 is shown and described in FIG. In this regard, the communication module 810 can manage communications with the ring 104 and the server 110, such as Figure 2 As shown. The communication module 810 can also manage peripheral devices that are not integrated into the device 805. In some cases, the communication module 810 can represent a physical connection or port to an external peripheral device. In some cases, the communication module 810 can utilize an operating system, such as Or another known operating system. In other cases, the communication module 810 can represent or interact with a wearable device (e.g., ring 104), a modem, a keyboard, a mouse, a touch screen, or a similar device. In some cases, the communication module 810 can be implemented as part of the processor 840. In some examples, a user can interact with the device 805 via the communication module 810, the user interface component 825, or via a hardware component controlled by the communication module 810.

[0159] In some cases, the device 805 may include a single antenna 815. However, in some other cases, the device 805 may have more than one antenna 815, which may be capable of simultaneously sending or receiving multiple wireless transmissions. The communication module 810 may communicate bidirectionally via one or more antennas 815, wired or wireless links as described herein. For example, the communication module 810 may represent a wireless transceiver and may communicate bidirectionally with another wireless transceiver. The communication module 810 may also include a modem to modulate data packets, provide the modulated data packets to one or more antennas 815 for transmission, and demodulate data packets received from one or more antennas 815.

[0160] The user interface component 825 can manage the storage and processing of data in the database 830. In some cases, the user can interact with the user interface component 825. In other cases, the user interface component 825 can run automatically without user interaction. The database 830 can be an example of a single database, a distributed database, multiple distributed databases, a data repository, a data lake, or an emergency backup database.

[0161] The memory 835 may include RAM and ROM. The memory 835 may store computer-readable, computer-executable software, including instructions that, when executed, enable the processor 840 to perform the various functions described herein. In some cases, the memory 835 may contain BIOS, etc., which may control basic hardware or software operations, such as interaction with peripheral components or devices.

[0162] 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, a discrete gate or transistor logic component, a discrete hardware component, 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 supporting methods and systems for sleep staging algorithms).

[0163] For example, the wearable application 820 may be configured as or otherwise support means for receiving a photoplethysmogram (PPG) signal representing a pulse waveform of a user from a wearable device, the pulse waveform including a first local maximum, a downward slope after the first local maximum, and a curved feature representing a transition from a systolic phase to a diastolic phase of a cardiac cycle. The wearable application 820 may be configured as or otherwise support means for extracting one or more morphological features associated with the location of the first local maximum, the value of the downward slope, the degree of the curved feature, or a combination thereof. The wearable application 820 may be configured as or otherwise support 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 extracting the one or more morphological features. The wearable application 820 may be configured as or otherwise support means for determining, based at least in part on the comparison, a cardiovascular health indicator indicating a cardiovascular health condition of the user relative to the chronological age of the user. The wearable application 820 may be configured as or otherwise support means for causing a graphical user interface to display an indication of the cardiovascular health indicator.

[0164] By including or configuring the wearable application 820 according to the examples described herein, the device 805 can support techniques for improving communication reliability, reducing latency, improving user experience associated with reduced processing, reducing power consumption, more efficiently utilizing communication resources, improving coordination between devices, extending battery life, improving utilization of processing power, or a combination thereof.

[0165] The wearable application 820 may include an application (e.g., an “app”), program, software, or other component configured to facilitate communication with the ring 104 , the server 110 , other user devices 106 , etc. For example, the wearable application 820 may include an application executable on the user device 106 that is configured to receive data (e.g., physiological data) from the ring 104 , perform processing operations on the received data, send and receive data with the server 110 , and present the data to the user 102 .

[0166] Fig. 9 A flowchart of a method 900 for supporting determination of cardiovascular health indicators from wearable physiological data according to aspects of the present disclosure is shown. The operations of the method 900 may be implemented by a user device or a component thereof as described herein. For example, the operations of the method 900 may be implemented by reference to Figures 1 to 8 The user equipment performs. In some examples, the user equipment may execute a set of instructions to control the functional elements of the user equipment to perform the functions. In addition, or as an alternative, the user equipment may use dedicated hardware to perform various aspects of the functions.

