TECHNIQUES FOR DETERMINING BLOOD PRESSURE BASED ON MORPHOLOGICAL CHARACTERISTICS OF PULSE

Abstract

Methods, systems, and devices for determining blood pressure based on morphological features of pulses are described. A system may include a wearable device employing one or more light-emitting components configured to emit light, one or more photodetectors configured to receive light, and a controller coupling the one or more light-emitting components to the one or more photodetectors. The wearable device may transmit light associated with multiple wavelengths and acquire photoplethysmogram (PPG) data including one or more PPG waveforms associated with the respective wavelengths. The system may determine respective sets of morphological features associated with each of the PPG waveforms based on systolic and diastolic peaks corresponding to the user's heartbeat.The system may determine one or more blood pressure measurement metrics for the user based at least in part on a comparison of the respective sets of morphological features.

Description

CROSS-REFERENCE

[0001] This patent application claims priority to U.S. Non-Provisional Patent Application No. 18 / 189,849 by Rantanen et al., entitled "TECHNIQUES FOR DETERMINING BLOOD PRESSURE BASED ON MORPHOLOGICAL FEATURES OF PULSES," filed March 24, 2023, which is assigned to the assignee of the present application and expressly incorporated herein by reference. FIELD OF TECHNOLOGY

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

[0003] Some wearable devices may be configured to collect data from users associated with blood pressure. However, wearable devices may not accurately indicate a user's blood pressure. That is, a wearable device may not accurately perform blood pressure measurements due to limitations in signal processing and adequate techniques that enable the wearable device's hardware to indicate the user's blood pressure. In some aspects, users may collect 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 the blood pressure device may be inconvenient for daily use, and in some cases, users may not have access to the blood pressure device to consistently collect blood pressure measurements.As a result, users may be unaware of certain conditions or diseases associated with high or low blood pressure measurements if blood pressure measurements are taken infrequently for the user. BRIEF DESCRIPTION OF THE DRAWINGS Fig. 1 illustrates an example of a system that supports techniques for determining blood pressure based on morphological features of pulses according to aspects of the present disclosure. Fig. 2 illustrates an example of a system that supports techniques for determining blood pressure based on morphological features of pulses according to aspects of the present disclosure. Fig. 3 illustrates an example of a wearable device system that supports techniques for determining blood pressure based on morphological features of pulses in accordance with aspects of the present disclosure. Fig. 4 illustrates an example of pulse morphology graphs supporting techniques for determining blood pressure based on morphological features of pulses according to aspects of the present disclosure. Fig. 5 illustrates an example of a graphical user interface (GUI) that supports techniques for determining blood pressure based on morphological features of pulses in accordance with aspects of the present disclosure. Fig. 6 illustrates a block diagram of an apparatus that supports techniques for determining blood pressure based on morphological features of pulses according to aspects of the present disclosure. Fig. 7 illustrates a block diagram of a portable device manager that supports techniques for determining blood pressure based on morphological features of pulses in accordance with aspects of the present disclosure. Fig. 8 illustrates a diagram of a system including an apparatus that supports techniques for determining blood pressure based on morphological features of pulses in accordance with aspects of the present disclosure. DETAILED DESCRIPTION

[0004] Traditionally, blood pressure could only be measured in a clinical setting, resulting in users measuring their blood pressure only on the few occasions per year they entered their doctor's office. Home blood pressure devices have allowed users to measure their blood pressure at home. However, traditional blood pressure devices rely on bulky arm cuffs that are not comfortable (or feasible) to wear consistently.

[0005] Some wearable devices have attempted to use light-based measurements to perform blood pressure measurements. However, wearable devices that have attempted to use light to collect physiological data necessary to determine blood pressure may be unable to perform blood pressure measurements due to signal processing limitations. Furthermore, a lack of information about the relationship between blood pressure and features of photoplethysmogram (PPG) waveforms may prevent some wearable devices from capturing accurate blood pressure measurements. As such, a system that conveniently measures blood pressure on a consistent (e.g., daily, hourly) basis may be beneficial to users' overall health.

[0006] Accordingly, aspects of the present disclosure are directed to systems that utilize a wearable device (e.g., a wearable wristband device, a wristwatch, a necklace, a wearable device worn on the chest, an extremity monitor) to determine one or more blood pressure measurement metrics (e.g., measurements) of the user. In particular, the systems described herein may enable wearable devices to determine blood pressure based on morphological characteristics of pulses (e.g., heartbeat pulses) of the user.

[0007] For example, a wearable device may be configured to collect PPG data from a user, wherein the PPG data includes one or more PPG waveforms associated with one or more wavelengths of light used to collect the PPG data. In some examples, PPG pulses within the respective PPG waveforms may indicate different morphological features based on different projected wavelengths used to collect the respective PPG pulses (e.g., a first wavelength, a second wavelength, and the like). In such cases, morphological features of the respective PPG waveforms may be used to determine blood pressure measurement metrics for the user.In particular, the system may acquire PPG data using one or more wavelengths of light, wherein the PPG data includes one or more PPG waveforms corresponding to the one or more wavelengths of light used to acquire the PPG data. Morphological features may then be identified within or between the raw PPG waveforms associated with the different wavelengths and / or within or between one or more derivatives (e.g., a first derivative, a second derivative, and the like) of the raw PPG waveforms.

[0008] In some examples, blood pressure may be determined using morphological features including correlation coefficients between each of the PPG waveforms of different wavelengths, delays between systolic and diastolic peaks of a second derivative of the PPG waveforms of different wavelengths, and differences in systolic peak times between PPG waveforms of different wavelengths. That is, the user's blood pressure may be determined (e.g., estimated) using the morphological features indicated by multiple PPG waveforms / pulses. As such, the system may use the wearable device placed at relative locations on the user to acquire PPG data, to determine morphological features from the PPG waveforms, and to determine one or more blood pressure measurement metrics of the user.

[0009] In some implementations, the system may use the wearable device to determine the one or more blood pressure measurement metrics for the user based on a comparison of morphological features of PPG waveforms. For example, the 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 photodetectors to receive light from the one or more light-emitting components. The light-emitting components and photodetectors may be coupled to a controller that transmits light associated with one or more wavelengths.

[0010] In some aspects, the system may cause light-emitting components of a wearable device to transmit first light associated with the first wavelength (e.g., red light) and second light associated with the second wavelength (e.g., infrared (IR) light, green light, etc.) during a time interval that includes a heartbeat of the user. The system may acquire PPG data from the user based on receiving the first light and the second light via the 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 set of morphological features associated with the first PPG waveform based on a first systolic peak and a first diastolic peak corresponding to the heartbeat for the first PPG waveform. Further, the system may determine a second set of morphological features associated with the second PPG waveform based on a second systolic peak and a second diastolic peak corresponding to the heartbeat for the second PPG waveform. Thus, the system may determine one or more blood pressure measurement metrics for the user based on a comparison of the first set of morphological features and the second set of morphological features.

[0011] Fig. Figure 1 illustrates an example of a system 100 that supports techniques for determining blood pressure based on morphological features of pulses according to aspects of the present disclosure. The system 100 includes a plurality of electronic devices (e.g., wearable devices 104, user devices 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.

[0012] The electronic devices may include any electronic devices known in the art, including wearable devices 104 (e.g., wearable wristband devices, wearable watch devices, etc.), user devices 106 (e.g., smartphones, laptops, tablets). The electronic devices associated with the respective users 102 may include one or more of the following functionalities: 1) measuring physiological data, 2) storing the measured data, 3) processing the data, 4) providing outputs (e.g., via graphical user interfaces (GUIs)) to a user 102 based on the processed data, and 5) communicating data with each other and / or other computing devices. Different electronic devices may perform one or more of the functionalities.

[0013] Example wearable devices 104 may include wearable computing devices, such as a ring computing device (hereinafter, a "ring") configured to be worn on the finger of a user 102, a wrist computing device (e.g., a smartwatch, a fitness band, or a bracelet) configured to be worn on the wrist of a user 102, and / or a head-mounted computing device (e.g., glasses / goggles). Wearable devices 104 may also include bands, straps (e.g., flexible or inflexible bands or straps), stick-on sensors, and the like, which may be positioned in other locations, such as bands around the head (e.g., a forehead headband), arm (e.g., a forearm band and / or bicep band), and / or leg (e.g., a thigh or calf band), behind the ear, under the armpit, and the like. Wearable devices 104 may also be attached to or contained within clothing.For example, wearable devices 104 may be contained in pockets and / or bags on clothing. As another example, wearable device 104 may be clipped and / or pinned to clothing or otherwise held near user 102. Example items of clothing may include, but are not limited to, hats, shirts, gloves, pants, socks, outerwear (e.g., jackets), and underwear. In some implementations, wearable devices 104 may be contained in other types of devices, such as exercise / sports devices used during physical activity. For example, wearable devices 104 may be attached to or contained within a bicycle, skis, a tennis racket, a golf club, and / or exercise weights.

[0014] Much of the present disclosure may be described in the context of a wearable wristband device 104. Accordingly, the terms "ring 104," "wearable device 104," and similar terms may be used interchangeably unless otherwise indicated herein. However, the use of the term "ring 104" is not intended to be limiting, as it is contemplated herein that aspects of the present disclosure may be performed using other wearable devices (e.g., wearable wristwatch devices, wearable necklace devices, wearable bracelet devices, wearable earring devices, wearable ankle devices, and the like).

[0015] In some aspects, user devices 106 may include portable mobile computing devices, such as smartphones and tablet computing devices. User devices 106 may also include personal computers, such as laptop and desktop computing devices. Other example user devices 106 may include server computing devices that can communicate with other electronic devices (e.g., via the Internet). In some implementations, computing devices may include medical devices, such as external portable computing devices (e.g., Holter monitors). Medical devices may also include implantable medical devices, such as pacemakers and cardioverter defibrillators. Other example user devices 106 may include home computing devices, such as Internet of Things (IoT) devices (e.g.,IoT devices), smart TVs, smart speakers, smart displays (e.g., video call displays), hubs (e.g., wireless communication hubs), security systems, smart appliances (e.g., thermostats and refrigerators), and fitness equipment.

[0016] Some electronic devices (e.g., wearable devices 104, user devices 106) may measure physiological parameters of respective users 102, such as photoplethysmography waveforms, continuous skin temperature, a pulse waveform, respiration rate, heart rate, heart rate variability (HRV), actigraphy, galvanic skin response, pulse oximetry, blood oxygen saturation (SpO2), blood glucose levels (e.g., glucose metrics), 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.,portable device 104), a mobile device application, or a server computing device may process received physiological data measured by other devices.

[0017] In some implementations, a 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, a user 102 may have a ring (e.g., wearable device 104) that measures physiological parameters. The user 102 may also have or be associated with a user device 106 (e.g., mobile device, smartphone), where the wearable device 104 and the user device 106 are communicatively coupled. In some cases, the user device 106 may receive data from the wearable device 104 and perform some / all of the calculations described herein. In some implementations, the user device 106 may also measure physiological parameters described herein, such as movement / activity parameters.

[0018] For example, as in Fig. 1, a first user 102-a (User 1) may operate or be associated with a wearable device 104-a (e.g., ring 104-a) and a user device 106-a, which may operate as described herein. In this example, the user device 106-a associated with the user 102-a may process / store physiological parameters measured by the ring 104-a. Comparably, a second user 102-b (User 2) may be associated with a ring 104-b, a wearable wristwatch device 104-c (e.g., wristwatch 104-c), and a user device 106-b, wherein the user device 106-b associated with the user 102-b may process / store physiological parameters measured by the ring 104-b and / or the wristwatch 104-c. In addition, an nth user 102-n (User N) may be associated with an arrangement of electronic devices described herein (e.g., ring 104-n, user device 106-n).In some aspects, wearable devices 104 (e.g., rings 104, wristwatches 104) and other electronic devices may be communicatively coupled to the user devices 106 of the respective users 102 via Bluetooth, Wi-Fi, and other wireless protocols.

[0019] In some implementations, the rings 104 (e.g., wearable devices 104) of the system 100 may be configured to collect physiological data from the respective users 102 based on arterial blood flow within the user's finger. In particular, a ring 104 may utilize one or more light-emitting components, such as LEDs (e.g., red LEDs, green LEDs), that emit light on the palm side of a user's finger to collect physiological data based on arterial blood flow within the user's finger. In general, the terms light-emitting components, light-emitting elements, 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), and the like.

[0020] In some cases, the system 100 may be configured to collect physiological data from the respective users 102 based on blood flow diffused into a skin microvascular bed comprising capillaries and arterioles. For example, the system 100 may collect PPG data based on a measured amount of blood diffused into the microvascular system of capillaries and arterioles. In some implementations, the ring 104 may capture the physiological data using a combination of both green and red LEDs. The physiological data may include any physiological data known in the art, including, but not limited to, temperature data, accelerometer data (e.g., motion / exercise data), heart rate data, HRV data, blood oxygen level data, or any combination thereof.

[0021] The use of both green and red LEDs can provide several advantages over other solutions, as red and green LEDs have been found to have their own distinct advantages when collecting physiological data under different conditions (e.g., light / dark, active / inactive) and across different parts of the body, and the like. For example, green LEDs have been found to perform better during exercise. Furthermore, the use of multiple LEDs (e.g., green and red LEDs) distributed around the ring 104 has been found to perform better compared to wearable devices that use LEDs positioned close together, such as within a wearable wristwatch device. Furthermore, the blood vessels in the finger (e.g., arteries, capillaries) are more accessible via LEDs compared to blood vessels in the wrist.Specifically, arteries in the wrist are positioned on the underside of the wrist (e.g., palmar side of the wrist), meaning that only capillaries on the upper side of the wrist (e.g., back of the palmar side of the wrist) are accessible, where wearable wristwatch devices and similar devices are typically worn. As such, the use of LEDs and other sensors within a ring 104 has been found to perform better compared to wearable devices worn on the wrist because the ring 104 can have greater access to arteries (compared to capillaries), resulting in stronger signals and more valuable physiological data.

[0022] The electronic devices of system 100 (e.g., user devices 106, portable devices 104) may be communicatively coupled to one or more servers 110 via wired or wireless communication protocols. For example, as shown in Fig. 1, the electronic devices (e.g., user devices 106) may be communicatively coupled to one or more servers 110 via a network 108. The network 108 may implement the Transmission Control Protocol and Internet Protocol (TCP / IP), such as the Internet, or may implement other protocols of the network 108. Network connections between the network 108 and the respective electronic devices may facilitate the transport of data via email, web, text messaging, email, or any other suitable form of interaction within a computer network 108. For example, in some implementations, the ring 104-a associated with the first user 102-a may be communicatively coupled to the user device 106-a, with the user device 106-a being communicatively coupled to the servers 110 via the network 108. In additional or alternative cases, portable devices 104 (e.g.Rings 104, wristwatches 104) can be directly communicatively coupled to the network 108.

[0023] The system 100 may provide an on-demand database service between the user devices 106 and the one or more servers 110. In some cases, the servers 110 may receive data from the user devices 106 via the network 108 and may store and analyze the data. Likewise, the servers 110 may provide data to the user devices 106 via the network 108. In some cases, the servers 110 may be located in one or more data centers. The servers 110 may be used for data storage, management, and processing. In some implementations, the servers 110 may provide a web-based interface to the user device 106 via web browsers.

[0024] In some aspects, the system 100 may detect periods of time during which a user 102 is sleeping and classify periods of time during which the user 102 is sleeping into one or more sleep stages (e.g., sleep stage classification). For example, as in Fig. 1, the 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, the ring 104-a may collect physiological data associated with the user 102-a, including temperature, heart rate, HRV, respiratory rate, and the like. In some aspects, data collected by the ring 104-a may be input to a machine learning classifier, where the machine learning classifier is configured to determine periods of time when the user 102-a is asleep (or asleep). Furthermore, the machine learning classifier may be configured to classify periods of time into different sleep stages, including a waking sleep stage, a rapid eye movement (REM) sleep stage, a light sleep stage (non-REM (NREM)), and a deep sleep stage (NREM).In some aspects, the classified sleep stages may be displayed to the user 102-a via a GUI of the user device 106-a. The sleep stage classification may be used to provide a user 102-a with feedback regarding the user's sleep patterns, such as recommended sleep times, recommended wake-up times, and the like. Furthermore, in some implementations, sleep stage classification techniques described herein may be used to calculate scores for the respective user, such as sleep scores, readiness scores, and the like.

