Techniques for determining blood pressure based on morphological characteristics of pulse waves

Abstract

Methods, systems, and devices for determining blood pressure based on morphological features of pulse waves are described. A system may include a wearable device using one or more light-emitting components configured to emit light, one or more photodetectors configured to receive light, and a controller that couples the light-emitting components to the one or more photodetectors. The wearable device may transmit light associated with multiple wavelengths and acquire PPG data including one or more photoplethysmography (PPG) waveforms associated with each wavelength. The system may determine each group of morphological features associated with each PPG waveform based on systolic and diastolic peaks corresponding to the user's heartbeat. The system may determine one or more blood pressure indices for the user based at least in part on a comparison of each group of morphological features.

Description

Technical Field

[0001] [Cross-reference] This application claims the benefit of U.S. Non-Provisional Application No. 18 / 189,849, filed Mar. 24, 2023, by Rantanen et al., entitled "TECHNIQUES FOR DETERMINING BLOOD PRESSURE BASED ON MORPHOLOGICAL FEATURES OF PULSES", which has been assigned to the assignee of the present application and is hereby incorporated by reference in its entirety.

[0002] [Technical Field] The following relates to wearable devices and data processing, including techniques for determining blood pressure based on morphological features of pulse waves.

Background Art

[0003] Some wearable devices may be configured to collect data related to blood pressure from a user. However, wearable devices may not be able to accurately indicate a user's blood pressure. That is, wearable devices may not be able to accurately perform blood pressure measurements due to signal processing limitations or the lack of appropriate techniques in the wearable device hardware to enable the indication of a user's blood pressure. In some aspects, a user may obtain blood pressure measurements in a clinical setting (e.g., a doctor's appointment) using a blood pressure device (e.g., a sphygmomanometer, a blood pressure cuff, a blood pressure monitor). However, the size and shape of blood pressure devices can be inconvenient for daily use, and in some cases, a user may not be able to consistently obtain blood pressure measurements using the blood pressure device. As a result, if blood pressure measurements are rarely taken for a user, the user may not notice specific conditions or diseases associated with high or low blood pressure measurements.

Brief Description of the Drawings

[0004] [Figure 1]This figure shows an example of a system that supports a technique for determining blood pressure based on the morphological characteristics of pulse waves, according to aspects of this disclosure.

[0005] [Figure 2] This figure shows an example of a system that supports a technique for determining blood pressure based on the morphological characteristics of pulse waves, according to aspects of this disclosure.

[0006] [Figure 3] This figure shows an example of a wearable device system that supports a technique for determining blood pressure based on the morphological characteristics of a pulse wave, according to aspects of this disclosure.

[0007] [Figure 4] This figure shows an example of a pulse wave morphology graph that supports a technique for determining blood pressure based on the morphological characteristics of pulse waves, according to aspects of this disclosure.

[0008] [Figure 5] This figure shows an example of a graphical user interface (GUI) that supports a technique for determining blood pressure based on morphological characteristics of pulse waves, according to aspects of this disclosure.

[0009] [Figure 6] A block diagram of an apparatus supporting a technique for determining blood pressure based on morphological characteristics of pulse waves, as described in this disclosure, is shown.

[0010] [Figure 7] A block diagram of a wearable device manager supporting a technique for determining blood pressure based on morphological characteristics of pulse waves, as described in this disclosure, is shown.

[0011] [Figure 8] A diagram of a system including a device that supports a technique for determining blood pressure based on the morphological characteristics of a pulse wave, as described in this disclosure. [Modes for carrying out the invention]

[0012] Traditionally, blood pressure could only be measured in a clinical setting, meaning users could only measure their blood pressure a few times a year when visiting their doctor's office. Home blood pressure monitors have made it possible for users to measure their blood pressure at home. However, conventional blood pressure devices rely on bulky arm cuffs that are not (or are not) comfortable to wear consistently.

[0013] Some wearable devices have attempted to use light-based measurements to measure blood pressure. However, wearable devices that attempt to use light to acquire the physiological data necessary to determine blood pressure may not be able to measure blood pressure due to limitations in signal processing. In addition, the lack of information about the relationship between blood pressure and the characteristics of photoplethysmography (PPG) waveforms may prevent some wearable devices from obtaining accurate blood pressure measurements. Therefore, a system that conveniently measures blood pressure on a consistent basis (e.g., daily, hourly) could be beneficial for the user's overall health.

[0014] Accordingly, aspects of this disclosure relate to systems that utilize a wearable device (e.g., a wearable ring device, a watch, a necklace, a chest-worn wearable device, a limb monitor) to determine one or more blood pressure indicators (e.g., measurements) of a user. In particular, the systems described herein may enable the wearable device to determine blood pressure based on the morphological characteristics of the user's pulse wave (e.g., heart pulse wave).

[0015] For example, a wearable device may be configured to acquire PPG data from a user, and the PPG data includes one or more PPG waveforms associated with one or more wavelengths of light used to acquire the PPG data. In some examples, the PPG pulse waves within each PPG waveform may exhibit different morphological features based on different projection wavelengths (e.g., a first wavelength, a second wavelength, etc.) used to acquire each PPG pulse wave. In such cases, the morphological features of each PPG waveform may be used to determine the user's blood pressure index. Specifically, the system may acquire PPG data using light of one or more wavelengths, and the PPG data includes one or more PPG waveforms corresponding to one or more wavelengths of light used to acquire the PPG data. Morphological features may then be identified in or between the raw PPG waveforms associated with different wavelengths and / or in or between one or more derivatives (e.g., the first derivative, the second derivative, etc.) of the raw PPG waveforms.

[0016] In some cases, blood pressure may be determined using morphological features that include correlation coefficients between each of the PPG waveforms of different wavelengths, delays between the systolic and diastolic peaks of the second derivatives of the PPG waveforms of different wavelengths, and differences in systolic peak timing between the PPG waveforms of different wavelengths. In other words, a user's blood pressure may be determined (e.g., estimated) using morphological features represented by multiple PPG waveforms / pulse waves. Thus, a system may acquire PPG data using a wearable device positioned relative to the user, determine morphological features from the PPG waveforms, and determine one or more blood pressure indices for the user.

[0017] In some implementations, the system may use a wearable device to determine one or more blood pressure indices for a user based on a comparison of morphological features of PPG waveforms. For example, a single wearable device may include one or more light-emitting components (e.g., one or more light-emitting diodes (LEDs)) configured to emit light associated with a first wavelength and a second wavelength, and a photodetector that receives light from the one or more light-emitting components. The light-emitting components and the photodetector may be coupled to a controller that transmits light associated with one or more wavelengths.

[0018] In some respects, the system may cause the light-emitting components of a wearable device to transmit a first light associated with a first wavelength (e.g., red light) and a second light associated with a second wavelength (e.g., infrared (IR) light, green light, etc.) during time intervals including the user's heartbeat. The system may acquire PPG data from the user based on receiving the first and second light through one or more photodetectors. The PPG data may include a first PPG waveform using the first light associated with the first wavelength and a second PPG waveform using the second light associated with the second wavelength. The system may determine a first group of morphological features associated with the first PPG waveform based on the first systolic peak and first diastolic peak corresponding to the heartbeat of the first PPG waveform. Furthermore, the system may determine a second group of morphological features associated with the second PPG waveform based on the second systolic peak and second diastolic peak corresponding to the heartbeat of the second PPG waveform. Therefore, the system can determine one or more blood pressure indicators for the user based on a comparison between a first group of morphological features and a second group of morphological features.

[0019] Figure 1 shows an example of a system 100 that supports a technique for determining blood pressure based on morphological features of a pulse wave, according to aspects of the present disclosure. The system 100 includes a plurality of electronic devices (e.g., a wearable device 104, a user device 106) that can be worn and / or operated by one or more users 102. The system 100 further includes a network 108 and one or more servers 110.

[0020] 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.) and user devices 106 (e.g., smartphones, laptops, tablets). The electronic devices associated with each user 102 may include one or more of the following functions: 1) measuring physiological data, 2) storing the measured data, 3) processing the data, 4) providing output to the user 102 based on the processed data (e.g., via a graphical user interface (GUI)), and 5) communicating data with each other and / or other computing devices. Different electronic devices may perform one or more of these functions.

[0021] Exemplary wearable device 104 can include wearable computing devices such as a ring-shaped computing device (hereinafter, “ring”) configured to be worn on a finger of user 102, a wrist computing device (e.g., a smartwatch, a fitness band, or a bracelet) configured to be worn on a wrist of user 102, and / or a head-mounted computing device (e.g., glasses / goggles). Wearable device 104 can also include bands, straps (e.g., flexible or non-flexible bands or straps), adhesive sensors, etc., which can be placed at other locations such as a band around the head (e.g., a forehead band), an arm (e.g., a forearm band and / or a biceps band), and / or a leg (e.g., a thigh or calf band), behind the ear, under the armpit, etc. Wearable device 104 may be attached to clothing or included in clothing. For example, wearable device 104 may be included in a pocket and / or a pouch of clothing. As another example, wearable device 104 may be clipped and / or pinned to clothing or otherwise maintained within the vicinity of user 102. Examples of clothing include, but are not limited to, hats, shirts, gloves, pants, socks, jackets (e.g., jackets), and underwear. In some implementations, wearable device 104 can be included with other types of devices such as training / sports devices used during physical activities. For example, wearable device 104 may be attached to or included in a bicycle, skis, a tennis racket, a golf club, and / or training weights.

[0022] Much of the present disclosure may be described in the context of the ring wearable device 104. Thus, the terms "ring 104", "wearable device 104", and similar terms may be used interchangeably herein unless otherwise described. However, the use of the term "ring 104" should not be considered limiting, and aspects of the present disclosure contemplate that other wearable devices (e.g., wristwatch wearable devices, necklace wearable devices, bracelet wearable devices, earring wearable devices, anklet wearable devices, etc.) may be used.

[0023] In some aspects, the user device 106 may include handheld mobile computing devices such as smartphones and tablet computing devices. The user device 106 may also include personal computers such as laptop and desktop computing devices. Other exemplary user devices 106 may include server computing devices that can communicate with other electronic devices (e.g., via the Internet). In some implementations, the computing device may include medical devices such as external wearable computing devices (e.g., Holter monitors). Medical devices may also include implanted medical devices such as pacemakers and defibrillators. Other exemplary user devices 106 may include home computing devices such as Internet of Things (IoT) devices (e.g., IoT devices), smart TVs, smart speakers, smart displays (e.g., video call displays), hubs (e.g., wireless communication hubs), security systems, smart home appliances (e.g., thermostats and refrigerators), and fitness devices.

[0024] Some electronic devices (e.g., wearable device 104, user device 106) may measure the physiological parameters of each user 102, such as photoplethysmography waveforms, continuous skin temperature, pulse wave waveforms, respiratory rate, heart rate, heart rate variability (HRV), actigraphy, electrodermal response, pulse oximetry, blood oxygen saturation (SpO2), blood glucose levels (e.g., glucose index), and / or other physiological parameters. Some electronic devices that measure physiological parameters may also perform some / all of the calculations described herein. Some electronic devices may not measure physiological parameters but may perform some / all of the calculations described herein. For example, a ring (e.g., wearable device 104), a mobile device application, or a server computing device may process the received physiological data measured by other devices.

[0025] 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) for measuring physiological parameters. User 102 may also have or be associated with a user device 106 (e.g., a mobile device, smartphone), and the wearable device 104 and user device 106 are coupled to communicate with 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.

[0026] For example, as shown in Figure 1, a first user 102-a (user 1) may operate or associate a wearable device 104-a (e.g., a ring 104-a) and a user device 106-a, which may operate as described herein. In this example, the user device 106-a associated with user 102-a may process / store physiological parameters measured by the ring 104-a. In comparison, a second user 102-b (user 2) may associate a ring 104-b, a wristwatch-type wearable device 104-c (e.g., a watch 104-c), and a user device 106-b, and the user device 106-b associated with user 102-b may process / store physiological parameters measured by the ring 104-b and / or the watch 104-c. Furthermore, the nth user 102-n (user N) may be associated with the arrangement of the electronic devices described herein (e.g., ring 104-n, user device 106-n).

[0027] In some implementations, the ring 104 of system 100 (e.g., wearable device 104) may be configured to collect physiological data from each user 102 based on arterial blood flow in the user's finger. In particular, the ring 104 may collect physiological data based on arterial blood flow in the user's finger by utilizing one or more light-emitting components, such as LEDs (e.g., red LEDs, green LEDs), that emit light on the palm side of the user's finger. Generally, the terms light-emitting component, light-emitting element, and similar terms may include, but are not limited to, LEDs, micro-LEDs, mini-LEDs, laser diodes (LDs) (e.g., vertical-cavity surface-emitting lasers (VCSELs)), etc.

[0028] In some cases, system 100 may be configured to collect physiological data from each user 102 based on blood flow diffused into the microvascular bed of the skin having capillaries and arterioles. For example, system 100 may collect PPG data based on the measured amount of blood diffused into the microvascular system of capillaries and arterioles. In some implementations, ring 104 may acquire physiological data using a combination of both green and red LEDs. Physiological data may include, but are not limited to, temperature data, accelerometer data (e.g., motion / exercise data), heart rate data, HRV data, blood oxygen saturation data, or any combination thereof, any physiological data known in the art.

[0029] Red and green LEDs have been shown to have distinct advantages of their own, such as when acquiring physiological data through different parts of the body under different conditions (e.g., light / dark, active / inactive), so the use of both green and red LEDs may offer several advantages over other solutions. For example, green LEDs have been shown to perform better during exercise. Furthermore, using multiple LEDs (e.g., green and red LEDs) dispersed around the ring 104 has been shown to perform better than wearable devices that utilize LEDs placed close together, such as in a wristwatch-type wearable device. In addition, the blood vessels in the fingers (e.g., arteries, capillaries) are more accessible via LEDs than the blood vessels in the wrist. In particular, the arteries in the wrist are located at the bottom of the wrist (e.g., the palm side of the wrist), which means that only capillaries are accessible at the top of the wrist (e.g., the back of the wrist), where wearable watch devices and similar devices are typically worn. Therefore, it has been found that utilizing LEDs and other sensors within the ring 104 results in superior performance compared to wearable devices worn on the wrist, as the ring 104 has greater access to arteries (compared to capillaries), thereby potentially yielding stronger signals and more valuable physiological data.

[0030] Electronic devices of system 100 (e.g., user device 106, wearable device 104) can be communicatively coupled to one or more servers 110 via wired or wireless communication protocols. For example, as shown in Figure 1, an electronic device (e.g., user device 106) can be communicatively coupled to one or more servers 110 via network 108. Network 108 may implement a transport control protocol such as the Internet and the Internet Protocol (TCP / IP), or it may implement other network 108 protocols. Network connections between network 108 and each electronic device can facilitate the transport of data via email, the web, text messages, postal mail, or any other suitable form of interaction within the computer network 108. For example, in some implementations, a first user 102-a associated with ring 104-a may be communicatively coupled to user device 106-a, and user device 106-a is communicatively coupled to server 110 via network 108. In additional or alternative cases, the wearable device 104 (e.g., ring 104, watch 104) may be coupled to the network 108 in a way that allows it to communicate directly with the network.

[0031] System 100 may provide an on-demand database service between a user device 106 and one or more servers 110. In some cases, the server 110 may receive data from the user device 106 via a network 108, and may store and analyze the data. Similarly, the server 110 may provide data to the user device 106 via the network 108. In some cases, the server 110 may be located in one or more data centers. The server 110 may be used for data storage, management, and processing. In some implementations, the server 110 may provide a web-based interface to the user device 106 via a web browser.

[0032] In some aspects, system 100 may detect periods when user 102 is asleep and classify those periods into one or more sleep stages (e.g., sleep stage classifications). For example, as shown in Figure 1, user 102-a may be associated with a wearable device 104-a (e.g., ring 104-a) and a user device 106-a. In this example, ring 104-a may collect physiological data associated with user 102-a, including body temperature, heart rate, HRV, respiratory rate, etc. In some aspects, the data collected by ring 104-a may be input into a machine learning classifier, which is configured to determine the duration of time when user 102-a is asleep (or has been asleep). Furthermore, the machine learning classifier may be configured to classify the duration into different sleep stages, including wakefulness, rapid eye movement (REM) sleep, light sleep (non-REM) sleep, and deep sleep (NREM). In some aspects, the classified sleep stages may be displayed to user 102-a via the GUI of user device 106-a. Sleep stage classification may be used to provide feedback to user 102-a regarding the user's sleep patterns, such as recommended bedtime and recommended wake-up time. Furthermore, in some implementations, the sleep stage classification techniques described herein may be used to calculate scores for each user, such as a Sleep Score and a Readiness Score.

