Activity monitoring to augment a personalized blood pressure model

Abstract

In some implementations, a device can obtain activity information indicative of an activity performed by a person. The device can update a model used to determine a blood pressure of the person based at least in part on the obtained activity information. The device can detect acoustic waves corresponding to a photoacoustic response of a blood vessel of the person to light emitted by a light source system. The device can estimate the blood pressure based at least in part on the updated model and the acoustic waves. In some embodiments, the device can include a wearable device worn by the person.

Description

[0001] Priority Statement

[0002] This application claims priority to U.S. Patent Application No. 18 / 486,866, filed October 13, 2023, entitled “ACTIVITY MONITORING TO AUGMENT PERSONALIZED BLOOD PRESSURE MODEL,” which is incorporated herein by reference for all purposes. Technical Field

[0003] This disclosure relates in general to devices and systems that use a variety of types of sensors. Background Technology

[0004] A variety of sensing technologies and algorithms are being implemented in devices to enable a wide range of biometric and biomedical applications, including health and wellness monitoring. This trend stems in part from the availability limitations of traditional measurement devices in terms of continuous, non-invasive, and / or dynamic monitoring. Some of these devices are or include photoacoustic or optical sensors. While some previously deployed devices provide acceptable results, improved detection devices and systems are desirable. Summary of the Invention

[0005] The systems, methods, and apparatus disclosed herein each have several aspects, and no single aspect is solely responsible for the desired properties disclosed herein.

[0006] An example apparatus includes: a control system configured to acquire activity information indicating an activity performed by a device user, and to update a model for determining the device user's blood pressure based at least in part on the acquired activity information. The one or more processors may further be configured to: a light source system including a light-emitting component; and a receiver system configured to detect an acoustic wave corresponding to the photoacoustic response of the device user's blood vessels to light emitted by the light source system, wherein the control system is further configured to estimate blood pressure based at least in part on the updated model and the acoustic wave.

[0007] An example method for blood pressure estimation according to this disclosure may include: obtaining activity information indicating an activity performed by a person. The method may further include: updating a model used to determine the person's blood pressure based at least in part on the obtained activity information. The method may further include: detecting an acoustic wave corresponding to the photoacoustic response of the person's blood vessels to light emitted by a light source system. The method may further include: estimating blood pressure based at least in part on the updated model and the acoustic wave.

[0008] An example apparatus includes: components for acquiring activity information indicating an activity performed by a person. The apparatus may further include components for updating a model for determining the person's blood pressure based at least in part on the acquired activity information. The apparatus may further include components for detecting sound waves corresponding to the photoacoustic response of the person's blood vessels to light emitted by a light source system. The apparatus may further include components for estimating blood pressure based at least in part on the updated model and the sound waves.

[0009] Details of one or more specific embodiments of the subject matter described in this disclosure are set forth in the accompanying drawings and the description below. Other features, aspects, and advantages will become apparent from the description, drawings, and claims. Note that the relative dimensions in the following drawings may not be drawn to scale. Attached Figure Description

[0010] Figure 1 An example of a blood pressure monitoring device based on photoacoustic volume plethysmography (which may be referred to herein as PAPG) is shown.

[0011] Figure 2 An example of a blood pressure monitoring device based on photoplethysmography (PPG) is shown.

[0012] Figure 3 An example of heart rate waveform (HRW) features that can be extracted according to some specific implementations is shown.

[0013] Figure 4 An example of a device that can be used in a system for estimating blood pressure based at least in part on pulse transit time (PTT) is shown.

[0014] Figure 5 A cross-sectional side view of a portion of an artery through which a pulse is propagating.

[0015] Figure 6A An example monitoring device, designed to be worn around the wrist, is shown according to some specific implementations.

[0016] Figure 6B An example monitoring device designed to be worn on a finger is shown according to some specific implementations.

[0017] Figure 6C An example monitoring device designed to reside on an earbud-type headset is shown according to some specific implementations.

[0018] Figure 7 This is a block diagram illustrating example components of a PAPG-based device according to some disclosed specific implementations.

[0019] Figure 8This is a block diagram illustrating a first technique for enhancing blood pressure estimation using activity information based on some disclosed specific implementations.

[0020] Figure 9 This is a block diagram illustrating a second technique for enhancing blood pressure estimation based on some disclosed specific implementations of activity information.

[0021] Figure 10 This is a flowchart of a method for expanding a personalized blood pressure model based on the use of activity monitoring in accordance with the implementation plan.

[0022] Figure 11 This is a flowchart of another method 1100 for expanding personalized blood pressure models based on the use of activity monitoring in accordance with the implementation plan.

[0023] Similar reference numerals and names in the various figures indicate similar elements. Detailed Implementation

[0024] The following description relates to certain specific embodiments intended to describe various aspects of this disclosure. However, those skilled in the art will readily recognize that the teachings herein can be applied in many different ways. Some of the concepts and examples provided in this disclosure are particularly applicable to blood pressure monitoring applications or the monitoring of other physiological parameters. However, some specific embodiments are also applicable to other types of biosensing applications and other fluid flow systems. The described specific embodiments can be implemented in any device, apparatus, or system that includes the means disclosed herein. Furthermore, it is contemplated that the described specific embodiments can be included in or associated with a variety of electronic devices such as, but not limited to: mobile phones, cellular phones with multimedia network enabled, mobile TV receivers, wireless devices, smartphones, smart cards, wearable devices (such as wristbands, armbands, wrist straps, rings, headbands, patches, chest straps, anklets, etc.), Bluetooth devices, etc. ®Devices, personal data assistants (PDAs), wireless email receivers, handheld or portable computers, laptops, notebook computers, smart e-books, tablet computers, printers, copiers, scanners, fax machines, GPS receivers / navigators, cameras, digital media players, game consoles, wristwatches, clocks, computers, television monitors, flat panel displays, electronic reading devices (e.g., e-readers), mobile health devices, computer monitors, automotive displays (including odometer and speedometer displays, etc.), cockpit controls and / or displays, camera view displays (such as displays for vehicle rearview cameras), building structures, microwave ovens, refrigerators, stereo systems, tape recorders or players, DVD players, CD players, VCRs, radios, portable memory chips, washing machines, dryers, washer / dryer units, parking meters, car doors, autonomous or semi-autonomous vehicles, drones, Internet of Things (IoT) devices, etc. Therefore, this teaching is not intended to be limited to the specific implementations depicted and described with reference to the accompanying drawings; rather, as will be apparent to those skilled in the art, this teaching has broad applicability.

[0025] Accurate, non-invasive, and continuously monitoring wearable devices can be used for both clinical and consumer applications, such as measuring physiological parameters like a user's blood pressure. Measurements of arterial signals and heart rate waveforms from arteries are crucial for determining and predicting arterial (e.g., blood pressure) measurements. Non-invasive health monitoring devices, such as those based on photoacoustic plethysmography (PAPG), offer various potential advantages over more invasive devices, such as cuff-based or catheter-based blood pressure measurement devices. Some PAPG-based wearable devices may include a plate or interface for transmitting light and sound signals. The plate or interface can be light-transmitting and ideally should have an acoustic impedance closely matched to that of human skin. As discussed in more detail elsewhere in this document, PAPG can measure arterial waveforms and characteristics (such as diameter and pulse wave velocity) at various depths, which can then be used to estimate blood pressure. Photoplethysmography (PPG) can also be used to monitor a user's health non-invasively by transmitting light and receiving reflected light from a target object.

[0026] While PAPG and PPG alone are useful and advantageous for non-invasive monitoring, as mentioned above, obtaining accurate measurements from the target remains challenging. For example, blood pressure measurements based on a single modality (such as PAPG, PPG, acoustic, or pressure) may not provide sufficient information to produce adequate resolution or measurement quality of interest, particularly regarding arterial compliance and dilation. However, current, recent, and / or historical activity information can help increase the accuracy of blood pressure measurements.

[0027] Therefore, the various aspects provided in this disclosure generally relate to methods for enhancing the accuracy of a user's blood pressure measurement by using activity information that indicates activities performed by the user.

[0028] Some aspects more specifically involve: obtaining activity information indicating an activity performed by a user; updating a model for determining the user's blood pressure based at least in part on the obtained activity information; detecting (e.g., using a PAPG device) acoustic waves corresponding to the photoacoustic response of the user's blood vessels to light emitted by a light source system; and estimating blood pressure based at least in part on the updated model and the acoustic waves. Activity information may include, for example, an identifier of the activity, an indication of the type of activity, sensor data indicating the activity, or any combination thereof. Activity information can be sensed using, for example, a gyroscope, accelerometer, magnetometer, altimeter, inertial measurement unit (IMU), or any combination thereof. Furthermore, in some specific implementations, machine learning can be used to train a machine learning model that can accurately estimate physiological characteristics of the blood vessels (e.g., PTT, pulse wave velocity (PWV)) or user parameters (e.g., blood pressure), or examine such physiological characteristics or parameters given the activity information.

[0029] Specific embodiments of the subject matter described in this disclosure can be implemented to achieve one or more of the following potential advantages. The embodiments described below utilize activity information to better identify and predict measurements more accurately than individual measurements (e.g., PAPG or PPG), while remaining non-invasive. Additionally or alternatively, highly accurate (e.g., more invasive) measurements captured during the setup process can be used to determine an accurate model that can subsequently be used for accurate, non-invasive measurements via a wearable device. In some embodiments, non-invasive measurements can be performed by the wearable device, thereby achieving the aforementioned advantages in a user-friendly manner.

[0030] Additional details will follow the initial description of the relevant systems and technologies.

[0031] Figure 1 An example of blood pressure monitoring based on photoacoustic volume plethysmography (which may be referred to herein as PAPG) is shown. Figure 1 The same example of arteries, veins, arterioles, venules, and capillaries inside a body part (in this example, finger 115) is shown. In some examples, Figure 1The light source shown can be coupled to a light source system (not shown) located away from a body part (e.g., finger 115). In some embodiments, the light source can be an opening in an optical fiber or other waveguide. Such an opening can also be connected to an opening in an interface that can contact a body part. In some embodiments, the light source system may include one or more LEDs, one or more laser diodes, etc. In this example, the light source has emitted light (in some examples, green, red, and / or near-infrared (NIR) light) that has penetrated the tissue of the finger 115 in the irradiated area.