[0167] At 905, the method may include receiving a photoplethysmogram (PPG) signal representing a pulse waveform of the user from a wearable device, the pulse waveform including a first local maximum, a downward slope after the first local maximum, and a curved feature representing a transition from a systolic phase to a diastolic phase of the cardiac cycle. The operations of 905 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 905 may be performed by the data acquisition component 725, as described in reference to Figure 7 described.

[0168] At 910, the method may include extracting one or more morphological features associated with the location of the first local maximum, the value of the downward slope, the degree of the curved feature, or a combination thereof. The operation of 910 may be performed according to examples disclosed herein. In some examples, aspects of the operation of 910 may be described by reference to Figure 7 The morphological feature component 730 described above performs the above steps.

[0169] At 915, the method may include comparing the one or more morphology 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 morphology features. The operations of 915 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 915 may be described with reference to Figure 7 The comparison component 735 described above performs the above.

[0170] At 920, the method may include determining a cardiovascular health indicator based at least in part on the comparison, the indicator indicating the cardiovascular health of the user relative to the chronological age of the user. The operations of 920 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 920 may be performed as described in reference to Figure 7 The cardiovascular indicator component 740 is executed.

[0171] At 925, the method may include causing a graphical user interface to display an indication of a cardiovascular health indicator. The operations of 925 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 925 may be performed as described in reference to Figure 7 The user interface component 745 executes.

[0172] Fig.10 A flowchart of a method 1000 for supporting determination of cardiovascular health indicators based on wearable physiological data according to aspects of the present disclosure is shown. The operations of the method 1000 may be implemented by a user device or a component thereof as described herein. For example, the operations of the method 1000 may be implemented by reference to Figures 1 to 8The user equipment performs. In some examples, the user equipment may execute a set of instructions to control the functional elements of the user equipment to perform the functions. In addition, or as an alternative, the user equipment may use dedicated hardware to perform various aspects of the functions.

[0173] At 1005, the method may include receiving a photoplethysmogram (PPG) signal representing a pulse waveform of a user from a wearable device, the pulse waveform including a first local maximum, a downward slope after the first local maximum, and a curved feature representing a transition from a systolic phase to a diastolic phase of a cardiac cycle. The operation of 1005 may be performed according to examples disclosed herein. In some examples, aspects of the operation of 1005 may be performed as described in reference to Figure 7 The data acquisition component 725 executes.

[0174] At 1010, the method may include extracting one or more morphological features associated with the location of the first local maximum, the value of the downward slope, the degree of the curved feature, or a combination thereof. The operations of 1010 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1010 may be performed as described in reference to Figure 7 The morphological feature component 730 is executed.

[0175] 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 according to examples disclosed herein. In some examples, aspects of the operations of 1015 may be as described in reference to Figure 7 The morphological feature component 730 is executed.

[0176] At 1020, the method may include identifying one or more local maxima or one or more local minima of a first derivative of the pulse waveform or a second derivative of the pulse waveform, or both, wherein one or more morphological features are associated with one or more local maxima or one or more local minima of a first derivative of the pulse waveform or a second derivative of the pulse waveform, or both. The operations of 1020 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1020 may be performed as described in reference to Figure 7 The morphological feature component 730 described above performs the above steps.

[0177] At 1025, the method may include comparing the one or more morphology 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 morphology features. The operations of 1025 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1025 may be performed as described in reference to Figure 7 The comparison component 735 described above performs the above.

[0178] At 1030, the method may include determining a cardiovascular health indicator based at least in part on the comparison, the indicator indicating the cardiovascular health of the user relative to the user's chronological age. The operations of 1030 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1030 may be as described in reference to Figure 7 The cardiovascular indicator component 740 is executed.

[0179] At 1035, the method may include causing a graphical user interface to display an indication of a cardiovascular health indicator. The operations of 1035 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1035 may be as described in reference to Figure 7 The user interface component 745 executes.

[0180] Fig.11 A flowchart of a method 1100 for supporting determination of cardiovascular health indicators based on wearable physiological data according to aspects of the present disclosure is shown. The operations of the method 1100 may be implemented by a user device or a component thereof as described herein. For example, the operations of the method 1100 may be implemented by reference to Figures 1 to 8 The user equipment performs. In some examples, the user equipment may execute a set of instructions to control the functional elements of the user equipment to perform the functions. In addition, or as an alternative, the user equipment may use dedicated hardware to perform various aspects of the functions.