[0025] In some aspects, system 100 may utilize circadian rhythm-derived features to further enhance physiological data collection, data processing methods, and other techniques described herein. The term circadian rhythm may refer to a natural, internal process that regulates an individual's sleep-wake cycle, which repeats approximately every 24 hours. In this regard, techniques described herein may utilize circadian rhythm adaptation models to enhance physiological data collection, analysis, and data processing. For example, a circadian rhythm adaptation model may be input into a machine learning classifier along with physiological data collected from user 102-a via wearable device 104-a.In this example, the circadian rhythm adaptation model may be configured to "weight" or adjust physiological data collected throughout a user's approximately 24-hour natural circadian rhythm. In some implementations, the system may initially start with a "baseline" circadian rhythm adaptation model and may modify the baseline model using physiological data collected from each user 102 to generate tailored, individualized circadian rhythm adaptation models specific to each respective user 102.

[0026] In some aspects, the system 100 may utilize other biological rhythms to further enhance the collection, analysis, and processing of physiological data by phasing these other rhythms. For example, if a weekly rhythm is detected within an individual's baseline data, the model may be configured to adjust data "weights" per weekday.Biological rhythms that may require fitting to the model by this procedure include: 1) ultradian (faster than a day) rhythms, including sleep cycles in a sleep state, and oscillations of less than one hour to several hours of periodicity in the measured physiological variables during wakefulness; 2) circadian rhythms; 3) non-endogenous daily rhythms shown to be imposed in addition to circadian rhythms, such as in work schedules; 4) weekly rhythms or other artificial time periodicities imposed exogenously (e.g., in a hypothetical culture with 12-day "weeks," 12-day rhythms could be used); 5) multi-day ovarian rhythms in females and spermatogenesis rhythms in males; 6) lunar rhythms (relevant for individuals living with low or no artificial light); and 7) seasonal rhythms.

[0027] Biological rhythms are not always stationary. For example, many women experience variability in ovarian cycle length across cycles, and ultradian rhythms are not expected to occur at exactly the same time or with the same periodicity across days, even within a user. As such, signal processing techniques sufficient to quantify the frequency composition while maintaining the temporal resolution of these rhythms in physiological data can be used to improve the detection of these rhythms, to assign the phase of each rhythm to each measured time point, and thereby modify fitting models and comparisons of time intervals.The biological rhythm adaptation models and parameters can be added in linear or nonlinear combinations, as appropriate to more accurately capture the dynamic physiological baselines of an individual or group of individuals. In some aspects, the biological models and parameters can be used in any combination.

[0028] In some aspects, the respective devices of system 100 may support techniques for determining blood pressure based on morphological features of PPG waveforms collected from a user 102. As described herein, system 100 may utilize a wearable device 104 (e.g., a wearable wristband device, a wristwatch or bracelet, a necklace, a wearable device worn on the chest, a headband or strap, an extremity monitor) to determine one or more blood pressure measurement metrics (e.g., measurements) of user 102. That is, system 100 may utilize wearable device 104 (which user 102 may wear on a consistent basis) to determine blood pressure based on morphological features of PPG data (e.g., PPG data / waveforms associated with heartbeat pulses) of user 102.In some examples, PPG waveforms may indicate different morphological features based on different projected wavelengths (e.g., a first wavelength, a second wavelength, and the like). In some examples, pulses from a user's heartbeat may be identified from one or more PPG waveforms acquired using one or more wavelengths. In particular, system 100 may acquire PPG data using multiple different wavelengths, and morphological features may be identified within or among the raw PPG waveforms / signals of the PPG data corresponding to different wavelengths. Subsequently, the morphological features of the respective PPG waveforms / signals may be evaluated to determine or estimate the user's blood pressure.

[0029] In some examples, blood pressure may be determined using morphological features including correlation coefficients between each of the PPG waveforms of different wavelengths, delays between systolic and diastolic peak values ​​of a second derivative of the PPG waveforms of different wavelengths, and differences in systolic peak times between PPG waveforms of different wavelengths. That is, the blood pressure of the user 102 may be determined (e.g., estimated) using the morphological features indicated by heartbeat pulses detected within the plurality of respective PPG waveforms / signals. As such, the system 100 may use the wearable device 104 placed at relative locations on the user 102 to collect PPG data including multiple PPG signals / waveforms (e.g.,PPG waveforms corresponding to different wavelengths) to determine morphological features from the PPG waveforms and to determine one or more blood pressure measurement metrics of the user 102.

[0030] Those skilled in the art should recognize that one or more aspects of the disclosure may be implemented in a system 100 to additionally or alternatively solve problems other than those described above. Furthermore, aspects of the disclosure may provide technical improvements to "conventional" systems or processes as described herein. However, the description and accompanying drawings include only exemplary technical improvements resulting from the implementation of aspects of the disclosure and, accordingly, do not represent all technical improvements provided within the scope of the claims.

[0031] Fig. 2 illustrates an example of a system 200 that supports techniques for determining blood pressure based on morphological features of pulses according to aspects of the present disclosure. The system 200 may implement or be implemented by the system 100. In particular, the system 200 illustrates 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 Fig. 1 described.

[0032] In some aspects, ring 104 may be configured to be worn around a user's finger and may determine one or more physiological parameters of the user when worn around the user's finger. Example measurements and determinations may include, but are not limited to, the user's skin temperature, pulse waveforms, respiratory rate, heart rate, HRV, blood oxygen levels, and the like.

[0033] The system 200 further includes a user device 106 (e.g., a smartphone) in communication with the ring 104. For example, the ring 104 may be in wireless and / or wired communication with the user device 106. In some implementations, the ring 104 may send measured and processed data (e.g., temperature data, PPG data, motion / accelerometer data, ring input data, and the like) to the user device 106. The user device 106 may also send data to the ring 104, such as firmware / configuration updates of the ring 104. The user device 106 may process data. In some implementations, the user device 106 may transmit data to the server 110 for processing and / or storage.

[0034] The ring 104 may include a housing 205, which may include an inner housing 205-a and an outer housing 205-b. In some aspects, the housing 205 of the ring 104 may store or otherwise contain various components of the ring, including, but not limited to, device electronics, a power source (e.g., battery 210 and / or capacitor), one or more substrates (e.g., printed circuit boards) interconnecting the device electronics and / or the power source, and the like. The device electronics may include device modules (e.g., hardware / software) such as: a processing module 230-a, a memory 215, a communication module 220-a, a power module 225, and the like. The device electronics may also include one or more sensors. Example sensors may include one or more temperature sensors 240, a PPG sensor assembly (e.g., PPG system 235), and one or more motion sensors 245.

[0035] The sensors may include associated modules (not illustrated) configured to communicate with the respective components / modules of the ring 104 and generate signals associated with the respective sensors. In some aspects, each of the components / modules of the ring 104 may be communicatively coupled to 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 light sensors (e.g., LEDs), oximeters, and the like.

[0036] The one with reference to Fig. 2 is provided for illustrative purposes only. Thus, the ring 104 may include additional or alternative components such as those shown in Fig. 2. Other rings 104 that provide the functionality described herein may be manufactured. For example, rings 104 may be manufactured with fewer components (e.g., sensors). In one specific example, a ring 104 may be manufactured with a single temperature sensor 240 (or other sensor), a power source, and device electronics configured to read the single temperature sensor 240 (or other sensor). In another specific example, a temperature sensor 240 (or other sensor) may be attached to a user's finger (e.g., using a clip, spring-loaded clips, etc.). In this case, the sensor may be wired to another computing device, such as a wrist-worn computing device, that reads the temperature sensor 240 (or other sensor).In other examples, a ring 104 that includes additional sensors and processing functionality may be manufactured.

[0037] The housing 205 may include one or more components of the housing 205. The housing 205 may include a component of the outer housing 205-b (e.g., a shell) and a component of the inner housing 205-a (e.g., a molded part). The housing 205 may include additional components (e.g., additional layers) that may be incorporated into Fig. 2 are not explicitly illustrated. For example, in some implementations, the ring 104 may include one or more insulating layers that electrically isolate the device electronics and other conductive materials (e.g., electrical traces) from the outer housing 205-b (e.g., an outer metal housing 205-b). The housing 205 may provide structural support for the device electronics, the battery 210, the substrate(s), and other components. For example, the housing 205 may protect the device electronics, the battery 210, and the substrate(s) from mechanical forces, such as pressure and shock. The housing 205 may also protect the device electronics, the battery 210, and the substrate(s) from water and / or other chemicals.

[0038] The outer housing 205-b may be made of one or more materials. In some implementations, the outer housing 205-b may include a metal, such as titanium, which may provide strength and abrasion resistance while being relatively lightweight. The outer housing 205-b may also be made of other materials, such as polymers. In some implementations, the outer housing 205-b may be both protective and decorative.

[0039] 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 another material. In some implementations, the inner housing 205-a may be transparent. For example, the inner housing 205-a may be transparent to light emitted by the PPG LEDs. In some implementations, the inner housing 205-a component 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 within a metallic shell of the outer housing 205-b.

[0040] The ring 104 may include one or more substrates (not illustrated). The device electronics and the battery 210 may be included on the one or more substrates. For example, the device electronics and the battery 210 may be mounted on one or more substrates. Example 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-mounted devices (e.g., surface mount technology (SMT) devices) on a flexible PCB. In some implementations, the one or more substrates (e.g., one or more flexible PCBs) may include electrical traces that provide electrical communication between device electronics. The electrical traces may also connect the battery 210 to the device electronics.

[0041] The device electronics, battery 210, and substrates can be arranged in the ring 104 in various ways. In some implementations, a substrate containing device electronics can be mounted along the underside of the ring 104 (e.g., the lower half) so that the sensors (e.g., PPG system 235, temperature sensors 240, motion sensors 245, and other sensors) interface with the underside of the user's finger. In these implementations, the battery 210 can be included along the upper portion of the ring 104 (e.g., on another substrate).

[0042] The various components / modules of ring 104 represent functionality (e.g., circuits and other components) that may be included in ring 104. Modules may include any discrete and / or integrated electronic circuit components that implement analog and / or digital circuits capable of producing the functions attributed to the modules herein. For example, the modules may include analog circuits (e.g., amplification circuits, filter circuits, analog-to-digital converter circuits, and / or other signal conditioning circuits). The modules may also include digital circuits (e.g., combinational or sequential logic circuits, memory circuits, etc.).

[0043] The memory 215 (memory module) of the ring 104 may include any volatile, non-volatile, magnetic, or electrical media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically erasable programmable read-only memory (EEPROM), flash memory, or any other storage device. The memory 215 may store any of the data described herein. For example, the memory 215 may be configured to store data (e.g., motion data, temperature data, PPG data) collected by the respective sensors and the PPG system 235. Further, the memory 215 may include instructions that, when executed by one or more processing circuits, cause the modules to perform various functions attributed to the modules herein. The device electronics of the ring 104 described herein are merely exemplary device electronics.Accordingly, the types of electronic components used to implement device electronics may vary based on design considerations.

[0044] The functions attributed to the modules of ring 104 described herein may be embodied as one or more processors, hardware, firmware, software, or any combination thereof. The representation of various features as modules is intended to highlight different functional aspects and does not necessarily imply that such modules must be implemented by separate hardware / software components. Instead, the functionality associated with one or more modules may be performed by separate hardware / software components or integrated into common hardware / software components.

[0045] The processing module 230-a of the ring 104 may include one or more processors (e.g., processing units), microcontrollers, digital signal processors, systems on a chip (SOCs), and / or other processing devices. The processing module 230-a communicates with the modules included in the ring 104. For example, the processing module 230-a may send / receive data to / from the modules and other components of the ring 104, such as the sensors. As described herein, the modules may be implemented by various circuit components. Accordingly, the modules may also be referred to as circuits (e.g., a communication circuit and power circuit).

[0046] Processing module 230-a may communicate with memory 215. Memory 215 may include computer-readable instructions that, when executed by processing module 230-a, cause processing module 230-a to perform the various functions attributed to processing module 230-a herein. In some implementations, processing module 230-a (e.g., a microcontroller) may include additional features associated with other modules, such as communication functionality provided by communication module 220-a (e.g., an integrated Bluetooth Low Energy transceiver) and / or additional on-board memory 215.

[0047] The communication module 220-a may include circuitry that provides wireless and / or wired communication with the user device 106 (e.g., communication module 220-b of the user device 106). In some implementations, the communication modules 220-a, 220-b may include wireless communication circuitry, such as Bluetooth circuitry and / or Wi-Fi circuitry. In some implementations, the communication modules 220-a, 220-b may include wired communication circuitry, such as Universal Serial Bus (USB) communication circuitry. Using the communication module 220-a, the ring 104 and the user device 106 may be configured to communicate with each other. The processing module 230-a of the ring may be configured to send / receive data to / from the user device 106 via the communication module 220-a.Example data may include, but is not limited to, motion data, temperature data, pulse waveforms, heart rate data, HRV data, PPG data, and status updates (e.g., charging status, battery charge level, and / or configuration settings of the ring 104). The ring's processing module 230-a may also be configured to receive updates (e.g., software / firmware updates) and data from the user device 106.

[0048] The ring 104 may include a battery 210 (e.g., a rechargeable battery 210). An example battery 210 may include a lithium-ion or lithium-polymer type battery 210, although various battery 210 options are possible. The battery 210 may be wirelessly charged. 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 geometry that matches the curvature of the ring 104. In some aspects, a charger or other power source may include additional sensors that may be used to collect data in addition to, or to supplement, the data collected by the ring 104 itself.Additionally, a charger or other power source for the ring 104 may function as a user device 106, in which case the charger or other power source for the ring 104 may be configured to receive data from the ring 104, store and / or process data received from the ring 104, and communicate data between the ring 104 and the servers 110.

[0049] In some aspects, the ring 104 includes a power module 225 that can control the charging of the battery 210. For example, the power module 225 can interface with an external wireless charger that charges the battery 210 when it interfaces with the ring 104. The charger can include a reference structure that mates with a reference structure of the ring 104 to create a specific alignment with the ring 104 during charging of the ring 104. The power module 225 can also regulate the voltage(s) of the device electronics, regulate the power output to the device electronics, and monitor the state of charge of the battery 210. In some implementations, the battery 210 can include a protection circuit module (PCM) that protects the battery 210 from high-current discharge, overvoltage during charging of the ring 104, and undervoltage during discharging of the ring 104.The 225 power module may also include electrostatic discharge (ESD) protection.

[0050] The one or more temperature sensors 240 may be electrically coupled to the processing module 230-a. The temperature sensor 240 may be configured to generate a temperature signal (e.g., temperature data) indicative of a temperature read or sensed by the temperature sensor 240. The processing module 230-a may determine a temperature of the user at the location of the temperature sensor 240. For example, in the ring 104, temperature data generated by the temperature sensor 240 may indicate a temperature of a user at the user's finger (e.g., skin temperature). In some implementations, the temperature sensor 240 may contact the user's skin. In other implementations, a portion of the housing 205 (e.g., the inner housing 205-a) may form a barrier (e.g., a thin, thermally conductive barrier) between the temperature sensor 240 and the user's skin.In some implementations, portions of the ring 104 configured to contact the user's finger may include thermally conductive portions and thermally insulating portions. The thermally conductive portions may conduct heat from the user's finger to the temperature sensors 240. The thermally insulating portions may isolate portions of the ring 104 (e.g., the temperature sensor 240) from the ambient temperature.

[0051] In some implementations, temperature sensor 240 may generate a digital signal (e.g., temperature data) that processing module 230-a may use to determine the temperature. As another example, in cases where temperature sensor 240 includes a passive sensor, processing module 230-a (or a temperature sensor 240 module) may measure a current / voltage generated by temperature sensor 240 and determine the temperature based on the measured current / voltage. Example temperature sensors 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.

[0052] Processing module 230-a may sample the user's temperature over time. For example, processing module 230-a may sample the user's temperature according to a sampling rate. An example sampling rate may include one sample per second, although processing module 230-a may be configured to sample the temperature signal at other sampling rates higher or lower than one sample per second. In some implementations, processing module 230-a may continuously sample the user's temperature throughout the day and night. Sampling at a sufficient rate (e.g., one sample per second) throughout the day may provide sufficient temperature data for the analysis described herein.