[0032] exist Figure 1 In the example shown, the blood vessels (and components of the blood itself) are heated by incident light from a light source and are emitting acoustic waves 102. In this example, the emitted acoustic waves 102 include ultrasound. According to this specific embodiment, the acoustic emission 102 is detected by an ultrasound receiver, which in this example is a piezoelectric receiver. The photoacoustic emission 102 from the irradiated tissue detected by the piezoelectric receiver can be used to detect volume changes in the blood in the irradiated area of ​​the finger 115, which correspond to physiological data within the irradiated tissue of the finger 115, such as heart rate waveforms. Although some tissue areas shown as irradiated are offset from the tissue areas shown as generating photoacoustic emission 102, this is merely for illustrative purposes. It should be understood that the irradiated tissue is actually the tissue that generates photoacoustic emission. Furthermore, it should be understood that the maximum level of photoacoustic emission will generally be generated along the same axis as the maximum level of irradiation.

[0033] Optical techniques such as those based on optical volumetric spectroscopy (PPG) systems and Figure 1 One important difference between PAPG-based methods is that Figure 1 The sound waves shown travel much slower than the reflected light waves involved in PPG. Therefore, based on Figure 1 Depth discrimination based on the arrival time of sound waves is possible, whereas depth discrimination based on the arrival time of light waves in a PPG may not be feasible. This depth discrimination allows some of the disclosed specific implementations to isolate sound waves received from different blood vessels.

[0034] Based on some such examples, this depth discrimination allows for the differentiation of arterial heart rate waveforms from venous heart rate waveforms and other heart rate waveforms. Therefore, blood pressure estimation based on the depth discrimination PAPG method can be significantly more accurate than blood pressure estimation based on PPG-based methods.

[0035] Figure 2 An example of a blood pressure monitoring device based on photoplethysmography (PPG) is shown. Figure 2 Examples of arteries, veins, arterioles, venules, and capillaries of the circulatory system are shown, including those inside the finger 115. Figure 2In the example shown, an electrocardiogram (ECG) sensor has detected a proximal arterial pulse near the heart 216. The following describes some examples of measuring arterial pulse transit time (PTT) based on arterial pulses measured by two sensors, in some specific implementations where one of the two sensors can be an ECG sensor.

[0036] according to Figure 2 The example shown includes a light source, such as one or more lasers or light-emitting diodes (LEDs), emitting light (in some examples, green, red, infrared, and / or near-infrared (NIR) light) that has penetrated the tissue of the finger 115 in the irradiated area. The reflections from these tissues, detected by a photodetector, can be used to detect changes in blood volume in the irradiated area of ​​the finger 115 corresponding to a heart rate waveform.

[0037] like Figure 2 As shown in heart rate waveform 218, microvascular heart rate waveform 219 has a different shape and phase shift compared to arterial heart rate waveform 217. In this simplified example, the detected heart rate waveform 221 is a combination of microvascular heart rate waveform 219 and arterial heart rate waveform 217. In some cases, the response of one or more other vessels may also be a portion of the heart rate waveform 221 detected by a PPG-based blood pressure monitoring device.

[0038] Figure 3 An example of heart rate waveform (HRW) features that can be extracted according to some specific implementations is shown. Figure 3 The horizontal axis represents time, and the vertical axis represents signal amplitude. The cardiac cycle is indicated by the time between adjacent peaks of the HRW (Heart Rate Wave). The systolic and diastolic time intervals are indicated below the horizontal axis. During the systolic phase of cardiac circulation, as the pulse travels along the artery through a specific location, the arterial wall expands according to the pulse waveform and the elastic properties of the arterial wall. Accompanying this expansion is a corresponding increase in blood volume at the specific location or region, and with the increase in blood volume, one or more properties in that region change accordingly. Conversely, during the diastolic phase of cardiac circulation, blood pressure in the artery decreases and the arterial wall constricts. Accompanying this constriction is a corresponding decrease in blood volume at the specific location, and with the decrease in blood volume, one or more properties in that region change accordingly.

[0039] Figure 3The illustrated HRW features represent the width of the contraction and / or diastolic portions of the HRW curve at different "heights" indicated by the percentage of maximum amplitude. For example, the SW50 feature is the width of the contraction portion of the HRW curve at a "height" of 50% of maximum amplitude. In some embodiments, the HRW features used for blood pressure estimation may include some or all of the HRW features SW10, SW25, SW33, SW50, SW66, SW75, DW10, DW25, DW33, DW50, DW66, and DW75. In other embodiments, additional HRW features may be used for blood pressure estimation. In some cases, such additional HRW features may include the sum and ratio of SW and DW at one or more "heights," such as (DW75 + SW75), DW75 / SW75, (DW66 + SW66), DW66 / SW66, (DW50 + SW50), DW50 / SW50, (DW33 + SW33), DW33 / SW33, (DW25 + SW25), DW25 / SW25, and / or (DW10 + SW10), DW10 / SW10. Other implementations may use other HRW features for blood pressure estimation. In some cases, such additional HRW features may include sums, differences, ratios, and / or other calculations based on more than one "height," such as (DW75 + SW75) / (DW50 + SW50), (DW50 + SW50 / (DW10 + SW10), etc.

[0040] Figure 4 An example of a device that can be used in a system for estimating blood pressure based at least in part on pulse transit time (PTT) is shown. The number, type, and arrangement of elements are presented by way of example only, as far as the other figures provided herein are concerned. According to this example, system 400 includes at least two sensors. In this example, system 400 includes at least an electrocardiogram sensor 405 and a device 410 configured to be mounted on a finger of a person 401. In this example, device 410 is or includes means configured to perform at least some of the PAPG methods disclosed herein. For example, device 410 may be or may include Figure 7 The device 700 or similar device.

[0041] As shown in Figure 420, PAT comprises two components: pre-ejection phase (PEP, the time required to convert the electrical signal into mechanical pumping force and isovolumetric contraction to open the aortic valve) and PTT. The start time of PAT can be estimated based on the QRS complex (the electrical signal characteristics of ventricular electrical stimulation). As shown in Figure 420, in this example, the start of PAT can be calculated based on the R-wave peak value measured by ECG sensor 405, and the end of PAT can be detected via analysis of the signal provided by device 410. In this example, it is assumed that the end of PAT corresponds to the intersection between the tangent of the local minimum detected by device 410 and the tangent of the maximum slope / first derivative of the sensor signal after the minimum time.

[0042] There are many known blood pressure estimation algorithms based on PTT and / or PAT, some of which are outlined in Table 1 and described in their corresponding texts on pages 5–10 of Sharma, M. et al., “Cuff-Less and Continuous Blood Pressure Monitoring: A Methodological Review (“Sharma”)” (published in Technologies, Vol. 5, No. 21, 2017, Multidisciplinary Digital Publishing Institute (MDPI), which are incorporated herein by reference.

[0043] Some previously disclosed methods involve calculating blood pressure based on PTT and / or PAT measured by a sensor system including a PPG sensor, according to one or more equations in Sharma's Table 1 or other known equations. As noted above, some disclosed PAPG-based implementations are configured to distinguish arterial HRW from other HRWs. Such implementations provide more accurate PTT and / or PAT measurements compared to those measured by a PPG sensor. Therefore, the disclosed PAPG-based implementations provide more accurate blood pressure estimates, even when the blood pressure estimate is based on previously known formulas.

[0044] Other embodiments of system 400 may not include electrocardiogram sensor 405. In some such embodiments, device 415, configured to be mounted on the wrist of person 401, may be or may include means configured to perform at least some of the PAPG methods disclosed herein. For example, device 415 may be or may include Figure 7Device 700 or similar devices. According to some such examples, device 415 may include a light source system and two or more ultrasonic receivers. See below for reference. Figure 6A Describe an example. In some examples, device 415 may include an array of ultrasonic receivers.

[0045] In some specific embodiments of system 400 excluding electrocardiogram sensor 405, device 410 may include a light source system and two or more ultrasound receivers. See below. Figure 6B An example is described.

[0046] Figure 5 A cross-sectional side view of a portion of an artery 500 through which a pulse 502 propagates is shown. Figure 5 The boxed arrows indicate the direction of blood flow and pulse propagation. As illustrated, the propagating pulse 502 causes strain in the arterial wall 504, which manifests as an expansion of the arterial wall's diameter (and therefore its cross-sectional area), termed "dilation." The actual spatial length L of the propagating pulse along the artery (in the direction of blood flow) is typically comparable to the length of a limb, such as the distance from the subject's shoulder to their wrist or fingers, and is usually less than one meter (m). However, the length L of the propagating pulse can vary considerably between different subjects and, for a given subject, may depend on various factors that vary significantly over time. The spatial length L of the pulse will generally decrease with increasing distance from the heart until the pulse reaches the capillaries.

[0047] As described above, certain specific implementations involve devices, systems, and methods for estimating blood pressure or other cardiovascular characteristics based on estimations of arterial dilation waveforms. Unless otherwise indicated, the terms “estimate,” “measure,” “calculate,” “infer,” “derive,” “evaluate,” “determine,” and “monitor” are used interchangeably herein where appropriate. Similarly, derivatives of the roots of these terms are also used interchangeably where appropriate; for example, the terms “estimate,” “measure,” “calculate,” “infer,” and “determine” are also used interchangeably herein. In some implementations, the pulse wave velocity (PWV) of the propagating pulse can be estimated by measuring its pulse transit time (PTT) as the pulse travels from a first physical location along the artery to a second physical location further distal along the artery. However, either version of the PTT can be used for blood pressure estimation purposes. The physical distance between the first and second physical locations is assumed. If it is deterministic, then PWV can be estimated as the physical spatial distance the pulse travels. Divide by the physical distance traveled by the pulse The quotient of the time taken (PTT). Typically, a first sensor located at a first physical location is used to determine the start time when the pulse arrives at or travels through the first physical location (also referred to herein as the "first time location"). A second sensor located at a second physical location is used to determine the end time when the pulse arrives at that point or travels through the second physical location and continues through the remainder of the arterial branch (also referred to herein as the "second time location"). In such examples, PTT represents the time distance (or time difference) between the first time location and the second time location (start time and end time).