[0033] In some respects, system 100 may leverage features derived from circadian rhythms to further improve physiological data acquisition, data processing procedures, and other techniques described herein. The term circadian rhythm may refer to a natural internal process that regulates an individual's sleep-wake cycle, repeating approximately every 24 hours. In this regard, the techniques described herein may utilize circadian rhythm adjustment models to improve physiological data acquisition, analysis, and data processing. For example, a circadian rhythm adjustment model may be input into a machine learning classifier along with physiological data collected from user 102-a via a wearable device 104-a. In this example, the circadian rhythm adjustment model may be configured to "weight" or adjust the physiological data collected over the user's natural, approximately 24-hour circadian rhythm. In some implementations, the system may initially start with a "baseline" circadian rhythm adjustment model and modify the baseline model using physiological data collected from each user 102 to generate an adjusted, individual circadian rhythm adjustment model specific to each respective user 102.

[0034] In some respects, system 100 may utilize other biological rhythms to further improve the collection, analysis, and processing of physiological data by phase of these other rhythms. For example, if a weekly rhythm is detected within an individual's baseline data, the model may be configured to adjust the “weights” of the data by day of the week. Biological rhythms that may require adjustment to the model in this manner include: 1) ultradian (rhythms faster than 24 hours, including sleep cycles in sleep states and periodic oscillations of less than an hour to several hours in physiological variables measured during wakefulness); 2) circadian rhythms; 3) non-endogenous daily rhythms that are shown to be imposed on top of circadian rhythms, such as in work schedules; 4) weekly rhythms, or other artificial time periodicities that are exogenously imposed (e.g., a 12-day rhythm may be used in a hypothetical culture with a 12-day “week”); 5) multi-day ovarian rhythms in women and spermatogenesis rhythms in men; 6) lunar rhythms (associated with individuals living with little or no artificial light); and 7) seasonal rhythms.

[0035] Biological rhythms are not always stationary. For example, many women experience variability in ovarian cycle length throughout the cycle, and ultradian rhythms are not expected to occur at exactly the same time or with the same periodicity across days, even within a single user. Therefore, the detection of these rhythms can be improved by using signal processing techniques sufficient to quantify the frequency composition while maintaining the temporal resolution of these rhythms in physiological data, and by assigning the phase of each rhythm to each measured point in time, thereby correcting adjustment models and time interval comparisons. Biological rhythm adjustment models and parameters can be added, as appropriate, in a combination of linear or nonlinear approaches to more accurately capture the dynamic physiological baseline of an individual or group of individuals.

[0036] In some respects, each device of system 100 may support a technique for determining blood pressure based on the morphological features of PPG waveforms collected from user 102. As described herein, system 100 may use a wearable device 104 (e.g., a wearable ring device, a watch or bracelet, a necklace, a chest-worn wearable device, a headband or strap, a limb monitor) to determine one or more blood pressure indices (e.g., measurements) of user 102. That is, system 100 may utilize a wearable device 104 (which user 102 may consistently wear) to determine blood pressure based on the morphological features of user 102's PPG data (e.g., PPG data / waveforms associated with heart rate pulse waves). In some examples, PPG waveforms may display different morphological features based on different projection wavelengths (e.g., a first wavelength, a second wavelength, etc.). In some examples, pulse waves from the user's heart rate may be identified from one or more PPG waveforms acquired using one or more wavelengths. Specifically, system 100 can acquire PPG data using multiple different wavelengths, and morphological features can be identified within or between the raw PPG waveforms / signals of the PPG data corresponding to different wavelengths. The morphological features of each PPG waveform / signal can then be evaluated to determine or estimate the user's blood pressure.

[0037] In some examples, blood pressure may be determined using morphological features that include correlation coefficients between each of the PPG waveforms of different wavelengths, delays between the systolic and diastolic peaks of the second derivatives of the PPG waveforms of different wavelengths, and differences in systolic peak timing between the PPG waveforms of different wavelengths. That is, user 102's blood pressure may be determined (e.g., estimated) using morphological features represented by the heart rate pulse waves captured within each of the multiple PPG waveforms / signals. Thus, system 100 can use a wearable device 104 positioned relative to user 102 to acquire PPG data containing multiple PPG signals / waveforms (e.g., PPG waveforms corresponding to different wavelengths), determine morphological features from the PPG waveforms, and determine one or more blood pressure indices for user 102.

[0038] Those skilled in the art will understand that one or more aspects of this disclosure may be implemented in System 100 to further, or instead, solve problems other than those described above. Furthermore, aspects of this disclosure may provide technical improvements to the “conventional” systems or processes described herein. However, the description and accompanying drawings only include illustrative technical improvements resulting from implementing aspects of this disclosure and therefore do not represent all of the technical improvements provided in the claims.

[0039] Figure 2 shows an example of a system 200 that supports a technique for determining blood pressure based on morphological features of a pulse wave, according to aspects of this disclosure. System 200 may implement or be implemented by system 100. In particular, system 200 shows an example of a ring 104 (e.g., a wearable device 104), a user device 106, and a server 110, as described with reference to Figure 1.

[0040] In some respects, the ring 104 may be configured to be worn around the user's finger and may determine one or more user physiological parameters when worn around the user's finger. Exemplary measurements and determinations may include, but are not limited to, the user's skin temperature, pulse wave pattern, respiratory rate, heart rate, HRV, blood oxygen saturation, etc.

[0041] The system 200 further includes a user device 106 (e.g., a smartphone) that communicates with the ring 104. For example, the ring 104 may communicate with the user device 106 wirelessly and / or via a wired connection. In some implementations, the ring 104 may transmit measured and processed data (e.g., temperature data, PPG data, motion / accelerometer data, ring input data, etc.) to the user device 106. The user device 106 may also transmit data to the ring 104, such as firmware / configuration updates for the ring 104. The user device 106 may process the data. In some implementations, the user device 106 may transmit the data to the server 110 for processing and / or storage.

[0042] The ring 104 may include a housing 105 comprising an inner housing 205-a and an outer housing 205-b. In some aspects, the housing 205 of the ring 104 may house, or otherwise include, various components of the ring, including, but not limited to, device electronics, power supplies (e.g., a battery 210, and / or capacitors), and one or more substrates (e.g., a printable circuit board) interconnecting the device electronics and / or power supplies. 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, etc. The device electronics may also include one or more sensors. Exemplary sensors may include one or more temperature sensors 240, a PPG sensor assembly (e.g., a PPG system 235), and one or more motion sensors 245.

[0043] The sensors may include associated modules (not shown) configured to communicate with each component / module of the ring 104 and generate signals associated with each sensor. In some aspects, each component / module of the ring 104 may be coupled to communicate with one another via wired or wireless connections. Furthermore, the ring 104 may include additional and / or alternative sensors or other components configured to collect physiological data from the user, including, for example, light sensors (e.g., LEDs), oxygen meter, etc.

[0044] The ring 104 illustrated and described with reference to Figure 2 is provided for illustrative purposes only. Therefore, the ring 104 may include additional or alternative components, such as those shown in Figure 2. Other rings 104 providing the functions described herein may be manufactured. For example, a ring 104 with fewer components (e.g., sensors) may be manufactured. In a particular example, a ring 104 may be manufactured having a single temperature sensor 240 (or other sensor), a power supply, and device electronics configured to read the single temperature sensor 240 (or other sensor). In another particular example, the temperature sensor 240 (or other sensor) may be attached to the user's finger (e.g., using a clamp, spring clamp, etc.). In this case, the sensor may be wired to another computing device, such as a wrist-worn computing device, that reads the temperature sensor 240 (or other sensor). In another example, a ring 104 including additional sensors and processing functions may be manufactured.

[0045] The housing 205 may include one or more housing 205 components. The housing 205 may include an outer housing 205-b component (e.g., a shell) and an inner housing 205-a component (e.g., a molded part). The housing 205 may include additional components (e.g., additional layers) not explicitly shown in Figure 2. For example, in some implementations, the ring 104 may include one or more insulating layers that electrically insulate the device electronics and other conductive materials (e.g., electrical traces) from the outer housing 205-b (e.g., a metal outer housing 205-b). The housing 205 may provide structural support for the device electronics, battery 210, substrate(s), and other components. For example, the housing 205 may protect the device electronics, battery 210, and substrate(s) from mechanical forces such as pressure and shock.

[0046] The outer housing 205-b may be manufactured from one or more materials. In some implementations, the outer housing 205-b may contain a metal such as titanium, which can provide strength and wear resistance at a relatively light weight. The outer housing 205-b may be manufactured from other materials such as polymers. In some implementations, the outer housing 205-b may be both protective and decorative.

[0047] The inner housing 205-a may be configured to interface with the user's finger. The inner housing 205-a may be formed from a polymer (e.g., a medical-grade polymer) or other material. In some implementations, the inner housing 205-a may be translucent. For example, the inner housing 205-a may be translucent to light emitted by a PPG light-emitting diode (LED). In some implementations, the inner housing 205-a components may be molded onto the outer housing 205-b. For example, the inner housing 205-a may include a polymer molded (e.g., injection-molded) to fit into the outer housing 205-b metal shell.

[0048] The ring 104 may include one or more substrates (not shown). The device electronics and battery 210 may be contained on one or more substrates. For example, the device electronics and battery 210 may be mounted on one or more substrates. Exemplary substrates may include one or more printed circuit boards (PCBs), such as a flexible PCB (e.g., polyimide). In some implementations, the electronics / battery 210 may include surface mount devices (e.g., surface mount technology (SMT) devices) on the flexible PCB. In some implementations, one or more substrates (e.g., one or more flexible PCBs) may include electrical traces that provide electrical communication between the device electronics. The electrical traces may also connect the battery 210 to the device electronics.

[0049] The device electronics, battery 210, and circuit board can be arranged within the ring 104 in various ways. In some implementations, one circuit board containing the device electronics may 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 fingers. In these implementations, the battery 210 may be included along the top portion of the ring 104 (e.g., on another circuit board).

[0050] The various components / modules of ring 104 represent the functions (e.g., circuits and other components) that may be included in ring 104. A module may include any discrete and / or integrated electronic circuit components that implement analog and / or digital circuits capable of generating the functions assigned to the module herein. For example, a module may include analog circuits (e.g., amplifiers, filtering circuits, analog-to-digital converters, and / or other signal conditioning circuits). A module may include digital circuits (e.g., combinational or sequential logic circuits, memory circuits, etc.).

[0051] The memory 215 (memory module) of ring 104 may include any volatile, non-volatile, magnetic, or electrical medium, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically erasable programmable ROM (EEPROM), flash memory, or any other memory device. The memory 215 may store any of the data described herein. For example, the memory 215 may be configured to store data collected by the respective sensors and PPG system 235 (e.g., motion data, temperature data, PPG data). Furthermore, the memory 215 may include instructions, when executed by one or more processing circuits, that cause the modules to perform various functions assigned to the modules herein. The device electronics of ring 104 described herein are merely illustrative device electronics. Therefore, the types of electronic components used to implement the device electronics may vary depending on design considerations.

[0052] The functions assigned to the modules of ring 104 as described herein may be embodied as one or more processors, hardware, firmware, software, or any combination thereof. The descriptions of different characteristics of the modules are intended to highlight different functional aspects and do not necessarily imply that such modules must be realized by separate hardware / software components. Rather, the functions associated with one or more modules may be performed by separate hardware / software components or integrated within a common hardware / software component.

[0053] The processing module 230-a of ring 104 may include one or more processors (e.g., processing units), which may include a microcontroller, a digital signal processor, a system-on-a-chip (SOC), and / or other processing devices. The processing module 230-a communicates with modules included in ring 104. For example, the processing module 230-a may send data to and receive data from other components of ring 104, such as modules and sensors. As described herein, modules may be implemented by various circuit components. Thus, modules may also be referred to as circuits (e.g., communication circuits and power circuits).

[0054] The processing module 230-a may communicate with memory 215. Memory 215 may contain computer-readable instructions that, when executed by the processing module 230-a, cause the processing module 230-a to perform various functions assigned to the processing module 230-a herein. In some implementations, the processing module 230-a (e.g., a microcontroller) may include additional features associated with other modules, such as communication functions provided by the communication module 220-a (e.g., an integrated Bluetooth® Low Energy transceiver) and / or additional onboard memory 215.

[0055] The communication module 220-a may include circuitry that provides wired and / or wireless communication with the user device 106 (e.g., communication module 220-b of the user device 106). In some implementations, communication modules 220-a and 220-b may include wireless communication circuits such as Bluetooth and / or Wi-Fi circuits. In some implementations, communication modules 220-a and 220-b may include wired communication circuits such as Universal Serial Bus (USB) communication circuits. Using communication module 220-a, the ring 104 and the user device 106 may be configured to communicate with each other. The ring's processing module 230-a may be configured to send / receive data to and from the user device 106 via communication module 220-a. Illustrative data may include, but is not limited to, motion data, temperature data, pulse wave waveform, heart rate data, HRV data, PPG data, and status updates (e.g., charge status, battery charge level, and / or ring 104 configuration settings). The ring processing module 230-a may also be configured to receive updates (e.g., software / firmware updates) and data from the user device 106.

[0056] The ring 104 may include a battery 210 (e.g., a rechargeable battery 210). An exemplary battery 210 may include a lithium-ion or lithium-polymer type battery 210, but a variety of battery 210 options are possible. The battery 210 may be charged wirelessly. In some implementations, the ring 104 may include a power source other than the battery 210, such as a capacitor. The power source (e.g., battery 210 or capacitor) may have a curved shape to conform to the curve of the ring 104. In some aspects, the charger or other power source may be used to collect data in addition to the data collected by the ring 104 itself, or may include additional sensors that complement the data collected by the ring 210 itself. Furthermore, the charger or other power source of the ring 104 may function as a user device 106, in which case the charger or other power source of the ring 104 may be configured to receive data from the ring 104, store and / or process the data received from the ring 104, and communicate data between the ring 104 and the server 110.

[0057] In some aspects, the ring 104 may include a power module 225 that can control the charging of the battery 210. For example, the power module 225 may interface with an external wireless charger that charges the battery 210 when interfaced with the ring 104. The charger may include a reference structure that mates with a reference structure of the ring 104 to make a specific orientation with the ring 104 during charging. The power module 225 may also regulate the voltage of the device electronics, regulate the power output to the device electronics, and monitor the charge state of the battery 210. In some implementations, the battery 210 may include a protection circuit module (PCM) that protects the battery 210 from high-current discharge, overvoltage during charging, and undervoltage during discharging. The power module 225 may also include electrostatic discharge (ESD) protection.

[0058] One or more temperature sensors 240 may be electrically coupled to the processing module 230-a. The temperature sensors 240 may be configured to generate a temperature signal (e.g., temperature data) indicating the temperature read or sensed by the temperature sensors 240. The processing module 230-a may determine the user's body temperature at the location of the temperature sensors 240. For example, in the ring 104, the temperature data generated by the temperature sensors 240 may indicate the user's temperature (e.g., skin temperature) at the user's finger. In some implementations, the temperature sensors 240 may be in contact with 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 sensors 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 an insulating portion. The thermally conductive portion may conduct heat from the user's finger to the temperature sensors 240. The insulating portion can insulate the ring 104 (for example, the temperature sensor 240) from the ambient temperature.

[0059] In some implementations, the temperature sensor 240 may generate a digital signal (e.g., temperature data) that the processing module 230-a can use to determine the temperature. In another example, if the temperature sensor 240 includes a passive sensor, the processing module 230-a (or the temperature sensor 240 module) may measure the current / voltage generated by the temperature sensor 240 and determine the temperature based on the measured current / voltage. An exemplary temperature sensor 240 may include a thermistor such as a negative temperature coefficient (NTC) thermistor, or other types of sensors including resistors, transistors, diodes, and / or other electrical / electronic components.

[0060] The processing module 230-a may sample the user's body temperature over time. For example, the processing module 230-a may sample the user's body temperature according to a sampling rate. The processing module 230-a may be configured to sample the temperature signal at other sampling rates higher or lower than one sample per second, although exemplary sampling rates may include one sample per second. In some implementations, the processing module 230-a may continuously sample the user's body temperature throughout the day and night. Sampling at a sufficient rate throughout the day (e.g., one sample per second) may provide sufficient temperature data for the analysis described herein.

[0061] The processing module 230-a may store the sampled temperature data in memory 215. In some implementations, the processing module 230-a may process the sampled temperature data. For example, the processing module 230-a may determine the average temperature value over a period of time. In one example, the processing module 230-a may determine the average temperature value per minute by summing all the temperature values ​​collected over one minute and dividing by the number of samples over one minute. In a particular example where temperature is sampled at one sample per second, the average temperature might be the sum of all temperatures sampled in one minute divided by 60 seconds. Memory 215 may store the average temperature value over time. In some implementations, memory 215 may store the average temperature (e.g., one per minute) instead of the sampled temperatures to conserve memory 215.