[0053] The processing module 230-a may store the sampled temperature data in the memory 215. In some implementations, the processing module 230-a may process the sampled temperature data. For example, the processing module 230-a may determine average temperature values ​​over a period of time. In one example, the processing module 230-a may determine an average temperature value every minute by summing all temperature values ​​collected over the minute and dividing by the number of samples over the minute. In a specific example where the temperature is sampled at one sample per second, the average temperature may be a sum of all sampled temperatures for one minute divided by sixty seconds. The memory 215 may store the average temperature values ​​over time. In some implementations, the memory 215 may store average temperatures (e.g.,one per minute) instead of sampled temperatures to conserve memory 215.

[0054] The sampling rate that may be stored in memory 215 may be configurable. In some implementations, the sampling rate may be the same throughout the day and night. In other implementations, the sampling rate may change throughout the day / night. In some implementations, the ring 104 may filter / reject temperature readings, such as large temperature spikes that do not indicate physiological changes (e.g., a temperature spike from a hot shower). In some implementations, the ring 104 may filter / reject temperature readings that may not be reliable due to other factors, such as excessive movement during exercise 104 (e.g., as indicated by a motion sensor 245).

[0055] Ring 104 (e.g., the communication module) may transmit the sampled and / or average temperature data to user device 106 for storage and / or further processing. User device 106 may transmit the sampled and / or average temperature data to server 110 for storage and / or further processing.

[0056] Although the ring 104 is illustrated as including a single temperature sensor 240, the ring 104 may include multiple temperature sensors 240 at one or more locations, such as along the inner housing 205-a near the user's finger. In some implementations, the temperature sensors 240 may be standalone temperature sensors 240. Additionally or alternatively, one or more temperature sensors 240 may be included in other components (e.g., packaged with other components), such as the accelerometer and / or processor.

[0057] The processing module 230-a may collect and process data from multiple temperature sensors 240 in a similar manner described with respect to 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 the 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 at different locations on the finger.

[0058] The temperature sensors 240 on the ring 104 can sense distal temperatures on the user's finger (e.g., any finger). For example, one or more temperature sensors 240 on the ring 104 can sense a user's temperature from the underside of a finger or at another location on the finger. In some implementations, the ring 104 can continuously sense a distal temperature (e.g., at a sampling rate). Although the distal temperature measured by a ring 104 on the finger is described herein, other devices can measure the temperature at the same / different locations. In some cases, the distal temperature measured on a user's finger may be different from the temperature measured on a user's wrist or other external body location. Additionally, the distal temperature measured on a user's finger (e.g.,a "shell" temperature) from the user's core temperature. Thus, the ring 104 can provide a useful temperature signal that may not be detected at other internal / external locations on the body. In some cases, a continuous temperature measurement on the finger can detect temperature fluctuations (e.g., small or large fluctuations) that may not be evident in the core temperature. For example, a continuous temperature measurement on the finger can detect minute-to-minute or hour-to-hour temperature fluctuations that provide additional insight that may not be provided by other temperature measurements elsewhere on the body.

[0059] The ring 104 may include a PPG system 235. The PPG system 235 may include one or more optical transmitters that transmit light. The PPG system 235 may also include one or more optical receivers that receive light transmitted from the one or more optical transmitters. An optical receiver may generate a signal (hereinafter, a "PPG" signal) indicative of an amount of light received by the optical receiver. The optical transmitters may illuminate an area of ​​the user's finger. The PPG signal generated by the PPG system 235 may indicate the perfusion of blood in the illuminated area. For example, the PPG signal may indicate blood volume changes in the illuminated area caused by a user's pulse pressure. The processing module 230-a may sample the PPG signal and determine a pulse waveform of the user based on the PPG signal.The processing module 230-a may determine a variety of physiological parameters based on the user's pulse waveform, such as the user's respiratory rate, heart rate, HRV, oxygen saturation, and other circulatory parameters.

[0060] In some implementations, the PPG system 235 may be configured as a reflective PPG system 235, where the optical receiver(s) receive transmitted light reflected by the portion of the user's finger. In some implementations, the PPG system 235 may be configured as a transmissive PPG system 235, where the optical transmitter(s) and the optical receiver(s) are positioned opposite each other so that light is transmitted directly through a portion of the user's finger to the optical receiver(s).

[0061] The number and ratio of transmitters and receivers included in the PPG system 235 may vary. Example optical transmitters may include LEDs. The optical transmitters may transmit light in the IR spectrum and / or other spectra. Example optical receivers may include, among others, photosensors, phototransistors, and photodiodes. The optical receivers may be configured to generate PPG signals in response to the wavelengths received by the optical transmitters. The location of the transmitters and receivers may vary. Additionally, a single device may include reflective and / or transmissive PPG systems 235.

[0062] The Fig. The PPG system 235 illustrated 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 either side of the optical receiver. In this implementation, the PPG system 235 (e.g., optical receiver) may generate the PPG signal based on light received from one or both of the optical transmitters. In other implementations, other placements, combinations, and / or configurations of one or more optical transmitters and / or optical receivers are contemplated.

[0063] The processing module 230-a may control one or both of the optical transmitters to transmit light while sampling the PPG signal generated by the optical receiver. In some implementations, the processing module 230-a may cause the optical transmitter with the stronger received signal to transmit light while sampling the PPG signal generated by the optical receiver. For example, the selected optical transmitter may continuously emit light while sampling the PPG signal at a sampling rate (e.g., 250 Hz).

[0064] Sampling the PPG signal generated by PPG system 235 may result in a pulse waveform, which may be referred to as a "PPG." The pulse waveform may be indicative of blood pressure across multiple cardiac cycles. The pulse waveform may include peak values ​​indicative of cardiac cycles. Additionally, the pulse waveform may include respiration-induced variations that may be used to determine respiratory rate. Processing module 230-a may, in some implementations, store the pulse waveform in memory 215. Processing module 230-a may process the pulse waveform as it is generated and / or from memory 215 to determine user physiological parameters described herein.

[0065] Processing module 230-a may determine the user's heart rate based on the pulse waveform. For example, processing module 230-a may determine the heart rate (e.g., in beats per minute) based on the time between peaks in the pulse waveform. The time between peaks may be referred to as the interbeat interval (IBI). Processing module 230-a may store the determined heart rate values ​​and IBI values ​​in memory 215.

[0066] The processing module 230-a may determine the HRV over time. For example, the processing module 230-a may determine the HRV based on the variation in the IBIs. The processing module 230-a may store the HRV values ​​over time in the memory 215. In addition, the processing module 230-a may determine the user's respiratory rate over time. For example, the processing module 230-a may determine the respiratory rate based on frequency modulation, amplitude modulation, or baseline modulation of the user's IBI values ​​over a period of time. The respiratory rate may be calculated in breaths per minute or as another respiratory rate (e.g., breaths per 30 seconds). The processing module 230-a may store the user's respiratory rate values ​​over time in the memory 215.

[0067] The ring 104 may include one or more motion sensors 245, such as one or more accelerometers (e.g., 6-D accelerometers) and / or one or more gyroscopes (gyros). The motion sensors 245 may generate motion signals indicative of the sensors' motion. For example, the ring 104 may include one or more accelerometers that generate acceleration signals indicative of the acceleration of the accelerometers. As another example, the ring 104 may include one or more gyro sensors that generate gyro signals indicative of 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 a Bosch BM1160 inertial microelectromechanical system (MEMS) sensor, which can measure angular rates and accelerations in three perpendicular axes.

[0068] The processing module 230-a may sample the motion signals at a sampling rate (e.g., 50 Hz) and determine the motion of the ring 104 based on the sampled motion signals. For example, the processing module 230-a may sample acceleration signals to determine the acceleration of the ring 104. As another example, the processing module 230-a may sample a gyro signal to determine the angular motion. In some implementations, the processing module 230-a may store motion data in the memory 215. Motion data may include sampled motion data as well as motion data calculated based on the sampled motion signals (e.g., acceleration and angle values).

[0069] Ring 104 can store a variety of data described herein. For example, ring 104 can store temperature data, such as raw sampled temperature data and calculated temperature data (e.g., average temperatures). As another example, ring 104 can store PPG signal data, such as pulse waveforms and data calculated based on the pulse waveforms (e.g., heart rate values, IBI values, HRV values, and respiratory rate values). Ring 104 can also store motion data, such as sampled motion data indicating linear and angular motion.

[0070] The ring 104 or other computing device may calculate and store additional values ​​based on the sampled / calculated physiological data. For example, the processing module 230 may calculate and store various metrics, such as sleep metrics (e.g., a sleep score), activity metrics, and readiness metrics. The ring 104 or other computing / wearable device may calculate a variety of values / metrics related to movement. Example derived values ​​for movement data may include, but are not limited to, movement counts, regularity scores, intensity scores, metabolic equivalence of task values ​​(METs), and orientation scores. Movement counts, regularity scores, intensity scores, and METs may indicate an amount of user movement (e.g., speed / acceleration) over time.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.

[0071] In some implementations, movement counts and regularity values ​​may be determined by counting a number of acceleration peaks within one or more time periods (e.g., one or more periods from 30 seconds to 1 minute). Intensity values ​​may indicate a number of movements and the associated intensity (e.g., acceleration values) of the movements. The intensity values ​​may be categorized as low, medium, and high depending on associated threshold acceleration values. METs may be determined based on the intensity of movements during a time period (e.g., 30 seconds), the regularity / irregularity of the movements, and the number of movements associated with the different intensities.

[0072] In some implementations, processing module 230-a may compress the data stored in memory 215. For example, processing module 230-a may delete sampled data after performing calculations based on the sampled data. As another example, processing module 230-a may average data over longer periods of time to reduce the number of stored values. In a specific example, if average temperatures for a user over one minute are stored in memory 215, processing module 230-a may calculate average temperatures over a five-minute period for storage and then subsequently delete the one-minute average temperature data.The processing module 230-a may compress data based on a variety of factors, such as the total amount of used / available memory 215 and / or an elapsed time since the ring 104 last sent the data to the user device 106.

[0073] Although a user's physiological parameters may be measured by sensors included in a ring 104, other devices may measure a user's physiological parameters. For example, although a user's temperature may be measured by a temperature sensor 240 included in a ring 104, other devices may measure a user's temperature. In some examples, other wearable devices (e.g., wrist devices) may include sensors that measure a user's physiological parameters. Additionally, medical devices, such as external medical devices (e.g., wearable medical devices) and / or implantable medical devices, may measure a user's physiological parameters. One or more sensors on any type of computing device may be used to implement the techniques described herein.

[0074] The physiological measurements may be taken continuously throughout the day and / or night. In some implementations, the physiological measurements may be taken during 104 portions of the day and / or portions of the night. In some implementations, the physiological measurements may be taken in response to determining that the user is in a particular state, such as an active state, a resting state, and / or a sleeping state. For example, the ring 104 may take physiological measurements in a resting / sleeping state to capture cleaner physiological signals. In one example, the ring 104 or other device / system may detect when a user is resting and / or sleeping and capture physiological parameters (e.g., temperature) for that detected state.In some implementations, the physiological parameters collected by the device / other system may include the physiological parameters collected by the device / other system. The devices / systems may use the resting / sleep physiological data and / or other data when the user is in other states to implement the techniques of the present disclosure.

[0075] In some implementations, as previously described herein, ring 104 may be configured to collect, store, and / or process data, and may transmit any of the data described herein to user device 106 for storage and / or processing. In some aspects, user device 106 includes a wearable application 250, an operating system (OS), a web browser application (e.g., web browser 280), one or more additional applications, and a GUI 275. User device 106 may further include other modules and components, including sensors, audio devices, haptic feedback devices, and the like. Wearable application 250 may include an example of an application (e.g., "app") that may be installed on user device 106.The portable application 250 may be configured to collect data from the ring 104, store the collected data, and process the collected data as described herein. For example, the portable application 250 may include a user interface (UI) module 255, a collection module 260, a processing module 230-b, a communication module 220-b, and a storage module (e.g., database 265) configured to store application data.

[0076] The various data processing operations described herein may be performed by ring 104, user device 106, servers 110, or any combination thereof. For example, in some cases, data collected by ring 104 may be preprocessed and transmitted to user device 106. In this example, user device 106 may perform some data processing operations on the received data, may transmit the data to servers 110 for data processing, or both. For example, in some cases, user device 106 may perform processing operations that require relatively low processing power and / or operations that require relatively low latency, whereas user device 106 may transmit the data to servers 110 for processing that require relatively high processing power and / or operations that may allow for relatively higher latency.

[0077] In some aspects, the ring 104, the user device 106, and the server 110 of the system 200 may be configured to evaluate sleep patterns for a user. In particular, the respective components of the system 200 may be used to collect data from a user via the ring 104 and generate one or more scores (e.g., sleep score, readiness score) for the user based on the collected data. For example, as mentioned above herein, the ring 104 of the system 200 may be worn by a user to collect data from the user, including temperature, heart rate, HRV, and the like. In some aspects, scores for the user may be calculated for each respective day of sleep, such that a first day of sleep is associated with a first set of scores and a second day of sleep is associated with a second set of scores.Scores may be calculated for each respective sleep day based on data collected by the ring 104 during the respective sleep day. Scores may include, but are not limited to, sleep scores, readiness scores, and the like.

[0078] In some cases, "sleep days" may align with traditional calendar days, so that a given sleep day runs from midnight to midnight of that calendar day. In other cases, sleep days may be offset relative to calendar days. For example, sleep days may run from 4:00 PM (6:00 PM) one calendar day to 4:00 PM (6:00 PM) the following calendar day. In this example, 4:00 PM may serve as the "cut-off time," with data collected from the user before 4:00 PM counting toward the current sleep day and data collected from the user after 4:00 PM counting toward the following sleep day. Due to the fact that most individuals sleep the most at night, offsetting sleep days relative to calendar days may allow the system 200 to evaluate sleep patterns for users in a manner consistent with their sleep schedules.In some cases, users may be able to selectively adjust a timing of sleep days relative to calendar days (e.g., via the GUI) so that the sleep days align with the amount of time that the respective users typically sleep.

[0079] In some implementations, each total score for a user for each respective day (e.g., sleep score, readiness score) may be determined / calculated based on one or more "contributors," "factors," or "contributing factors." The sleep score may include any number of contributors. The "total sleep" contributor may refer to the sum of all sleep periods of the sleep day. The "efficiency" contributor may reflect the percentage of time spent asleep compared to time spent awake in bed and may be calculated using the efficiency average of long sleep periods (e.g., primary sleep period) of the sleep day, weighted by a duration of each sleep period.The "rest" contributor can indicate how restful the user's sleep is and can be calculated using the average of all sleep periods of the sleep day, weighted by a duration of each period. The rest contributor can be based on a "wake-up count" (e.g., the sum of all wake-ups (when the user wakes up) recorded during different sleep periods), excessive movement, and a "get-up count" (e.g., the sum of all get-up counts (when the user gets out of bed) recorded during different sleep periods).

[0080] The "REM sleep" contributor may refer to a total sum of REM sleep durations across all sleep periods of the sleep day, including REM sleep. Similarly, the "deep sleep" contributor may refer to a total sum of deep sleep durations across all sleep periods of the sleep day, including deep sleep. The "latency" contributor may mean how long (e.g., average, median, longest) it takes the user to fall asleep and may be calculated using the average length of sleep periods throughout the sleep day, weighted by a duration of each period and the number of such periods (e.g., consolidation of a given sleep stage or sleep stages may be its own contributor or weight other contributors).Finally, the contributor “timing” may refer to a relative timing of sleep periods within the sleep day and / or calendar day and may be calculated using the average of all sleep periods of the sleep day, weighted by a duration of each period.

[0081] As another example, a user's overall readiness score may be calculated based on a set of contributors, including: sleep, sleep balance, heart rate, HRV balance, recovery index, temperature, activity, activity balance, or any combination thereof. The readiness score may include any set of contributors. The "sleep" contributor may refer to the combined sleep score of all sleep periods within the sleep day. The "sleep balance" contributor may refer to a cumulative duration of all sleep periods within the sleep day. In particular, sleep balance may indicate to a user whether the sleep the user has received over a certain period of time (e.g., the last two weeks) is in balance with the user's needs.Typically, adults need 7-9 hours of sleep per night to stay healthy and alert, and perform at their best both mentally and physically. However, it's normal to have the occasional night of poor sleep, so the Sleep Balance contributor considers long-term sleep patterns to determine if each user's sleep needs are being met. The Resting Heart Rate contributor may report a lowest heart rate from the longest sleep period of the sleep day (e.g., primary sleep period) and / or the lowest heart rate from naps that occur after the primary sleep period.