[0048] The fact that the pulse expansion waveform is measured at two different physical locations means that the estimated PWV inevitably represents the total path distance traveled by the pulse between the first and second physical locations. The average value is calculated based on blood density. More specifically, PWV typically depends on several factors, including blood density. arterial wall stiffness (or conversely, elasticity), arterial diameter, arterial wall thickness, and blood pressure. Because both arterial wall elasticity and baseline resting diameter (e.g., the diameter at the end of ventricular diastole) vary significantly throughout the arterial system, the PWV estimate obtained from a PTT measurement is essentially an average (the total path length between the two locations where the measurement was performed). (Take the average from above).

[0049] In conventional methods for obtaining pulse wave velocity (PWV), electrocardiogram (ECG) sensors (which detect electrical signals from the heart) have been used to determine the onset time of the pulse at the heart. For example, the onset time can be estimated based on the QRS complex (the electrical signal characteristics of ventricular electrical stimulation). In such methods, different sensors positioned at a second location (e.g., the finger) are typically used to determine the end time of the pulse. As those skilled in the art will understand, there are numerous arterial discontinuities, branches, and variations along the entire path from the heart to the finger. Variations in PWV can reach or exceed one order of magnitude along various extensions of the entire path from the heart to the finger. Therefore, PWV estimations based on such a long path length are unreliable.

[0050] In the various specific embodiments described herein, PTT estimation is obtained based on measurements associated with an arterial dilation signal (also referred to as "arterial dilation data" or "sound waves corresponding to a photoacoustic response"), obtained by each of a first arterial dilation sensor 506 and a second arterial dilation sensor 508, respectively, approaching a first physical location and a second physical location along the artery of interest. In some specific embodiments, the first arterial dilation sensor 506 and the second arterial dilation sensor 508 are advantageously positioned near the first and second physical locations, and between the first and second physical locations, arterial properties of the artery of interest, such as wall elasticity and diameter, can be considered or assumed to be relatively constant. In this way, PWV calculated based on PTT estimation is more representative of the actual PWV along a specific segment of the artery. Subsequently, blood pressure is estimated based on PWV. This provides a more accurate representation of actual blood pressure. In some specific implementations, the separation distance between the first arterial dilation sensor 506 and the second arterial dilation sensor 508... The magnitude of the pulse (and therefore the distance between the first and second positions along the artery) can range from about 1 centimeter (cm) to tens of centimeters, long enough to distinguish the arrival of the pulse at the first physical position from the arrival of the pulse at the second physical position, but close enough to ensure arterial consistency. In some specific implementations, the distance between the first arterial dilation sensor 506 and the second arterial dilation sensor 508... The distance can be in the range of approximately 1 cm to approximately 30 cm, and in some embodiments, less than or equal to approximately 20 cm, and in some embodiments, less than or equal to approximately 10 cm, and in some embodiments, less than or equal to approximately 5 cm. In some other embodiments, the distance between the first arterial dilation sensor 506 and the second arterial dilation sensor 508... The distance can be less than or equal to 1 cm, for example, about 0.1 cm, about 0.25 cm, about 0.5 cm, or about 0.75 cm. For reference, a typical PWV can be about 15 m / s. Using a distance of about 5 cm between the first arterial dilation sensor 506 and the second arterial dilation sensor 508, and assuming a PWV of about 15 m / s means a PTT of about 3.3 ms for the monitoring device.

[0051] The distance between the first arterial dilation sensor 506 and the second arterial dilation sensor 508 The values ​​of the quantities can be pre-programmed into the memory within the monitoring device that integrates the sensors (e.g., such as a control system, as referred to below). Figure 7The memory described in 706, or the memory configured to communicate with it. As will be understood by those skilled in the art, in such an embodiment, the spatial length L of the pulse may be greater than the distance from the first arterial dilation sensor 506 to the second arterial dilation sensor 508. Therefore, although Figure 5 The illustrated pulse 502 is shown to have a spatial length L equivalent to the distance between the first arterial dilation sensor 506 and the second arterial dilation sensor 508, but in reality, each pulse can typically have a distance greater than and even much greater than (e.g., about an order of magnitude or more) the distance between the first arterial dilation sensor 506 and the second arterial dilation sensor 508. The spatial length L.

[0052] In some embodiments of the monitoring device disclosed herein, the first arterial dilation sensor 506 and the second arterial dilation sensor 508 are both sensors of the same sensor type. In some such embodiments, the first arterial dilation sensor 506 and the second arterial dilation sensor 508 are identical sensors. In such embodiments, each of the first arterial dilation sensor 506 and the second arterial dilation sensor 508 utilizes the same sensor technology with the same sensitivity to arterial dilation signals caused by propagating pulses, and has the same time delay and sampling characteristics. In some embodiments, each of the first arterial dilation sensor 506 and the second arterial dilation sensor 508 is configured for, for example, photoacoustic volumetric plethysmography (PAPG) sensing as disclosed elsewhere herein. Some such embodiments include a light source system and two or more ultrasound receivers. In some embodiments, each of the first arterial dilation sensor 506 and the second arterial dilation sensor 508 is configured for ultrasound sensing via the transmission of ultrasound signals and the reception of corresponding reflections. In some alternative embodiments, each of the first arterial dilation sensor 506 and the second arterial dilation sensor 508 can be configured for impedance plethysmography (IPG) sensing, also known in a biomedical context as bioimpedance sensing. In various embodiments, regardless of the type of sensor used, each of the first arterial dilation sensor 506 and the second arterial dilation sensor 508 functions broadly to capture and provide arterial dilation data indicating an arterial dilation signal generated by the propagation of a pulse through a portion of the artery adjacent to the corresponding sensor. For example, the arterial dilation data may be provided from the sensor to the processor in the form of a voltage signal generated or received by the sensor based on an ultrasound signal or impedance signal sensed by the corresponding sensor.

[0053] As described above, during the systolic phase of cardiac circulation, as the pulse travels along the arteries through a specific location, the arterial walls expand according to the pulse waveform and the elastic properties of the arterial walls. This expansion is accompanied by a corresponding increase in blood volume at that specific location or region, and with this increase in blood volume, one or more properties in that region change accordingly. Conversely, during the diastolic phase of cardiac circulation, blood pressure in the arteries decreases and the arterial walls constrict. This constriction is accompanied by a corresponding decrease in blood volume at that specific location, and with this decrease in blood volume, one or more properties in that region change accordingly.

[0054] In the context of bioimpedance sensing (or impedance plethysmography), blood in an artery has a higher conductivity than surrounding or adjacent skin, muscle, fat, tendons, ligaments, bone, lymph, or other tissues. The susceptivity (and therefore the dielectric constant) of blood also differs from that of other types of surrounding or nearby tissues. As a pulse propagates through a particular location, the corresponding increase in blood volume results in an increase in conductivity (and more generally, an increase in admittance, or equivalently, a decrease in impedance) at that location. Conversely, during the diastolic phase of cardiac circulation, the corresponding decrease in blood volume results in an increase in resistivity (and more generally, an increase in impedance, or equivalently, a decrease in admittance) at that location.

[0055] Bioimpedance sensors typically function by applying an electrically excited signal at an excitation carrier frequency to a region of interest via two or more input electrodes and detecting an output signal (or multiple output signals) via two or more output electrodes. In some more specific embodiments, the electrically excited signal is a current signal injected into the region of interest via the input electrodes. In some such embodiments, the output signal is a voltage signal representing the voltage response of tissue in the region of interest to the applied excitation signal. The detected voltage response signal is influenced by the different (and in some cases time-varying) electrical properties of the various tissues through which the injected excitation current signal passes. In some embodiments where the bioimpedance sensor is operable to monitor blood pressure, heart rate, or other cardiovascular characteristics, the detected voltage response signal is amplitude and phase modulated by the time-varying impedance (or conversely, admittance) of the underlying artery, as described above, which fluctuates in sync with the user's heartbeat. To determine various biological characteristics, information from the detected voltage response signal is typically demodulated from the excitation carrier frequency component using various analog or digital signal processing circuits, which may include passive and active components.

[0056] In some examples of incorporating ultrasound sensors, the measurement of arterial dilation may involve, for example, guiding ultrasound waves into the limb toward the artery via one or more ultrasound transducers. Such ultrasound sensors are also configured to receive reflected waves, at least in part based on the guided waves. The reflected waves may include scattered waves, specular reflections, or both. The reflected waves provide information about the arterial wall and, therefore, information about arterial dilation.

[0057] In some specific implementations, regardless of the type of sensor used for the first arterial dilation sensor 506 and the second arterial dilation sensor 508, both the first arterial dilation sensor 506 and the second arterial dilation sensor 508 may be arranged, assembled, or otherwise included within a single housing of a single monitoring device. As described above, the housing and other components of the monitoring device may be configured such that when the monitoring device is secured or otherwise physically coupled to a subject, both the first arterial dilation sensor 506 and the second arterial dilation sensor 508 are in contact with or near the user's skin at a first position and a second position, respectively, at a distance from each other. Furthermore, in some embodiments, it may be assumed that the various arterial properties along the arterial extension are relatively constant. In various embodiments, the housing of the monitoring device is a wearable housing, or is incorporated into or integrated with a wearable housing. In some embodiments, the wearable housing includes a physical coupling mechanism (or connection thereto) for detachable, non-invasive attachment to the user. The housing can be formed using any of a variety of suitable manufacturing processes, including injection molding and vacuum forming. Additionally, the housing can be made of any of a variety of suitable materials, including but not limited to plastics, metals, glass, rubber, and ceramics, or combinations of these or other materials. In certain embodiments, the housing and coupling mechanism enable fully non-bedridden use. In other words, some embodiments of the wearable monitoring devices described herein are non-invasive, non-physically inhibiting, and generally do not restrict the free and uninhibited movement of the subject's arms or legs, enabling continuous or periodic monitoring of cardiovascular characteristics (such as blood pressure) even when the subject is moving or otherwise engaging in physical activity. Therefore, monitoring devices facilitate and enable long-term wear and monitoring (e.g., continuously for days, weeks, or a month or more) of one or more biometrics of interest to obtain a better picture of such characteristics over extended durations, and generally, a better picture of the user's health.