[0062] The sampling rate that can be stored in memory 215 may be configurable. In some implementations, the sampling rate may be the same throughout the day and night. In other embodiments, the sampling rate may be varied throughout the day / night. In some implementations, ring 104 may filter / reject temperature readings such as large temperature spikes that do not indicate physiological changes (e.g., temperature spikes from a hot shower). In some implementations, ring 104 may filter / reject temperature readings that may be unreliable due to other factors such as excessive motion during exercise (e.g., as indicated by motion sensor 245).

[0063] Ring 104 (for example, a communication module) may transmit sampled and / or averaged temperature data to user device 106 for storage and / or further processing. User device 106 may transfer the sampled and / or averaged temperature data to server 110 for storage and / or further processing.

[0064] Although the ring 104 is shown as containing a single temperature sensor 240, the ring 104 may contain multiple temperature sensors 240 in one or more locations, such as positioned along the inner housing 205-a near the user's finger. In some implementations, the temperature sensor 240 may be a standalone temperature sensor 240. Furthermore, or instead, one or more temperature sensors 240 may be included with other components such as an accelerometer and / or a processor (for example, they may be packaged together with the other components).

[0065] The processing module 230-a can acquire and process data from multiple temperature sensors 240 in a similar manner to that described for a single temperature sensor 240. For example, the processing module 230 may individually sample, average, and store temperature data from each of the multiple temperature sensors 240. In other examples, the processing module 230-a may sample the sensors at different rates and average / store different values ​​for different sensors. In some implementations, the processing module 230-a may be configured to determine a single temperature based on the average of two or more temperatures determined by two or more temperature sensors 240 located at different positions on a finger.

[0066] The temperature sensors 240 on the ring 104 can acquire distal temperature at the user's finger (e.g., any finger). For example, one or more temperature sensors 240 on the ring 104 can acquire the user's temperature from the underside of the finger or at different locations on the finger. In some implementations, the ring 104 can acquire distal temperature continuously (e.g., at a certain sampling rate). While this specification describes distal temperature measured by the ring 104 at the finger, other devices may measure temperature at the same / different locations. In some cases, distal temperature measured at the user's finger may differ from temperature measured at the user's wrist or other external location. Furthermore, distal temperature measured at the user's finger (e.g., "shell" temperature) may differ from the user's core temperature. Thus, the ring 104 can provide a useful temperature signal that may not be acquired at other internal / external locations on the body. In some cases, continuous temperature measurement at the finger may capture temperature fluctuations (e.g., small or large fluctuations) that may not be apparent in core body temperature. For example, continuous temperature measurement on the finger can capture minute- or hourly temperature fluctuations, providing additional insights that may not be available through other temperature measurements on other parts of the body.

[0067] The ring 104 may include a PPG system 235. The PPG system 235 may include one or more light transmitters that transmit light. The PPG system 235 may also include one or more light receivers that receive light transmitted by one or more light transmitters. The light receivers may generate a signal (hereinafter, "PPG" signal) indicating the amount of light received by the light receivers. The light transmitters may illuminate an area of ​​the user's fingers. 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, such as the user's respiratory rate, heart rate, HRV, oxygen saturation, and other circulatory parameters, based on the user's pulse waveform.

[0068] In some implementations, the PPG system 235 may be configured as a reflective PPG system 235 in which the optical receiver(s) receive transmitted light reflected through the user's finger area. In some implementations, the PPG system 235 may be configured as a transmissive PPG system 235 in which the optical transmitter(s) and optical receiver(s) are arranged facing each other so that light is sent directly to the optical receiver(s) through a portion of the user's finger.

[0069] The number and ratio of transmitters and receivers included in the PPG system 235 may vary. Exemplary optical transmitters may include LEDs. Optical transmitters may transmit light in the IR spectrum and / or other spectra. Exemplary optical receivers may include, but are not limited to, optical sensors, phototransistors, and photodiodes. Optical receivers may be configured to generate a PPG signal depending on the wavelength received from the optical transmitter. The positions of the transmitters and receivers may vary. Furthermore, a single device may include reflective and / or transmissive PPG systems 235.

[0070] The PPG system 235 shown in Figure 2 may, in some implementations, include a reflective PPG system 235. In these implementations, the PPG system 235 may include a centrally located optical receiver (e.g., at the bottom of the ring 104) and two optical transmitters located on each side of the optical receiver. In this implementation, the PPG system 235 (e.g., the optical receiver) may generate a PPG signal based on light received from one or both of the optical transmitters. In other implementations, other arrangements, combinations, and / or configurations of one or more optical transmitters and / or optical receivers are contemplated.

[0071] The processing module 230-a may control one or both of the optical transmitters to transmit light while sampling the PPG signal generated by the optical receiver. In some implementations, the processing module 230-a may cause the optical transmitter with a stronger received signal to transmit light while sampling the PPG signal generated by the optical receiver. For example, the selected optical transmitter may continuously emit light while the PPG signal is sampled at a sampling rate (e.g., 250 Hz).

[0072] Sampling the PPG signal generated by the PPG system 235 may yield a pulse wave waveform that may be called a "PPG". The pulse wave waveform may show blood pressure versus time for multiple cardiac cycles. The pulse wave waveform may include peaks indicating cardiac cycles. Furthermore, the pulse wave waveform may include respiration-induced fluctuations that can be used to determine the respiratory rate. In some implementations, the processing module 230-a may store the pulse wave waveform in memory 215. The processing module 230-a may process the pulse wave waveform, both when it was generated and / or from memory 215, to determine the user's physiological parameters as described herein.

[0073] The processing module 230-a may determine the user's heart rate based on the pulse waveform. For example, the processing module 230-a may determine the heart rate (e.g., beats per minute) based on the time between peaks in the pulse waveform. The time between peaks may be referred to as the heart rate interval (IBI). The processing module 230-a may store the determined heart rate value and IBI value in the memory 215.

[0074] The processing module 230-a can determine HRV over time. For example, the processing module 230-a can determine HRV based on fluctuations in IBI. The processing module 230-a can store HRV values ​​over time in memory 215. Furthermore, the processing module 230-a can determine the user's respiratory rate over time. For example, the processing module 230-a can determine the respiratory rate based on frequency modulation, amplitude modulation, or baseline modulation of the user's IBI value over a period of time. The respiratory rate can be calculated as breaths per minute or other respiratory rates (e.g., breaths per 30 seconds). The processing module 230-a can store the user's respiratory rate over time in memory 215.

[0075] The ring 104 may include one or more motion sensors 245, such as one or more accelerometers (e.g., 6-D accelerometers) and / or one or more gyroscopes (gyro). The motion sensors 245 may generate motion signals indicating the movement of the sensors. For example, the ring 104 may include one or more accelerometers that generate acceleration signals indicating the acceleration of the accelerometers. As another example, the ring 104 may include one or more gyro sensors that generate gyro signals indicating angular motion (e.g., angular velocity) and / or changes in orientation. The motion sensors 245 may be included in one or more sensor packages. An exemplary accelerometer / gyro sensor is the Bosch BMI160 inertial microelectromechanical system (MEMS) sensor, which can measure angular velocity and acceleration on three vertical axes.

[0076] 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 gyro signal to determine the angular motion. In some implementations, the processing module 230-a may store the motion data in memory 215. The motion data may include the sampled motion data, as well as motion data calculated based on the sampled motion signals (e.g., acceleration values ​​and angular values).

[0077] The ring 104 can store various types of data as described herein. For example, the ring 104 can store temperature data such as raw sampled temperature data and calculated temperature data (e.g., mean temperature). As another example, the ring 104 can store PPG signal data such as pulse wave waveforms and data calculated based on the pulse wave waveforms (e.g., heart rate values, IBI values, HRV values, and respiratory values). The ring 104 can also store motion data such as sampled motion data showing linear and angular motion.

[0078] The ring 104 or other computing device may calculate and store additional values ​​based on sampled / calculated physiological data. For example, the processing module 230 may calculate and store various metrics such as sleep metrics (e.g., sleep score), activity metrics, and readiness metrics. In some implementations, these additional values / metrics may be referred to as “derived values.” The ring 104, or other computing / wearable device, may calculate various values / metrics related to motion. Exemplary derived values ​​for motion data may include, but are not limited to, motion count values, regularity values, intensity values, metabolic equivalents (METs: metabolic equivalence of task values), and orientation values. Motion count, regularity values, intensity values, and METs may indicate the amount of user movement over time (e.g., velocity / acceleration). Orientation values ​​may indicate how the ring 104 is oriented on the user’s finger and whether the ring 104 is worn on the left or right hand.

[0079] In some implementations, motion counts and regularity values ​​may be determined by counting the number of acceleration peaks within one or more time periods (e.g., one or more periods of 30 seconds to 1 minute). Intensity values ​​may indicate the number of movements and the associated intensity of the movements (e.g., acceleration values). Intensity values ​​may be classified as low, medium, and high depending on the associated threshold acceleration value. METs may be determined based on the intensity of the movements during a period (e.g., 30 seconds), the regularity / irregularity of the movements, and the number of movements associated with different intensities.

[0080] In some implementations, the processing module 230-a may compress the data stored in memory 215. For example, the processing module 230-a may perform calculations based on sampled data and then delete the sampled data. As another example, the processing module 230-a may average the data over a longer period to reduce the number of values ​​stored. In a particular example, if the average user temperature over one minute is stored in memory 215, the processing module 230-a may calculate and store the average temperature over a five-minute period and then erase the one-minute average temperature data. The processing module 230-a may compress the data based on various factors such as the total amount of memory 215 used / available and / or the elapsed time since ring 104 last sent data to the user device 106.

[0081] The user's physiological parameters may be measured by sensors included on the ring 104, but other devices may also measure the user's physiological parameters. For example, the user's body temperature may be measured by the temperature sensor 240 included on the ring 104, but other devices may also measure the user's body temperature. In some examples, other wearable devices (e.g., wrist devices) may include sensors that measure the user's physiological parameters. Furthermore, medical devices such as external medical devices (e.g., wearable medical devices) and / or implantable medical devices may measure the user's physiological parameters. One or more sensors on any type of computing device may be used to implement the techniques described herein.

[0082] Physiological measurements may be obtained continuously throughout the day and / or night. In some implementations, physiological measurements may be obtained between the daytime and / or nighttime portions. In some implementations, physiological measurements may be obtained in response to the user determining that they are in a particular state, such as active, resting, and / or sleeping. For example, ring 104 may perform physiological measurements in the resting / sleeping state to obtain a cleaner physiological signal. In one example, ring 104 or other device / system may detect when the user is resting and / or sleeping and obtain physiological parameters (e.g., temperature) of that detected state. The device / system may use resting / sleeping physiological data and / or other data when the user is in other states in order to implement the technology of this disclosure.

[0083] In some implementations, as described herein, the ring 104 may be configured to collect, store, and / or process data, and may transfer any of the data described herein to the user device 106 for storage and / or processing. In some aspects, the user device 106 includes a wearable application 250, an operating system (OS), a web browser application (e.g., a web browser 280), one or more additional applications, and a GUI 275. The user device 106 may further include other modules and components, such as sensors, audio devices, and haptic feedback devices. The wearable application 250 may include examples of applications (e.g., “Apps”) that can be installed on the user device 106. The wearable application 250 may be configured to take data from the ring 104, store the acquired data, and process the acquired data as described herein. For example, a wearable application 250 includes 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.

[0084] The various data processing operations described herein may be performed by the ring 104, the user device 106, the server 110, or any combination thereof. For example, in some cases, data collected by the ring 104 may be preprocessed and sent to the user device 106. In this example, the user device 106 may perform some data processing operations on the received data, send 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 send the data to the server 110 for processing operations that require relatively high processing power and / or operations that allow for relatively high latency.

[0085] In some aspects, the ring 104, user device 106, and server 110 of system 200 may be configured to evaluate the user's sleep patterns. In particular, each component of system 200 may be used to collect data from the user via the ring 104 and to generate one or more scores for the user (e.g., sleep score, readiness score) based on the collected data. For example, as previously stated herein, 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 is sleeping in order to evaluate the user's sleep during a given "sleep day". In some aspects, the score may be calculated for the user for each respective sleep day, such that the first sleep day is associated with the first group score, the second sleep day with the second group score, and so on. The score may be calculated for each sleep day based on the data collected by the ring 104 during each sleep day. The score may include, but is not limited to, a sleep score, a readiness score, etc.

[0086] In some cases, a “sleep day” may be aligned with a conventional calendar day, such that a given sleep day lasts from midnight to midnight on each calendar day. In other cases, a sleep day may be shifted relative to a calendar day. For example, a sleep day may last from 6:00 pm (18:00) on one calendar day to 6:00 pm (18:00) on the next calendar day. In this example, 6:00 pm may function as a “cutoff time,” where data collected from the user before 6:00 pm is counted for the current sleep day, and data collected from the user after 6:00 pm is counted for the next sleep day. Due to the fact that most individuals sleep most at night, shifting sleep days relative to calendar days may allow system 200 to evaluate the user’s sleep pattern in a way that is consistent with the user’s sleep schedule. In some cases, the user may be able to selectively adjust the timing of sleep days relative to calendar days (e.g., via a GUI) so that the sleep days align with the duration of sleep each user typically experiences.

[0087] In some implementations, each overall score for a user for each respective day (e.g., sleep score, readiness score) may be determined / calculated based on one or more “factors,” “contributing factors,” or “contributing factors.” For example, a user’s overall sleep score may be calculated based on a set of factors including total sleep, efficiency, rest, REM sleep, deep sleep, latency, timing, or any combination thereof. A sleep score may include any number of factors. The “total sleep” factor may refer to the sum of all sleep periods in a sleep day. The “efficiency” factor may reflect the proportion of time spent asleep compared to time awake while in bed and may be calculated using the efficiency average of the longer sleep periods in a sleep day (e.g., main sleep periods), weighted by the duration of each sleep period. The “rest” factor may indicate how restful a user’s sleep is and may be calculated using the average of all sleep periods in a sleep day, weighted by the duration of each period. Relaxation factors may also be based on "wake-up counts" (e.g., the sum of all wake-ups detected during different sleep periods (when the user wakes up)), excessive movement, and "get-up counts" (e.g., the sum of all get-ups detected during different sleep periods (when the user gets out of bed)).

[0088] The "REM sleep" factor may refer to the total duration of REM sleep across all sleep periods in a sleep day that include REM sleep. Similarly, the "deep sleep" factor may refer to the total duration of deep sleep across all sleep periods in a sleep day that include deep sleep. The "latency" factor may indicate how long it takes a user to fall asleep (e.g., average, median, longest) and may be calculated using the average of the longest sleep periods throughout the sleep day, weighted by the duration of each period and the number of such periods (e.g., a given sleep stage or a combination of multiple sleep stages may be a factor in itself or weighted by other factors). Finally, the "timing" factor may refer to the relative timing of sleep periods within a sleep day and / or calendar day and may be calculated using the average of all sleep periods in a sleep day, weighted by the duration of each period.

[0089] As another example, a user's overall readiness score may be calculated based on a set of factors including sleep, sleep balance, heart rate, HRV balance, recovery index, body temperature, activity, activity balance, or any combination thereof. The readiness score may include any number of factors. The “Sleep” factor may refer to the combined sleep score for all sleep periods within a sleep day. The “Sleep Balance” factor may refer to the cumulative duration of all sleep periods within a sleep day. In particular, sleep balance can indicate to a user whether the sleep they have had over a certain period (e.g., the past two weeks) is balanced with their needs. Typically, adults need 7-9 hours of sleep per night to be healthy, mentally sharp, and perform at their best both mentally and physically. However, since occasional sleepless nights are normal, the sleep balance factor takes long-term sleep patterns into account to determine whether each user's sleep needs are being met. The “resting heart rate” factor may represent the lowest heart rate from the longest sleep period of the sleep day (e.g., the primary sleep period), and / or the lowest heart rate from a nap that occurs after the primary sleep period.

[0090] Continuing to refer to the “factors” (e.g., contributing factors) of the readiness score, the “HRV balance” factor may represent the average peak HRV from the main sleep period and naps occurring after the main sleep period. The HRV balance factor can help users track their recovery status by comparing their HRV trend over a first period (e.g., two weeks) to the average HRV over a second, longer period (e.g., three months). The “recovery index” 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 during the first half of the night, at least six hours before the user wakes up, leaving the body time to recover for the next day. The “body temperature” factor may be calculated based on the longest sleep period (e.g., the main sleep period) or based on naps occurring after the longest sleep period if the user’s peak body temperature during the nap is at least 0.5°C higher than the peak body temperature during the longest period. In some respects, 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 the normal range (e.g., significantly higher or lower than 0.0), the body temperature factor may be highlighted (e.g., go to a “pay attention” state) or otherwise generate an alert to the user.