[0082] Further referring to the "contributors" (e.g., factors, contributing factors) of the readiness score, the "HRV Balance" contributor may indicate a highest HRV average from the primary sleep period and naps that occur after the primary sleep period. The HRV Balance contributor can help users track their recovery status by comparing their HRV trend over a first period (e.g., two weeks) with an average HRV over a second, longer period (e.g., three months). The "Recovery Index" contributor can be calculated based on the longest sleep period. The Recovery Index measures how long it takes for a user's resting heart rate to stabilize during the night.An indication of excellent recovery is that the user's resting heart rate stabilizes during the first half of the night, at least six hours before the user awakens, leaving the body time to recover for the next day. The "body temperature" contributor may be calculated based on the longest sleep period (e.g., primary sleep period) or based on a nap that occurs after the longest sleep period, when the user's highest temperature during the nap is at least 0.5°C higher than the highest temperature during the longest period. In some aspects, the ring may measure a 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 a user's body temperature is outside of its normal range (e.g.,significantly above or below 0.0), the contributor to the body temperature may be highlighted (e.g., enter a "pay attention" state) or otherwise generate an alert for the user.

[0083] In some aspects, the system 200 may support techniques for determining blood pressure based on relative timing of pulses (e.g., heartbeat pulses). To provide some context for the desire to determine blood pressure, in some examples, one or more users (e.g., individuals) 102 may measure blood pressure regularly in a clinical setting, and therefore, users 102 may measure blood pressure infrequently (e.g., once a year, twice a year). Furthermore, users 102 may measure not only blood pressure, but also health measurements related to heart rate, heart rate variability, cardiovascular age, arterial stiffness, AFib, ectopic beats, orthostatic testing, VO2 max and the like. As such, users 102 may unknowingly avoid taking preventative measures based on whether the health measurements indicate positive or negative measurements. That is, if users 102 were aware of one or more health measurements, users 102 may incorporate healthy lifestyle choices that include improving diet (e.g., eating fruits and vegetables), physical activity, sleep, stress management, or the like. However, as discussed, users 102 rarely visit primary care settings where one or more physicians may measure users' cardiovascular health (e.g., performing an electrocardiogram (ECG) test, measuring consecutive heartbeat pulses), and therefore users 102 may be unaware of risk factors, such as an increased risk of heart attack, stroke, heart failure, and other complications.

[0084] In particular, users 102 affected by cardiovascular health issues may implement lifestyle choices (e.g., modifications) to improve overall cardiovascular health over the long term. For example, users 102 monitoring cardiovascular health may be concerned about risk factors of heart disease and / or stroke and may refrain from an unhealthy diet (e.g., high salt intake), a lack of physical inactivity, tobacco use, alcohol, or the like. User 102 may refrain from these actions to avoid an increase in blood pressure, an increase in blood glucose, or an increase in blood lipids, which may cause user 102 to become overweight and / or obese. In some examples, users 102 may be concerned about one or more cardiovascular health measurements, such as cardiovascular age, otherwise known as heart age and vascular age.Specifically, heart age is an assessment of known risk factors for heart disease (e.g., age, gender, blood pressure, cholesterol) to estimate a user's 102 risk of a heart attack or stroke compared to a defined healthy range. In some cases, if heart age exceeds a user's 102 current age, the user 102 may be at risk of developing heart disease.

[0085] In other examples, users 102 may be concerned about vascular age, where a vascular aging test provides a measurement of the apparent age of the arteries of user 102 compared to healthy users 102. In some cases, user 102 may indicate a vascular age that exceeds user 102's chronological age, and user 102 may be at risk of developing cardiovascular disease (CVD). Thus, concerned users 102 may implement lifestyle changes such as increasing aerobic exercise, reducing calories, reducing sodium, including flavonoids in the diet, and other healthy dietary patterns to reduce arterial stiffness and blood pressure to reduce vascular aging. In some aspects, the tests that generate cardiovascular aging measurements may compare the data of one user 102 to multiple users 102.That is, the tests can compare the pulse waveform of the user 102 with typical pulse waveforms across different age groups.

[0086] In some aspects, cardiovascular health measurements may use blood pressure measurements to accurately predict the health and well-being of user 102. That is, blood pressure may indicate binary information including a systolic blood pressure measured by one or more arteries when user 102's heart is beating and a diastolic blood pressure measured by one or more arteries when user 102 is between heartbeats. In some examples, a classification of blood pressure may include either normal blood pressure or high blood pressure. For user 102, normal blood pressure may indicate a systolic blood pressure of less than 130 millimeters of mercury (mmHg) and a diastolic blood pressure of less than 80 mmHg. Alternatively, high blood pressure may indicate a systolic blood pressure of greater than 130 mmHg and a diastolic blood pressure of greater than 80 mmHg.

[0087] In some aspects, blood pressure measurements may indicate the pressure of circulating blood against the walls of blood vessels. In some cases, blood pressure may result from the user's 102 heart pumping blood through the circulatory system. That is, the heart pumps blood in the form of pulses (e.g., beats), with each pulse having a morphology (e.g., morphological features describing the size / shape of pulses). Further, each pulse may indicate a different morphology (e.g., size and shape) corresponding to blood pressure (e.g., high or low). For example, pulses acquired in different methods, such as PPG pulses and arterial blood pressure (ABP) pulses, may indicate different systolic peaks, diastolic peaks, dicrotic notches, pulse widths, pulse slopes, inflection points, and the like.

[0088] In some examples, a comparison of pulses may illustrate differences resulting from PPG pulses sensing signals noninvasively (e.g., a finger-clamp device) and ABP pulses sensing signals invasively (e.g., via a needle inserted directly into a user's vein). Further, the PPG pulses and ABP pulses may be compared over time on a graph to determine an in-phase analysis. That is, a morphology correlation (e.g., r) may be determined between the PPG and ABP waveforms to accurately determine whether the user falls into a particular blood pressure category, such as normotensive (e.g., normal blood pressure), prehypertensive (e.g., at risk for high blood pressure), or hypertensive (e.g., high blood pressure).Thus, for a blood pressure appropriate measuring system, the system may use multiple pulses to correctly indicate appropriate blood pressure categories of users 102 and determine whether users 102 are at risk for certain blood pressure conditions or diseases.

[0089] In some examples, pulses may be monitored via a random check, where a binary classification may indicate whether the pulse is normal or high. In other cases, pulses may be evaluated against a blood pressure trend (e.g., a blood pressure trend line). That is, the pulses may be compared to typical blood pressure trends for the user expressed at different times of the day. In some cases, pulses may be collected over a period of time (e.g., weekly, monthly, annually). Thus, blood pressure over a period of time may compare to the user's blood pressure, which may include the sum of nighttime, continuous blood pressure. In such cases, constant monitoring (e.g., chronic exposure) of blood pressure may be a key indicator (e.g., determinant) of cardiovascular risk.That is, the area under the blood pressure curve, in other words the cumulative cardiovascular risk, can be calculated by time multiplied by continuous blood pressure (e.g., area under the curve = time * continuous blood pressure). In some examples, pulses can be compared to typical nighttime blood pressure trends to determine whether a user has nocturnal hypertension based on a dip in pulses. For the user, a normal nighttime blood pressure trend may indicate a blood pressure drop of approximately 10% to 15% less than typical daytime pulses. Detecting blood pressure changes (e.g.,However, blood pressure changes (based on absolute values ​​and additional values) of more than 15% may indicate a warning that the user has elevated sodium levels, salt sensitivity, chronic kidney disease (CKD), congestive heart failure (CHF), diabetes, structural vascular disease, insomnia, or the like. Furthermore, the US Food and Drug Administration (FDA) allows pulse monitoring via spot checks or blood pressure trends to detect users' blood pressure, and therefore, technologies that support these methods would be beneficial to integrate with users outside of a hospital setting.

[0090] In some aspects, one or more caregivers (e.g., nurses, physicians) may use a blood pressure device (e.g., a sphygmomanometer, a blood pressure cuff, a blood pressure monitor) that monitors one or more pulses and determines blood pressure measurement metrics for each user 102. In some examples, the user 102 may use a home blood pressure device to collect blood pressure measurement metrics outside of a clinical setting. However, the blood pressure device may use one or more arm cuffs, which may be uncomfortable for the user to wear consistently. That is, one or more solutions that conveniently measure blood pressure on a daily basis may enable the user 102 to monitor health issues.However, conventional wearable devices 104 have been unable to perform blood pressure measurements due to signal processing limitations 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 may be desirable but still impractical.

[0091] Accordingly, aspects of system 200 may support techniques for determining blood pressure based on morphological features of heartbeat pulses of user 102. As described herein, system 200 may utilize a wearable device 104 (e.g., a wearable wristband device, a wristwatch, a necklace, a wearable device worn on the chest, an extremity monitor) to determine one or more blood pressure measurement metrics (e.g., measurements) of user 102. Further, 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 to receive light from the 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 transmit light associated with one or more wavelengths. That is, system 200 may enable wearable device 104, worn by users 102 on a consistent basis, to determine blood pressure based on morphological characteristics of pulses (e.g., heartbeat pulses) of user 102.

[0092] In some implementations, the system 200 may determine blood pressure measurement metrics for the 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 photodetectors near the surface of the skin to measure the volumetric variations in blood flow of the user 102. In some examples, a triple-LED (e.g., red, green, and IR) PPG system 235 may enable the wearable devices 104 to propagate multiple light waves into the tissue of the user 102 based on the specified wavelength of light to collect physiological data. In some implementations, the PPG system 235 may include one or more light-emitting components (e.g., LEDs) that transmit light associated with one or more wavelengths. In some examples, physiological data may include PPG data, acceleration data, pressure data, and the like.That is, the PPG system 235 may enable the system 200 to use various components to collect PPG data.

[0093] Additionally or alternatively, physiological data collected by wearable device 104 may include acceleration data (e.g., motion / exercise data) associated with user 102. In some cases, wearable devices 104 may collect acceleration data from the one or more motion sensors 245. For example, wearable device 104 may use motion sensors 245 to determine the heart rate of user 102 when user 102 is in motion (e.g., exercising). That is, system 200 may use acceleration data to indicate how motion affects blood flow and subsequently blood pressure.

[0094] In some aspects, the wearable device 104 may use the one or more light-emitting components to transmit a first light associated with the first wavelength and a second light associated with the second wavelength during a time interval that includes a heartbeat of the user 102. The system 200 may acquire PPG data from the user 102 based on receiving the first light and the second light via the one or more photodetectors. Further, 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, the respective PPG waveforms may include or illustrate one or more user heartbeat pulses exhibiting different morphological features based on different projected wavelengths (e.g., a first wavelength, a second wavelength, and the like).

[0095] As described herein, the system 200 may acquire PPG data including the first PPG waveform acquired using the first light and the second PPG waveform acquired using the second light. That is, each of the PPG waveforms / signals acquired using a plurality of different wavelengths may represent different 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, green PPG waveforms / signals, and the like. The system 200 may acquire a plurality of raw PPG waveforms / signals received from the PPG data and determine morphological features that exist within or between the raw PPG waveforms of the different wavelengths and one or more derivatives (e.g.,a first derivative, a second derivative, and the like) of the raw PPG waveforms. That is, the morphological features may be indicative of one or more blood pressure measurement metrics of the user 102.

[0096] Additionally or alternatively, the system 200 may collect data via one or more pressure sensors 246 (e.g., piezoelectric pressure sensors, etc.). In some cases, the physiological data may be collected during a time interval in which a pressure between the wearable device 104 and a tissue of the user 102 is changing. That is, the system 200 may include the wearable device 104 with an optical sensor contacting the skin of the user 102, and the optical sensor may restrict blood circulation at different skin tissue layers when the pressure between the optical sensor (and / or other surfaces of the wearable device 104) and the user's tissue is changing. Thus, the system 200 may determine a correlation between arterial blood pressure and pulse morphology and may consider the external pressure when determining the blood pressure of the user 102.

[0097] In some examples, external pressure may be applied to the optical sensor when the user 102 grasps an object, when the user 102 has swollen extremities (e.g., fingers) due to dehydration, or the like. In some cases, the PPG system 235 may not be able to transmit light through different skin tissue layers if the pressure between the optical sensor and the skin of the user 102 is increased. That is, the system 200 may instruct the user 102 to apply different pressures to observe how different wavelengths / PPG waveforms respond to changing pressures. Thus, the system 200 may determine how PPG data (e.g., PPG waveforms of different wavelengths) changes in response to different pressures between the wearable device 104 and the user's tissue. Accordingly, the system 200 may determine how PPG data (e.g.,PPG waveforms of different wavelengths) in response to varying pressures applied between the wearable device 104 and the user's tissue. In this regard, the system 200 may utilize morphological features associated with respective PPG waveforms when pressure is applied to account for changes in the blood pressure measurement metrics of the user 102. Furthermore, in some aspects, pressure sensors (e.g., piezoelectric sensors) may be used to identify vibrations or other pressure changes attributable to heartbeats, which may be used to determine when heartbeats are detected (e.g., pulse arrival times) and may therefore be used to determine blood pressure measurement metrics for the user.

[0098] In some aspects, the system may be configured to identify other circumstances or characteristics of the wearable device 104 and / or the user 102 that affect signal qualities or characteristics, such as the user's skin temperature, the rotation / fit of the ring (e.g., the tightness or looseness of the ring as determined using pressure sensors or PPG sensors). In such cases, the system may be configured to identify how such characteristics affect morphological features and compensate for such characteristics when determining morphological features from PPG waveforms. That is, PPG signals may be compensated using data from other sources, such as pressure sensors, temperature sensors, and the like.

[0099] In some aspects, the system 200 may determine the first set of morphological features associated with the first PPG waveform based on a first systolic peak value and a first diastolic peak value corresponding to the heartbeat (e.g., HeartbeatPulse) for the first PPG waveform. Further, the system 200 may determine the second set of morphological features associated with the second PPG waveform based on a second systolic peak value and a second diastolic peak value corresponding to the heartbeat for the second PPG waveform. That is, the system 200 may compare the first set of morphological features and the second set of morphological features to determine one or more blood pressure measurement metrics of the user 102.

[0100] For example, the system 200 may determine correlation coefficients between each of the PPG waveforms of different wavelengths, delays between systolic and diastolic peaks of a second derivative of the PPG waveforms of different wavelengths, and differences in systolic peak times between PPG waveforms of different wavelengths. That is, the blood pressure of the user 102 may be determined (e.g., estimated) using the morphological features indicated by multiple PPG pulses. As such, the system 200 may use the wearable device 104 placed at relative locations on the user 102 to acquire PPG data using the PPG system 335, to determine morphological features from the PPG waveforms, and to determine one or more blood pressure measurement metrics of the user 102 based on a comparison of morphological features.Further, the system 200 may determine blood pressure measurement metrics of the user 102 and may use acceleration data from the one or more motion sensors 245 to selectively adjust the blood pressure measurement metrics of the user 102.

[0101] The analysis of various morphological features of PPG waveforms that can be used to determine blood pressure measurement metrics is presented with reference to Fig. 4 further shown and described.

[0102] In some aspects, system 200 may transmit physiological data, such as PPG data, acceleration data, pressure data, and the like, collected from various physiological locations and / or penetration depths, from one or more wearable devices 104 to one or more user devices 106. User device 106 (and / or other components of system 200, such as server 110) may then determine blood pressure measurement metrics of user 102 based on the received physiological data. In some aspects, user device 106 may store blood pressure trends for user 102 in database 265. That is, the blood pressure trends may be indicative of normal (e.g., typical) blood pressure levels of user 102 and may be indicative of a baseline blood pressure measurement metric associated with user 102.Further, the user device 106 may compare the current blood pressure measurement metric to the baseline blood pressure measurement metric for differences (e.g., deviations). In some cases, the user 106 may determine notable differences between the baseline blood pressure measurement metric of the user 102 and the current blood pressure measurement metric of the user 102 and may use a GUI 275 of the user device 106 to display information associated with the differences. Thus, the GUI 275 may alert (e.g., notify) the user about current blood pressure measurement metrics of the user 102 compared to typical blood pressure trends of the user 102 and / or additional instructions the user 102 needs to follow to calibrate blood pressure measurement metrics.