[0058] In some implementations, the monitoring device can be positioned around the user's wrist as a strip or band, similar to a watch or fitness / activity tracker. Figure 6AAn example monitoring device 600, designed to be worn around the wrist according to some specific embodiments, is shown. In the illustrated example, the monitoring device 600 includes a housing 602 integrally formed, coupled, or otherwise integrated with a wristband 604. In some cases, a first arterial dilation sensor 606 and a second arterial dilation sensor 608 may each include an instance of the ultrasound receiver system described above and a portion of the light source system. In this example, the monitoring device 600 is coupled around the wrist such that the first arterial dilation sensor 606 and the second arterial dilation sensor 608 within the housing 602 are each positioned along a segment of the radial artery 610 (note that, viewed from the external or outer surface of the housing facing the subject, the sensors are typically concealed, while the monitoring device is coupled to the subject but exposed on the inner surface of the housing so that the sensors can obtain measurements from the underlying artery through the subject's skin). Also as shown, the first arterial dilation sensor 606 and the second arterial dilation sensor 608 are positioned at a fixed distance. Separately. In some other specific implementations, the monitoring device 600 may be similarly designed or adapted for positioning using strips or bands around the forearm, upper arm, ankle, lower leg, thigh, or fingers (all of which are referred to below as "limbs").

[0059] Figure 6B An example monitoring device 600 designed to be worn on a finger is shown according to some specific embodiments. In some cases, the first arterial dilation sensor 606 and the second arterial dilation sensor 608 may each include an instance of the ultrasound receiver described above and a portion of the light source system.

[0060] In some other embodiments, the monitoring device disclosed herein can be positioned on a user’s area of ​​interest without the use of strips or bands. For example, the first arterial dilation sensor 606 and the second arterial dilation sensor 608, along with other components of the monitoring device, can be enclosed in a housing that is secured to the user’s skin in the area of ​​interest using an adhesive or other suitable attachment mechanism (an example of a “patch” monitoring device).

[0061] Figure 6C An example monitoring device 600 designed to reside in an earphone is shown according to some specific embodiments. According to this example, the monitoring device 600 is coupled to the housing of an earphone 620. In some cases, a first arterial dilation sensor 606 and a second arterial dilation sensor 608 may each include an instance of the ultrasound receiver described above and a portion of a light source system.

[0062] Figure 7 This is a block diagram illustrating example components of a PAPG-based device 700 according to some disclosed specific embodiments. Device 700 may include a single device or a group of interconnected devices. Device 700 can be coupled with... Figures 4 to 6C The illustrated device may contain components or all of them. In this example, device 700 may include a photoacoustic sensor system 701, an acoustic sensor system 702, and a motion sensor system 703. Optionally (as shown by the dashed lines), some embodiments of device 700 may include a control system 706, an interface system 708, a noise reduction system 710, or a combination thereof. In a specific embodiment, an interface system 708 may be included (including, for example, a contact surface) to allow contact with the skin to maximize the sensitivity of the acoustic sensor system 702 and / or the motion sensor system 703.

[0063] In some implementations, the photoacoustic sensor system 701 may include an interface, a light source system, a receiver system, and may be an example of a PAPG-based blood pressure monitoring device as previously described. In some specific implementations, the photoacoustic sensor system 701 may also include a controller system or controller.

[0064] Some of the disclosed PAPG sensors described herein (such as photoacoustic sensor system 701) may include a pressure plate, a light source system, and an ultrasonic receiver system. According to some embodiments, the light source system may include a light source configured to generate and direct light. In some embodiments, the pressure plate may include an anti-reflective layer, a mirror layer, or a combination thereof. According to some embodiments, the pressure plate may have an outer surface or a layer on the outer surface having an acoustic impedance configured to approximate the acoustic impedance of human skin. In some embodiments, the pressure plate may have a surface adjacent to the ultrasonic receiver system, or a layer on that surface adjacent to the ultrasonic receiver system, whose acoustic impedance is configured to approximate the acoustic impedance of the ultrasonic receiver system.

[0065] Some of the disclosed PAPG sensors described herein (such as photoacoustic sensor system 701) may include an interface, a light source system, and an ultrasonic receiver system. Some such devices may not include a rigid pressure plate. According to some embodiments, the interface may be a physically flexible interface made of one or more suitable materials having one or more desired properties, such as acoustic properties of acoustic impedance, material flexibility, etc. In some embodiments, the interface may be a flexible interface that can contact a target object that can approach or contact the interface. There may be significant differences between such an interface and a pressure plate. In some embodiments, the light source system may be configured to guide light using one or more optical waveguides (e.g., optical fibers) configured to guide light toward a target object. According to some embodiments, the interface may have an outer surface or a layer on that outer surface whose acoustic impedance is configured to approximate that of human skin. Such an outer surface may have contact portions that can be contacted by a user or a part of the user's body (e.g., fingers, wrist). In some examples, an optical waveguide may be embedded in one or more acoustic matching layers configured to bring light transmitted by the optical waveguide very close to tissue. The outer surface and / or other portions of the interface may be malleable, flexible, adaptable, or otherwise at least partially conformable to the shape and contour of a user's body part. In some embodiments, the interface may have a surface adjacent to, or a layer on, the surface adjacent to, the ultrasonic receiver system, whose acoustic impedance is configured to approximate that of the ultrasonic receiver system. According to some examples, the receiver system may be or may include an array of ultrasonic receivers. In some examples, the photoacoustic sensor system 701 may include one or more separate ultrasonic transmitter elements or an array of one or more separate ultrasonic transmitter elements. In some examples, the ultrasonic transmitter may include an ultrasonic plane wave generator.

[0066] In some embodiments, at least a portion of the photoacoustic sensor system 701 (e.g., a receiver system, a light source system, or both) may include one or more sound-absorbing layers, sound-insulating materials, light-absorbing materials, light-reflecting materials, or combinations thereof. In some examples, the sound-insulating material may reside between at least a portion of the light source system and the receiver system. In some examples, at least a portion of the photoacoustic sensor system 701 (e.g., a receiver system, a light source system, or both) may include one or more electromagnetically shielded transmitting lines. In some such examples, the one or more electromagnetically shielded transmitting lines may be configured to reduce electromagnetic interference received by the receiver system from the light source system.

[0067] The controller control system 706 may include one or more general-purpose single-chip or multi-chip processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic components, discrete hardware components, or combinations thereof. The control system 706 may also include one or more memory devices (and / or be configured to communicate with them), such as one or more random access memory (RAM) devices, read-only memory (ROM) devices, etc. Therefore, the photoacoustic sensor system 701 may have a memory system including one or more memory devices. The control system 706 may be configured to receive and process data from a receiver system, for example, as described below. If the photoacoustic sensor system 701 includes an ultrasonic transmitter, the control system 706 may be configured to control the ultrasonic transmitter. In some specific implementations, the functionality of the control system 706 may be divided among one or more controllers or processors (such as between a dedicated sensor controller and an application processor in a mobile device).

[0068] In some examples, the control system 706 is communicatively coupled to the light source system and configured to control the light source system to emit light toward a target object on the outer surface of the interface. In some such examples, the control system 706 may be configured to receive from an ultrasound receiver system a signal corresponding to ultrasound waves generated by the target object in response to light from the light source system. In some examples, the control system 706 may be configured to identify one or more vascular signals, such as arterial or venous signals, from the ultrasound receiver system. In some such examples, the one or more arterial or venous signals may be, or may include, one or more vessel wall signals corresponding to ultrasound waves generated by one or more arterial or venous walls of the target object. In some such examples, the one or more arterial or venous signals may be, or may include, one or more arterial blood signals corresponding to ultrasound waves generated by blood within an artery of the target object, or one or more venous blood signals corresponding to ultrasound waves generated by blood within a vein of the target object.

[0069] In some examples, the control system 706 may be configured to determine or estimate one or more physiological parameters or cardiac characteristics based at least in part on one or more arterial signals, one or more venous signals, or a combination thereof. According to some examples, the cardiac characteristic may be blood pressure, or may include blood pressure. In some embodiments, the control system 706 may use data from the photoacoustic sensor system 701, along with data from other components of the device 700 (e.g., the acoustic sensor system 702 and the motion sensor system 703), to determine enhanced physiological parameters according to the various embodiments described herein.

[0070] In a further example, the control system 706 may be communicatively coupled to a receiver system. The receiver system may be configured to detect acoustic signals from a target object. The control system 706 may be configured to select at least one of a plurality of receiver elements from the receiver system. Such a selected receiver element may correspond to the optimal signal from the plurality of receiver elements. In some embodiments, the selection of at least one receiver element may be based on information about detected acoustic signals (e.g., arterial or venous signals) from the plurality of receivers. For example, the signal quality or signal strength of some signals (e.g., based on signal-to-noise ratio (SNR)) may be relatively higher than some other signals, or higher than a predetermined threshold or percentile, which may indicate the optimal signal. In some specific embodiments, the control system 706 may also be configured to determine or estimate at least one characteristic of a blood vessel, such as pulse wave velocity (indicating arterial stiffness), arterial size, or both, based on information about the detected acoustic signals.

[0071] Some specific implementations of device 700 may include an interface system 708. In some examples, interface system 708 may include a wired and / or wireless communication interface system. The communication interface system allows device 700 to communicate with other devices in the system. According to some embodiments, in addition to having an integrated motion sensor system 703 that can obtain activity information, or as an alternative, device 700 may communicate with one or more other devices (including sensors) that can provide device 700 with activity information including sensor data and / or data derived therefrom (e.g., identified motion or activity, etc.). Embodiments describing how such activity information can be used will be described in more detail thereafter.

[0072] In some implementations, interface system 708 may include a user interface system, one or more network interfaces, one or more interfaces between control system 706 and memory system, and / or one or more interfaces between control system 706 and one or more external device interfaces (e.g., ports or application processors), or combinations thereof. According to some examples where interface system 708 is present and includes a user interface system, the user interface system may include a microphone system, a speaker system, a haptic feedback system, a voice command system, one or more displays, or combinations thereof. According to some examples, interface system 708 may include a touch sensor system, a gesture sensor system, or combinations thereof. The touch sensor system (if present) may be a resistive touch sensor system, a surface capacitive touch sensor system, a projected capacitive touch sensor system, a surface acoustic wave touch sensor system, an infrared touch sensor system, any other suitable type of touch sensor system, or combinations thereof, or may include a resistive touch sensor system, a surface capacitive touch sensor system, a projected capacitive touch sensor system, a surface acoustic wave touch sensor system, an infrared touch sensor system, any other suitable type of touch sensor system, or combinations thereof.