[0091] In some respects, system 200 may support techniques for determining blood pressure based on the relative timing of pulse waves (e.g., heart rate pulse). To provide some context for the need to determine blood pressure, in some examples, one or more users (e.g., individuals) 102 may measure their blood pressure regularly in a clinical setting, and thus, user 102 may measure their blood pressure infrequently (e.g., once or twice a year). In addition, user 102 may not only be unable to measure blood pressure, but also other health measurements related to heart rate, heart rate variability, cardiovascular age, arteriosclerosis, AFib, ectopic pulse, orthostatic test, VO2max, etc. Therefore, user 102 may unknowingly fail to take precautions based on whether health measurements indicate a positive or negative measurement. That is, if user 102 were aware of one or more health measurements, user 102 could integrate healthy living options, including improving nutrition (e.g., eating fruits and vegetables), physical activity, sleep, and stress management. However, as explained, user 102 rarely visits a primary healthcare facility where one or more physicians can measure the user's cardiovascular health (for example, by performing an electrocardiogram (ECG) and measuring continuous heart rate), and therefore user 102 may be unaware of risk factors such as an increased risk of heart attack, stroke, heart failure, and other complications.

[0092] In particular, users 102 affected by cardiovascular health issues may make lifestyle choices (e.g., changes) to improve their overall cardiovascular health in the long term. For example, a user 102 monitoring their cardiovascular health may be concerned with risk factors for heart disease and / or stroke and may reduce unhealthy eating habits (e.g., high salt intake), lack of exercise, tobacco use, and alcohol. A user 102 may also reduce these behaviors to avoid increased blood pressure, increased blood sugar, or increased blood lipids, which could lead to them being overweight and / or obese. In some cases, a user 102 may be concerned with one or more cardiovascular health measures, such as cardiovascular age, also known as heart age and vascular age. Specifically, heart age is an assessment of well-known risk factors for heart disease (e.g., age, sex, blood pressure, cholesterol) to estimate the user 102's risk of heart attack or stroke compared to a defined health range. In some cases, when heart age exceeds the user 102's current age, the user 102 may have a modifiable risk of developing heart disease.

[0093] In another example, user 102 may have concerns about their vascular age, and a vascular age test may provide a measure of user 102's apparent arterial age compared to a healthy user 102. In some examples, user 102 may display a vascular age exceeding their chronological age, indicating that user 102 may be at risk of developing cardiovascular disease (CVD). Therefore, affected user 102 may reduce arteriosclerosis and blood pressure and mitigate vascular aging by implementing lifestyle changes such as increasing aerobic exercise, reducing calorie intake, reducing sodium including flavonoids in the diet, and other healthy eating patterns. In some aspects, tests that produce cardiovascular age measurements may compare data from one user 102 to that of multiple users 102. That is, the test may compare user 102's pulse waveform to typical pulse waveforms across different age groups.

[0094] In some respects, cardiovascular health measurements can utilize blood pressure measurements to accurately predict user 102's health and well-being. Specifically, blood pressure may represent binary information including systolic blood pressure measured from one or more arteries during user 102's heartbeat and diastolic blood pressure measured from one or more arteries between user 102's heartbeats. In some examples, blood pressure classifications may include either normal blood pressure or hypertension. For user 102, normal blood pressure may represent a systolic blood pressure of less than 130 millimeters of mercury (mmHg) and a diastolic blood pressure of less than 80 mmHg. Alternatively, hypertension may represent a systolic blood pressure greater than 130 mmHg and a diastolic blood pressure greater than 80 mmHg.

[0095] In some respects, blood pressure measurements can indicate the pressure of circulating blood against the walls of blood vessels. In some cases, blood pressure can result from the user's heart pumping blood through the circulatory system. That is, the heart pumps blood in the form of pulse waves (e.g., pulsations), and each pulse wave has morphology (e.g., morphological features describing the size / shape of the pulse wave). Furthermore, each pulse wave may exhibit different morphology (e.g., size and shape) corresponding to blood pressure (e.g., high or low). For example, pulse waves obtained in different ways, such as PPG pulse waves and arterial pressure (ABP) pulse waves, may exhibit different systolic peaks, diastolic peaks, overlapping peaks, pulse wave width, pulse wave slope, inflection points, etc.

[0096] In some examples, a comparison of pulse waves may show the difference between PPG pulse waves obtained non-invasively (e.g., by a finger clip device) and ABP pulse waves obtained invasively (e.g., by direct insertion into the user's vein via a needle). Furthermore, PPG and ABP pulse waves may be compared graphically over time to determine in-phase analysis. That is, morphological correlations (e.g., r) between PPG and ABP waveforms may be determined to accurately determine whether the user falls into a specific blood pressure category, such as normal blood pressure (e.g., normal blood pressure), prehypertension (e.g., at risk of hypertension), or hypertension (e.g., hypertension). Thus, in order for the system to measure blood pressure appropriately, the system may utilize multiple pulse waves to appropriately indicate the corresponding blood pressure category of user 102 and determine whether user 102 is at risk of a specific blood pressure condition or disease.

[0097] In some cases, pulse waves may be monitored via spot checks, and binary classification may indicate whether the pulse wave is normal or elevated. In other cases, pulse waves may be evaluated against a blood pressure trend (e.g., a blood pressure trend line). That is, pulse waves may be compared to a typical blood pressure trend of the user expressed at different times of the day. In some cases, pulse waves may be acquired over a period of time (e.g., weekly, monthly, yearly). Thus, blood pressure over a certain period of time may be compared to the user's blood pressure, which may include the sum of continuous blood pressure at night. In such cases, continuous monitoring of blood pressure (e.g., chronic exposure) may be an important indicator (e.g., a determinant) of cardiovascular risk. That is, the area under the blood pressure curve, in other words, the cumulative cardiovascular risk, may be calculated by multiplying the continuous blood pressure by the time (e.g., area under the curve = time * continuous blood pressure). In some cases, pulse waves may be compared to a typical nocturnal blood pressure trend to determine whether the user has nocturnal hypertension based on the pulse wave drop. For a user, a normal nocturnal blood pressure trend may show a blood pressure drop that is about 10-15% lower than a typical daytime pulse wave. However, detecting blood pressure changes exceeding 15% (e.g., changes based on absolute and additional values) may alert users to conditions such as elevated sodium, salt sensitivity, chronic kidney disease (CKD), congestive heart failure (CHF), diabetes, structural vascular disease, or insomnia. Furthermore, the U.S. Food and Drug Administration (FDA) permits monitoring pulse waves via spot checks or blood pressure trends to detect a user's blood pressure, and therefore, techniques supporting these methods would be beneficial for integration into users outside of hospital settings.

[0098] In some respects, one or more healthcare professionals (e.g., nurses, doctors) may use blood pressure devices (e.g., blood pressure monitors, blood pressure cuffs, blood pressure monitors) that may miss one or more pulse waves and determine each user's blood pressure index. In some cases, user 102 may use a home blood pressure device to obtain blood pressure index outside of a clinical setting. However, blood pressure devices may use one or more arm cuffs that may be uncomfortable for the user to wear consistently. In other words, one or more solutions for conveniently measuring blood pressure daily could enable user 102 to monitor health problems. However, conventional wearable devices 104 have been unable to perform blood pressure measurements due to limitations in signal processing and a lack of information about the relationship between blood pressure and PPG waveform characteristics. Therefore, methods and / or techniques for conveniently measuring blood pressure on a consistent basis are desired but have not yet been implemented.

[0099] Accordingly, aspects of system 200 may support techniques for determining blood pressure based on the morphological characteristics of user 102's heart rate pulse wave. As described herein, system 200 may determine one or more blood pressure indices (e.g., measured values) of user 102 using a wearable device 104 (e.g., a wearable ring device, a watch, a necklace, a chest-worn wearable device, a limb monitor). Furthermore, the wearable device 104 may include one or more light-emitting components (e.g., one or more LEDs) configured to emit light associated with a first wavelength and a second wavelength, and one or more photodetectors for receiving light from one or more light-emitting components. The light-emitting components and photodetectors may be coupled to a controller that causes the light-emitting components to emit light associated with one or more wavelengths. That is, system 200 may enable a wearable device 104 consistently worn by user 102 to determine blood pressure based on the morphological characteristics of user 102's pulse wave (e.g., heart rate pulse wave).

[0100] In some implementations, system 200 may determine a blood pressure index for user 102 using the PPG system 235. That is, the PPG system 235 may include one or more light-emitting components (e.g., LEDs) and a photodetector near the surface of the skin to measure volume fluctuations of user 102's blood flow. In some examples, a triple-LED (e.g., red, green, and IR) PPG system 235 may allow the wearable device 104 to propagate multiple light waves into user 102's tissues based on specified wavelengths of light to acquire physiological data. In some examples, the physiological data may include PPG data, acceleration data, pressure data, etc. That is, the PPG system 235 may allow system 200 to utilize different components to acquire PPG data.

[0101] Furthermore, or alternatively, the physiological data acquired by the wearable device 104 may include acceleration data (e.g., motion data) associated with user 102. In some cases, the wearable device 104 may acquire acceleration data from one or more motion sensors 245. For example, the wearable device 104 may use the motion sensors 245 to determine user 102's heart rate when user 102 is moving (e.g., exercising). That is, the system 200 may use the acceleration data to show how motion affects blood flow, and subsequently blood pressure.

[0102] In some aspects, the wearable device 104 may utilize one or more light-emitting components to transmit a first light associated with a first wavelength and a second light associated with a second wavelength during time intervals including the user 102's heartbeat. The system 200 may acquire PPG data from the user 102 based on receiving the first and second lights via one or more photodetectors. Furthermore, the PPG data may include a first PPG waveform associated with (e.g., acquired using) the first light associated with the first wavelength, and a second PPG waveform associated with (e.g., acquired using) the second light associated with the second wavelength. In some examples, each PPG waveform may include or represent one or more heartbeat pulse waves of the user exhibiting different morphological features based on different projection wavelengths (e.g., first wavelength, second wavelength, etc.).

[0103] As described herein, system 200 may acquire PPG data including a first PPG waveform acquired using a first light and a second PPG waveform acquired using a second light. That is, each of the PPG waveforms / signals acquired using multiple different wavelengths may exhibit various morphological features. In some examples, the PPG waveforms / signals may be identified as raw PPG waveforms / signals such as IR PPG waveforms / signals, red PPG waveforms / signals, and green PPG waveforms / signals. System 200 may acquire multiple raw PPG waveforms / signals received from the PPG data and determine morphological features that may be identified within or between the raw PPG waveforms of different wavelengths, as well as one or more derivatives of the raw PPG waveforms (e.g., first derivative, second derivative, etc.). That is, the morphological features may implicitly indicate one or more blood pressure indicators of user 102.

[0104] Furthermore, or alternatively, the system 200 may acquire data via one or more pressure sensors 246 (e.g., piezoelectric pressure sensors). In some cases, physiological data may be acquired during time intervals in which the pressure between the wearable device 104 and the user 102's tissue changes. That is, the system 200 may include a wearable device 104 having an optical sensor in contact with the user 102's skin, and the optical sensor may restrict blood circulation in different layers of skin tissue as the pressure between the optical sensor (and / or other surfaces of the wearable device 104) and the user's tissue changes. Thus, the system 200 may determine a correlation between arterial pressure and pulse wave morphology, and may take external pressure into consideration when determining the user 102's blood pressure.

[0105] In some cases, external pressure may be applied to the optical sensor when user 102 grasps an object or when user 102 has a swollen extremity (e.g., a finger) due to dehydration. In some cases, the PPG system 235 may not be able to transmit light through different skin tissue layers when the pressure between the optical sensor and user 102's skin is increased. That is, system 200 may instruct user 102 to apply different pressures to observe how different wavelengths / PPG waveforms respond to changing pressures. Thus, system 200 may determine how PPG data (e.g., PPG waveforms of different wavelengths) changes in response to various pressures applied between the wearable device 104 and the user's tissue. In this regard, system 200 may use morphological features associated with each PPG waveform when pressure is applied to account for changes in user 102's blood pressure index. Furthermore, in some respects, pressure sensors (e.g., piezoelectric sensors) can be used to identify vibrations or other pressure changes that can be attributed to a heartbeat, which can then be used to determine when a heartbeat is detected (e.g., pulse wave arrival time), and thus can be used to determine the user's blood pressure index.

[0106] In some aspects, the system may be configured to identify other conditions or characteristics of the wearable device 104 and / or user 102 that affect signal quality or characteristics, such as user skin temperature and ring rotation / fit (e.g., tightness or looseness of the ring, as determined using a pressure sensor or PPG sensor). In such cases, the system may be configured to identify how such characteristics affect morphological features and to compensate for such characteristics when determining the morphological features of the PPG waveform. That is, the PPG signal may be compensated using data from other sources such as a pressure sensor or a temperature sensor.

[0107] In some respects, the system 200 may determine a first group of morphological features associated with a first PPG waveform based on a first systolic peak and a first diastolic peak corresponding to the heartbeat (e.g., pulse wave) of the first PPG waveform. Furthermore, the system 200 may determine a second group of morphological features associated with a second PPG waveform based on a second systolic peak and a second diastolic peak corresponding to the heartbeat of the second PPG waveform. That is, the system 200 may compare the first group of morphological features with the second group of morphological features to determine one or more blood pressure indicators of user 102.

[0108] For example, system 200 may determine the correlation coefficient between each of the PPG waveforms of different wavelengths, the delay between the systolic and diastolic peaks of the second derivatives of the PPG waveforms of different wavelengths, and the difference in systolic peak timing between the PPG waveforms of different wavelengths. That is, user 102's blood pressure may be determined (e.g., estimated) using morphological features represented by multiple PPG pulse waves. Thus, system 200 may use a wearable device 104 positioned relative to user 102 to acquire PPG data via the PPG system 335, determine morphological features from the PPG waveforms, and determine one or more blood pressure indices for user 102 based on a comparison of morphological features. Furthermore, system 200 may determine user 102's blood pressure indices and selectively adjust user 102's blood pressure indices using acceleration data from one or more motion sensors 245.

[0109] An analysis of various morphological features of PPG waveforms that can be used to determine blood pressure indicators is further shown and explained with reference to Figure 4.

[0110] In some aspects, the system 200 may transfer physiological data, such as PPG data, acceleration data, and pressure data, acquired from various physiological locations and / or penetration depths, from one or more wearable devices 104 to one or more user devices 106. The user device 106 (and / or other components of the system 200, such as the server 110) may then determine the blood pressure index of user 102 based on the received physiological data. In some aspects, the user device 106 may store the blood pressure trend of user 102 in the database 265. That is, the blood pressure trend may indicate the normal (e.g., typical) blood pressure level of user 102, or it may indicate a baseline blood pressure index associated with user 102. Furthermore, the user device 106 may compare the current blood pressure index to the baseline blood pressure index and calculate the difference (e.g., deviation). In some cases, user 106 may determine a significant difference between user 102's baseline blood pressure index and user 102's current blood pressure index, and may use the GUI 275 of the user device 106 to display information associated with the difference. Therefore, GUI 275 may alert (e.g., notify) the user 102 of the current blood pressure index compared to the user 102's typical blood pressure trend, and / or any additional instructions the user 102 should follow to calibrate the blood pressure index.

[0111] Figure 3 shows an example of a wearable device system that supports a technique for determining blood pressure based on morphological features of a pulse wave, according to aspects of the present disclosure. In some implementations, the wearable device system 300 may implement, or be implemented by, aspects of systems 100 and 200 as described with reference to Figures 1 and 2.

[0112] For example, the wearable device system 300 may be implemented by a wearable device 104 (e.g., a ring 104), a user device 106, one or more servers 110, or any combination thereof. In the following description of the wearable device system 300, the operations may be performed in a different order than the exemplary order shown, or the operations may be performed in a different order or at different times. Some operations may also be omitted from the wearable device system 300, and other operations may be added to the wearable device system 300. In this example, the wearable device system 300 may be referred to as system 300. Thus, “wearable device 104-b” may be used interchangeably with “wearable ring device” unless otherwise specified herein.

[0113] In the example shown in Figure 3, the system 300 may use one or more wearable devices 104 (e.g., wearable device 104-b) to determine the blood pressure index of user 102 based on the morphological features of PPG data acquired using each wearable device. For example, wearable device 104-b may use PPG technology that includes a triple LED (e.g., red, green, and IR) system, which allows wearable device 104-b to propagate multiple light waves into different tissue layers 320 of user 102 based on the wavelength of light. That is, the depth of light penetration into user 102's skin (e.g., wavelength range) increases with wavelength from the UV to the visible light range and through the IR range.