[0181] At 1105, the method may include receiving a photoplethysmogram (PPG) signal representing a pulse waveform of the user from a wearable device, the pulse waveform including a first local maximum, a downward slope after the first local maximum, and a curved feature representing a transition from a systolic phase to a diastolic phase of the cardiac cycle. The operations of 1105 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1105 may be performed as described in reference to Figure 7 The data acquisition component 725 executes.

[0182] At 1110, the method may include extracting one or more morphological features associated with the location of the first local maximum, the value of the downward slope, the degree of the curved feature, or a combination thereof. The operations of 1110 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1110 may be as described in reference to Figure 7 The morphological feature component 730 is executed.

[0183] At 1115, the method may include comparing the one or more morphology 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 morphology features. The operations of 1115 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1115 may be performed as described in reference to Figure 7 The comparison component 735 described above performs the above.

[0184] At 1120, the method may include determining which of the plurality of baseline PPG signal morphologies matches the one or more morphology features based at least in part on the comparison, wherein determining the cardiovascular health indicator is based at least in part on the determination. The operations of 1120 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1120 may be performed as described in reference to Figure 7 The comparison component 735 described above performs the above.

[0185] At 1125, the method may include determining a cardiovascular health indicator indicating a cardiovascular health condition of the user relative to the user's chronological age based at least in part on the comparison. The operations of 1125 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1125 may be performed as described in reference to Figure 7 The cardiovascular indicator component 740 is executed.

[0186] At 1130, the method may include causing a graphical user interface to display an indication of a cardiovascular health indicator. The operations of 1130 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1130 may be performed as described in reference to Figure 7 The user interface component 745 executes.

[0187] It is worth noting that the above methods describe possible implementations, operations and steps may be rearranged or otherwise modified, and other implementations are possible. In addition, aspects of two or more methods may be combined.

[0188] A method is described. The method may include: receiving a photoplethysmogram (PPG) signal representing a pulse waveform of a user from a wearable device, the pulse waveform including a first local maximum, a downward slope after the first local maximum, and a curved feature representing a transition from a systolic phase to a diastolic phase of a cardiac cycle; extracting one or more morphological features associated with a location of the first local maximum, a value of the downward slope, a degree of the curved feature, or a combination thereof; based at least in part on extracting the one or more morphological features, 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; determining a cardiovascular health indicator indicative of a cardiovascular health condition of the user relative to the chronological age of the user based at least in part on the comparison; and causing a graphical user interface to display an indication of the cardiovascular health indicator.

[0189] 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 executed by the processor to cause the apparatus to receive a photoplethysmogram (PPG) signal representing a pulse waveform of a user from a wearable device, the pulse waveform including a first local maximum, a downward slope after the first local maximum, and a curved feature representing a transition from a systolic phase to a diastolic phase of a cardiac cycle; extract one or more morphological features associated with the location of the first local maximum, the value of the downward slope, the degree of the curved feature, or a combination thereof; based at least in part on extracting the one or more morphological features, 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; determine a cardiovascular health indicator indicating a cardiovascular health condition of the user relative to the chronological age of the user based at least in part on the comparison; and cause a graphical user interface to display an indication of the cardiovascular health indicator.

[0190] Another apparatus is described. The apparatus may include: means for receiving a photoplethysmogram (PPG) signal representing a pulse waveform of a user from a wearable device, the pulse waveform including a first local maximum, a downward slope after the first local maximum, and a curved feature representing a transition from a systolic phase to a diastolic phase of a cardiac cycle; means for extracting one or more morphological features associated with a location of the first local maximum, a value of the downward slope, a degree of the curved feature, or a combination thereof; 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; means for determining a cardiovascular health indicator indicative of a cardiovascular health condition of the user relative to the chronological age of the user based at least in part on the comparison; and means for causing a graphical user interface to display an indication of the cardiovascular health indicator.