[0073] In some examples, interface system 708 may include a force sensor system. The force sensor system (if present) may be a piezoresistive sensor, a capacitive sensor, a thin-film sensor (e.g., a polymer-based thin-film sensor), another suitable type of force sensor, or a combination thereof, or may include a piezoresistive sensor, a capacitive sensor, a thin-film sensor (e.g., a polymer-based thin-film sensor), another suitable type of force sensor, or a combination thereof. If the force sensor system includes a piezoresistive sensor, the piezoresistive sensor may include silicon, metal, polycrystalline silicon, glass, or a combination thereof. In some embodiments, the ultrasonic fingerprint sensor and the force sensor system may be mechanically coupled. In some embodiments, the force sensor system may be mechanically coupled to a pressure plate. In some such examples, the force sensor system may be integrated into the circuitry of the ultrasonic fingerprint sensor. In some examples, interface system 708 may include an optical sensor system, one or more cameras, or a combination thereof.

[0074] According to some examples, device 700 may include a noise reduction system 710. For example, noise reduction system 710 may include one or more mirrors configured to reflect light from the light source system away from the receiver system. In some implementations, noise reduction system 710 may include one or more sound-absorbing layers, sound-insulating materials, light-absorbing materials, light-reflecting materials, or combinations thereof. In some examples, noise reduction system 710 may include sound-insulating materials that may reside between, on, or in combination with, at least a portion of the light source system and the receiver system. In some examples, noise reduction system 710 may include one or more electromagnetically shielded transmission lines. In some such examples, the one or more electromagnetically shielded transmission lines may be configured to reduce electromagnetic interference received by the receiver system from circuitry of the light source system, receiver system circuitry, or combinations thereof.

[0075] According to some embodiments, the motion sensor system 703 may include one or more motion sensors that can be used, for example, to obtain activity information. Apple, the motion sensor system 703 may include one or more accelerometers, gyroscopes, altimeters, and / or other inertial or motion sensors capable of determining the motion of artist 700 and / or one or more devices communicatively coupled thereto. According to some embodiments, the motion sensor system 703 may also be used to obtain additional information about tissue movement, eliminate environmental noise or motion, improve signal quality, and enhance physiological measurements related to heart rate, blood pressure, etc.

[0076] Device 700 can be used in a variety of different contexts, many examples of which are disclosed herein. For example, in some embodiments, a mobile device may include a photoacoustic sensor system 701, an acoustic sensor system 702, and a motion sensor system 703. In some such examples, the mobile device may be a smartphone. In some embodiments, a wearable device may include a photoacoustic sensor system 701, an acoustic sensor system 702, and a motion sensor system 703. Wearable devices may be, for example, wristbands, armbands, wrist straps, watches, rings, headbands, or patches. In some embodiments, a wearable device may be one or a pair of earbuds, headphones, headrest mounts, headbands, headphones, or another head-worn or head-mounted device. Figure 6C An example monitoring device designed to reside in an in-ear headphone is shown.

[0077] As previously noted, PAPG devices (e.g., device 700) may be able to determine blood pressure and other physiological measurements using PAPG measurements alone. For example, models that correlate PAPG measurements (e.g., PTT, PWV) with blood pressure can be used to determine blood pressure estimates corresponding to PAPG measurements. However, using this model is a limitation because each user may have different correlations between PAPG measurements and blood pressure. Therefore, the "one model for all" approach to BP estimation can be further improved using personalization. This personalization can be implemented via machine learning (ML) models (e.g., deep learning). However, this personalization may be limited by PAPG measurement information. Additional information such as an individual's various lifestyle choices, physical activity, sleep, resting heart rate (HR), and similar factors can help personalize and ultimately ensure more accurate blood pressure estimates.

[0078] As described in this article, a “model” characterizes the correlation between PAPG measurements and blood pressure. Typically, these correlation models can be represented by a curve on a blood pressure graph of a specific PAPG measurement (e.g., PWV), and personalized models can represent a curve personalized for a specific user of the PAPG device. Thus, such models enable the estimation of accurate blood pressure using the correlation between a specific user's blood pressure and PAPG measurements.

[0079] With this in mind, the embodiments described herein involve obtaining and utilizing activity information to determine a personalized model for an individual, which can be used to accurately determine blood pressure estimates based on PAPG measurement information via a PAPG device (e.g., a wearable device as previously described). Depending on the desired functionality, different techniques may be implemented in the embodiments to provide enhanced blood pressure estimation using activity information. For example, according to a first technique (referring below) Figure 8 (In more detail), activity information acquired over time can be used to personalize a general model in the general manner described above. According to the second technique (see below) Figure 9 (To be described in more detail), a calibration process can be performed during which a well-calibrated device performs a blood pressure measurement while the user performs various activities, and the PAPG device or other devices (e.g., microphone, piezoelectric device, EKG combined with PAPG / oscilloscope technology) acquire measurements, thereby enabling the selection and subsequent use of a model that accurately correlates PAPG measurements with blood pressure.

[0080] Figure 8The diagram 800 illustrates the first technique, in which a general model 805 is personalized for a specific user, based not only on PAPG measurements 810 but also on activity information 815 regarding activities performed by that specific user. As indicated, this personalization can be performed by an ML process 820 (e.g., a deep learning ML model), which can modify the general model 805 to provide a personalized model 825, as illustrated. The personalized model 825 can then be used to obtain a more accurate blood pressure estimate for that specific user.

[0081] The general model 805 may include an initial correlation model for relating PAPG measurements to blood pressure. Depending on the desired functionality, the general model 805 may include a model that performs well on a variety of demographic data, thereby achieving relatively accurate modeling for a variety of user types. According to some embodiments, the general model 805 may be “tuned” using one or more features (not shown) that are correlated with blood pressure, such as age, health, weight, etc. These features may be input by, for example, a user via a user interface (e.g., which may be part of interface system 708, as previously described).

[0082] ML processing 820 can generate a personalized model 825 by performing additional tuning based on PAPG measurement 810 and activity information 815, which can increase the accuracy of blood pressure estimation. That is, the personalized model 825 can be used to accurately estimate a particular user's blood pressure based at least in part on knowledge of various activities the user engages in (e.g., exercise, sleep, etc.). More specifically, according to some embodiments, activity information 815 can be obtained while the user is experiencing various activities, which allows ML processing 820 to perform additional and / or enhanced tuning (e.g., compared to tuning based on PAPG measurement 810 and / or initial tuning of a general model 805 alone). This additional / enhanced tuning results in a more personalized model 825, which provides a more accurate blood pressure estimate. Depending on the desired functionality, model tuning may involve updating the weights of personal characteristics derived from the PAPG signal and / or determining which relevant characteristics (e.g., health, age, etc.) and / or individual-specific weights to use. According to some implementation schemes, activity information 815 may be correlated with PAPG measurement 810 to determine certain characteristics (e.g., resting heart rate (HR)) that can be used to tune the personalized model 825.

[0083] It should be clarified that such personalization may be ongoing. That is, the ML processing 820 may continuously tune the personalization model 825 based on activity information 815 acquired over time. Furthermore, according to some implementations, daily and / or long-term (e.g., historical) activity information may be used as additional features in the personalization model 825. The personalization model 825 can then be used to determine a blood pressure estimate based on PAPG measurements 810 (which may or may not be performed during a specific activity). Depending on the specific implementation, the PAPG device may not be able to obtain PAPG measurements 810 during some activities. However, recent and / or historical activity information is used to tune the personalization model 825 and can therefore be taken into account when determining a blood pressure estimate based on PAPG measurements 810.

[0084] The source and / or content of the activity information 815 may vary depending on the desired functionality. As noted elsewhere herein, the activity information 815 may be obtained via: (i) sensors and / or systems incorporated into the PAPG device (e.g., motion sensor system 703) and / or (ii) one or more sources (e.g., sensors, systems, devices) that are separate from but communicatively coupled to the PAPG device. These one or more sources may include separate wearable devices worn by the user of the PAPG device. For example, an activity tracker and / or smartwatch may include a separate device capable of providing the PAPG device with the activity information 815 for generating a personalized model 825.

[0085] Regarding the content, activity information 815 may include sensor data (e.g., from accelerometers, altimeters, gyroscopes / gyroscopes, IMUs, etc.) and / or high-level information derived therefrom. High-level information may include identifiers of a specific activity (e.g., jogging, walking, cycling, sleeping, etc.), identifiers of the category or type of activity (e.g., high-tempo or low-tempo activity, active or non-active, etc.), or combinations thereof. For example, high-level information may be provided where a separate wearable device (e.g., an activity tracker, a smartwatch, etc.) determines the activity or type of activity based on raw sensor information. Depending on some implementations, an indication of activity intensity may also be provided. For example, running speed may be included in activity information 815 indicating running.

[0086] It can be noted that in some embodiments, PAPG measurement 810 can indicate activity and can be used to provide activity information 815. However, other embodiments may use activity information 815 that includes non-PAPG measurements to provide additional data to the ML processing 820 of the personalized model 825. Because PAPG measurement 810 may be affected by factors other than activity (e.g., heat, pressure, etc.), using activity information 815 that includes non-PAPG measurements helps ensure that the data indicates a specific activity or activity type.

[0087] Figure 9 Example Figure 900 illustrates a second technique in which a calibration process can be performed to determine a model that accurately correlates PAPG measurements with the blood pressure of a particular user. According to this technique, accurate non-PAPG blood pressure measurements (which serve as a baseline true measurement of blood pressure) can be performed during the calibration process using a well-calibrated device while the user performs various activities. PAPG measurements can be acquired concurrently by the PAPG device, and the correlation between the non-PAPG blood pressure measurements yields data points 905, which can be used to determine a model 910 that can be used to correlate PAPG measurements with blood pressure. Once model 910 is determined, an accurate blood pressure estimate can be determined using the PAPG device (after the calibration process) based on the PAPG measurements, which are generally less invasive than the non-PAPG blood pressure measurements used during the calibration process to determine model 910.

[0088] For example, oscillometric and / or electrocardiogram (EKG) methods can be used to perform non-PAPG blood pressure measurements. Depending on the specific implementation, this information may be obtained by a PAPG device (e.g., if it has oscillometric and / or EKG capabilities) and / or a separate device. In some implementations, a separate device may be communicatively coupled to the PAPG device and may provide non-PAPG blood pressure measurements directly to the PAPG device (e.g., via a wired or wireless connection). Additionally or alternatively, the user may provide user input including non-PAPG blood pressure measurements via a user interface.