[0114] In system 300, a light-emitting component 310 (e.g., an LED) and a photodetector 315 may be coupled to a controller to transmit light to a location 325 associated with one or more wavelengths and to acquire measurements. In this example, the light-emitting component 310 may transmit first light associated with a wavelength range (e.g., IR light, red light, etc.) that penetrates to a depth or location 325-a in the epidermal layer 320, and the first light returns to the photodetector 315 along with acquired PPG data. In addition, the light-emitting component 310 may transmit second light associated with a wavelength range (e.g., IR light, red light, green light, blue light, etc.) that penetrates to a depth or location 325-b in the epidermal layer 320, and the second light returns to the photodetector 315 along with additional acquired PPG data.

[0115] In other words, the wearable device 104-b may utilize light in different wavelength ranges to acquire PPG data at different locations 325 or penetration depths in the user's tissue. That is, the system 300 may enable the controller of the wearable device 104-b to transmit blue light associated with a wavelength of about 460 nanometers (nm), green light associated with a wavelength of about 530 nm, red light associated with a wavelength of about 660 nm, or IR light associated with a wavelength of about 940 nm, each of which can reach one or more tissue layers 320 (e.g., an epidermal layer 320-a of about 0.3 mm, a dermal layer 320-b of about 1.0 mm, and a subcutaneous tissue layer 320-c of about 3.0 mm). That is, each of which can reach one or more layers of tissue where blood vessels are located, such as capillaries in the epidermis that are closest to the user's skin, arterioles in the middle layer of the dermis, and arteries in the deepest layer of the subcutaneous tissue. Therefore, the system 300 may use a light-emitting component 310 and a photodetector 315 coupled to a controller on the wearable device 104-b to transmit one or more lights associated with a wavelength range to one or more tissue layers 320 at multiple locations 325 in order to acquire physiological data.

[0116] Furthermore, a single wearable device 104-b may transmit light from the light-emitting component 310 through one or more tissue layers 320 to the photodetector 315 to acquire PPG data during a time interval, the time interval may include the heartbeat of the user 102. In some examples, the wearable device 104-b may acquire PPG data via the photodetector 315 based on first and second light. The PPG data may include a first PPG waveform acquired via first light associated with a first wavelength and a second PPG waveform acquired using second light associated with a second wavelength.

[0117] Furthermore, each of the PPG waveforms, such as the first PPG waveform and the second PPG waveform, may exhibit different morphological characteristics. That is, the wearable device 104-b may determine a first group of morphological characteristics associated with the first PPG waveform based on the first systolic peak and the first diastolic peak corresponding to the heartbeat in the first PPG waveform. The wearable device 104-b may also determine a second group of morphological characteristics associated with the second PPG waveform based on the second systolic peak and the second diastolic peak corresponding to the heartbeat in the second PPG waveform. Thus, the wearable device 104-c may acquire PPG data from one or more wavelengths that penetrate to different tissue depths (e.g., the epidermal layer 320-a, the dermal layer 320-b, and the subcutaneous tissue layer 320-c). Therefore, the system 300 may be configured to use a single wearable device 104-b to transmit light associated with each wavelength, acquire PPG data from user 102, determine each group of morphological features associated with each PPG waveform, and determine one or more blood pressure indices of user 102 based on a comparison of each group of morphological features.

[0118] In some cases, the light emitted by the light-emitting components 310 (e.g., LEDs) of the wearable device 104-b may be measured by multiple photodetectors 315. In some cases, the light-emitting components 310 and photodetectors 315 may be arranged at different radial positions on the inner surface of the wearable device 104-b. For example, in some cases, multiple light-emitting components 310 and multiple photodetectors (PDs) may be arranged around the inner surface of the wearable device 104-b in an alternating arrangement pattern (e.g., LED, PD, LED, PD) with regular or irregular spacing between each component. In other cases, multiple photodetectors 315 may be arranged adjacent to each other at the same or similar radial positions on the wearable device 104-b.

[0119] In some cases, measuring light using multiple photodetectors 315 (for example, multiple photodetectors 315 located at the same distance from a common light-emitting component 310) may allow the wearable device 104-b) to determine the phase difference of the optical signals received by each photodetector 315. Such parallel measurement of light by multiple photodetectors 315 may enable more robust and reliable PPG data acquisition. For example, in some cases, the phase difference between light measured by multiple PDs 315 may be used to determine the velocity of blood moving through blood vessels, which may then be used to further determine or estimate blood pressure.

[0120] Various morphological features of PPG waveforms that can be used to determine blood pressure indices can be further shown and explained with reference to Figure 4.

[0121] Figure 4 shows an example of a pulse wave morphology graph 400 supporting a technique for determining blood pressure based on the morphological features of a pulse wave, according to aspects of the present disclosure. In some implementations, the pulse wave morphology graph 400 may implement aspects of system 100, system 200, and system 300, or be implemented by them, as described with reference to Figures 1-3. For example, the wearable device system 400 may be implemented by a wearable device 104 (e.g., a ring 104), a user device 106, one or more servers 110, or any combination thereof. In the following description of the pulse wave morphology graph 4400, the actions may be performed in an order different from the exemplary order shown, or the actions may be performed in a different order or at different times. Some actions may also be omitted from the pulse wave morphology graph 400, and other actions may be added to the pulse wave morphology graph 400.

[0122] In the example in Figure 4, the pulse wave morphology graph 400 may display multiple graphs 405. That is, each of the graphs 405 may show signal intensity that changes over time and may display different morphological features (e.g., pulse wave size, pulse wave shape). Graph 405-a may show a raw PPG waveform 410 (e.g., a raw PPG signal). For example, the raw PPG waveform 410 may represent a raw IR PPG signal 425-a, a raw red PPG signal 425-b, and a raw green PPG signal 425-c. That is, each of the raw PPG waveforms 410 may show signal intensity that changes over time and may display different morphological features (e.g., peaks, troughs). Furthermore, each of the raw PPG waveforms 410 may include one or more systolic peaks and one or more diastolic peaks. In some examples, a wearable device may transmit one or more wavelength-associated lights during a time interval having the user's heartbeat. In other words, the wearable device can receive the transmitted light and obtain PPG data from the user in the form of a raw PPG waveform 410.

[0123] In some examples, the raw PPG waveforms 410 may represent PPG data acquired at different penetration depths, depending on the different wavelengths of light used, as described herein. Furthermore, each of the raw PPG waveforms 410 may represent one or more light measurements associated with wavelength ranges to one or more tissue layers at multiple locations. Thus, graph 405-a may show that the raw IR PPG signal 425-a exceeds the raw red PPG signal 425-b and the raw green PPG signal 425-c over a time interval.

[0124] In some implementations, the first derivative 415 can be calculated for each of the raw PPG waveforms 410. That is, graph 405-b may show the gradient signals 430-a, 430-b, and 430-c of the raw PPG signals 425-a, 425-b, and 425-c, respectively. In this regard, since the first derivative 415 is derived from the raw PPG waveforms 410, the characteristics / features of the first derivative 415 can be considered to be the morphological features of each of the raw PPG waveforms 410.

[0125] Each of the raw PPG waveforms 410 may be filtered (e.g., a bandpass filtered PPG signal), and its respective first derivative 415 (e.g., a gradient signal 430) may be calculated for each of the raw PPG waveforms 410 for display in graph 405-b. In some examples, the first derivative may be identified as the velocity PPG (VPG), and one or more prominent positive peaks found in the first derivative 415 indicate the point in time when the raw PPG waveform 410 is increasing at the highest rate. Thus, the VPG represents the velocity of the raw PPG waveform 410 and may represent the systolic and diastolic rises of the PPG pulse wave waveform.

[0126] In some respects, the second derivative 420 can be calculated for each of the raw PPG waveforms 410 (e.g., raw PPG signals 425). That is, graph 405-c may show the second derivatives of the raw PPG signals 435-a, 435-b, and 435-c (e.g., curvature signals 435-a, 435-b, and 435-c), respectively. In this regard, since the second derivative 420 is derived from the raw PPG waveform 410, the characteristics / features of the second derivative 420 can be considered to be the morphological features of each raw PPG waveform 410.

[0127] Each of the raw PPG waveforms 410 may be further filtered, and for each of the raw PPG waveforms 410, its respective second derivative 420 may be calculated for display in graph 405. In some cases, the second derivative 420 (e.g., curvature signal 435) may be identified as acceleration PPG (APG), with multiple peaks identified for blood pressure assessment. In Figure 4, the second derivative 420 may show five peaks, including several upward and downward peaks present for the measured PPG pulse wave. That is, one or more timings of the APG peaks (e.g., the number of samples after the pulse wave started), one or more ratios, and one or more calculated features may be used as input for a classifier that maps the feature values ​​to a blood pressure index. Thus, identifying the peaks of the second derivative 420 may enable the system to determine the user's accurate blood pressure index.

[0128] In some respects, one or more morphological features of the raw PPG waveform 410 can be identified from graphs 405-a, 405-b, and 405-c. That is, graph 405-a may show the raw PPG waveform 410 which can be identified as one or more PPG waveforms. In some implementations, the system can determine the correlation coefficient between the raw IR PPG signal 425-a and the raw red PPG signal 425-b. However, in other examples, the system can determine the correlation coefficient between different PPG signals having different wavelength ranges, and is not limited to the raw IR PPG signal 425-a for a first wavelength range and the raw red PPG signal 435-b for a second wavelength range. That is, the system can compare the morphological features of the raw PPG waveform 410 displayed on graph 405-a, such as one or more systolic peaks 440 and diastolic peaks 445 for determining blood pressure indices. Therefore, the raw PPG waveforms 410 representing multiple PPG waveforms may contain each group of morphological features that the system can utilize to determine the user's accurate blood pressure index.

[0129] In some examples, Graph 405-a may show a raw PPG waveform 410 indicating the timing of the systolic peak 440 of systolic blood pressure. That is, Graph 405-a may show one or more delays of the systolic peak 440 of the raw PPG waveform 410. Specifically, Graph 405-a may show one or more delays between a first wavelength including a first wavelength range associated with the raw IR PPG signal 425 and a second wavelength including a second wavelength range associated with the raw green PPG signal 425-c. Furthermore, the system may determine the timing based on the systolic peak 440 of the raw PPG waveform 410. For example, the system may determine a first timing for the systolic peak 440 of the raw IR PPG signal 425-a (e.g., a first PPG waveform) and a second timing for the systolic peak 440 of the raw green PPG signal 425-c (e.g., a second PPG waveform), where a first group of morphological features and a second group of morphological features include the first and second timings. The system may determine a delay between the systolic peak 440 of the raw IR PPG signal 425 and the systolic peak 440-c of the raw green PPG signal 425-a based on the first and second timings. Thus, the system may use the delay to determine the user's blood pressure index.

[0130] Furthermore, or alternatively, additional morphological features may be identified from graph 405-a. Specifically, graph 405-a may show raw PPG waveforms 410 calculated from predicting blood pressure indices from a single pulse wave. That is, raw PPG waveforms 410 may represent systolic, diastolic, and mean blood pressure. Thus, the system may identify morphological features such as the ratio of the areas under each of the PPG waveforms 410, comparing the area under one or more curves from different phases of the PPG pulse wave (e.g., feature = area under the curve of the raw IR PPG signal 425-a (AUC_IR) / area under the curve of the raw green PPG signal 425-c (AUC_GRE)). Therefore, the system may use this morphological feature, which calculates the area under one or more curves in graph 405-a, to determine the user's blood pressure indices.

[0131] In some respects, additional morphological features can be identified from graph 405-b. Specifically, graph 405-b may show the first derivative 415 (e.g., gradient signal 430) calculated for each of the raw PPG waveforms 410 shown in graph 405-a. That is, the first derivative 415 can be calculated from predicting a blood pressure index from a single pulse wave. That is, the first derivative 415 may represent systolic blood pressure. Thus, the system can identify morphological features such as the ratio of the maximum gradients between the first derivatives 415 of the PPG waveforms 410. In other words, a morphological feature that can be used to determine blood pressure may include the ratio of the maximum gradients of the raw PPG signal 425 (and / or the ratio of the peaks of the gradient signal 430) (for example, feature = maximum gradient for red PPG signal 425-b shown as the peak of red gradient signal 430-b (MS_RED) / maximum gradient of raw green PPG signal 425-c shown as the peak of green gradient signal 430-c (MS_GRE)). Thus, the system may use a morphological feature that includes the maximum gradient of the raw PPG signal 425 in graph 405-a (for example, the peak of the gradient signal 430 in graph 405-b) to determine the user's blood pressure index.

[0132] In some implementations, one or more morphological features can be identified from graph 405-c. That is, graph 405-c may show the second derivative 420 of the raw PPG waveform 410 shown in graph 405-a (e.g., curvature signal 435), such as the second derivative of the raw PPG waveform 410. In other examples, graph 405-c may show the second derivatives 420 representing diastolic and mean blood pressure. In some aspects, a system having a controller that couples one or more light-emitting components to one or more photodetectors may determine the curvature signals 435-a, 435-b, and 435-c (e.g., the second derivatives of the raw PPG waveform 410), respectively, of the raw PPG signals 425-a, 425-b, and 425-c.

[0133] In other words, the system may determine the peak of the curvature signal 435 corresponding to the systolic peak 440 of the raw PPG signal 425, and the morphological features of each PPG waveform 410 include the timing of the peaks of each curvature signal 435. In particular, the system may determine the relative delay between the timings of the peaks of the curvature signal 435, and the relative timing of the peaks (e.g., the delay between the peaks of the curvature signal 435) can be used as a morphological feature used to determine the blood pressure index.

[0134] As previously stated herein, aspects of this disclosure may utilize PPG data to determine the morphological features of PPG waveforms, thereby using the morphological features of PPG waveforms (e.g., the morphological features shown and described in graph 405 of Figure 4) to determine the user's blood pressure index. In some implementations, the wearable device 104 may collect PPG data in the form of one or more groups of PPG pulse waves (as shown in the raw PPG signal 425) to measure the user's specific physiological parameters. However, not all PPG pulse waves exhibit the same morphological features or characteristics. In other words, PPG pulse waves can exhibit a variety of shapes and characteristics. That is, the morphological features of PPG pulse waves (e.g., PPG pulse wave amplitude, duration, slope, curvature, and relationships between peaks) may differ from PPG pulse wave to PPG pulse wave, and some PPG pulse waves may not accurately represent physiological measurements. Furthermore, or alternatively, factors such as light, pressure, user posture (e.g., whether the user is sitting or standing), or user hydration status (e.g., the user may have swollen fingers due to dehydration) may affect the accuracy of PPG data. In particular, systems that use inaccurate PPG pulse waves or fail to account for additional factors affecting PPG data may yield unreliable physiological measurements. That is, multiple systems may benefit from one or more techniques for identifying PPG pulse waves that accurately represent the physiological indicators of one or more users.

[0135] Accordingly, in some implementations, the system of this disclosure may be configured to identify one or more “representative” (e.g., common, mean) PPG pulse waves that accurately represent the user’s physiological indicators, and the identified representative PPG pulse waves of the PPG waveform may be used to identify morphological features and / or blood pressure measurements. That is, the techniques described herein may be used to identify high-quality PPG pulse waves that accurately reflect the user’s physiological indicators in order to determine blood pressure measurements.

[0136] To identify one or more PPG pulse waves that accurately represent a user's physiological indicators (for example, to identify PPG pulse waves used to determine pulse wave observation time and / or blood pressure indicators), the wearable device 104 may acquire PPG data from the user, including a first group of PPG pulse waves. In some aspects, the system may compare multiple morphological features from the first group of PPG pulse waves for each specific physiological measurement. Furthermore, based on the comparison of multiple morphological features of the first group of PPG pulse waves, the system may determine one or more PPG profiles (e.g., one or more representative PPG pulse waves, one or more common pulse wave templates) for each specific physiological indicator. That is, each of the one or more PPG profiles may include a group of multiple morphological value ranges for multiple morphological features. In some examples, each PPG profile may represent a representative (e.g., common, mean) pulse wave calculated from the first group of PPG pulse waves for each specific physiological measurement.

[0137] In addition, the system may acquire additional PPG data from the user via the wearable device 104. In some cases, the system may acquire additional PPG data from the user as a second group of PPG pulse waves. In some implementations, the system may determine that one or more PPG pulse waves from the second group of PPG pulse waves match one or more PPG profiles from the first group of PPG pulse waves. That is, the system may detect that multiple morphological feature values ​​of the second group of PPG pulse waves satisfy multiple morphological value ranges of one or more PPG profiles. In other words, the system may identify which PPG pulse waves from the second group of PPG pulse waves "match" a PPG profile.