[0103] Fig. 3 illustrates an example of a wearable device system 300 that supports techniques for determining blood pressure based on morphological features of pulses according to aspects of the present disclosure. In some implementations, the wearable device system 300 may implement or be implemented by aspects of the system 100 and the system 200, as described with reference to Fig. 1 and Fig. 2 described.

[0104] 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 different orders 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 a system 300. Accordingly, the term "a wearable device 104-b" may be used interchangeably with "a wearable wristband device" unless otherwise noted herein.

[0105] In the example of Fig. 3, the system 300 may utilize one or more wearable devices 104 (e.g., wearable device 104-b) to determine a blood pressure measurement metric for the user 102 based on morphological characteristics of PPG data acquired using the respective wearable devices. For example, the wearable device 104-b may utilize PPG techniques that include a triple-LED (e.g., red, green, and IR) system, allowing the wearable device 104-b to propagate multiple light waves into different tissue layers 320 of the user 102 based on the wavelength of light. That is, the penetration depth (e.g., wavelength range) of light into the skin of the user 102 increases with wavelength from the UV to the visible light range and through the IR range.

[0106] In system 300, a light-emitting component 310 (e.g., LED) and a photodetector 315 may be coupled to a controller to transmit light to locations 325 associated with one or more wavelengths and to acquire measurements. Light-emitting component 310 may be coupled to a controller to transmit light to locations 325 associated with one or more wavelengths. In this example, light-emitting component 310 may transmit first light associated with a range of wavelengths (e.g., IR light, red light, etc.) that penetrates to a depth or location 325-a at epidermal layer 320-a, where the first light travels back to photodetector 315 with acquired PPG data. Additionally, light-emitting component 310 may transmit second light associated with a range of wavelengths (e.g., red light, green light, blue light, etc.) that penetrates to a depth or location 325-b at the hypodermal layer 320-c, where the second light travels back to the photodetector 315 with additional acquired PPG data.

[0107] In other words, the wearable device 104-b can use light of different wavelength ranges to acquire PPG data at different locations 325 or penetration depths of the user's tissue. That is, the system 300 can 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, where each of the transmitted light waves can reach one or more tissue layers 320 (e.g., an epidermal layer 320-a at about 0.3 millimeters (mm), a dermal layer 320-b at about 1.0 mm, a hypodermal layer 320-c at about 3.0 mm).That is, each of the transmitted lights can reach the one or more tissue layers where the blood vessels are located, such as the capillaries closest to the user's skin 102 at the epidermis, the arterioles located at the middle layer at the dermis, and the arteries located at the deepest layer at the hypodermis. Thus, the system 300 can utilize 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 wavelength ranges to one or more tissue layers 320 at multiple locations 325 to acquire PPG data.

[0108] Further, the single wearable device 104-b may transmit the first and second lights from the light-emitting component 310 through one or more fabric layers 320 and back to the photodetector 315 to acquire PPG data during a time interval, where the time interval may include a heartbeat of the user 102. In some examples, the wearable device 104-b may acquire PPG data based on the first light and the second light via the photodetector 315. The PPG data may include 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.

[0109] Furthermore, each of the PPG waveforms, such as the first PPG waveform and the second PPG waveform, may represent different morphological features. That is, the wearable device 104-b may determine a first set of morphological features associated with the first PPG waveform based on a first systolic peak and a first diastolic peak corresponding to the heartbeat within the first PPG waveform. Further, the wearable device 104-b may determine a second set of morphological features associated with the second PPG waveform based on a second systolic peak and a second diastolic peak corresponding to the heartbeat within the second PPG waveform. Accordingly, the wearable device 104-c may acquire PPG data from one or more wavelengths corresponding to different tissue penetration depths (e.g.,the epidermal layer 320-a, the dermal layer 320-b, at the hypodermal layer 320-c). Thus, the system 300 may use a single wearable device 104-b to transmit respective lights associated with respective wavelengths, acquire PPG data from the user 102, determine respective sets of morphological features associated with each of the PPG waveforms, and determine one or more blood pressure measurement metrics for the user 102 based on the comparison of the respective sets of morphological features.

[0110] In some cases, light emitted by light-emitting components 310 of the portable device 104-b (e.g., LEDs) may be measured by multiple photodetectors 315. In some cases, the light-emitting components 310 and photodetectors 315 may be positioned at different radial positions on an inner peripheral surface of the portable device 104-b. For example, in some cases, multiple light-emitting components 310 and multiple PDs may be arranged in an interleaved pattern (e.g., LED, PD, LED, PD) at regular or irregular intervals between the respective components around the inner peripheral surface of the portable device 104-b. In other cases, multiple photodetectors 315 may be positioned adjacent to one another at the same or similar radial position of the portable device 104-b.

[0111] In some cases, measuring light with multiple photodetectors 315 (e.g., multiple photodetectors 315 that are the same distance from a common light-emitting component 310) may enable the portable device 104-b to determine phase differences in light signals received at the respective photodetectors 315. Such parallel measurement of light by multiple photodetectors 315 may enable more robust and reliable PPG data collection. For example, in some cases, phase differences between light measured at multiple PDs 315 may be used to determine a velocity of blood moving through the blood vessels, which may be used to further determine or estimate blood pressure.

[0112] Various morphological features of PPG waveforms that can be used to determine blood pressure measurement metrics can be described with reference to Fig. 4 will be further shown and described.

[0113] Fig. 4 illustrates an example of pulse morphology graphs 400 that support techniques for determining blood pressure based on morphological features of pulses according to aspects of the present disclosure. In some implementations, pulse morphology graphs 400 may implement or be implemented by aspects of system 100, system 200, and system 300, as described with reference to Fig. 1- Fig. 3. For example, pulse morphology graphs 400 may illustrate PPG data acquired from a user via 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 pulse morphology graphs 400, the operations may be performed in a different order than the exemplary order shown, or the operations may be performed in different orders or at different times. Some operations may also be omitted from pulse morphology graphs 400, and other operations may be added to pulse morphology graphs 400.

[0114] In the example of Fig. 4, the pulse morphology graphs 400 may display multiple graphs 405. That is, each of the graphs 405 may illustrate varying signal strengths over time and may display different morphological features (e.g., pulse size, pulse shape). Raw PPG waveforms 410 (e.g., raw PPG signals) may be depicted in graph 405-a. For example, the raw PPG waveforms 410 may depict 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 vary in signal strength over time and may display different morphological features (e.g., peaks, valleys). Further, 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 lights associated with wavelengths during a time interval corresponding to a user's heartbeat. That is, the wearable device may receive the transmitted light and collect PPG data from the user in the form of raw PPG waveforms 410.

[0115] In some examples, the raw PPG waveforms 410 may show PPG data acquired at different penetration depths due to different wavelengths of light used, as described herein. Further, each of the raw PPG waveforms 410 may represent measurements of one or more lights associated with wavelength ranges to one or more tissue layers at multiple locations. Thus, the graph 405-a may represent signal strengths at which the raw IR PPG signal 425-a exceeds the raw red PPG signal 425-b and the raw green PPG signal 425-c during the time interval.

[0116] In some implementations, first derivatives 415 may be calculated for each of the raw PPG waveforms 410. That is, graph 405-b may represent slope 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 derivatives 415 are derived from the raw PPG waveforms 410, properties / characteristics of the first derivatives 415 may be considered morphological features of the respective raw PPG waveforms 410.

[0117] Each of the raw PPG waveforms 410 may be filtered (e.g., a bandpass filtered PPG signal), and respective first derivatives 415 (e.g., slope signals 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 a velocity PPG (VPG), where one or more prominent positive peaks found in the first derivatives 415 indicate times at which the raw PPG waveforms 410 are growing at a highest rate. Therefore, the VPG shows the velocity of the raw PPG waveforms 410 and may represent the systolic slope and diastolic slope of the PPG pulse waveforms.

[0118] In some aspects, second derivatives 420 may be calculated for each of the raw PPG waveforms 410 (e.g., raw PPG signals 425). That is, graph 405-c may represent second derivatives (e.g., curvature signals 435-a, 435-b, and 435-c) of the raw PPG signals 435-a, 435-b, and 435-c, respectively. In this regard, since the second derivatives 420 are derived from the raw PPG waveforms 410, properties / characteristics of the second derivatives 420 may be considered morphological features of the respective raw PPG waveforms 410.

[0119] Each of the raw PPG waveforms 410 may be additionally filtered, and respective second derivatives 420 may be calculated for each of the raw PPG waveforms 410 for display in graph 405-c. In some cases, the second derivatives 420 (e.g., curvature signals 435) may be identified as acceleration PPG (APG), identifying multiple peaks for the blood pressure measurement. In Fig. 4, second derivatives 420 may illustrate five peak values, including some up-peak values ​​and down-peak values ​​present for measured PPG pulses. That is, one or more APG peak time points (e.g., a number of samples after a pulse begins), one or more ratios, and one or more calculated features may be used as inputs to a classifier that maps the feature values ​​into blood pressure measurement metrics. Thus, identifying peak values ​​for second derivatives 420 may enable the system to determine accurate blood pressure measurement metrics for the user.

[0120] In some aspects, one or more morphological features of the raw PPG waveforms 410 may be identified from the graphs 405-a, 405-b, and 405-c. That is, the graph 405-a may illustrate the raw PPG waveforms 410, which may be identified as one or more PPG waveforms. In some implementations, a system may determine a correlation coefficient between the raw IR PPG signal 425-a and the raw red PPG signal 425-b. However, in other examples, the system may determine correlation coefficients between different PPG signals having different wavelength ranges and is not limited to just 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 may compare morphological features of the raw PPG waveforms 410 displayed on the graph 405-a, such as one or more systolic peak values ​​440 and diastolic peak values ​​445, to determine blood pressure measurement metrics. Thus, the raw PPG waveforms 410 representing multiple PPG waveforms may include respective sets of morphological features that the system may use to determine accurate blood pressure measurement metrics of the user.

[0121] In some examples, graph 405-a may illustrate raw PPG waveforms 410 indicating times of systolic peak values ​​440 for systolic blood pressure. That is, graph 405-a may illustrate one or more delays of the systolic peak values ​​440 of the raw PPG waveforms 410. In particular, graph 405-a may depict one or more delays between a first wavelength including a first wavelength range associated with raw IR PPG signal 425-a and a second wavelength including a second wavelength range associated with raw green PPG signal 425-c. Further, the system may determine times based on the systolic peak values ​​440 of raw PPG waveforms 410. For example, the system may determine a first time point of the systolic peak 440 of the raw IR PPG signal 425-a (e.g.,the first PPG waveform) and a second time point of the systolic peak value 440 of the raw green PPG signal 425-c (e.g., the second PPG waveform), wherein the first set of morphological features and the second set of morphological features include the first time point and the second time point. The system may determine a delay between the systolic peak value 440 of the raw IR PPG signal 425-a and the systolic peak value 440 of the raw green PPG signal 425-c based on the first time point and the second time point. Thus, the system may use the delay to determine blood pressure measurement metrics of the user.

[0122] Additionally or alternatively, additional morphological features may be identified from the graph 405-a. That is, the graph 405-a may represent the raw PPG waveforms 410 calculated from predicting blood pressure measurement metrics from individual pulses. That is, the raw PPG waveforms 410 may represent systolic, diastolic, and mean average blood pressures. Accordingly, the system may identify a morphological feature, such as a ratio of the area under each of the PPG waveforms 410 (e.g., features = area under the curve for the raw IR PPG signal 425-a (AUC_IR) / area under the curve for the raw green PPG signal 425-c (AUC_GRE)), that compares areas under one or more curves from different phases of PPG pulses. Thus, the system may use this morphological feature of calculating the area under one or more curves of the graph 405-a to determine blood pressure measurement metrics of the user.

[0123] In some aspects, additional morphological features may be identified from graph 405-b. That is, graph 405-b may represent first derivatives 415 (e.g., slope signals 430) calculated for each of the raw PPG waveforms 410 illustrated in graph 405-a. That is, first derivatives 415 may be calculated from predicting blood pressure measurement metrics from individual pulses. That is, first derivatives 415 may represent systolic blood pressures. Accordingly, the system may identify a morphological feature, such as a ratio of maximum slopes between the first derivatives 415 of the PPG waveforms 410. In other words, a morphological feature that may be used to determine blood pressure may include a ratio of the maximum slopes of the raw PPG signals 425 (and / or a ratio of the peak values ​​of the slope signals 430) (e.g.,Feature = maximum slope for the red PPG signal 425-b, represented as the peak value of the red slope signal 430-b (MS_RED) / maximum slope for the raw green PPG signal 425-c, represented as the peak value of the green slope signal 430-c (MS_GRE)). Thus, the system can use morphological features including the maximum slopes of the raw PPG signals 425 in graph 405-a (e.g., peak values ​​of the slope signals 430 in graph 405-b) to determine user blood pressure measurement metrics.

[0124] In some implementations, one or more morphological features may be identified from graph 405-c. That is, graph 405-c may illustrate the second derivatives 420 (e.g., curvature signals 435) of the raw PPG waveforms 410 illustrated in graph 405-a, such as second derivatives of the raw PPG waveforms 410. In other examples, graph 405-c may depict second derivatives 420 representing diastolic pressure and mean average blood pressures. In other examples, graph 405-c may depict second derivatives 420 representing diastolic pressure and mean blood pressure values. In some aspects, a system having a controller coupling one or more light-emitting components to one or more photodetectors may determine curvature signals 435-a, 435-b, and 435-c of the raw PPG signals 425-a, 425-b, and 425-c, respectively (e.g., the second derivatives of the raw PPG waveforms 410).

[0125] That is, the system may determine peak values ​​of the curvature signals 435 corresponding to the systolic peak values ​​440 of the raw PPG signals 425, wherein morphological features of the respective PPG waveforms 410 include the times of the peak values ​​of the respective curvature signals 435. In particular, the system may determine relative delays between the times of the peak values ​​of the curvature signals 435 and may use the relative times of the peak values ​​(e.g., delay between peak values ​​of the curvature signals 435) as a morphological feature used to determine blood pressure measurement metrics.

[0126] As previously described herein, aspects of the present disclosure may use PPG data to determine morphological features of PPG waveforms, and may thereby determine the morphological features of the PPG waveforms (e.g., morphological features depicted in the graphs 405 of Fig. 4) to determine blood pressure measurement metrics for a user. In some implementations, a wearable device 104 may collect PPG data in the form of one or more sets of PPG pulses (as shown in raw PPG signals 425) to measure certain physiological parameters of the user. In some implementations, the wearable device 104 may collect PPG data in the form of one or more sets of PPG pulses (as shown in raw PPG signals 425) to measure certain physiological parameters of the user. However, not all PPG pulses may exhibit the same morphological features or characteristics. In other words, PPG pulses may exhibit varying shapes and characteristics. That is, morphological features of PPG pulses (e.g.,PPG pulse amplitude, duration, slope, curvature, relationships between peaks) may vary from one PPG pulse to the next, and some PPG pulses may inaccurately represent a physiological measurement. Additionally or alternatively, factors such as light, pressure, user posture (e.g., the user is sitting or standing), or user hydration (e.g., the user may have swollen fingers due to lack of hydration) may affect the accuracy of PPG data. In particular, a system that uses inaccurate PPG pulses or fails to account for additional factors that affect PPG data may result in unreliable physiological measurements. That is, multiple systems may benefit from one or more techniques for identifying PPG pulses that accurately represent the physiological metrics of one or more users.

[0127] Accordingly, in some implementations, the systems of the present disclosure may be configured to identify one or more "representative" (e.g., common, average) PPG pulses that accurately represent the user's physiological metrics, and may use identified representative PPG pulses from PPG waveforms to identify morphological features and / or blood pressure measurements. That is, techniques described herein may be used to identify PPG pulses that are of high quality and accurately reflect the user's physiological metrics to determine blood pressure measurements.

[0128] To identify the one or more PPG pulses that accurately represent the user's physiological metrics (e.g., to identify PPG pulses used to determine pulse observation times and / or blood pressure measurement metrics), a wearable device 104 may collect PPG data including a first set of PPG pulses from the user. In some aspects, the system may compare multiple morphological features from the first set of PPG pulses for each particular physiological measurement. Further, the system may determine one or more PPG profiles (e.g., one or more representative PPG pulses, one or more common pulse templates) for each particular physiological metric based on the comparison of the multiple morphological features of the first set of PPG pulses.That is, each of the one or more PPG profiles may include a set of multiple morphological value ranges for the multiple morphological features. In some examples, each of the PPG profiles may represent a representative (e.g., common, average) pulse calculated from the first set of PPG pulses for each particular physiological measurement.