[0191] A non-transitory computer-readable medium storing code is described. The code may include instructions executable by a processor to: receive a photoplethysmogram (PPG) signal representing a pulse waveform of a user from a wearable device, the pulse waveform including a first local maximum, a downward slope after the first local maximum, and a curved feature representing a transition from a systolic phase to a diastolic phase of a cardiac cycle; extract one or more morphological features associated with a location of the first local maximum, a value of the downward slope, a degree of the curved 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 extracting the one or more morphological features; determine a cardiovascular health indicator indicative of a cardiovascular health condition of the user relative to the chronological age of the user based at least in part on the comparison; and cause a graphical user interface to display an indication of the cardiovascular health indicator.

[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, apparatus, or instructions for calculating a first derivative of a pulse waveform, a second derivative of the pulse waveform, or both and identifying one or more local maxima or one or more local minima of the first derivative of the pulse waveform or the second derivative of the pulse waveform, or both, wherein the one or more morphological features may be associated with one or more local maxima or one or more local minima of the first derivative of the pulse waveform or the second derivative of the pulse waveform, or both.

[0193] 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, apparatus, or instructions for determining a magnitude, a position, or both of a first local maximum, wherein the one or more morphological features may be associated with the magnitude, the position, or both of the first local maximum.

[0194] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, extracting one or more morphological features can include operations, features, apparatus, or instructions for identifying the presence of a second local maximum of a pulse waveform, wherein a curvature feature representing a transition from a systolic phase to a diastolic phase of the cardiac cycle can be associated with the second local maximum.

[0195] 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, apparatus, or instructions for identifying one or more positive slopes or one or more negative slopes of the pulse waveform, wherein a downward slope after a first local maximum may be associated with the one or more negative slopes of the pulse waveform.

[0196] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, apparatus, or instructions for determining which of a plurality of baseline PPG signal morphologies matches one or more morphology features based at least in part on the comparison, wherein determining a cardiovascular health indicator may be based at least in part on the determination.

[0197] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, devices, or instructions for calculating a deviation of one or more morphology features relative to one or more features from a plurality of baseline PPG signal morphologies based at least in part on the comparison, wherein determining a cardiovascular health indicator may be based at least in part on calculating the deviation.

[0198] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, apparatus, or instructions for receiving, via a user device, data related to a user's health record from a wearable device, physiological data from the wearable device, or both, and adjusting a cardiovascular health metric based at least in part on receiving the indication, wherein causing a graphical user interface to display the indication may be based at least in part on adjusting the cardiovascular health metric.

[0199] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, means, or instructions for causing a graphical user interface of a user device associated with a user to display a message associated with a cardiovascular health indicator.

[0200] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the message also includes recommendations for improving cardiovascular health indicators, trends related to cardiovascular health indicators, educational content related to cardiovascular health indicators, an adjusted set of activity goals, an adjusted set of sleep goals, or a combination thereof.

[0201] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, apparatus, or instructions for identifying a plurality of baseline PPG signal morphologies associated with a plurality of chronological ages based at least in part on receiving the PPG signal, wherein the comparing may be based at least in part on identifying the plurality of baseline PPG signal morphologies.

[0202] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, apparatus, or instructions for inputting a PPG signal into a machine learning classifier, wherein determining a cardiovascular health indicator may be based at least in part on inputting the PPG signal into the machine learning classifier.

[0203] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, a plurality of baseline PPG signal morphologies associated with a plurality of chronological ages may be extracted from physiological data associated with a plurality of users.

[0204] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the wearable device includes a wearable ring device.

[0205] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, a wearable device collects physiological data from a user based on arterial blood flow, capillary blood flow, arteriolar blood flow, or a combination thereof.

[0206] The descriptions set forth herein in conjunction with the accompanying drawings describe example configurations and do not represent all examples that may be implemented or within the scope of the claims. The term "exemplary" as used herein means "as an example, instance, or illustration," rather than "preferably" or "better than other examples." The detailed description includes specific details intended to aid in understanding the techniques. However, the techniques may be implemented without these specific details. In some cases, well-known structures and devices are shown in block diagram form to avoid obscuring the concepts of the examples.

[0207] In the drawings, similar components or features may have the same reference label. In addition, various components of the same type may be distinguished by following the reference label with a dash and a second label that distinguishes the similar components. If only the first reference label is used in the specification, the description applies to any one of the similar components having the same first reference label regardless of the second reference label.