[0138] Subsequently, the system may determine one or more physiological indicators associated with the user based on one or more PPG profiles from a first group of PPG pulse waves and one or more PPG pulse waves from a second group of PPG pulse waves that match. In other words, the system may perform physiological measurements of the user using PPG pulse waves that "match" the PPG profile (for example, the system may use "representative" PPG pulse waves). For example, the system / wearable device 104 may be configured to determine morphological features and therefore blood pressure measurements using PPG pulse waves that match the PPG profile in the PPG waveform.

[0139] Conversely, the system may detect that one or more PPG pulse waves from a second group of PPG pulse waves do not match one or more PPG profiles from the second group of PPG pulse waves, and may refrain from using that particular physiological indicator associated with the user, or may take this information into consideration. In other words, the system / wearable device may not utilize PPG pulse waves that do not match PPG profiles within the PPG waveform to determine morphological characteristics and / or blood pressure measurements.

[0140] In some aspects, a wearable device may use the existing hardware features of the wearable device to identify one or more representative PPG pulse waves for each user. In some examples, the system may define one or more PPG pulse wave profiles (e.g., one or more PPG templates) that represent a user's typical PPG pulse waves. That is, the system may acquire one or more PPG pulse waves and compare each of them to one or more PPG pulse wave profiles. In such cases, the system may determine one or more PPG profiles by identifying common (e.g., average) values ​​(e.g., average length, amplitude, slope, etc.) of multiple PPG pulse waves. For example, the system may define one or more PPG pulse wave profiles based on common PPG pulse waves acquired from a user via a daytime calibration sequence. That is, the calibration sequence may be initiated to define a valid sample to determine which of the PPG pulse waves are suitable (e.g., reliable) for performing physiological measurements. In some cases, the system may utilize varying correlations between different signal paths to find the optimal measurement time for PPG pulse waves.

[0141] In some implementations, the system may take user posture estimation into account and determine different groups of PPG profiles based on the user's different postures. For example, the system may detect user posture (e.g., the user may be standing, sitting, or lying down) that may affect the signal quality index of the PPG pulse wave. Thus, the system can use the PPG pulse wave profile, calibration sequence, and additional factors to select accurate PPG pulse waves with appropriate signal quality indexes that represent the user's physiological indicators (e.g., a first group of PPG profiles when the user is standing, and a second group of PPG profiles when the user is sitting).

[0142] Figure 5 shows an example of a system 500 that supports a technique for determining blood pressure based on morphological features of a pulse wave, according to aspects of the present disclosure. The GUI 500 may implement or be implemented by aspects of system 100, system 200, system 300, pulse wave morphology graph 400, timing diagram 400, or any combination thereof. For example, the GUI 500 may be implemented for a user in a user device connected to a wearable device (e.g., a wearable ring device, watch, necklace, or any other wearable device), and may be an example of user 102, user device 106, and wearable device 104, as described with reference to Figures 1 to 4.

[0143] GUI 500 shows a set of application pages, including application page 605-a, application page 605-b, and application page 605, which may be displayed to the user (for example, user 102 and GUI 275 as described with reference to Figures 1-5) via GUI 500. In some examples, the user may open application page 505-a to view scores belonging to the user. For example, application page 505-a may display sleep scores, readiness scores, etc. In some examples, application page 505-a may display a blood pressure alert 510 that indicates an alert to the user. For example, blood pressure alert 510 may indicate that a change in the user's blood pressure has been detected. That is, the user may select the user's blood pressure history 515. Furthermore, or alternatively, the user may decide that their blood pressure measurements may need to be recalibrated. That is, the user may have experienced changes that have affected their blood pressure indicators. For example, the user may have traveled to a higher altitude and is experiencing swelling of the extremities (e.g., fingers) due to dehydration. Furthermore, users may select Box 520 for blood pressure calibration to enable the wearable device to obtain accurate blood pressure indicators.

[0144] In some examples, the user may see a blood pressure history feature 515 and an application page 505-b appear on the user's device. The application page 505-b may show blood pressure 525 taken on the day and at a specific (e.g., current) time. In some examples, blood pressure 525 may show several measurements, such as systolic blood pressure 530 measured from one or more arteries when the user's heart is beating, and diastolic blood pressure 535 measured from one or more arteries when the user's heart is between beats. In some examples, the blood pressure classification may include either normal blood pressure or hypertension. For the user, normal blood pressure may show a systolic blood pressure of less than 130 mmHg and a diastolic blood pressure of less than 80 mmHg. Alternatively, hypertension may show a systolic blood pressure of greater than 130 mmHg and a diastolic blood pressure of greater than 80 mmHg. In addition, pulse wave 540 may be displayed, representing a heart rate measurement (e.g., the number of times the user's heart beats per minute). Furthermore, a pulse wave of 540 can be calculated by subtracting the diastolic blood pressure of 535 from the systolic blood pressure of 525.

[0145] Furthermore, or alternatively, application page 505-b may display the user's blood pressure history 545 for a specific period. In the example in Figure 5, the blood pressure history 545 shows a night period on a particular date with two trend lines. That is, the blood pressure history 545 may show a baseline (e.g., normal, typical, trend) blood pressure index 550-a associated with the user and a blood pressure index 550-b for a night period on a particular date. That is, application page 505-b on the user device may graphically show a comparison between a blood pressure index 550-a representing the user's typical nighttime blood pressure trend and a blood pressure index 550-b representing the user's blood pressure trend for the last night period. That is, GUI 500 may display and / or show information associated with the difference between the baseline blood pressure index 550-a and the blood pressure index 550-b (e.g., your blood pressure is ±X compared to your mean blood pressure).

[0146] In some cases, GUI 500 may share information that could indicate whether a user has nocturnal hypertension based on a drop in pulse wave. For a user, a normal nocturnal blood pressure trend may show a blood pressure drop of approximately 10% to 15% lower than a typical daytime pulse wave. However, detecting a blood pressure change of more than 15% (e.g., a change in blood pressure based on absolute and additional values) may alert the user that they have elevated sodium, salt sensitivity, CKD, CHF, diabetes, structural vascular disease, insomnia, etc. That is, a user may look at a blood pressure history over a period of time to determine whether the blood pressure index 550 could indicate a potential cardiovascular health risk 545.

[0147] In some examples, the blood pressure calibration 520 function and application page 505-c may appear on the user device. In such examples, the user may select the blood pressure calibration 520 function and take a blood pressure test 555. That is, application page 505-c may indicate that the user may take a blood pressure test 555 and follow a set of instructions. For example, instruction box 560-a may indicate that the user applies a first pressure to the wearable device (e.g., presses the wearable device and pulls a fist to apply pressure to the wearable ring) over a specified time interval, pauses (e.g., stops) and does not apply pressure to the ring, reducing the pressure to a normative state, and applies a second pressure to the wearable device again over another specified time interval. Furthermore, the blood pressure test 555 may display one or more interval boxes, and in Figure 5, the time interval 560-b box allows the user to start a time interval period for applying each pressure to the wearable device. In other words, the Timer Countdown 560-c box may indicate the time in seconds for the user to apply a first pressure and when to apply a second pressure, or the time period in seconds for the user to rest during the time interval. In some cases, the user may refrain from applying additional pressure to the wearable device, but may select the Blood Pressure Calibration 520 function for the user device to collect one or more blood pressure trends of the user. Thus, the Blood Pressure Test 555 may calibrate the user's blood pressure and create one or more blood pressure trends that accurately represent the user's blood pressure. Furthermore, the user device may display the Blood Pressure Test 555, allowing the wearable device to monitor pulse wave observation times for different pulse waves at variable tissue penetration depth and / or location.

[0148] Figure 6 shows a block diagram 600 of a device 605 that supports a technique for determining blood pressure based on morphological features of a pulse wave, according to aspects of the present disclosure. Device 605 may include an input module 610, an output module 615, and a wearable device manager 620. Device 605 may also include a processor. Each of these components may communicate with one another (for example, via one or more buses).

[0149] For example, the wearable device manager 620 may include a light-emitting component 625, a photodetector component 630, a controller component 635, a wavelength component 640, a PPG data acquisition manager 645, a first form component 650, a second form component 655, a blood pressure component 660, or any combination thereof. In some examples, the wearable device manager 620 or its various components may be configured to perform various operations (e.g., receiving, monitoring, transmitting) using or in other ways with the input module 610, the output module 615, or both. For example, the wearable device manager 620 may receive information from the input module 610 and transmit information to the output module 615, or be integrated with the input module 610, the output module 615, or both to receive information, transmit information, or perform various other operations as described herein.

[0150] The light-emitting component 625 is configured as a means for one or more light-emitting components configured to emit light associated with at least a first wavelength and a second wavelength, or can otherwise support such means. The photodetector component 630 is configured as a means for one or more photodetectors configured to receive light emitted by one or more light-emitting components, or can otherwise support such means. The controller component 635 is configured as a means for a controller that is communicatively coupled to one or more light-emitting components and one or more photodetectors, or can otherwise support such means. The wavelength component 640 is configured as a means for using one or more light-emitting components to emit first light associated with a first wavelength and second light associated with a second wavelength during a time interval including the user's heartbeat, or can otherwise support such means. The PPG data acquisition manager 645 is configured, or may support, means for acquiring PPG data from a user, at least in part on receiving first and second light through one or more photodetectors, the PPG data including at least a first PPG waveform acquired using first light associated with a first wavelength and a second PPG waveform acquired using second light associated with a second wavelength. The first morphological component 650 is configured, or may support, means for determining a first group of morphological features associated with the first PPG waveform, at least in part on a first systolic peak and a first diastolic peak corresponding to a heartbeat in the first PPG waveform. The second morphological component 655 is configured, or may support, means for determining a second group of morphological features associated with the second PPG waveform, at least in part on a second systolic peak and a second diastolic peak corresponding to a heartbeat in the second PPG waveform. The blood pressure component 660 is configured as a means for determining the user's blood pressure index, or may support it, based at least in part on a comparison between a first group of morphological features and a second group of morphological features.

[0151] The PPG data acquisition manager 645 is configured as a means for acquiring PPG data from a user using a wearable device, and the PPG data is collected during a time interval including the user's heartbeat, and the PPG data includes at least a first PPG waveform acquired using first light associated with a first wavelength and a second PPG waveform acquired using second light associated with a second wavelength. The first morphological component 650 is configured as a means for determining a first group of morphological features associated with a first PPG waveform, at least partially based on a first systolic peak and a first diastolic peak corresponding to a heartbeat in the first PPG waveform, and may support such determination. The second morphological component 655 is configured as a means for determining a second group of morphological features associated with a second PPG waveform, at least partially based on a second systolic peak and a second diastolic peak corresponding to a heartbeat in the second PPG waveform, and may support such determination. The blood pressure component 660 is configured as a means for determining the user's blood pressure index, or may support it, based at least in part on a comparison between a first group of morphological features and a second group of morphological features.

[0152] Figure 7 shows a block diagram 700 of a wearable device manager 720 that supports a technique for determining blood pressure based on morphological features of a pulse wave, according to an aspect of this disclosure. The wearable device manager 720 may be an example of an aspect of the wearable device manager 720, the wearable device manager 620, or both, as described herein. The wearable device manager 720 or its various components may be an example of means for performing various aspects of the technique for determining blood pressure based on morphological features of a pulse wave, as described herein. For example, the wearable device manager 720 may include a light-emitting component 725, a photodetector component 730, a controller component 735, a wavelength component 740, a PPG data acquisition manager 745, a first morphology component 750, a second morphology component 755, a blood pressure component 760, a baseline blood pressure component 765, a blood pressure difference component 770, a user interface manager 775, a correlation coefficient component 780, a curvature component 785, a first peak component 790, a second peak component 795, a timing delay component 7100, a timing component 7105, a physiological data acquisition manager 7110, a PPG waveform component 7115, or any combination thereof. Each of these components may communicate with one another directly or indirectly (for example, via one or more buses).

[0153] The light-emitting component 725 is configured as a means for one or more light-emitting components configured to emit light associated with at least a first wavelength and a second wavelength, or may otherwise support such means. The photodetector component 730 is configured as a means for one or more photodetectors configured to receive light emitted by one or more light-emitting components, or may otherwise support such means. The controller component 735 is configured as a means for a controller that is communicatively coupled to one or more light-emitting components and one or more photodetectors, or may otherwise support such means. The wavelength component 740 is configured as a means for using one or more light-emitting components to emit first light associated with a first wavelength and second light associated with a second wavelength during a time interval including the user's heartbeat, or may otherwise support such means. The PPG data acquisition manager 745 is configured, or may support, means for acquiring PPG data from a user, at least in part on receiving first and second light through one or more photodetectors, the PPG data including at least a first PPG waveform acquired using first light associated with a first wavelength and a second PPG waveform acquired using second light associated with a second wavelength. The first morphological component 750 is configured, or may support, means for determining a first group of morphological features associated with the first PPG waveform, at least in part on a first systolic peak and a first diastolic peak corresponding to a heartbeat in the first PPG waveform. The second morphological component 755 is configured, or may support, means for determining a second group of morphological features associated with the second PPG waveform, at least in part on a second systolic peak and a second diastolic peak corresponding to a heartbeat in the second PPG waveform. The blood pressure component 760 is configured as a means for determining the user's blood pressure index, or may support it, based at least in part on a comparison between a first group of morphological features and a second group of morphological features.

[0154] In some examples, a baseline blood pressure index associated with the user is determined. In some examples, the difference between the baseline blood pressure index and the current blood pressure index is determined. In some examples, information associated with the difference between the baseline blood pressure index and the current blood pressure index is displayed on the user device's GUI.

[0155] In some cases, the correlation coefficient between the first PPG waveform and the second PPG waveform was determined at least partially based on a comparison of the first group of morphological features with the second group of morphological features, and the blood pressure index was determined at least partially based on the correlation coefficient.

[0156] In some examples, the first wavelength includes a first wavelength range associated with IR light. In some examples, the second wavelength includes a second wavelength range associated with red light.

[0157] In some examples, a first curvature signal associated with the relative curvature of a first PPG waveform and a second curvature signal associated with the relative curvature of a second PPG waveform are determined. In some examples, the first peak of the first curvature signal corresponding to the first systolic peak of the first PPG waveform is determined, and the first group of morphological features includes the first timing of the first peak. In some examples, the second peak of the second curvature signal corresponding to the second systolic peak of the second PPG waveform is determined, and the second group of morphological features includes the second timing of the second peak. In some examples, the delay between the first timing of the first peak and the second timing of the second peak is determined at least in part based on a comparison between the first group of morphological features and the second group of morphological features, and the blood pressure index is at least in part based on the delay.

[0158] In some examples, the first curvature signal and the second curvature signal include the second derivatives of the first PPG waveform and the second PPG waveform, respectively.

[0159] In some examples, the first wavelength includes a first wavelength range associated with IR light, red light, or both. In some examples, the second wavelength includes a second wavelength range associated with green light.

[0160] In some examples, the first timing of the first systolic peak of the first PPG waveform and the second timing of the second systolic peak of the second PPG waveform are determined, and the first group of morphological features and the second group of morphological features include the first and second timings, respectively. In some examples, the delay between the first systolic peak of the first PPG waveform and the second systolic peak of the second PPG waveform is determined, at least partially based on the first and second timings, and the blood pressure index is at least partially based on the delay.

[0161] In some examples, the first wavelength includes a first wavelength range associated with red light. In some examples, the second wavelength includes a second wavelength range associated with green light.

[0162] In some examples, PPG data is acquired during a time interval in which the pressure between the wearable device and the user's tissue changes from a first pressure to a second pressure. In some examples, a first group of morphological features and a second group of morphological features include the responses of the first and second PPG waveforms, respectively, to the change from the first to the second pressure.

[0163] In some examples, the GUI of a user device displays instructions for the user to selectively change the pressure from a first pressure to a second pressure during a time interval, and acquiring PPG data throughout the entire time interval is at least partially based on these instructions.

[0164] In some examples, physiological data is acquired from the user via a wearable device, and this physiological data includes at least PPG data and acceleration data associated with the user's movement. In some examples, blood pressure indices are selectively adjusted based at least partially on the acceleration data.

[0165] In some examples, wearable devices include wearable ring devices.

[0166] In some examples, the first group of morphological features includes a first systolic peak, a first diastolic peak, or a first amplitude of both. In some examples, the second group of morphological features includes a second systolic peak, a first diastolic peak, or a second amplitude of both.