[0129] Additionally, the system may collect additional PPG data from the user via the portable device 104. In some cases, the system may collect the additional PPG data from the user as a second set of PPG pulses. In some implementations, the system may determine that one or more PPG pulses from the second set of PPG pulses match the one or more PPG profiles from the first set of PPG pulses. That is, the system may detect that multiple morphological feature values ​​of the second set of PPG pulses satisfy the multiple morphological value ranges of the one or more PPG profiles. In other words, the system may identify which PPG pulses of the second set of PPG pulses "match" the PPG profiles.

[0130] The system may then determine one or more physiological metrics associated with the user based on the one or more PPG pulses from the second set of PPG pulses that match the one or more PPG profiles from the first set of PPG pulses. In other words, the system may use the PPG pulses that "match" the PPG profiles (e.g., the system may use "representative" PPG pulses) to perform physiological measurements for the user. For example, the system / wearable device 104 may be configured to use PPG pulses within PPG waveforms that match the PPG profiles to determine morphological features and, therefore, blood pressure measurements.

[0131] Conversely, the system may detect that the one or more PPG pulses from the second set of PPG pulses do not match the one or more PPG profiles from the second set of PPG pulses and may refrain from using that particular physiological metric associated with the user or otherwise consider that information. In other words, the system / wearable device may not use PPG pulses within PPG waveforms that do not match PPG profiles to determine morphological features and / or blood pressure measurements.

[0132] In some aspects, the wearable device may identify the one or more representative PPG pulses for each user using the existing hardware features of the wearable device. In some examples, the system may define the one or more PPG pulse profiles (e.g., one or more PPG templates) that represent common PPG pulses of the user. That is, the system may acquire one or more PPG pulses and may compare each of the PPG pulses to each other to determine the one or more PPG pulse profiles. In such cases, the system may determine the one or more PPG profiles by identifying common (e.g., average) values ​​(e.g., average length, amplitude, slope, or the like) of the plurality of PPG pulses.For example, the system can define one or more PPG pulse profiles based on ordinary PPG pulses acquired by the user via a time-of-day calibration sequence. That is, the calibration sequence can be initiated to define valid samples to determine which of the PPG pulses are suitable (e.g., reliable) for performing physiological measurements. In some cases, the system can use a changing correlation between different signal pathways to find an optimal measurement time for the PPG pulses.

[0133] In some implementations, the system may consider the user's posture estimation and may determine different sets of PPG profiles based on different user postures. For example, the system may detect the user's posture (e.g., the user may be standing, sitting, lying down, or the like), which may affect the signal quality metrics of the PPG pulses. Thus, the system may use the PPG pulse profiles, the calibration sequence, and additional factors to select accurate PPG pulses with appropriate signal quality metrics that represent the user's physiological metrics (e.g., first set of PPG profiles when the user is standing, and second set of PPG profiles when the user is sitting).

[0134] Fig. 5 illustrates an example of a GUI 500 that supports techniques for determining blood pressure based on morphological features of pulses according to aspects of the present disclosure. The GUI 500 may implement or be implemented by aspects of the system 100, the system 200, the system 300, and the pulse morphology graph 400, or any combination thereof. For example, the GUI 500 may be implemented for a user at a user device associated with a wearable device (e.g., a wearable wristband device, a wristwatch, a necklace, or any other wearable device), and may be examples of a user 102, a user device 106, and a wearable device 104, as described with reference to FIG. Fig. 1-4 described.

[0135] The GUI 500 illustrates a series of application pages, including an application page 505-a, an application page 505-b, and an application page 505-c, that may be displayed to a user via the GUI 500 (e.g., a user 102 and a GUI 275, as described with reference to the Fig. 1-5). In some examples, the user may open application page 505-a to view scores associated with the user. For example, application page 505-a may display a sleep score, a readiness score, and the like. In some examples, application page 505-a may illustrate a blood pressure alert 510 that displays alerts for 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 a blood pressure history 515 of the user. Additionally or alternatively, the user may determine that blood pressure measurements for the user need to be recalibrated. That is, the user may have experienced a change that has affected blood pressure measurement metrics. For example, the user may have traveled to a higher altitude and is experiencing swollen extremities (e.g., fingers) due to dehydration.Further, the user may select a box to calibrate blood pressure 520 to enable the wearable device to capture accurate blood pressure measurement metrics.

[0136] In some examples, the user may select the blood pressure history feature 515, and the application page 505-b may appear on the user device. The application page 505-b may show a blood pressure 525 taken on the current day and at a specific (e.g., current) time. In some examples, the blood pressure 525 may represent multiple measurements, such as a systolic blood pressure 530, which is measured by one or more arteries when the user's heart is beating, and a diastolic blood pressure 535, which is measured by one or more arteries when the user's heart is between heartbeats. In some examples, a classification of blood pressure may include either normal blood pressure or high blood pressure. For the user, normal blood pressure may indicate a systolic blood pressure of less than 130 mmHg and a diastolic blood pressure of less than 80 mmHg.Alternatively, high blood pressure may indicate a systolic blood pressure greater than 130 mmHg and a diastolic blood pressure greater than 80 mmHg. Additionally, a pulse 540 may be displayed and represents a measurement of heart rate (e.g., the number of times the user's heart beats per minute). Furthermore, the pulse 540 may be calculated by subtracting the diastolic blood pressure 535 from the systolic blood pressure 525.

[0137] Additionally or alternatively, the application page 505-b may illustrate a blood pressure history 545 of the user for a particular term. In the example of Fig. 5, the blood pressure trend 545 represents a nighttime term for a particular date with two trend lines. That is, the blood pressure trend 545 may illustrate a baseline (e.g., normal, typical, trend) blood pressure measurement metric 550-a associated with the user and a blood pressure measurement metric 550-b for the nighttime term on the particular date. That is, the application page 505-b of the user device may graphically illustrate comparisons between the blood pressure measurement metric 550-a representing typical blood pressure trends for a typical nighttime term of the user and the blood pressure measurement metric 550-b representing the blood pressure trends for the user's most recent nighttime term. That is, the GUI 500 may display and / or indicate information associated with the difference between the baseline blood pressure measurement metric 550-a and the blood pressure measurement metric 550-b (e.g., your blood pressure is ±X compared to your average blood pressure).

[0138] In some examples, the GUI 500 may share information that may indicate whether the user has nocturnal hypertension based on a dip in pulses. For the user, a normal nighttime blood pressure trend may indicate a blood pressure drop of approximately 10% to 15% less than typical daytime pulses. However, detecting blood pressure changes (e.g., blood pressure changes based on absolute values ​​and additional values) of greater than 15% may indicate a warning that the user has elevated sodium levels, salt sensitivity, CKD, CHF, diabetes, structural vascular disease, insomnia, or the like. That is, the user may view the blood pressure history 545 over time to determine whether the blood pressure measurement metrics 550 may indicate potential cardiovascular health risks.

[0139] In some examples, the user may select the blood pressure calibration feature 520, and the application page 505-c may appear on the user device. In such examples, the user may select the blood pressure calibration feature 520 and may undergo a blood pressure test 555. That is, the application page 505-c may indicate that the user may take the blood pressure test 555 and follow a set of instructions. For example, an instruction field 560-a may indicate that the user applies an initial pressure to the wearable device during a time interval (e.g., presses against the wearable device, applies strong shocks to apply pressure to a wearable ring), rests during a time interval (e.g.,paused) and does not apply a respective pressure to the ring to reduce the pressure to a normative state, and during the time interval, again applies a second pressure to the wearable device for a different specified time. Furthermore, the blood pressure test 555 may display one or more interval fields in which . Fig. 5, a time interval field 560-b allows the user to start the time interval period for applying respective pressures to the wearable device. That is, a timer countdown field 560-c may specify time in seconds for the user to apply the first pressure and when to apply the second pressure, or a period in seconds for the user to rest between time intervals. In some cases, the user may refrain from applying additional pressure to the wearable device, but may select the blood pressure calibration feature 520 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 pressures and may generate one or more blood pressure trends that accurately represent the user's blood pressures.Furthermore, the user device may display the blood pressure test 555, which allows the portable device to monitor pulse observation times of different pulses at variable tissue penetration depths and / or locations.

[0140] Fig. 6 illustrates a block diagram 600 of a device 605 that supports techniques for determining blood pressure based on morphological features of pulses according to aspects of the present disclosure. The device 605 may include an input module 610, an output module 615, and a portable device manager 620. The device 605 may also include a processor. Each of these components may be in communication with each other (e.g., via one or more buses).

[0141] For example, the portable device manager 620 may include a light-emitting component 625, a photodetector component 630, a control component 635, a wavelength component 640, a PPG data acquisition manager 645, a first morphology component 650, a second morphology component 655, a blood pressure component 660, or any combination thereof. In some examples, the portable device manager 620 or various components thereof may be configured to perform various operations (e.g., receiving, monitoring, transmitting) using or otherwise in cooperation with the input module 610, the output module 615, or both.For example, the portable device manager 620 may receive information from the input module 610, transmit information to the output module 615, or be integrated in combination with the input module 610, the output module 615, or both to receive information, transmit information, or perform various other operations as described herein.

[0142] The light-emitting component 625 may be configured as a means for, or otherwise support, one or more light-emitting components configured to emit light associated with at least a first wavelength and a second wavelength. The photodetector component 630 may be configured as a means for, or otherwise support, one or more photodetectors configured to receive light emitted by the one or more light-emitting components.The control component 635 may be configured as or otherwise support means for a controller communicatively coupled to the one or more light-emitting components and the one or more photodetectors, the controller configured to: The wavelength component 640 may be configured as or otherwise support means for transmitting, during a time interval including a heartbeat of a user and using the one or more light-emitting components, first light associated with the first wavelength and second light associated with the second wavelength.The PPG data acquisition manager 645 may be configured or otherwise support means for acquiring PPG data from the user based at least in part on receiving the first light and the second light via the 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. The first morphology component 650 may be configured or otherwise support means for determining a first set of morphological features associated with the first PPG waveform based at least in part on a first systolic peak and a first diastolic peak corresponding to the heartbeat within the first PPG waveform.The second morphology component 655 may be configured or otherwise assist in determining a second set of morphological features associated with the second PPG waveform based at least in part on a second systolic peak and a second diastolic peak corresponding to the heartbeat within the second PPG waveform. The blood pressure component 660 may be configured or otherwise assist in determining a blood pressure measurement metric for the user based at least in part on a comparison of the first set of morphological features and the second set of morphological features.

[0143] The PPG data acquisition manager 645 may be configured or otherwise support means for acquiring PPG data from a user using a portable device, wherein the PPG data is collected during a time interval including a heartbeat of the user, the PPG data comprising 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 morphology component 650 may be configured or otherwise support means for determining a first set of morphological features associated with the first PPG waveform based at least in part on a first systolic peak value and a first diastolic peak value corresponding to the heartbeat within the first PPG waveform.The second morphology component 655 may be configured or otherwise assist in determining a second set of morphological features associated with the second PPG waveform based at least in part on a second systolic peak and a second diastolic peak corresponding to the heartbeat within the second PPG waveform. The blood pressure component 660 may be configured or otherwise assist in determining a blood pressure measurement metric for the user based at least in part on a comparison of the first set of morphological features and the second set of morphological features.

[0144] Fig. 7 illustrates a block diagram 700 of a wearable device manager 720 that supports techniques for determining blood pressure based on morphological features of pulses, according to aspects of the present disclosure. Wearable device manager 720 may be an example of aspects of wearable device manager or wearable device manager 620, or both, as described herein. Wearable device manager 720, or various components thereof, may be an example of means for performing various aspects of techniques for determining blood pressure based on morphological features of pulses, as described herein.For example, the wearable device manager 720 may include a light-emitting component 725, a photodetector component 730, a control 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 time delay component 7100, a time component 7105, a physiological data acquisition manager 7110, a PPG waveform component 7115, or any combination thereof. Each of these components may communicate with each other directly or indirectly (e.g., via one or more buses).

[0145] The light-emitting component 725 may be configured as a means for, or otherwise support, one or more light-emitting components configured to emit light associated with at least a first wavelength and a second wavelength. The photodetector component 730 may be configured as a means for, or otherwise support, one or more photodetectors configured to receive light emitted by the one or more light-emitting components.The control component 735 may be configured as or otherwise support means for a controller communicatively coupled to the one or more light-emitting components and the one or more photodetectors, the controller configured to: The wavelength component 740 may be configured as or otherwise support means for transmitting, during a time interval including a heartbeat of a user and using the one or more light-emitting components, first light associated with the first wavelength and second light associated with the second wavelength.The PPG data acquisition manager 745 may be configured or otherwise support means for acquiring PPG data from the user based at least in part on receiving the first light and the second light via the 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. The first morphology component 750 may be configured or otherwise support means for determining a first set of morphological features associated with the first PPG waveform based at least in part on a first systolic peak and a first diastolic peak corresponding to the heartbeat within the first PPG waveform.The second morphology component 755 may be configured or otherwise assist in determining a second set of morphological features associated with the second PPG waveform based at least in part on a second systolic peak and a second diastolic peak corresponding to the heartbeat within the second PPG waveform. The blood pressure component 760 may be configured or otherwise assist in determining a blood pressure measurement metric for the user based at least in part on a comparison of the first set of morphological features and the second set of morphological features.

[0146] In some examples, determining a baseline blood pressure measurement metric associated with the user. In some examples, determining a difference between the baseline blood pressure measurement metric and the blood pressure measurement metric. In some examples, causing a GUI of a user device to display information associated with the difference between the baseline blood pressure measurement metric and the blood pressure measurement metric.

[0147] In some examples, determining a correlation coefficient between the first PPG waveform and the second PPG waveform based at least in part on the comparison of the first set of morphological features and the second set of morphological features, wherein the blood pressure measurement metric is based at least in part on the correlation coefficient.

[0148] In some examples, the first wavelength comprises a first wavelength range associated with IR light. In some examples, the second wavelength comprises a second wavelength range associated with red light.

[0149] In some examples, determining a first curvature signal associated with a relative curvature of the first PPG waveform and a second curvature signal associated with a relative curvature of the second PPG waveform. In some examples, determining a first peak value of the first curvature signal corresponding to the first systolic peak value of the first PPG waveform, wherein the first set of morphological features includes a first time point of the first peak value. In some examples, determining a second peak value of the second curvature signal corresponding to the second systolic peak value of the second PPG waveform, wherein the second set of morphological features includes a second time point of the second peak value.In some examples, determining a delay between the first time point of the first peak value and the second time point of the second peak value based at least in part on the comparison of the first and second sets of morphological features, wherein the blood pressure measurement metric is based at least in part on the delay.

[0150] In some examples, the first curvature signal and the second curvature signal comprise second derivatives of the first PPG waveform and the second PPG waveform, respectively.

[0151] In some examples, the first wavelength comprises a first wavelength range associated with IR light, red light, or both. In some examples, the second wavelength comprises a second wavelength range associated with green light.

[0152] In some examples, determining a first time point of the first systolic peak of the first PPG waveform and a second time point of the second systolic peak of the second PPG waveform, wherein the first set of morphological features and the second set of morphological features comprise the first time point and the second time point, respectively. In some examples, determining a delay between the first systolic peak of the first PPG waveform and the second systolic peak of the second PPG waveform based at least in part on the first time point and the second time point, wherein the blood pressure measurement metric is based at least in part on the delay.

[0153] In some examples, the first wavelength comprises a first wavelength range associated with red light. In some examples, the second wavelength comprises a second wavelength range associated with green light.

[0154] In some examples, the PPG data is acquired during a time interval in which a pressure between the wearable device and a user's tissue changes from a first pressure to a second pressure. In some examples, the first set of morphological features and the second set of morphological features include responses of the first PPG waveform and the second PPG waveform, respectively, to the change from the first pressure to the second pressure.

[0155] In some examples, causing a GUI of a user device to display instructions for the user to selectively change the pressure from the first pressure to the second pressure during the time interval, wherein collecting the PPG data throughout the time interval is based at least in part on the instructions.