[0089] Depending on the desired functionality, the PAPG device user can be prompted to engage in various activities during the calibration process. For example, using visual or audio cues via a user interface, the PAPG device can prompt the user (wearing the PAPG device) to perform various activities such as sitting, standing, running, walking, etc. These various activities can be selected to cause significant changes in blood pressure and other vital signs (e.g., PWV). Non-PAPG blood pressure measurements and PAPG measurements (e.g., PWV, PTT) are obtained, thereby producing data point 905 from which model 910 can be determined. According to some embodiments, the PAPG device can continue to prompt the user to engage in various activities until the change in data point 905 is sufficient to allow the determination of model 910.

[0090] According to some implementations, model 910 can be determined via ML or a fitting process to select a model from multiple candidate models based on the model that best fits the data point 905. According to some implementations, the multiple candidate models may include one or more of the following:

[0091]

[0092] • Inverse square model: BP = A / (PTT) 2+B,

[0093] • Inverse proportional model: BP = A / (PTT) + B,

[0094] • Linear model: BP = A(PTT) + B, or

[0095]

[0096] Regarding the model described above, BP is blood pressure, and A and B are fitting parameters associated with arterial wall stiffness. Like the model itself, variations in A and B are individualized characteristics that vary from person to person. The calibration and fitting process not only leads to the selection of which model to use but also determines the values ​​of A and B. According to some implementations, information other than the measurements taken during the calibration process can be used to determine / select the model 910. This information may include, for example, age, weight, health status, and / or other information that may be known to be relevant to certain models.

[0097] It is important to note that, according to some implementations, recalibration (e.g., re-executing the calibration process) can be performed upon the occurrence of a triggering event, where the triggering event indicates the need for recalibration. Triggering events may include the elapsed amount of a previously calibrated threshold time. This threshold time may be based on known drift in the calibrated sensor, changes in age factors (e.g., the user's age changes the threshold amount), or the like. Additional triggering events may include changes in other blood pressure-related factors (e.g., weight, health, etc.) or the like. These triggering events may be determined based on user input, time, since input, or the like.

[0098] Figure 10 This is a flowchart of a method 1000 for expanding a personalized blood pressure model based on activity monitoring according to the implementation plan. Examples of its execution are shown below. Figure 10 The functional components in one or more boxes shown in the diagram may be implemented by hardware and / or software components of a PAPG device or similar device. Example components of a PAPG device are as follows: Figure 7 As illustrated above.

[0099] At box 1010, functionality includes: obtaining activity information indicating activities performed by a person. As noted above (e.g., see reference...). Figure 8The person may include the device user as described elsewhere herein. More specifically, the person may include the wearer of the PAPG device (or, if the PAPG device has more than one component, one or more components of the PAPG device). As also noted in the embodiments described above, activity information may include one or more different types of information. Thus, according to some embodiments of method 1000, activity information may include: an identifier of the activity, an indication of the category of the activity, sensor data indicating the activity, or any combination thereof. Additionally or alternatively, activity information may include data obtained from one or more sensors. In such embodiments, such one or more sensors may include: a gyroscope, an accelerometer, a magnetometer, an altimeter, an inertial measurement unit (IMU), or any combination thereof. As noted above, the embodiments may obtain information from one or more sources, which may include systems, devices, sensors, etc., that are separable from the PAPG device. Thus, according to some embodiments of method 1000, activity information may be obtained from a device communicatively coupled to the device.

[0100] Components for performing functionality at frame 1010 may include: a photoacoustic sensor system 701, an acoustic sensor system 702, a motion sensor system 703, a control system 706, and / or other components of a PAPG device or similar apparatus, such as Figure 7 exemplified.

[0101] At box 1020, the functionality includes: updating the model used to determine a person's blood pressure based at least in part on the obtained activity information. As previously noted (e.g., regarding...). Figure 8 The models all include a correlation model that correlates blood pressure with detected sound waves (e.g., PAPG measurements), thereby enabling a person's blood pressure to be determined from the detected sound waves. According to some embodiments, the models include a plurality of features associated with blood pressure. In such embodiments, updating the model may include: adding one or more features to a plurality of features, subtracting one or more features from a plurality of features, adjusting one or more weights applied to a plurality of features, or any combination thereof. Furthermore, in such embodiments, the plurality of features may include historical activity information. Updating historical activity information may be based at least in part on the obtained activity information.

[0102] Components used to perform functionality at frame 1020 may include: a photoacoustic sensor system 701, an acoustic sensor system 702, a motion sensor system 703, a control system 706, and / or other components of a PAPG device or similar apparatus, such as Figure 7 exemplified.

[0103] At box 1030, functionality includes: detecting an acoustic wave corresponding to the photoacoustic response of a human blood vessel to light emitted by a light source system. The acoustic wave may include a PAPG measurement and / or the PAPG measurement may be derived from the acoustic wave, as described in the embodiments above. As previously noted, the PAPG measurement may include PTT, PWV, HRW, or similar measurements, or any combination thereof.

[0104] Components used to perform functionality at frame 1030 may include: a photoacoustic sensor system 701, an acoustic sensor system 702, a motion sensor system 703, a control system 706, and / or other components of a PAPG device or similar apparatus, such as Figure 7 exemplified.

[0105] At box 1040, the functionality includes: estimating blood pressure at least in part based on the updated model and the sound waves. As previously noted, the blood pressure estimated based on the updated model may be based on the correlation between the sound waves (e.g., PAPG measurements) provided by the updated model and the corresponding blood pressure.

[0106] Components used to perform functionality at frame 1040 may include: a photoacoustic sensor system 701, an acoustic sensor system 702, a motion sensor system 703, a control system 706, and / or other components of a PAPG device or similar apparatus, such as Figure 7 exemplified.

[0107] Figure 11 This is a flowchart of another method 1100 for expanding a personalized blood pressure model based on the use of activity monitoring according to the implementation plan. (Examples are provided.) Figure 11 The functional components in one or more boxes shown in the diagram may be implemented by hardware and / or software components of a PAPG device or similar device. Example components of a PAPG device are as follows: Figure 7 As illustrated above.

[0108] At box 1120, the functionality includes: obtaining a set of photoacoustic measurements, at least in part, by detecting acoustic waves corresponding to the photoacoustic response of a person's blood vessels to light, wherein different photoacoustic measurements of this set of measurements are performed while the person performs a set of activities. The person may include a device user, as described herein. As noted above (e.g., regarding...) Figure 9 According to some embodiments, light may be emitted by a light source system of the device, which may include a light-emitting component. Additionally, sound waves may be detected by a receiver system of the device. Also, as noted above, the device may prompt a person to participate in various activities within the group to help ensure that a sufficient number of blood pressure measurements are taken to determine a model for estimating blood pressure.

[0109] Components used to perform functionality at frame 1120 may include: a photoacoustic sensor system 701, an acoustic sensor system 702, a motion sensor system 703, a control system 706, and / or other components of a PAPG device or similar apparatus, such as Figure 7 exemplified.

[0110] It can be noted that although PAPG devices can take a set of photoacoustic measurements during calibration (as previously discussed...), Figure 9 (As discussed), but the implementation is not limited thereto. Other devices and / or measurements may be used to achieve model determination (at box 1140) and subsequent blood pressure measurements using the determined model. For example, other devices may be used to obtain the measurements. Additionally or alternatively, some measurements (e.g., PWV) may be measured using multiple technologies other than PAPG (e.g., PPG sensors, microphones, piezoelectric devices, or force sensors).

[0111] At box 1130, functionality includes: obtaining a set of reference blood pressure measurements of a person, wherein each reference blood pressure measurement in the set of reference blood pressure measurements corresponds to a corresponding photoacoustic measurement in the set of photoacoustic measurements. As noted above (e.g., reference... Figure 9 Reference blood pressure measurements can be obtained from a well-calibrated device, which may differ from the PAPG device. These reference blood pressure measurements can be used as “reference true” blood pressure measurements and may have corresponding photoacoustic measurements that allow for the determination of a correspondence (e.g., such as...). Figure 9 (As illustrated). According to some embodiments, the reference blood pressure measurement may be obtained from a data source, including a device communicatively coupled to the device, one or more sensors of the device, or both. According to some embodiments, the data source includes an electrocardiogram (EKG) or oscillometric device. According to some embodiments, PAPG technology may be used to determine blood pressure.

[0112] Components used to perform functionality at frame 1130 may include: a photoacoustic sensor system 701, an acoustic sensor system 702, a motion sensor system 703, a control system 706, and / or other components of a PAPG device or similar apparatus, such as Figure 7 exemplified.

[0113] At box 1140, the functionality includes: determining a model for estimating a person's blood pressure from subsequent photoacoustic measurements based on the correlation between the set of reference blood pressure measurements and the set of photoacoustic measurements. As noted in the embodiments described above, determining the model may include: selecting a model from a predetermined set of candidate models. In such embodiments, the method may further include: performing a fitting process to select from the predetermined set of candidate models a model that best fits the data derived from the correlation between the set of reference blood pressure measurements and the set of photoacoustic measurements. Also noted, the fitting process may be performed by an ML model and may also result in the determination of one or more model parameters.

[0114] Additionally, according to some embodiments, recalibration can be performed. In such embodiments, the method may further include: determining a recalibration device; and prompting a person to perform a new set of activities. Thus, some embodiments may include: prompting a person to perform the new set of activities; and providing instructions for recalibration via a UI. The UI may further provide audio and / or visual cues to perform the new set of activities.

[0115] According to some embodiments, method 1100 may further include: estimating blood pressure using the determined model used to do so. That is, some embodiments may further include, after determining the model: obtaining subsequent photoacoustic measurements; and determining a person's blood pressure based at least in part on the subsequent photoacoustic measurements and the model.

[0116] Components used to perform functionality at frame 1140 may include: a photoacoustic sensor system 701, an acoustic sensor system 702, a motion sensor system 703, a control system 706, and / or other components of a PAPG device or similar apparatus, such as Figure 7 exemplified.

[0117] As used in this article, the phrase “at least one of the items” refers to any combination of these items, including a single member. As an example, “at least one of a, b, or c” is intended to cover: a, b, c, ab, ac, bc, and abc.