[0167] In some examples, the PPG data acquisition manager 745 may be configured or supportive of means for acquiring PPG data from a user using a wearable device, the PPG data being collected during a time interval including the user's heartbeat, and the PPG data including at least a first PPG waveform acquired using first light associated with a first wavelength and a second PPG waveform acquired using second light associated with a second wavelength. In some examples, the first morphological component 750 may be configured or supportive of means for determining a first group of morphological features associated with a first PPG waveform, at least partially based on a first systolic peak and a first diastolic peak corresponding to a heartbeat in the first PPG waveform. In some examples, the second morphological component 755 may be configured or supportive of means for determining a second group of morphological features associated with a second PPG waveform, at least partially based on a second systolic peak and a second diastolic peak corresponding to a heartbeat in the second PPG waveform. In some examples, the blood pressure component 760 may be configured as a means for determining the user's blood pressure index, or may support it, based at least in part on a comparison between a first group of morphological features and a second group of morphological features.

[0168] In some examples, the baseline blood pressure component 765 may be configured as a means for determining a baseline blood pressure index associated with the user, or may support this. In some examples, the blood pressure difference component 770 may be configured as a means for determining the difference between the baseline blood pressure index and the blood pressure index. In some examples, the user interface manager 775 may be configured as a means for displaying information associated with the difference between the baseline blood pressure index and the blood pressure index on the GUI of the user device, or may support this.

[0169] In some examples, the correlation coefficient component 780 is configured, or may support, for determining the correlation coefficient between a first PPG waveform and a second PPG waveform, at least partially based on a comparison of a first group of morphological features with a second group of morphological features, and the blood pressure index is at least partially based on the correlation coefficient.

[0170] In some examples, to support the acquisition of PPG data, the light-emitting component 725 is configured or may support means for transmitting first and second light using the light-emitting component of the wearable device. In some examples, to support the acquisition of PPG data, the photodetector component 730 is configured or may support means for receiving first and second light using the photodetector of the wearable device. In some examples, to support the acquisition of PPG data, the PPG waveform component 7115 is configured or may support means for generating first and second PPG waveforms, at least in part, based on receiving first and second light, respectively, via the photodetector.

[0171] In some examples, the first wavelength includes a first wavelength range associated with IR light. In some examples, the second wavelength includes a second wavelength range associated with red light.

[0172] In some examples, the curvature component 785 is configured or may support means for determining a first curvature signal associated with the relative curvature of a first PPG waveform and a second curvature signal associated with the relative curvature of a second PPG waveform. In some examples, the first peak component 790 is configured or may support means for determining a first peak of the first curvature signal corresponding to a first systolic peak of the first PPG waveform, and the first group of morphological features includes a first timing of the first peak. In some examples, the second peak component 795 is configured or may support means for determining a second peak of the second curvature signal corresponding to a second systolic peak of a second PPG waveform, and the second group of morphological features includes a second timing of the second peak. In some examples, the timing delay component 7100 is configured, or may support, for determining the delay between the first timing of the first peak and the second timing of the second peak, at least in part, based on a comparison between a first group of morphological features and a second group of morphological features, and the blood pressure index is at least in part based on the delay.

[0173] In some examples, the first curvature signal and the second curvature signal include the second derivatives of the first PPG waveform and the second PPG waveform, respectively.

[0174] In some examples, the first wavelength includes a first wavelength range associated with IR light, red light, or both. In some examples, the second wavelength includes a second wavelength range associated with green light.

[0175] In some examples, the timing component 7105 is configured or may support means for determining a first timing of a first systolic peak in a first PPG waveform and a second timing of a second systolic peak in a second PPG waveform, where a first group of morphological features and a second group of morphological features include the first timing and the second timing, respectively. In some examples, the timing delay component 7100 is configured or may support means for determining a delay between the first systolic peak in a first PPG waveform and the second systolic peak in a second PPG waveform, at least partially based on the first timing and the second timing, where the blood pressure index is at least partially based on the delay.

[0176] In some examples, the first wavelength includes a first wavelength range associated with red light. In some examples, the second wavelength includes a second wavelength range associated with green light.

[0177] In some examples, PPG data is acquired during a time interval in which the pressure between the wearable device and the user's tissue changes from a first pressure to a second pressure. In some examples, a first group of morphological features and a second group of morphological features include the responses of the first and second PPG waveforms, respectively, to the change from the first to the second pressure.

[0178] In some examples, the user interface manager 775 is configured, or may support, for displaying instructions to the user on the user device's GUI to selectively change the pressure from a first pressure to a second pressure during a time interval, and acquiring PPG data throughout the entire time interval is at least partially based on these instructions.

[0179] In some examples, the physiological data acquisition manager 7110 is configured as a means for acquiring physiological data from a user via a wearable device, or may otherwise support such means, and the physiological data includes at least PPG data and acceleration data associated with the user's movement. In some examples, the blood pressure component 760 is configured as a means for selectively adjusting a blood pressure index based at least in part on acceleration data, or may support such adjustment.

[0180] In some examples, wearable devices include wearable ring devices.

[0181] In some examples, the first group of morphological features includes a first systolic peak, a first diastolic peak, or a first amplitude of both. In some examples, the second group of morphological features includes a second systolic peak, a first diastolic peak, or a second amplitude of both.

[0182] Figure 8 shows a diagram of system 800 including a device 805 that supports a technique for determining blood pressure based on morphological features of a pulse wave, according to aspects of this disclosure. Device 805 may be an example of, or include, components of, device 605 as described herein. Device 805 may include an example of a wearable device 104 as previously stated herein. Device 805 may include components for bidirectional communication, including components for transmitting and receiving communications with a user device 106 and a server 110, such as a wearable device manager 820, a communications module 810, an antenna 815, a sensor component 825, a power module 830, a memory 835, a processor 840, and a wireless device 850. These components communicate electronically via one or more buses (e.g., bus 845) or may be coupled (e.g., operably, communicatively, functionally, electronically, electrically).

[0183] For example, the wearable device manager 820 may be configured as a means for one or more light-emitting components configured to emit light associated with at least a first wavelength and a second wavelength, or may otherwise support such means. The wearable device manager 820 may be configured as a means for one or more photodetectors configured to receive light emitted by one or more light-emitting components, or may otherwise support such means. The wearable device manager 820 may be configured as a means for a controller communicatively coupled to one or more light-emitting components and one or more photodetectors, or may otherwise support such means. The wearable device manager 820 may be configured as a means for using one or more light-emitting components to emit first light associated with a first wavelength and second light associated with a second wavelength during a time interval including the user's heartbeat, or may support such means. The wearable device manager 820 may be configured or supportive of means for acquiring PPG data from a user, at least in part on receiving first and second light through one or more photodetectors, the PPG data including at least a first PPG waveform acquired using first light associated with a first wavelength and a second PPG waveform acquired using second light associated with a second wavelength. The wearable device manager 820 may be configured or supportive of means for determining a first group of morphological features associated with a first PPG waveform, at least in part on a first systolic peak and a first diastolic peak corresponding to a heartbeat in the first PPG waveform. The wearable device manager 820 may be configured or supportive of means for determining a second group of morphological features associated with a second PPG waveform, at least in part on a second systolic peak and a second diastolic peak corresponding to a heartbeat in the second PPG waveform.The wearable device manager 820 is configured, or may support, a means for determining a user's blood pressure index based at least in part on a comparison between a first group of morphological features and a second set of morphological features.

[0184] For example, the wearable device manager 820 may be configured as a means for acquiring PPG data from a user using a wearable device, the PPG data being collected during a time interval including the user's heartbeat, and the PPG data including at least a first PPG waveform acquired using first light associated with a first wavelength and a second PPG waveform acquired using second light associated with a second wavelength. The wearable device manager 820 may be configured as a means for determining a first group of morphological features associated with a first PPG waveform, at least partially based on a first systolic peak and a first diastolic peak corresponding to a heartbeat in the first PPG waveform. The wearable device manager 820 may be configured as a means for determining a second group of morphological features associated with a second PPG waveform, at least partially based on a second systolic peak and a second diastolic peak corresponding to a heartbeat in the second PPG waveform. The wearable device manager 820 is configured, or may support, a means for determining a user's blood pressure index based at least in part on a comparison between a first group of morphological features and a second group of morphological features.

[0185] By including or configuring a wearable device manager 820 in accordance with the examples described herein, device 805 may support a technique for determining blood pressure based on morphological characteristics of pulse waves. That is, device 805 may improve the user experience by communicating blood pressure indicators to the user in a timely manner without the need to visit a doctor for a blood pressure test.

[0186] The methods described above illustrate possible implementations; note that the operations and steps may be rearranged or, in some cases, modified, and other implementations are possible. Furthermore, aspects from two or more of the methods may be combined.

[0187] A method is described. The method may include one or more light-emitting components configured to emit light associated with at least a first wavelength and a second wavelength, one or more photodetectors configured to receive light emitted by one or more light-emitting components, and a controller communicatively coupled to one or more light-emitting components and one or more photodetectors, wherein the controller, at least in part, obtains PPG data from the user based on transmitting a first light associated with the first wavelength and a second light associated with the second wavelength using one or more light-emitting components during a time interval including the user's heartbeat, and receiving the first light and the second light via one or more photodetectors. The PPG data is configured to include at least a first PPG waveform acquired using first light associated with a first wavelength and a second light associated with a second wavelength, to determine a first group of morphological features associated with the first PPG waveform based at least partially on the first systolic peak and first diastolic peak corresponding to the heartbeat in the first PPG waveform, to determine a second group of morphological features associated with the second PPG waveform based at least partially on the second systolic peak and second diastolic peak corresponding to the heartbeat in the second PPG waveform, and to determine the user's blood pressure index based at least partially on a comparison of the first group of morphological features and the second group of morphological features.

[0188] An apparatus is described. The apparatus may include a processor, memory coupled to the processor, and instructions stored in the memory. Instructions may be executable by the processor to cause the apparatus to include one or more light-emitting components configured to emit light associated with at least a first wavelength and a second wavelength, one or more photodetectors configured to receive light emitted by one or more light-emitting components, and a controller communicatively coupled to one or more light-emitting components and one or more photodetectors, the controller at least in part on the basis of transmitting a first light associated with the first wavelength and a second light associated with the second wavelength using one or more light-emitting components during a time interval including the user's heartbeat, and receiving the first light and the second light via one or more photodetectors, the user The system is configured to acquire PPG data from the user, the PPG data comprising at least a first PPG waveform acquired using first light associated with a first wavelength and a second light associated with a second wavelength, a first group of morphological features associated with the first PPG waveform is determined at least partially based on the first systolic peak and first diastolic peak corresponding to the heartbeat in the first PPG waveform, a second group of morphological features associated with the second PPG waveform is determined at least partially based on the second systolic peak and second diastolic peak corresponding to the heartbeat in the second PPG waveform, and the user's blood pressure index is determined at least partially based on a comparison of the first group of morphological features and the second group of morphological features.

[0189] Another apparatus is described. The apparatus comprises means for one or more light-emitting components configured to emit light associated with at least a first wavelength and a second wavelength; means for one or more photodetectors configured to receive light emitted by one or more light-emitting components; and means for a controller communicatively coupled to one or more light-emitting components and one or more photodetectors, wherein the controller comprises means for transmitting a first light associated with the first wavelength and a second light associated with the second wavelength using one or more light-emitting components during a time interval including the user's heartbeat, and means for obtaining PPG data from the user, at least in part on receiving the first light and the second light via one or more photodetectors. The PPG data may include means for determining a first group of morphological features associated with the first PPG waveform, at least based on a first PPG waveform acquired using first light associated with a first wavelength and a second PPG waveform acquired using second light associated with a second wavelength; means for determining a first group of morphological features associated with the first PPG waveform, at least partially based on a first systolic peak and a first diastolic peak corresponding to a heartbeat in the first PPG waveform; means for determining a second group of morphological features associated with the second PPG waveform, at least partially based on a second systolic peak and a second diastolic peak corresponding to a heartbeat in the second PPG waveform; and means for determining a user's blood pressure index, at least partially based on a comparison of the first group of morphological features and the second group of morphological features.

[0190] A non-temporary computer-readable medium for storing a code is described. The code may include instructions executable by a processor to one or more light-emitting components configured to emit light associated with at least a first wavelength and a second wavelength, one or more photodetectors configured to receive light emitted by one or more light-emitting components, and a controller communicatively coupled to one or more light-emitting components and one or more photodetectors, the controller at least in part on transmitting a first light associated with the first wavelength and a second light associated with the second wavelength using one or more light-emitting components during a time interval including the user's heartbeat, and receiving the first light and the second light via one or more photodetectors, the user PPG data is obtained from the data, and the PPG data includes at least a first PPG waveform obtained using first light associated with a first wavelength and a second light associated with a second wavelength. A first group of morphological features associated with the first PPG waveform is determined at least partially based on the first systolic peak and first diastolic peak corresponding to the heartbeat in the first PPG waveform, a second group of morphological features associated with the second PPG waveform is determined at least partially based on the second systolic peak and second diastolic peak corresponding to the heartbeat in the second PPG waveform, and the user's blood pressure index is determined at least partially based on a comparison of the first group of morphological features and the second group of morphological features.

[0191] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for determining a baseline blood pressure index associated with a user, determining the difference between the baseline blood pressure index and the blood pressure index, and causing the GUI of the user device to display information associated with the difference between the baseline blood pressure index and the blood pressure index.

[0192] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for determining a correlation coefficient between a first PPG waveform and a second PPG waveform, at least in part on a comparison of a first group of morphological features with a second group of morphological features, and the blood pressure index may be at least in part on the correlation coefficient.

[0193] In some examples of the methods, apparatus, and non-transient computer-readable media described herein, the first wavelength includes a first wavelength range associated with IR light, and the second wavelength includes a second wavelength range associated with red light.

[0194] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for determining a first curvature signal associated with the relative curvature of a first PPG waveform and a second curvature signal associated with the relative curvature of a second PPG waveform, determining a first peak of the first curvature signal corresponding to a first systolic peak of the first PPG waveform, wherein a first group of morphological features includes a first timing of the first peak, determining a second peak of the second curvature signal corresponding to a second systolic peak of the second PPG waveform, wherein a second group of morphological features includes a second timing of the second peak, and determining a delay between the first timing of the first peak and the second timing of the second peak, at least in part on a comparison of the first and second groups of morphological features, wherein a blood pressure index may be at least in part on the delay.

[0195] In some examples of the methods, apparatus, and non-temporary computer-readable media described herein, the first curvature signal and the second curvature signal include the second derivatives of the first PPG waveform and the second PPG waveform, respectively.

[0196] In some examples of the methods, apparatus, and non-transient computer-readable media described herein, the first wavelength includes a first wavelength range associated with IR light, red light, or both, and the second wavelength includes a second wavelength range associated with green light.

[0197] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for determining a first timing of a first systolic peak of a first PPG waveform and a second timing of a second systolic peak of a second PPG waveform, wherein a first group of morphological features and a second group of morphological features each include a first timing and a second timing, respectively, and the delay between the first systolic peak of the first PPG waveform and the second systolic peak of the second PPG waveform may be determined at least in part on the first timing and the second timing, and the blood pressure index may be determined at least in part on the delay.

[0198] In some examples of the methods, apparatus, and non-transient computer-readable media described herein, the first wavelength includes a first wavelength range associated with red light, and the second wavelength includes a second wavelength range associated with green light.

[0199] In some examples of the methods, apparatus, and non-temporary computer-readable media described herein, PPG data may be acquired during a time interval in which the pressure between the wearable device and the user's tissue can change from a first pressure to a second pressure, and the first group of morphological features and the second group of morphological features include the responses of the first PPG waveform and the second PPG waveform to the change from the first pressure to the second pressure, respectively.

[0200] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for causing a GUI of a user device to display instructions to the user for selectively changing a pressure from a first pressure to a second pressure during a time interval, and acquiring PPG data over the entire time interval may be at least partially based on these instructions.

[0201] Some examples of methods, apparatus, and non-temporary computer-readable media described herein include actions, features, means, or instructions for acquiring physiological data from a user via a wearable device, wherein the physiological data includes at least PPG data and acceleration data associated with the user's movement, and for selectively adjusting a blood pressure index based at least in part on the acceleration data.

[0202] In some examples of the methods, apparatus, and non-temporary computer-readable media described herein, wearable devices include wearable ring devices.

[0203] In some examples of the methods, apparatus, and non-transient computer-readable media described herein, a first group of morphological features includes a first systolic peak, a first diastolic peak, or a first amplitude of both, and a second group of morphological features includes a second systolic peak, a first diastolic peak, or a second amplitude of both.