[0208] The information and signals described herein may be represented using any of a variety of techniques and technologies. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the above description may be represented by voltage, current, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0209] The various illustrative blocks and modules described in connection with the disclosure herein may be implemented or executed with 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 in an alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices (e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, a combination of one or more microprocessors and a DSP core, or any other such configuration).

[0210] The functions described herein can be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, these functions can be stored on a computer-readable medium or transmitted via a computer-readable medium as one or more instructions or codes. Other examples and implementations are within the scope of the present disclosure and the appended claims. For example, due to the nature of software, the above functions can be implemented using software executed by a processor, hardware, firmware, hard wiring, or any combination of these. The features that implement the functions can also be physically located in various locations, including distribution, so that the parts of the functions are implemented in different physical locations. In addition, as used herein, including in the claims, the "or" used in a list of items (e.g., a list of items beginning with phrases such as "at least one of..." or "one or more of...") indicates a list containing a list, so that, for example, a list of at least one of A, B, or C indicates A or B or C or AB or AC or BC or ABC (i.e., A and B and C). In addition, as used herein, the phrase "based on" should not be interpreted as referring to a closed set of conditions. For example, the exemplary steps described as "based on condition A" can be based on condition A and condition B at the same time without departing from the scope of the present disclosure. In other words, as used herein, the phrase "based on" should be interpreted in the same manner as the phrase "based at least in part on."

[0211] Computer-readable media include non-transitory computer storage media and communication media, including any media that facilitates the transfer of computer programs from one place to another. Non-transitory storage media can be any available media that can be accessed by a general or special computer. For example, but not limited to, non-transitory computer-readable media can include RAM, ROM, electrically erasable programmable ROM (EEPROM), compact disk (CD) ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other non-transitory media that can be used to carry or store the required program code device in the form of instructions or data structures and can be accessed by a general or special computer or a general or special processor. In addition, any connection can be appropriately referred to as a computer-readable medium. For example, if the software is transmitted from a website, server or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL) or wireless technologies such as infrared, radio and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL or wireless technologies such as infrared, radio and microwave are included in the definition of the medium. Disk and disc, as used herein, include CDs, laser discs, optical discs, digital versatile discs (DVDs), floppy disks, and Blu-ray discs, where disks typically reproduce data magnetically, while discs reproduce data optically using lasers. Combinations of the above are also included within the scope of computer-readable media.

[0212] The description herein is intended to enable those skilled in the art to make or use the present disclosure. Various modifications of the present disclosure are obvious to those skilled in the art, and the general principles defined herein can be applied to other variations without departing from the scope of the present disclosure. Therefore, the present disclosure is not limited to the examples and designs described herein, but should be given the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method comprising: receiving a photoplethysmogram (PPG) signal representing a pulse waveform of the user from a wearable device, the pulse waveform comprising a first local maximum, a downward slope after the first local maximum, and a curved feature representing a transition from a systolic phase to a diastolic phase of a cardiac cycle; extracting one or more morphological features related to the location of the first local maximum, the value of the downward slope, the degree of the curved feature, or a combination thereof; Based at least in part on extracting the one or more morphology features, comparing the one or more morphology features to one or more features from a plurality of baseline PPG signal morphologies associated with a plurality of chronological ages; determining a cardiovascular health indicator indicative of a cardiovascular health condition of the user relative to a chronological age of the user based at least in part on the comparison; and A graphical user interface is caused to display an indication of the cardiovascular health indicator.

2. The method according to claim 1, wherein extracting the one or more morphological features further comprises: calculating a first derivative of the pulse waveform, a second derivative of the pulse waveform, or both; and One or more local maxima or one or more local minima of a first derivative of the pulse waveform or a second derivative of the pulse waveform, or both, are identified, wherein the one or more morphological features are associated with the one or more local maxima or the one or more local minima of the first derivative of the pulse waveform or the second derivative of the pulse waveform, or both.

3. The method according to claim 1, wherein extracting the one or more morphological features further comprises: A magnitude, a location, or both of the first local maximum is determined, wherein the one or more morphological features are associated with the magnitude, the location, or both of the first local maximum.