[0118] The various exemplary logics, logic blocks, modules, circuits, and algorithmic processes described in conjunction with the specific implementations disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. The interchangeability of hardware and software has been broadly described in terms of functionality and illustrated in the aforementioned exemplary components, blocks, modules, circuits, and processes. Whether such functionality is implemented in hardware or software depends on the specific application and the design constraints imposed on the overall system.

[0119] Hardware and data processing means for implementing the various exemplary logic, logic blocks, modules, and circuits described herein can be implemented or executed using general-purpose single-chip or multi-chip processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic components, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor can be a microprocessor, or any conventional processor, controller, microcontroller, or state machine. A processor can also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors combined with a DSP core, or any other such configuration. In some specific implementations, specific processes and methods can be performed by circuitry specific to a given function.

[0120] In one or more aspects, the described functionality may be implemented in hardware, digital electronic circuits, computer software, firmware, including the structures disclosed in this specification and their structural equivalents or any combination thereof. Specific implementations of the subject matter described in this specification may also be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a computer storage medium for execution by a data processing apparatus or for controlling the operation of a data processing apparatus.

[0121] If implemented in software, the functions can be stored as one or more instructions or codes on or transmitted via a computer-readable medium such as a non-transitory medium. The processes of the methods or algorithms disclosed herein can be implemented in a processor-executable software module that can reside on a computer-readable medium. Computer-readable media include both computer storage media and communication media, including any medium capable of transferring a computer program from one location to another. Storage media can be any available medium accessible to a computer. By way of example and not limitation, non-transitory media can include RAM, ROM, EEPROM, CD-ROM or other optical disc storage devices, disk storage devices or other magnetic storage devices, or any other medium that can be used to store desired program code in the form of instructions or data structures and is accessible to a computer. Additionally, any connection can be appropriately referred to as a computer-readable medium. As used herein, disks and optical discs include compact optical discs (CDs), laser discs, optical discs, digital versatile optical discs (DVDs), floppy disks, and Blu-ray discs, wherein disks typically magnetically reproduce data, while optical discs optically reproduce data using lasers. Combinations of the above should also be included within the scope of computer-readable media. In addition, the operation of a method or algorithm may reside as a set of code and instructions or any combination of code and instructions on a machine-readable medium and a computer-readable medium that may be incorporated into a computer program product.

[0122] Various modifications to the specific embodiments described herein may be apparent to those skilled in the art, and the general principles defined herein may be applied to other specific embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not intended to be limited to the specific embodiments shown herein, but is to be accorded the widest scope consistent with the claims, principles, and novel features disclosed herein. The word “exemplary” (if any) is used herein specifically to mean “serving as an example, instance, or illustration.” Any specific embodiment described herein as “exemplary” is not necessarily to be construed as superior to or better than other specific embodiments.

[0123] Certain features described in this specification in the context of a single embodiment may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments. Furthermore, although features may be described above as operating in certain combinations and even originally claimed in this way, one or more features from the claimed combination may be removed from that combination in some cases, and the claimed combination may be for sub-combinations or variations thereof.

[0124] Similarly, although operations are depicted in a specific order in the figures, this should not be construed as requiring such operations to be performed in the shown specific order or sequential order, or to perform all illustrated operations to achieve the desired result. In some environments, multitasking and parallel processing are advantageous. Furthermore, the separation of the various system components in the embodiments described above should not be construed as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products. Additionally, other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve the desired result.

[0125] It should be understood that unless features in any particular embodiment of the description are explicitly identified as incompatible with each other, or the surrounding context suggests that they are mutually exclusive and not easily combined in a complementary and / or supporting sense, the general conception and ideas of this disclosure may be selectively combined with specific features of those complementary embodiments to provide one or more comprehensive but slightly different technical solutions. Therefore, it should also be understood that the above description is given by way of example only and may be modified in detail within the scope of this disclosure.

[0126] Various modifications to the specific embodiments described in this disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other specific embodiments without departing from the spirit or scope of this disclosure. Therefore, the appended claims are not intended to limit them to the specific embodiments shown herein, but are to be accorded the broadest scope consistent with this disclosure, the principles disclosed herein, and the novel features.

[0127] Additionally, certain features described in this specification within the context of individual embodiments may also be implemented in combination within a single embodiment. Conversely, the various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments. Furthermore, although features may be described above as functioning in certain combinations and even originally claimed in this way, in some cases, one or more features from the claimed combination may be removed from that combination, and the claimed combination may involve sub-combinations or variations thereof.

[0128] Similarly, although operations are depicted in a specific order in the figures, this should not be construed as requiring such operations to be performed in the indicated specific order or sequential order, or to perform all illustrated operations to achieve the desired result. Furthermore, the figures may schematically depict one or more example processes in the form of flowcharts. However, other operations not depicted may be incorporated into the schematically illustrated example processes. For example, one or more additional operations may be performed before, after, simultaneously with, or between any of the illustrated operations. Moreover, the various operations in the described and illustrated operations may themselves include multiple sub-operations and collectively refer to multiple sub-operations. For example, each operation in the operations described above may itself involve the execution of a process or algorithm. Furthermore, in some embodiments, the various operations in the described and illustrated operations may be combined or performed in parallel. Similarly, the separation of the various system components in the embodiments described above should not be construed as requiring this separation in all embodiments. Therefore, other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims may be performed in a different order and still achieve the desired result.

[0129] Specific implementation examples are described in the following numbered clauses:

[0130] Clause 1: An apparatus comprising: a control system configured to: acquire activity information indicating an activity performed by a device user, and update a model for determining the device user's blood pressure based at least in part on the acquired activity information; a light source system including a light-emitting component; and a receiver system configured to detect an acoustic wave corresponding to a photoacoustic response of the device user's blood vessels to light emitted by the light source system; and wherein the control system is further configured to estimate blood pressure based at least in part on the updated model and the acoustic wave.

[0131] Clause 2: The apparatus according to Clause 1, wherein in order to obtain the activity information, the control system is configured to obtain: an identifier of the activity, an indication of the category of the activity, sensor data indicating the activity, or any combination thereof.

[0132] Clause 3: The apparatus according to any one of Clauses 1 to 2 further includes one or more sensors, wherein the control system is configured to use data from the one or more sensors to obtain the activity information.

[0133] Clause 4: The device of claim 3, wherein the one or more sensors comprise: a gyroscope, an accelerometer, a magnetometer, an altimeter, an inertial measurement unit (IMU), or any combination thereof.

[0134] Clause 5: The apparatus of any one of Clauses 1 to 4, wherein, in order to obtain the activity information, the control system is configured to receive the activity information from a device communicatively coupled to the apparatus.

[0135] Clause 6: An apparatus according to any one of Clauses 1 to 5, wherein the model comprises a plurality of blood pressure-related features, and wherein, in order to update the model, the control system is configured to: add one or more features to the plurality of features, subtract one or more features from the plurality of features, adjust one or more weights applied to the plurality of features, or any combination thereof.

[0136] Clause 7: The apparatus according to Clause 6, wherein the plurality of features include historical activity information, and wherein, in order to update the model, the control system is configured to update the historical activity information at least in part based on the obtained activity information.

[0137] Clause 8: The apparatus according to any one of Clauses 1 to 7, wherein the control system is configured to derive photoacoustic volumetric (PAPG) measurements from the acoustic waves.

[0138] Clause 9: The apparatus according to Clause 8, wherein, in order to derive the PAPG measurement, the control system is configured to derive heart rate waveform (HRW), pulse transit time (PTT), pulse wave velocity (PWV), or any combination thereof.

[0139] Clause 10: A method for blood pressure estimation, the method comprising: obtaining activity information indicating an activity performed by a person; updating a model for determining the person's blood pressure based at least in part on the obtained activity information; detecting an acoustic wave corresponding to the photoacoustic response of the person's blood vessels to light emitted by a light source system; and estimating the blood pressure based at least in part on the updated model and the acoustic wave.

[0140] Clause 11: The method according to Clause 10 further includes: controlling the light source system to emit the light.

[0141] Clause 12: The method according to Clause 11, wherein the light source system is incorporated into a device used by the person, and wherein the detection of the sound waves is performed by a receiver system of the device.

[0142] Clause 13: The method according to Clause 12, wherein obtaining the activity information includes: receiving the activity information at the device from a device communicatively coupled to the device.

[0143] Clause 14: The method according to any one of Clauses 12 to 13, wherein the device includes a wearable device worn by the person.

[0144] Clause 15: The method according to any one of Clauses 10 to 14, wherein the activity information includes: an identifier of the activity, an indication of the category of the activity, sensor data indicating the activity, or any combination thereof.

[0145] Clause 16: The method according to any one of Clauses 10 to 15, wherein the activity information is obtained from one or more sensors.

[0146] Clause 17: The method of claim 16, wherein the one or more sensors comprise: a gyroscope, an accelerometer, a magnetometer, an altimeter, an inertial measurement unit (IMU), or any combination thereof.

[0147] Clause 18: The method according to any one of Clauses 10 to 17, wherein the model comprises a plurality of features related to blood pressure, and wherein updating the model comprises: adding one or more features to the plurality of features, subtracting one or more features from the plurality of features, adjusting one or more weights applied to the plurality of features, or any combination thereof.

[0148] Clause 19: The method according to Clause 18, wherein the plurality of features includes historical activity information, and wherein updating the model comprises: updating the historical activity information based at least in part on the obtained activity information.

[0149] Clause 20: The method according to any one of Clauses 10 to 19, the method further comprising: deriving photoacoustic volumetric plethysmography (PAPG) measurements from the acoustic waves.

[0150] Clause 21: The PAPG measurement described in accordance with Clause 20 includes heart rate waveform (HRW), pulse transit time (PTT), pulse wave velocity (PWV), or any combination thereof.

[0151] Clause 22: An apparatus comprising: means for acquiring activity information indicating an activity performed by a person; means for updating a model for determining the person's blood pressure based at least in part on the acquired activity information; means for detecting an acoustic wave corresponding to the photoacoustic response of the person's blood vessels to light emitted by a light source system; and means for estimating the blood pressure based at least in part on the updated model and the acoustic wave.