[0204] A method is described. The method involves acquiring PPG data from a user using a wearable device, wherein the PPG data is collected during a time interval including the user's heartbeat, and the PPG data includes at least a first PPG waveform acquired using first light associated with a first wavelength and a second PPG waveform acquired using second light associated with a second wavelength; determining a first group of morphological features associated with the first PPG waveform, at least partially based on a first systolic peak and a first diastolic peak corresponding to the heartbeat in the first PPG waveform; determining a second group of morphological features associated with the second PPG waveform, at least partially based on a second systolic peak and a second diastolic peak corresponding to the heartbeat in the second PPG waveform; and determining the user's blood pressure index, at least partially based on a comparison of the first group of morphological features and the second group of morphological features.

[0205] A device is described. The device may include a processor, memory coupled to the processor, and instructions stored in the memory. The processor may execute the command to cause the device to acquire PPG data from a user using a wearable device, wherein the PPG data is collected during a time interval including the user's heartbeat, and the PPG data includes at least a first PPG waveform acquired using first light associated with a first wavelength and a second PPG waveform acquired using second light associated with a second wavelength; to determine a first group of morphological features associated with the first PPG waveform, at least based on a first systolic peak and a first diastolic peak corresponding to the heartbeat in the first PPG waveform; to determine a second group of morphological features associated with the second PPG waveform, at least based on a second systolic peak and a second diastolic peak corresponding to the heartbeat in the second PPG waveform; and to determine the user's blood pressure index, at least based on a comparison of the first group of morphological features and the second group of morphological features.

[0206] Another apparatus is described. The apparatus is a means for acquiring PPG data from a user using a wearable device, wherein the PPG data is collected during a time interval including the user's heartbeat, and the PPG data includes at least a first PPG waveform acquired using first light associated with a first wavelength and a second PPG waveform acquired using second light associated with a second wavelength; means for determining a first group of morphological features associated with the first PPG waveform, at least based on a first systolic peak and a first diastolic peak corresponding to the heartbeat in the first PPG waveform; means for determining a second set of morphological features associated with the second PPG waveform, at least based on a second systolic peak and a second diastolic peak corresponding to the heartbeat in the second PPG waveform; and means for determining a blood pressure index of the user, at least based on a comparison of the first group of morphological features and the second set of morphological features.

[0207] A non-temporary computer-readable medium for storing code is described. The code may include instructions executable by a processor to obtain PPG data from a user using a wearable device, wherein the PPG data is collected during a time interval including the user's heartbeat, and the PPG data includes at least a first PPG waveform acquired using first light associated with a first wavelength and a second PPG waveform acquired using second light associated with a second wavelength; to determine a first group of morphological features associated with the first PPG waveform, at least based on a first systolic peak and a first diastolic peak corresponding to the heartbeat in the first PPG waveform; to determine a second group of morphological features associated with the second PPG waveform, at least based on a second systolic peak and a second diastolic peak corresponding to the heartbeat in the second PPG waveform; and to determine a blood pressure index of the user, at least based on a comparison of the first group of morphological features and the second group of morphological features.

[0208] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for determining a baseline blood pressure index associated with a user, determining the difference between the baseline blood pressure index and the blood pressure index, and causing the GUI of the user device to display information associated with the difference between the baseline blood pressure index and the blood pressure index.

[0209] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for determining a correlation coefficient between a first PPG waveform and a second PPG waveform, at least in part on a comparison of a first group of morphological features with a second group of morphological features, and the blood pressure index may be at least in part on the correlation coefficient.

[0210] In some examples of the methods, apparatus, and non-temporary computer-readable media described herein, acquiring PPG data may include operations, features, means, or instructions for transmitting first and second light using a light-emitting component of a wearable device, receiving the first and second light using a photodetector of the wearable device, and generating first and second PPG waveforms based at least in part on receiving the first and second light through the photodetector, respectively.

[0211] In some examples of the methods, apparatus, and non-transient computer-readable media described herein, the first wavelength includes a first wavelength range associated with IR light, and the second wavelength includes a second wavelength range associated with red light.

[0212] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for performing: determining a first curvature signal associated with the relative curvature of a first PPG waveform and a second curvature signal associated with the relative curvature of a second PPG waveform; determining a first peak of the first curvature signal corresponding to a first systolic peak of the first PPG waveform, wherein a first group of morphological features includes a first timing of the first peak; determining a second peak of the second curvature signal corresponding to a second systolic peak of the second PPG waveform, wherein a second group of morphological features includes a second timing of the second peak; and determining a delay between the first timing of the first peak and the second timing of the second peak, at least in part on a comparison of the first and second groups of morphological features, such that a blood pressure index may be at least in part on the delay.

[0213] In some examples of the methods, apparatus, and non-temporary computer-readable media described herein, the first curvature signal and the second curvature signal include the second derivatives of the first PPG waveform and the second PPG waveform, respectively.

[0214] In some examples of the methods, apparatus, and non-transient computer-readable media described herein, the first wavelength includes a first wavelength range associated with IR light, red light, or both, and the second wavelength includes a second wavelength range associated with green light.

[0215] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for determining a first timing of a first systolic peak of a first PPG waveform and a second timing of a second systolic peak of a second PPG waveform, wherein a first group of morphological features and a second group of morphological features each include a first timing and a second timing, respectively, and determining a delay between the first systolic peak of the first PPG waveform and the second systolic peak of the second PPG waveform, at least partially based on the first timing and the second timing, wherein a blood pressure index may be at least partially based on the delay.

[0216] In some examples of the methods, apparatus, and non-transient computer-readable media described herein, the first wavelength includes a first wavelength range associated with red light, and the second wavelength includes a second wavelength range associated with green light.

[0217] In some examples of the methods, apparatus, and non-temporary computer-readable media described herein, PPG data may be acquired during a time interval in which the pressure between the wearable device and the user's tissue can change from a first pressure to a second pressure, and the first group of morphological features and the second group of morphological features include the responses of the first PPG waveform and the second PPG waveform to the change from the first pressure to the second pressure, respectively.

[0218] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for causing a GUI of a user device to display instructions to the user for selectively changing a pressure from a first pressure to a second pressure during a time interval, and acquiring PPG data over the entire time interval may be at least partially based on these instructions.

[0219] Some examples of methods, apparatus, and non-temporary computer-readable media described herein include actions, features, means, or instructions for acquiring physiological data from a user via a wearable device, wherein the physiological data includes at least PPG data and acceleration data associated with the user's movement, and for selectively adjusting a blood pressure index based at least in part on the acceleration data.

[0220] In some examples of the methods, apparatus, and non-temporary computer-readable media described herein, wearable devices include wearable ring devices.

[0221] In some examples of the methods, apparatus, and non-transient computer-readable media described herein, a first group of morphological features includes a first systolic peak, a first diastolic peak, or a first amplitude of both, and a second group of morphological features includes a second systolic peak, a first diastolic peak, or a second amplitude of both.

[0222] The descriptions provided herein with respect to the accompanying drawings describe exemplary configurations and do not necessarily represent all examples that may be implemented or fall within the scope of the claims. The term “exemplary” as used herein means “acting as an example, case, or illustration,” and does not mean “preferred” or “advantageous over other examples.” However, these 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 described.

[0223] In the accompanying drawings, similar components or features may have the same reference label. Furthermore, different components of the same type may be distinguished by following the reference label with a dash and a second label that distinguishes similar components. Where only the first reference label is used herein, its description is applicable to any one of the similar components having the same first reference label, notwithstanding the second reference label.

[0224] The information and signals described herein may be represented using any of the following different techniques and methods. For example, the data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the above description may be represented by voltage, electric current, electromagnetic waves, magnetic fields or magnetic particles, light fields or optical particles, or any combination thereof.

[0225] The various exemplary blocks and modules described in relation to the disclosure herein may be implemented using general-purpose processors, DSPs, ASICs, FPGAs or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The general-purpose processor may be a microprocessor, but alternatively, 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, multiple microprocessors, one or more microprocessors working with a DSP core, or any other such configuration).

[0226] The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. When implemented in software executed by a processor, the functions may be stored or transmitted as one or more instructions or codes on a computer-readable medium. Other examples and implementations are within the scope of this disclosure and the accompanying claims. For example, depending on the nature of the software, the functions described above may be implemented using software executed by a processor, hardware, firmware, hardwiring, or any combination thereof. Features implementing the functions may also be physically located in various locations, including being distributed so that parts of the functions are implemented in different physical locations. Also, as used herein, including in the claims, "or" in a list of items (for example, a list of items ending with a phrase such as "at least one of" or "one or more of") indicates an inclusive list, such as a list of at least one of A, B, or C meaning A or B or C or AB or AC or BC or ABC (i.e., A and B and C). Also, as used herein, the phrase "based on" should not be interpreted as a reference to a limited set of conditions. For example, an exemplary step described as “based on Condition A” may be based on both Condition A and Condition B without departing from the scope of this disclosure. In other words, as used herein, the phrase “based on” shall be construed in the same way as the phrase “based at least in part on.”

[0227] Computer-readable media include both non-temporary computer storage media and communication media, including any media that facilitates the transfer of computer programs from one location to another. Non-temporary storage media can be any available media that can be accessed by a general-purpose or dedicated computer. Examples, but not limitations, of non-temporary computer-readable media may 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-temporary media that can be used to carry or store desired program code means in the form of instructions or data structures, and can be accessed by a general-purpose or dedicated computer or general-purpose or dedicated processor. Any connection is also appropriately referred to as computer-readable media. For example, if software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as IR, radio, and microwave, then coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as IR, radio, and microwave are included in the definition of media. As used herein, the terms "disk" and "disc" include CDs, laserdiscs, optical discs, digital multipurpose discs (DVDs), floppy disks (registered trademark), and Blu-ray discs, where a "disk" typically reproduces data magnetically, and a "disc" reproduces data optically using a laser. Combinations of the above also fall within the range of computer-readable media.

[0228] The descriptions herein are provided to enable those skilled in the art to create or use this disclosure. Various modifications to this disclosure will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the scope of this disclosure. Accordingly, this disclosure is not limited to the examples and designs described herein and should be given the broadest scope that corresponds to the principles and novel features disclosed herein.

Claims

1. A wearable device for measuring blood pressure, One or more light-emitting components configured to emit light associated with at least a first wavelength and a second wavelength, One or more photodetectors configured to receive light emitted by one or more light-emitting components, The controller comprises one or more light-emitting components and one or more photodetectors, wherein the controller is, During a time interval including the user's heartbeat, one or more light-emitting components are used to transmit a first light associated with the first wavelength and a second light associated with the second wavelength. Acquiring photoplethysmography (PPG) data from the user, at least in part, based on receiving the first light and the second light through one or more photodetectors, wherein the PPG data includes at least a first PPG waveform acquired using the first light associated with the first wavelength and a second PPG waveform acquired using the second light associated with the second wavelength. Determining a first group of morphological features associated with the first PPG waveform, based at least partially on the first systolic peak and the first diastolic peak corresponding to the heartbeat in the first PPG waveform, Determining a second group of morphological features associated with the second PPG waveform, at least partially based on the second systolic peak and second diastolic peak corresponding to the heartbeat in the second PPG waveform, A wearable device configured to determine the user's blood pressure index based at least in part on a comparison between a first group of morphological features and a second group of morphological features.

2. The aforementioned controller, To determine the baseline blood pressure index associated with the user, To determine the difference between the baseline blood pressure index and the blood pressure index, The wearable device according to claim 1, further configured to display information associated with the difference between the baseline blood pressure index and the blood pressure index on the graphical user interface of the user device.

3. The aforementioned controller, The correlation coefficient between the first PPG waveform and the second PPG waveform is determined at least partially based on the comparison between the first group of morphological features and the second group of morphological features, wherein the blood pressure index is at least partially based on the correlation coefficient. The wearable device according to claim 1, further configured to perform the following:

4. The wearable device according to claim 3, wherein the first wavelength includes a first wavelength range associated with infrared light, and the second wavelength includes a second wavelength range associated with red light.

5. The aforementioned controller, To determine a first curvature signal associated with the relative curvature of the first PPG waveform and a second curvature signal associated with the relative curvature of the second PPG waveform, Determining a first peak of the first curvature signal corresponding to the first systolic peak of the first PPG waveform, wherein the first group of morphological features includes a first timing of the first peak. Determining the second peak of the second curvature signal corresponding to the second systolic peak of the second PPG waveform, wherein the second group of morphological features includes the second timing of the second peak. The delay between the first timing of the first peak and the second timing of the second peak is determined at least in part on the comparison between the first group of morphological features and the second group of morphological features, wherein the blood pressure index is at least in part on the delay. The wearable device according to claim 1, further configured to perform the following:

6. The wearable device according to claim 5, wherein the first curvature signal and the second curvature signal each include the first PPG waveform and the second derivative of the second PPG wave, respectively.

7. The wearable device according to claim 5, wherein the first wavelength includes a first wavelength range associated with infrared light, red light, or both, and the second wavelength includes a second wavelength range associated with green light.

8. The aforementioned controller, The method involves determining the first timing of the first systolic peak of the first PPG waveform and the second timing of the second systolic peak of the second PPG waveform, wherein the first group of morphological features and the second group of morphological features each include the first timing and the second timing, respectively. The method involves determining the delay between the first systolic peak of the first PPG waveform and the second systolic peak of the second PPG waveform, based at least partially on the first and second timings, wherein the blood pressure index is based at least partially on the delay. The wearable device according to claim 1, further configured to perform the following:

9. The wearable device according to claim 8, wherein the first wavelength includes a first wavelength range associated with red light, and the second wavelength includes a second wavelength range associated with green light.

10. The wearable device according to claim 1, wherein the PPG data is acquired during a time interval in which the pressure between the wearable device and the user's tissue changes from a first pressure to a second pressure, and the first group of morphological features and the second group of morphological features each include the response of the first PPG waveform and the second PPG waveform to the change from the first pressure to the second pressure, respectively.

11. The aforementioned controller, The wearable device according to claim 10, further configured to display instructions to the user for selectively changing the pressure from a first pressure to a second pressure during the time interval, and to collect the PPG data over the entire time interval, at least in part, based on the instructions.

12. The aforementioned controller, The acquisition of physiological data from the user via the wearable device, wherein the physiological data includes at least the PPG data and acceleration data associated with the user's movements. Selectively adjusting the blood pressure index based at least partially on the acceleration data, The wearable device according to claim 1, further configured to perform the following:

13. The wearable device according to claim 1, wherein the wearable device includes a wearable ring device.

14. The wearable device according to claim 1, wherein the first group of morphological features includes a first systolic peak, a first diastolic peak, or a first amplitude of both, and the second group of morphological features includes a second systolic peak, a first diastolic peak, or a second amplitude of both.

15. A method for measuring blood pressure, The method involves acquiring photoplethysmography (PPG) data from a user using a wearable device, wherein the PPG data is collected during a time interval including the user's heartbeat, and the PPG data includes at least a first PPG waveform acquired using first light associated with a first wavelength, and a second PPG waveform acquired using second light associated with a second wavelength. Determining a first group of morphological features associated with the first PPG waveform, based at least partially on the first systolic peak and the first diastolic peak corresponding to the heartbeat in the first PPG waveform, Determining a second group of morphological features associated with the second PPG waveform, at least partially based on the second systolic peak and second diastolic peak corresponding to the heartbeat in the second PPG waveform, The user's blood pressure index is determined at least partially based on a comparison between the first group of morphological features and the second group of morphological features. Methods that include...

16. To determine the baseline blood pressure index associated with the user, To determine the difference between the baseline blood pressure index and the blood pressure index, The graphical user interface of the user device displays information associated with the difference between the baseline blood pressure index and the blood pressure index, The method according to claim 15, further comprising:

17. The method of claim 15, further comprising determining a correlation coefficient between the first PPG waveform and the second PPG waveform, at least in part on the comparison between the first group of morphological features and the second group of morphological features, wherein the blood pressure index is at least in part on the correlation coefficient.

18. Obtaining the aforementioned PPG data means The light-emitting component of the wearable device is used to transmit the first light and the second light, The photodetector of the wearable device is used to receive the first light and the second light, The first PPG waveform and the second PPG waveform are generated, at least partially based on receiving the first light and the second light, respectively, through the photodetector. The method according to claim 17, including the method described in claim 17.

19. The method according to claim 17, wherein the first wavelength includes a first wavelength range associated with infrared light, and the second wavelength includes a second wavelength range associated with red light.

20. To determine a first curvature signal associated with the relative curvature of the first PPG waveform and a second curvature signal associated with the relative curvature of the second PPG waveform, Determining a first peak of the first curvature signal corresponding to the first systolic peak of the first PPG waveform, wherein the first group of morphological features includes a first timing of the first peak. Determining the second peak of the second curvature signal corresponding to the second systolic peak of the second PPG waveform, wherein the second group of morphological features includes the second timing of the second peak. The delay between the first timing of the first peak and the second timing of the second peak is determined at least in part on the comparison of the first and second groups of morphological features, wherein the blood pressure index is at least in part on the delay. The method according to claim 15, further comprising:

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