4. The method according to claim 1, wherein extracting the one or more morphological features further comprises: The presence of a second local maximum of the pulse waveform is identified, wherein the curved feature representing a transition from the systolic phase to the diastolic phase of the cardiac cycle is associated with the second local maximum.

5. The method according to claim 1, wherein extracting the one or more morphological features further comprises: One or more positive slopes or one or more negative slopes of the pulse waveform are identified, wherein the downward slope after the first local maximum is associated with the one or more negative slopes of the pulse waveform.

6. The method according to claim 1, further comprising: Determining which of the plurality of baseline PPG signal morphologies matches the one or more morphology features based at least in part on the comparing, wherein determining the cardiovascular health indicator is based at least in part on the determining.

7. The method according to claim 1, further comprising: A deviation of the one or more morphology features relative to the one or more features from a plurality of baseline PPG signal morphologies is calculated based at least in part on the comparison, wherein determining the cardiovascular health indicator is based at least in part on calculating the deviation.

8. The method according to claim 1, further comprising: receiving, via a user device, an indication of data from the wearable device related to a health record of the user, physiological data from the wearable device, or both; and The cardiovascular fitness indicator is adjusted based at least in part on receiving the indication, wherein causing the graphical user interface to display the indication is based at least in part on adjusting the cardiovascular fitness indicator.

9. The method according to claim 1, further comprising: A graphical user interface of a user device associated with the user is caused to display a message associated with the cardiovascular health indicator.

10. The method of claim 9, wherein the message further comprises suggestions for improving the cardiovascular health indicator, trends associated with the cardiovascular health indicator, educational content associated with the cardiovascular health indicator, an adjusted set of activity goals, an adjusted set of sleep goals, or a combination thereof.

11. The method according to claim 1, further comprising: The plurality of baseline PPG signal morphologies associated with the plurality of chronological ages are identified based at least in part on receiving the PPG signal, wherein the comparing is based at least in part on identifying the plurality of baseline PPG signal morphologies.

12. The method according to claim 1, further comprising: The PPG signal is input into a machine learning classifier, wherein determining the cardiovascular health indicator is based at least in part on inputting the PPG signal into 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. An apparatus comprising: processor; a memory coupled to the processor; as well as Instructions stored in the memory and executable by the processor, the instructions being configured to cause the apparatus to: receiving a photoplethysmogram (PPG) signal representing a pulse waveform of the user from the wearable device, the pulse waveform comprising a first local maximum, a downward slope after the first local maximum, and a curved feature representing a transition from a systolic phase to a diastolic phase of a cardiac cycle; extracting one or more morphological features related to the location of the first local maximum, the value of the downward slope, the degree of the curved feature, or a combination thereof; Based at least in part on extracting the one or more morphology features, comparing the one or more morphology features to one or more features from a plurality of baseline PPG signal morphologies associated with a plurality of chronological ages; determining a cardiovascular health indicator indicative of a cardiovascular health condition of the user relative to a chronological age of the user based at least in part on the comparison; and A graphical user interface is caused to display an indication of the cardiovascular health indicator.

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

18. The apparatus of claim 16, wherein the instructions for extracting the one or more morphological features are further executable by the processor to cause the apparatus to: A magnitude, a location, or both of the first local maximum is determined, wherein the one or more morphological features are associated with the magnitude, the location, or both of the first local maximum.

19. A non-transitory computer-readable medium storing code, the code comprising instructions executable by a processor, the instructions for: receiving a photoplethysmogram (PPG) signal representing a pulse waveform of the user from a wearable device, the pulse waveform comprising a first local maximum, a downward slope after the first local maximum, and a curved feature representing a transition from a systolic phase to a diastolic phase of a cardiac cycle; extracting one or more morphological features related to the location of the first local maximum, the value of the downward slope, the degree of the curved feature, or a combination thereof; Based at least in part on extracting the one or more morphology features, comparing the one or more morphology features to one or more features from a plurality of baseline PPG signal morphologies associated with a plurality of chronological ages; determining a cardiovascular health indicator indicative of a cardiovascular health condition of the user relative to a chronological age of the user based at least in part on the comparison; and A graphical user interface is caused to display an indication of the cardiovascular health indicator.

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

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