[0152] Clause 23: The apparatus according to Clause 22, wherein the apparatus includes a wearable device configured to detect the sound waves and estimate the blood pressure when worn by the person.

[0153] Clause 24: The apparatus according to any one of Clauses 22 to 23, wherein the component for obtaining the activity information includes components for obtaining: an identifier of the activity, an indication of the category of the activity, sensor data indicating the activity, or any combination thereof.

[0154] Clause 25: The apparatus according to any one of Clauses 22 to 24, wherein the component for obtaining the activity information includes a component for obtaining the activity information from one or more sensors.

[0155] Clause 26: The device of claim 25, wherein the one or more sensors comprise: a gyroscope, an accelerometer, a magnetometer, an altimeter, an inertial measurement unit (IMU), or any combination thereof.

[0156] Clause 27: An apparatus according to any one of Clauses 22 to 26, wherein the model includes a plurality of blood pressure-related features, and wherein the components for updating the model include: components for adding one or more features to the plurality of features, components for subtracting one or more features from the plurality of features, components for adjusting one or more weights applied to the plurality of features, or any combination thereof.

[0157] Clause 28: The apparatus according to Clause 27, wherein the plurality of features include historical activity information, and wherein the component for updating the model includes a component for updating the historical activity information based at least in part on the obtained activity information.

[0158] Clause 29: The apparatus according to any one of Clauses 22 to 28 further includes components for deriving photoacoustic volumetric plethysmography (PAPG) measurements from the acoustic waves.

[0159] Clause 30: The PAPG measurement of the apparatus described in Clause 29 includes heart rate waveform (HRW), pulse transit time (PTT), pulse wave velocity (PWV), or any combination thereof.

[0160] Clause 31: An apparatus comprising: a light source system including a light-emitting component; and a receiver system configured to perform photoacoustic measurements by detecting acoustic waves corresponding to the photoacoustic response of a blood vessel of a device user to light emitted by the light source system; and a control system configured to: use the light source system and the receiver system to obtain a set of photoacoustic measurements, wherein different photoacoustic measurements of the set of photoacoustic measurements are performed simultaneously with the device user performing a set of activities; obtain a set of reference blood pressure measurements of the device user, wherein each reference blood pressure measurement of the set of reference blood pressure measurements corresponds to a corresponding photoacoustic measurement of the set of photoacoustic measurements; and determine a model for estimating the blood pressure of the device user from subsequent photoacoustic measurements based on the correlation between the set of reference blood pressure measurements and the set of photoacoustic measurements.

[0161] Clause 32: The device according to Clause 31, wherein the control system is further configured to, after determining the model: obtain subsequent photoacoustic measurements using the light source system and the receiver system; and determine the blood pressure of the device user based at least in part on the subsequent photoacoustic measurements and the model.

[0162] Clause 33: An apparatus according to any one of Clauses 31 to 32, wherein the control system is configured to obtain the set of reference blood pressure measurements from a data source, the data source including a device communicatively coupled to the apparatus, one or more sensors of the apparatus, or both.

[0163] Clause 34: The apparatus described in Clause 33, wherein the data source includes an electrocardiogram (EKG) or oscillometric device.

[0164] Clause 35: An apparatus according to any one of Clauses 31 to 34, wherein, in order to determine the model, the control system is configured to select the model from a predetermined set of candidate models.

[0165] Clause 36: The apparatus according to Clause 35, wherein, in order to select the model, the control system is configured to perform a fitting process to select from a predetermined set of candidate models the model that best fits the data derived from the correlation between the set of reference blood pressure measurements and the set of photoacoustic measurements.

[0166] Clause 37: The apparatus according to any one of Clauses 31 to 36, wherein the control system is further configured to: determine to recalibrate the apparatus; and prompt the user of the apparatus to perform a new set of activities.

[0167] Clause 38: The apparatus according to Clause 37, wherein the apparatus includes a user interface (UI), and the control system is configured to provide instructions for the recalibration via the UI in order to prompt the user of the apparatus to perform the new set of activities.

[0168] Clause 39: The apparatus according to any one of Clauses 31 to 38, wherein the set of photoacoustic measurements includes heart rate waveform (HRW), pulse transit time (PTT), pulse wave velocity (PWV), or any combination thereof.

[0169] Clause 40: An apparatus having components for performing the functions performed by an apparatus according to any one of Clauses 1 to 9 or Clauses 31 to 39.

[0170] Clause 41: A non-transitory computer-readable medium storing instructions, the instructions including code for performing functions performed by an apparatus according to any one of Clauses 1 to 9 or Clauses 31 to 39.

[0171] Clause 42: A method for performing functions performed by an apparatus according to any one of Clauses 1 to 9 or Clauses 31 to 39.

[0172] Clause 43: A system comprising one or more devices configured to perform functions performed by means of any one of Clauses 1 to 9 or Clauses 31 to 39.

Claims

1. An apparatus, the apparatus comprising: The control system is configured to: Obtain activity information indicating activities performed by the device user, and The model used to determine the blood pressure of the user of the device is updated at least in part based on the obtained activity information; A light source system, the light source system including a light-emitting component; and A receiver system configured to detect acoustic waves corresponding to the photoacoustic response of a blood vessel of the device user to light emitted by the light source system; and The control system is further configured to estimate blood pressure based at least in part on the updated model and the sound waves.

2. The apparatus of claim 1, wherein in order to obtain the activity information, the control system is configured to obtain: The identifier of the activity. Indication of the category of the activity, Sensor data indicating the activity, or Any combination of them.

3. The apparatus of claim 1, further comprising one or more sensors, wherein the control system is configured to use data from the one or more sensors to obtain the activity information.

4. The apparatus of claim 3, wherein the one or more sensors comprise: Gyroscope, accelerometer, Magnetometer altimeter, Inertial Measurement Unit (IMU), or Any combination of them.

5. The apparatus of claim 1, wherein, in order to obtain the activity information, the control system is configured to receive the activity information from a device communicatively coupled to the apparatus.

6. The apparatus of claim 1, wherein the model includes a plurality of blood pressure-related features, and wherein, in order to update the model, the control system is configured to: Add one or more features to the plurality of features. Subtract one or more features from the plurality of features Adjust one or more weights applied to the plurality of features, or Any combination of them.

7. The apparatus of claim 6, wherein the plurality of features include historical activity information, and wherein, in order to update the model, the control system is configured to update the historical activity information at least in part based on the obtained activity information.

8. The apparatus of claim 1, wherein the control system is configured to derive photoacoustic volumetric plethysmography (PAPG) measurements from the acoustic waves.

9. The apparatus of claim 8, wherein, in order to derive the PAPG measurement, the control system is configured to derive heart rate waveform (HRW), pulse transit time (PTT), pulse wave velocity (PWV), or any combination thereof.

10. A method for estimating blood pressure, the method comprising: To obtain activity information that instructs activities to be performed by people; The model used to determine the person's blood pressure is updated, at least in part, based on the obtained activity information; Detecting the acoustic waves corresponding to the photoacoustic response of the blood vessels of the person to light emitted by the light source system; and Blood pressure is estimated at least in part based on the updated model and the sound waves.

11. The method according to claim 10, further comprising: Control the light source system to emit the light.

12. The method of claim 11, wherein the light source system is incorporated into a device used by the person, and wherein the detection of the sound waves is performed by a receiver system of the device.

13. The method of claim 12, wherein obtaining the activity information comprises: The activity information is received at the device from a device communicatively coupled to the device.

14. The method of claim 12, wherein the device comprises a wearable device worn by the person.

15. The method of claim 10, wherein the activity information includes: The identifier of the activity. Indication of the category of the activity, Sensor data indicating the activity, or Any combination of them.

16. The method of claim 10, wherein the activity information is obtained from one or more sensors.

17. The method of claim 16, wherein the one or more sensors comprise: Gyroscope, accelerometer, Magnetometer altimeter, Inertial Measurement Unit (IMU), or Any combination of them.

18. The method of claim 10, wherein the model comprises a plurality of blood pressure-related features, and wherein updating the model comprises: Add one or more features to the plurality of features. Subtract one or more features from the plurality of features Adjust one or more weights applied to the plurality of features, or Any combination of them.

19. The method of claim 18, wherein the plurality of features includes historical activity information, and wherein updating the model comprises: The historical activity information is updated based at least in part on the activity information obtained.

20. The method according to claim 10, further comprising: Photoacoustic volumetric plethysmography (PAPG) measurements are derived from the sound waves.

21. The method of claim 20, wherein the PAPG measurement comprises heart rate waveform (HRW), pulse transit time (PTT), pulse wave velocity (PWV), or any combination thereof.

22. An apparatus comprising: Components used to obtain activity information indicating activities performed by humans; Components for updating the model used to determine the person's blood pressure, at least in part, based on the obtained activity information; A component for detecting acoustic waves corresponding to the photoacoustic response of the blood vessels of the person to light emitted by the light source system; and A component for estimating blood pressure based at least in part on the updated model and the sound waves.

23. The apparatus of claim 22, wherein the apparatus includes a wearable device configured to detect the sound waves and estimate the blood pressure when worn by the person.

24. The apparatus of claim 22, wherein the component for obtaining the activity information includes components for obtaining: The identifier of the activity. Indication of the category of the activity, Sensor data indicating the activity, or Any combination of them.

25. The apparatus of claim 22, wherein the component for obtaining the activity information includes a component for obtaining the activity information from one or more sensors.

26. The apparatus of claim 25, wherein the one or more sensors comprise: Gyroscope, accelerometer, Magnetometer altimeter, Inertial Measurement Unit (IMU), or Any combination of them.

27. The apparatus of claim 22, wherein the model includes a plurality of blood pressure-related features, and wherein the component for updating the model includes: A component for adding one or more features to the plurality of features. A component used to subtract one or more features from the plurality of features. A component for adjusting one or more weights applied to the plurality of features, or Any combination of them.

28. The apparatus of claim 27, wherein the plurality of features include historical activity information, and wherein the component for updating the model includes a component for updating the historical activity information based at least in part on the obtained activity information.

29. The apparatus of claim 22, further comprising components for deriving photoacoustic volumetric plethysmography (PAPG) measurements from the acoustic waves.

30. The apparatus of claim 29, wherein the PAPG measurement comprises heart rate waveform (HRW), pulse transit time (PTT), pulse wave velocity (PWV), or any combination thereof.

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