[0156] In some examples, collecting 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 movement. In some examples, selectively adjusting the blood pressure measurement metric based at least in part on the acceleration data.

[0157] In some examples, the wearable device comprises a wearable wristband device.

[0158] In some examples, the first set of morphological features includes a first amplitude of the first systolic peak, the first diastolic peak, or both. In some examples, the second set of morphological features includes a second amplitude of the second systolic peak, the first diastolic peak, or both.

[0159] In some examples, the PPG data acquisition manager 745 may be configured or otherwise support means for acquiring PPG data from a user using a wearable device, wherein the PPG data is collected during a time interval including a heartbeat of the user, the PPG data comprising 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 morphology component 750 may be configured or otherwise support means for determining a first set of morphological features associated with the first PPG waveform based at least in part on a first systolic peak value and a first diastolic peak value corresponding to the heartbeat within the first PPG waveform. In some examples, the second morphology component 755 may be configured or otherwise support means for determining a second set of morphological features associated with the second PPG waveform based at least in part on a second systolic peak value and a second diastolic peak value corresponding to the heartbeat within the second PPG waveform.In some examples, the blood pressure component 760 may be configured to provide or otherwise assist in determining a blood pressure measurement metric for the user based at least in part on a comparison of the first set of morphological features and the second set of morphological features.

[0160] In some examples, the baseline blood pressure component 765 may be configured or otherwise support means for determining a baseline blood pressure measurement metric associated with the user. In some examples, the blood pressure difference component 770 may be configured or otherwise support means for determining a difference between the baseline blood pressure measurement metric and the blood pressure measurement metric. In some examples, the user interface manager 775 may be configured or otherwise support means for causing a GUI of a user device to display information associated with the difference between the baseline blood pressure measurement metric and the blood pressure measurement metric.

[0161] In some examples, the correlation coefficient component 780 may be configured as, or otherwise assist in, determining a correlation coefficient between the first PPG waveform and the second PPG waveform based at least in part on the comparison of the first set of morphological features and the second set of morphological features, wherein the blood pressure measurement metric is based at least in part on the correlation coefficient.

[0162] In some examples, to assist in acquiring the PPG data, the light-emitting component 725 may be configured as, or otherwise support, means for transmitting the first light and the second light using a light-emitting component of the portable device. In some examples, the photodetector component 730 may be configured as, or otherwise support, means for receiving the first light and the second light using a photodetector of the portable device. In some examples, the PPG waveform component 7115 may be configured as, or otherwise support, means for generating the first PPG waveform and the second PPG waveform based at least in part on receiving the first light and the second light, respectively, via the photodetector.

[0163] In some examples, the first wavelength comprises a first wavelength range associated with IR light. In some examples, the second wavelength comprises a second wavelength range associated with red light.

[0164] In some examples, the curvature component 785 may be configured as, or otherwise support, means for determining a first curvature signal associated with a relative curvature of the first PPG waveform and a second curvature signal associated with a relative curvature of the second PPG waveform. In some examples, the first peak component 790 may be configured as, or otherwise support, means for determining a first peak value of the first curvature signal corresponding to the first systolic peak value of the first PPG waveform, wherein the first set of morphological features includes a first time point of the first peak value.In some examples, the second peak component 795 may be configured as, or otherwise assist in, determining a second peak of the second curvature signal corresponding to the second systolic peak of the second PPG waveform, wherein the second set of morphological features includes a second time of the second peak. In some examples, the time delay component 7100 may be configured as, or otherwise assist in determining a delay between the first time of the first peak and the second time of the second peak based at least in part on the comparison of the first and second sets of morphological features, wherein the blood pressure measurement metric is based at least in part on the delay.

[0165] In some examples, the first curvature signal and the second curvature signal comprise second derivatives of the first PPG waveform and the second PPG waveform, respectively.

[0166] In some examples, the first wavelength comprises a first wavelength range associated with IR light, red light, or both. In some examples, the second wavelength comprises a second wavelength range associated with green light.

[0167] In some examples, the time component 7105 may be configured or otherwise support means for determining a first time point of the first systolic peak of the first PPG waveform and a second time point of the second systolic peak of the second PPG waveform, wherein the first set of morphological features and the second set of morphological features comprise the first time point and the second time point, respectively. In some examples, the time delay component 7100 may be configured or otherwise support means for determining a delay between the first systolic peak of the first PPG waveform and the second systolic peak of the second PPG waveform based at least in part on the first time point and the second time point, wherein the blood pressure measurement metric is based at least in part on the delay.

[0168] In some examples, the first wavelength comprises a first wavelength range associated with red light. In some examples, the second wavelength comprises a second wavelength range associated with green light.

[0169] In some examples, the PPG data is acquired during a time interval in which a pressure between the wearable device and a user's tissue changes from a first pressure to a second pressure. In some examples, the first set of morphological features and the second set of morphological features include responses of the first PPG waveform and the second PPG waveform, respectively, to the change from the first pressure to the second pressure.

[0170] In some examples, the user interface manager 775 may be configured or otherwise support means for causing a GUI of a user device to display instructions to the user to selectively change the pressure from the first pressure to the second pressure during the time interval, wherein collecting the PPG data throughout the time interval is based at least in part on the instructions.

[0171] In some examples, the physiological data acquisition manager 7110 may be configured or otherwise support means for acquiring 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 movement. In some examples, the blood pressure component 760 may be configured or otherwise support means for selectively adjusting the blood pressure measurement metric based at least in part on the acceleration data.

[0172] In some examples, the wearable device comprises a wearable wristband device.

[0173] In some examples, the first set of morphological features includes a first amplitude of the first systolic peak, the first diastolic peak, or both. In some examples, the second set of morphological features includes a second amplitude of the second systolic peak, the first diastolic peak, or both.

[0174] Fig.8 illustrates a diagram of a system 800 including a device 805 that supports techniques for determining blood pressure based on morphological features of pulses according to aspects of the present disclosure. The device 805 may be an example of or include the components of a device 605 as described herein. The device 805 may include an example of a wearable device 104 as previously described herein. The device 805 may include components for bidirectional communication, including components for sending and receiving communication with a user device 106 and a server 110, such as a wearable device manager 820, a communication 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 may be in electronic communication or otherwise coupled (e.g., operationally, communicatively, functionally, electronically, electrically) via one or more buses (e.g., a bus 845).

[0175] For example, the portable device manager 820 may be configured as a means for or otherwise supporting one or more light-emitting components configured to emit light associated with at least a first wavelength and a second wavelength. The portable device manager 820 may be configured as a means for or otherwise supporting one or more photodetectors configured to receive light emitted by the one or more light-emitting components.The portable device manager 820 may be configured as or otherwise support means for a controller communicatively coupled to the one or more light-emitting components and the one or more photodetectors, the controller configured to: The portable device manager 820 may be configured as or otherwise support means for transmitting, during a time interval including a heartbeat of a user and using the one or more light-emitting components, first light associated with the first wavelength and second light associated with the second wavelength.The portable device manager 820 may be configured or otherwise support means for acquiring PPG data from the user based at least in part on receiving the first light and the second light via the 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. The portable device manager 820 may be configured or otherwise support means for determining a first set of morphological features associated with the first PPG waveform based at least in part on a first systolic peak value and a first diastolic peak value corresponding to the heartbeat within the first PPG waveform.The portable device manager 820 may be configured or otherwise support means for determining a second set of morphological features associated with the second PPG waveform based at least in part on a second systolic peak and a second diastolic peak corresponding to the heartbeat within the second PPG waveform. The portable device manager 820 may be configured or otherwise support means for determining a blood pressure measurement metric for the user based at least in part on a comparison of the first set of morphological features and the second set of morphological features.

[0176] For example, the portable device manager 820 may be configured or otherwise support means for collecting PPG data from a user using a portable device, wherein the PPG data is collected during a time interval including a heartbeat of the user, the PPG data comprising at least a first PPG waveform collected using first light associated with a first wavelength and a second PPG waveform collected using second light associated with a second wavelength.The portable device manager 820 may be configured or otherwise support means for determining a first set of morphological features associated with the first PPG waveform based at least in part on a first systolic peak value and a first diastolic peak value corresponding to the heartbeat within the first PPG waveform. The portable device manager 820 may be configured or otherwise support means for determining a second set of morphological features associated with the second PPG waveform based at least in part on a second systolic peak value and a second diastolic peak value corresponding to the heartbeat within the second PPG waveform.The portable device manager 820 may be configured or otherwise support means for determining a blood pressure measurement metric for the user based at least in part on a comparison of the first set of morphological features and the second set of morphological features.

[0177] By including or configuring the portable device manager 820 according to examples described herein, the device 805 may support techniques for determining blood pressure based on morphological features of pulses. That is, the device 805 may enhance the user experience by communicating blood pressure measurement metrics to the user in a timely manner without requiring a visit to a physician for a blood pressure test.

[0178] It should be noted that the methods described above describe possible implementations, and that the acts and steps may be rearranged or otherwise modified, and other implementations are possible. Furthermore, aspects of two or more of the methods may be combined.

[0179] 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 the one or more light-emitting components, a controller communicatively coupled to the one or more light-emitting components and the one or more photodetectors, wherein the controller is configured to transmit, during a time interval including a user's heartbeat and using the one or more light-emitting components, first light associated with the first wavelength and second light associated with the second wavelength,Acquiring PPG data from the user based at least in part on receiving the first light and the second light via the one or more photodetectors, the PPG data comprising 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 set of morphological features associated with the first PPG waveform based at least in part on a first systolic peak and a first diastolic peak corresponding to the heartbeat within the first PPG waveform; determining a second set of morphological features associated with the second PPG waveform based at least in part on a second systolic peak and a second diastolic peak;corresponding to the heartbeat within the second PPG waveform, and determining a blood pressure measurement metric for the user based at least in part on a comparison of the first set of morphological features and the second set of morphological features.

[0180] A device is described. The device may include a processor, a memory coupled to the processor, and instructions stored in the memory. The instructions may be executable by the processor to cause the device 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 the one or more light-emitting components, a controller communicatively coupled to the one or more light-emitting components and the one or more photodetectors, the controller configured to transmit, during a time interval including a heartbeat of a user and using the one or more light-emitting components,of first light associated with the first wavelength and second light associated with the second wavelength, acquiring PPG data from the user based at least in part on receiving the first light and the second light via the one or more photodetectors, the PPG data comprising 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 set of morphological features associated with the first PPG waveform based at least in part on a first systolic peak and a first diastolic peak corresponding to the heartbeat within the first PPG waveform, determining a second set of morphological features,associated with the second PPG waveform based at least in part on a second systolic peak value and a second diastolic peak value corresponding to the heartbeat within the second PPG waveform, and determining a blood pressure measurement metric for the user based at least in part on a comparison of the first set of morphological features and the second set of morphological features.

[0181] Another device is described. The device may include 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 the one or more light-emitting components, means for a controller communicatively coupled to the one or more light-emitting components and the one or more photodetectors, the controller configured to transmit, during a time interval including a user's heartbeat and using the one or more light-emitting components, first light associated with the first wavelength and second light associated with the second wavelength,Means for acquiring PPG data from the user based at least in part on receiving the first light and the second light via the one or more photodetectors, the PPG data comprising 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; means for determining a first set of morphological features associated with the first PPG waveform based at least in part on a first systolic peak and a first diastolic peak corresponding to the heartbeat within the first PPG waveform; means for determining a second set of morphological features associated with the second PPG waveform;based at least in part on a second systolic peak value and a second diastolic peak value corresponding to the heartbeat within the second PPG waveform, and means for determining a blood pressure measurement metric for the user based at least in part on a comparison of the first set of morphological features and the second set of morphological features.

[0182] A non-transitory computer-readable medium storing code is described. The code may include instructions executable by a processor to transmit 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 the one or more light-emitting components, a controller communicatively coupled to the one or more light-emitting components and the one or more photodetectors, the controller configured to transmit, during a time interval including a heartbeat of a user and using the one or more light-emitting components, first light associated with the first wavelength and second light,associated with the second wavelength, acquiring PPG data from the user based at least in part on receiving the first light and the second light via the one or more photodetectors, the PPG data comprising 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 set of morphological features associated with the first PPG waveform based at least in part on a first systolic peak and a first diastolic peak corresponding to the heartbeat within the first PPG waveform, determining a second set of morphological features associated with the second PPG waveform,based at least in part on a second systolic peak value and a second diastolic peak value corresponding to the heartbeat within the second PPG waveform, and determining a blood pressure measurement metric for the user based at least in part on a comparison of the first set of morphological features and the second set of morphological features.

[0183] Some examples of the method, apparatus, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for determining a baseline blood pressure measurement metric associated with the user, determining a difference between the baseline blood pressure measurement metric and the blood pressure measurement metric, and causing a GUI of a user device to display information associated with the difference between the baseline blood pressure measurement metric and the blood pressure measurement metric.

[0184] Some examples of the method, apparatus, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for determining a correlation coefficient between the first PPG waveform and the second PPG waveform based at least in part on the comparison of the first set of morphological features and the second set of morphological features, wherein the blood pressure measurement metric may be based at least in part on the correlation coefficient.

[0185] In some examples of the method, apparatus, and non-transitory computer-readable medium described herein, the first wavelength comprises a first wavelength range associated with IR light, and the second wavelength comprises a second wavelength range associated with red light.

[0186] Some examples of the method, apparatus, and non-transitory computer-readable medium described herein may further comprise operations, features, means, or instructions for determining a first curvature signal associated with a relative curvature of the first PPG waveform and a second curvature signal associated with a relative curvature of the second PPG waveform, determining a first peak value of the first curvature signal corresponding to the first systolic peak value of the first PPG waveform, wherein the first set of morphological features comprises a first time point of the first peak value, determining a second peak value of the second curvature signal corresponding to the second systolic peak value of the second PPG waveform, wherein the second set of morphological features comprises a second time point of the second peak value,and determining a delay between the first time point of the first peak value and the second time point of the second peak value based at least in part on the comparison of the first and second sets of morphological features, wherein the blood pressure measurement metric may be based at least in part on the delay.

[0187] In some examples of the method, apparatus, and non-transitory computer-readable medium described herein, the first curvature signal and the second curvature signal comprise second derivatives of the first PPG waveform and the second PPG waveform, respectively.

[0188] In some examples of the method, apparatus, and non-transitory computer-readable medium described herein, the first wavelength comprises a first wavelength range associated with IR light, red light, or both, and the second wavelength comprises a second wavelength range associated with green light.

[0189] Some examples of the method, apparatus, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for determining a first time point of the first systolic peak of the first PPG waveform and a second time point of the second systolic peak of the second PPG waveform, wherein the first set of morphological features and the second set of morphological features comprise the first time point and the second time point, 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 based at least in part on the first time point and the second time point, wherein the blood pressure measurement metric may be based at least in part on the delay.

[0190] In some examples of the method, apparatus, and non-transitory computer-readable medium described herein, the first wavelength comprises a first wavelength range associated with red light, and the second wavelength comprises a second wavelength range associated with green light.

[0191] In some examples of the method, devices, and non-transitory computer-readable medium described herein, the PPG data may be acquired during a time interval in which a pressure between the wearable device and a user's tissue may be changed from a first pressure to a second pressure, and the first set of morphological features and the second set of morphological features include responses of the first PPG waveform and the second PPG waveform, respectively, to the change from the first pressure to the second pressure.

[0192] Some examples of the method, apparatus, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for causing a GUI of a user device to display instructions to the user to selectively change the pressure from the first pressure to the second pressure during the time interval, wherein collecting the PPG data throughout the time interval may be based at least in part on the instructions.

[0193] Some examples of the method, devices, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for acquiring 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 movement, and selectively adjusting the blood pressure measurement metric based at least in part on the acceleration data.

[0194] In some examples of the method, apparatus, and non-transitory computer-readable medium described herein, the wearable device comprises a wearable wristband device.

[0195] In some examples of the method, apparatus, and non-transitory computer-readable medium described herein, the first set of morphological features comprises a first amplitude of the first systolic peak, the first diastolic peak, or both, and the second set of morphological features comprises a second amplitude of the second systolic peak, the first diastolic peak, or both.

[0196] A method is described. The method may include acquiring PPG data from a user using a wearable device, wherein the PPG data is collected during a time interval including a heartbeat of the user, the PPG data comprising 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 set of morphological features associated with the first PPG waveform based at least in part on a first systolic peak and a first diastolic peak corresponding to the heartbeat within the first PPG waveform; determining a second set of morphological features associated with the second PPG waveform;based at least in part on a second systolic peak value and a second diastolic peak value corresponding to the heartbeat within the second PPG waveform, and determining a blood pressure measurement metric for the user based at least in part on a comparison of the first set of morphological features and the second set of morphological features.

[0197] A device is described. The device may include a processor, a memory coupled to the processor, and instructions stored in the memory. The instructions may be executable by the processor to cause the device to collect PPG data from a user using a wearable device, wherein the PPG data is collected during a time interval including a heartbeat of the user, the PPG data comprising 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 set of morphological features associated with the first PPG waveform based at least in part on a first systolic peak value and a first diastolic peak value;corresponding to the heartbeat within the first PPG waveform, determining a second set of morphological features associated with the second PPG waveform based at least in part on a second systolic peak value and a second diastolic peak value corresponding to the heartbeat within the second PPG waveform, and determining a blood pressure measurement metric for the user based at least in part on a comparison of the first set of morphological features and the second set of morphological features.

[0198] Another device is described. The device may include means for acquiring PPG data from a user using a portable device, wherein the PPG data is collected during a time interval including a heartbeat of the user, the PPG data comprising 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 set of morphological features associated with the first PPG waveform based at least in part on a first systolic peak and a first diastolic peak corresponding to the heartbeat within the first PPG waveform; means for determining a second set of morphological features;associated with the second PPG waveform based at least in part on a second systolic peak value and a second diastolic peak value corresponding to the heartbeat within the second PPG waveform, and means for determining a blood pressure measurement metric for the user based at least in part on a comparison of the first set of morphological features and the second set of morphological features.

[0199] A non-transitory computer-readable medium storing code is described. The code may include instructions executable by a processor to acquire PPG data from a user using a wearable device, wherein the PPG data is collected during a time interval including a heartbeat of the user, the PPG data comprising 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 set of morphological features associated with the first PPG waveform based at least in part on a first systolic peak and a first diastolic peak corresponding to the heartbeat within the first PPG waveform;Determining a second set of morphological features associated with the second PPG waveform based at least in part on a second systolic peak and a second diastolic peak corresponding to the heartbeat within the second PPG waveform, and determining a blood pressure measurement metric for the user based at least in part on a comparison of the first set of morphological features and the second set of morphological features.

[0200] Some examples of the method, apparatus, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for determining a baseline blood pressure measurement metric associated with the user, determining a difference between the baseline blood pressure measurement metric and the blood pressure measurement metric, and causing a GUI of a user device to display information associated with the difference between the baseline blood pressure measurement metric and the blood pressure measurement metric.

[0201] Some examples of the method, apparatus, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for determining a correlation coefficient between the first PPG waveform and the second PPG waveform based at least in part on the comparison of the first set of morphological features and the second set of morphological features, wherein the blood pressure measurement metric may be based at least in part on the correlation coefficient.

[0202] In some examples of the method, apparatus, and non-transitory computer-readable medium described herein, acquiring the PPG data may include operations, features, means, or instructions for transmitting the first light and the second light using a light-emitting component of the portable device, receiving the first light and the second light using a photodetector of the portable device, and generating the first PPG waveform and the second PPG waveform based at least in part on receiving the first light and the second light, respectively, via the photodetector.

[0203] In some examples of the method, apparatus, and non-transitory computer-readable medium described herein, the first wavelength comprises a first wavelength range associated with IR light, and the second wavelength comprises a second wavelength range associated with red light.

[0204] Some examples of the method, apparatus, and non-transitory computer-readable medium described herein may further comprise operations, features, means, or instructions for determining a first curvature signal associated with a relative curvature of the first PPG waveform and a second curvature signal associated with a relative curvature of the second PPG waveform, determining a first peak value of the first curvature signal corresponding to the first systolic peak value of the first PPG waveform, wherein the first set of morphological features comprises a first time point of the first peak value, determining a second peak value of the second curvature signal corresponding to the second systolic peak value of the second PPG waveform, wherein the second set of morphological features comprises a second time point of the second peak value,and determining a delay between the first time point of the first peak value and the second time point of the second peak value based at least in part on the comparison of the first and second sets of morphological features, wherein the blood pressure measurement metric may be based at least in part on the delay.

[0205] In some examples of the method, apparatus, and non-transitory computer-readable medium described herein, the first curvature signal and the second curvature signal comprise second derivatives of the first PPG waveform and the second PPG waveform, respectively.

[0206] In some examples of the method, apparatus, and non-transitory computer-readable medium described herein, the first wavelength comprises a first wavelength range associated with IR light, red light, or both, and the second wavelength comprises a second wavelength range associated with green light.

[0207] Some examples of the method, apparatus, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for determining a first time point of the first systolic peak of the first PPG waveform and a second time point of the second systolic peak of the second PPG waveform, wherein the first set of morphological features and the second set of morphological features comprise the first time point and the second time point, 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 based at least in part on the first time point and the second time point, wherein the blood pressure measurement metric may be based at least in part on the delay.

[0208] In some examples of the method, apparatus, and non-transitory computer-readable medium described herein, the first wavelength comprises a first wavelength range associated with red light, and the second wavelength comprises a second wavelength range associated with green light.

[0209] In some examples of the method, devices, and non-transitory computer-readable medium described herein, the PPG data may be acquired during a time interval in which a pressure between the wearable device and a user's tissue may be changed from a first pressure to a second pressure, and the first set of morphological features and the second set of morphological features include responses of the first PPG waveform and the second PPG waveform, respectively, to the change from the first pressure to the second pressure.

[0210] Some examples of the method, apparatus, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for causing a GUI of a user device to display instructions to the user to selectively change the pressure from the first pressure to the second pressure during the time interval, wherein collecting the PPG data throughout the time interval may be based at least in part on the instructions.

[0211] Some examples of the method, devices, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for acquiring 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 movement, and selectively adjusting the blood pressure measurement metric based at least in part on the acceleration data.

[0212] In some examples of the method, apparatus, and non-transitory computer-readable medium described herein, the wearable device comprises a wearable wristband device.

[0213] In some examples of the method, apparatus, and non-transitory computer-readable medium described herein, the first set of morphological features comprises a first amplitude of the first systolic peak, the first diastolic peak, or both, and the second set of morphological features comprises a second amplitude of the second systolic peak, the first diastolic peak, or both.

[0214] The description set forth herein, in conjunction with the accompanying drawings, describes exemplary configurations and does not represent all examples that may be implemented or that fall within the scope of the claims. The term "exemplary" as used herein means "serving as an example, instance, or illustration" rather than "preferred" or "advantageous over other examples." However, these techniques may be practiced without these specific details. In some instances, well-known structures and devices are shown in block diagram form to avoid obscuring the concepts of the described examples.

[0215] In the accompanying figures, similar components or features may have the same reference numeral. Furthermore, different components of the same type may be distinguished by following the reference numeral with a prime and a second label distinguishing between the similar components. If only the first reference numeral is used in the description, the description applies to any of the similar components with the same first reference numeral, regardless of the second reference numeral.

[0216] Information and signals described herein may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referred to in the foregoing description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0217] The various illustrative blocks and modules described in connection with the disclosure herein may be implemented or executed using a general-purpose processor, a DSP, an ASIC, an FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof configured to perform the functions described herein. A general-purpose processor may be a microprocessor, but alternatively, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices (e.g.,a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration).

[0218] The functions described herein may be implemented in hardware, processor-executed software, firmware, or any combination thereof. When implemented in processor-executed software, the functions may be stored on or transmitted over a computer-readable medium as one or more instructions or code. Other examples and implementations are within the scope of the disclosure and the appended claims. For example, due to the nature of software, the functions described above may be implemented using processor-executed software, hardware, firmware, hardwiring, or combinations of any of these. Features that implement functions may also be physically located in different locations, including being distributed so that portions of functions are implemented in different physical locations.As used herein, including in the claims, "or," as used in a list of items (e.g., a list of items introduced by a phrase such as "at least one of" or "one or more of"), indicates an inclusive list, such that, for example, a list of at least one of A, B, or CA, or B, or C, or AB, or AC, or BC, or ABC (i.e., A, B, and C). As used herein, the phrase "based on" is also not intended to be construed as referring to a closed set of states. For example, an exemplary step described as "based on state A" may be based on both state A and state B without departing from the scope of the present disclosure. In other words, as used herein, the phrase "based on" is intended to be construed in the same manner as the phrase "based at least in part on."

[0219] Computer-readable media includes both non-transitory computer storage media and communications media, including any medium that facilitates the transfer of a computer program from one location to another. A non-transitory storage medium may be any available medium that can be accessed by a general-purpose or special-purpose computer. By way of example and not limitation, non-transitory computer-readable media may include RAM, ROM, electrically erasable programmable ROM (EEPROM), compact disk (CD) ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to carry or store desired program code means in the form of instructions or data structures and that can be accessed by a general-purpose or special-purpose computer or processor.Furthermore, any connection is properly referred to as a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair cable, digital subscriber line (DSL), or wireless technologies such as IR, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair cable, DSL, or wireless technologies such as IR, radio, and microwave are included in the definition of medium. Disk and disc, as used herein, include CD, laser disc, optical disc, digital versatile disc (DVD), floppy disk, and Blu-ray Disc, where disks typically reproduce data magnetically, while discs reproduce data optically using lasers. Combinations of the foregoing are also included within the scope of computer-readable media.

[0220] The description herein is provided to enable any person skilled in the art to make or use the disclosure. Various modifications of the disclosure will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the scope of the disclosure. Thus, the disclosure is not limited to the examples and designs described herein, but is intended to be accorded the broadest scope consistent with the principles and novel features disclosed herein. QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited patent literature

[0000] US 18 / 189,849

[0001]

Claims

A portable device for measuring blood pressure, comprising: 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 the one or more light-emitting components; and a controller communicatively coupled to the one or more light-emitting components and the one or more photodetectors, the controller configured to: transmit, during a time interval including a user's heartbeat and using the one or more light-emitting components, first light associated with the first wavelength and second light associated with the second wavelength;Acquiring photoplethysmogram (PPG) data from the user based at least in part on receiving the first light and the second light via the one or more photodetectors, the PPG data comprising 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 set of morphological features associated with the first PPG waveform based at least in part on a first systolic peak value and a first diastolic peak value corresponding to the heartbeat within the first PPG waveform;Determining a second set of morphological features associated with the second PPG waveform based at least in part on a second systolic peak value and a second diastolic peak value corresponding to the heartbeat within the second PPG waveform; andDetermining a blood pressure measurement metric for the user based at least in part on a comparison of the first set of morphological features and the second set of morphological features; The portable device of claim 1, wherein the controller is further configured to:determine a baseline blood pressure measurement metric associated with the user;determine a difference between the baseline blood pressure measurement metric and the blood pressure measurement metric; andcause a graphical user interface of a user device to display information associated with the difference between the baseline blood pressure measurement metric and the blood pressure measurement metric. The portable device of claim 1, wherein the controller is further configured to:determine a correlation coefficient between the first PPG waveform and the second PPG waveform based at least in part on the comparison of the first set of morphological features and the second set of morphological features, wherein the blood pressure measurement metric is based at least in part on the correlation coefficient. The portable device of claim 3, wherein the first wavelength comprises a first wavelength range associated with infrared light, and wherein the second wavelength comprises a second wavelength range associated with red light. The portable device of claim 1, wherein the controller is further configured to:determine a first curvature signal associated with a relative curvature of the first PPG waveform and a second curvature signal associated with a relative curvature of the second PPG waveform;determine a first peak value of the first curvature signal corresponding to the first systolic peak value of the first PPG waveform, wherein the first set of morphological features includes a first time point of the first peak value;determine a second peak value of the second curvature signal corresponding to the second systolic peak value of the second PPG waveform, wherein the second set of morphological features includes a second time point of the second peak value;anddetermining a delay between the first time point of the first peak value and the second time point of the second peak value based at least in part on the comparison of the first and second sets of morphological features, wherein the blood pressure metric is based at least in part on the delay.; The portable device of claim 5, wherein the first curvature signal and the second curvature signal comprise second derivatives of the first PPG waveform and the second PPG waveform, respectively. The portable device of claim 5, wherein the first wavelength comprises a first wavelength range associated with infrared light, red light, or both, and wherein the second wavelength comprises a second wavelength range associated with green light. The wearable device of claim 1, wherein the controller is further configured to: determine a first time point of the first systolic peak of the first PPG waveform and a second time point of the second systolic peak of the second PPG waveform, wherein the first set of morphological features and the second set of morphological features comprise the first time point and the second time point, respectively; and determine a delay between the first systolic peak of the first PPG waveform and the second systolic peak of the PPG waveform based at least in part on the first time point and the second time point, wherein the blood pressure measurement metric is based at least in part on the delay. The portable device of claim 8, wherein the first wavelength comprises a first wavelength range associated with red light, and wherein the second wavelength comprises a second wavelength range associated with green light. The wearable device of claim 1, wherein the PPG data is acquired during a time interval in which a pressure between the wearable device and a tissue of the user is changed from a first pressure to a second pressure, wherein the first set of morphological features and the second set of morphological features comprise responses of the first PPG waveform and the second PPG waveform, respectively, to the change from the first pressure to the second pressure. The portable device of claim 10, wherein the controller is further configured to:cause a graphical user interface of a user device to display instructions to the user to selectively change the pressure from the first pressure to the second pressure during the time interval, wherein collecting the PPG data throughout the time interval is based at least in part on the instructions. The wearable device of claim 1, wherein the controller is further configured to:collect physiological data from the user via the wearable device, the physiological data comprising at least the PPG data and acceleration data associated with the user's movement; andselectively adjust the blood pressure measurement metric based at least in part on the acceleration data. The wearable device of claim 1, wherein the wearable device comprises a wearable wristband device. The portable device of claim 1, wherein the first set of morphological features comprises a first amplitude of the first systolic peak, the first diastolic peak, or both, and wherein the second set of morphological features comprises a second amplitude of the second systolic peak, the first diastolic peak, or both. A method for measuring blood pressure, comprising:acquiring photoplethysmogram (PPG) data from a user using a wearable device, wherein the PPG data is collected during a time interval that includes a heartbeat of the user, the PPG data comprising 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 set of morphological features associated with the first PPG waveform based at least in part on a first systolic peak value and a first diastolic peak value corresponding to the heartbeat within the first PPG waveform;Determining a second set of morphological features associated with the second PPG waveform based at least in part on a second systolic peak value and a second diastolic peak value corresponding to the heartbeat within the second PPG waveform; andDetermining a blood pressure measurement metric for the user based at least in part on a comparison of the first set of morphological features and the second set of morphological features. The method of claim 15, further comprising: determining a baseline blood pressure measurement metric associated with the user; determining a difference between the baseline blood pressure measurement metric and the blood pressure measurement metric; and causing a graphical user interface of a user device to display information associated with the difference between the baseline blood pressure measurement metric and the blood pressure measurement metric. The method of claim 15, further comprising: determining a correlation coefficient between the first PPG waveform and the second PPG waveform based at least in part on the comparison of the first set of morphological features and the second set of morphological features, wherein the blood pressure measurement metric is based at least in part on the correlation coefficient. The method of claim 17, wherein acquiring the PPG data comprises: transmitting the first light and the second light using a light-emitting component of the portable device; receiving the first light and the second light using a photodetector of the portable device; and generating the first PPG waveform and the second PPG waveform based at least in part on receiving the first light and the second light, respectively, via the photodetector. The method of claim 17, wherein the first wavelength comprises a first wavelength range associated with infrared light, and wherein the second wavelength comprises a second wavelength range associated with red light. The method of claim 15, further comprising: determining a first curvature signal associated with a relative curvature of the first PPG waveform and a second curvature signal associated with a relative curvature of the second PPG waveform; determining a first peak value of the first curvature signal corresponding to the first systolic peak value of the first PPG waveform, wherein the first set of morphological features comprises a first time point of the first peak value; determining a second peak value of the second curvature signal corresponding to the second systolic peak value of the second PPG waveform, wherein the second set of morphological features comprises a second time point of the second peak value;anddetermining a delay between the first time point of the first peak value and the second time point of the second peak value based at least in part on the comparison of the first and second sets of morphological features, wherein the blood pressure metric is based at least in part on the delay.;

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