Monitoring cardiovascular health using sensor data

Abstract

To provide a computing system, computer implemented method, and wearable computing device for determining a cardiovascular response on the basis of at least in part of sensor data of a wearable device.SOLUTION: A computing system receives impedance plethysmography (IPG) data generated by an IPG sensor disposed at a lower side of a housing of a wearable device. The computing system receives photoplethysmography (PPG) data generated by a PPG sensor disposed at the lower side of the housing and proximate to the IPG sensor. Then, the computing system determines a cardiovascular response on the basis of at least in part of a comparison of the IPG data and the PPG data, such as a peripheral myogenic response.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Application No. 63 / 658,238, having a filing date of June 10, 2024. Applicant claims priority to and the benefit of the above-listed applications, which are incorporated herein by reference in their entireties.

[0002] The present disclosure relates generally to monitoring cardiovascular health using noninvasive sensor data obtained from one or more sensors on a wearable computing device. More specifically, the present disclosure relates to passively monitoring myogenic responses using sensor data obtained from at least one or more photoplethysmography (PPG) sensors and one or more impedance plethysmography (IPG) sensors on a wearable computing device, where the monitored myogenic responses are indicative of different cardiovascular health indicators. [Background technology]

[0003] Peripheral vasoconstriction and vasodilation are directly related to many aspects of cardiovascular health and are affected by diseases including hypertension, atherosclerosis, peripheral arterial disease, Raynaud's phenomenon, diabetes mellitus, heart failure, and aging. However, peripheral myogenic responses such as peripheral vasoconstriction and peripheral vasodilation are complex physiological processes that are also affected by a wide range of factors, such as temperature changes, posture, hydration, exercise, and stress, making them difficult to accurately monitor using wearable consumer computing devices. For example, photoplethysmography (PPG) sensors are optical sensors used to measure pulsatile blood flow by measuring changes in blood volume in superficial vascular beds. However, PPG signals are prone to degradation during vasoconstriction due to reduced blood flow in superficial tissues and are sensitive to movement and device wear, making it difficult to isolate the effects of vasoconstriction in PPG data.

[0004] Other technologies for monitoring indicators related to cardiovascular health include electrocardiogram (ECG) technology, which can be used to monitor electrical activity related to the heart. However, ECG sensors for wearable consumer computing devices require the user to actively take the measurement.

[0005] Therefore, there is a need for systems and methods for passively monitoring cardiovascular health indicators, such as peripheral myogenic response, that can be used as indicators for detecting cardiovascular health-related conditions. Summary of the Invention

[0006] Aspects and advantages of embodiments of the present disclosure will be set forth in part in the description that follows, or may be learned from the description, or may be learned by practice of the embodiments.

[0007] In one aspect, a system for monitoring cardiovascular health may include a housing for a wearable computing device, the housing having an upper side and a lower side, the lower side facing a user's skin when the wearable computing device is worn by the user. The system may further include an impedance plethysmography (IPG) sensor disposed on the lower side of the housing, the IPG sensor having a pair of excitation electrodes and a pair of sensing electrodes configured to contact the user's skin, the IPG sensor configured to generate IPG data indicative of a voltage passing between the pair of sensing electrodes due to a current applied to the pair of excitation electrodes. The system may also include a photoplethysmography (PPG) sensor disposed on the lower side of the housing and proximate to the IPG sensor, the PPG sensor may include an emitter configured to emit light and a detector configured to detect the light emitted from the emitter, the PPG sensor may be configured to generate PPG data indicative of an amount of light detected by the detector. Additionally, the system may include a computing system configured to receive IPG data, receive PPG data, and determine a myogenic response based at least in part on a comparison of the PPG data and the IPG data.

[0008] In a further aspect, a computer-implemented method capable of performing the functions described above with respect to a computing system.

[0009] In yet another aspect, a wearable computing device is provided that is capable of performing the functions described above with respect to the computing system.

[0010] These and other features, aspects, and advantages of various embodiments of the present disclosure will become better understood with reference to the following description and appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the present disclosure and, together with the detailed description, serve to explain associated principles.

[0011] Detailed descriptions of embodiments directed to those skilled in the art are set forth herein with reference to the accompanying drawings. [Brief explanation of the drawings]

[0012] [Figure 1] FIG. 1 illustrates a front perspective view of a wearable computing device on a user's wrist, according to one embodiment of the disclosure. [Figure 2] 2 illustrates a rear perspective view of the wearable computing device of FIG. 1 according to an embodiment of the present disclosure. [Figure 3] FIG. 3 illustrates another rear view of the wearable computing device of FIGS. 1 and 2, particularly showing an IPG sensor and a PPG sensor, according to an embodiment of the present disclosure. [Figure 4] 1 illustrates various components of an exemplary system that may be utilized in accordance with one embodiment of the present disclosure. [Figure 5] 1 shows a schematic diagram of an exemplary set of devices capable of communication, according to one embodiment of the present disclosure. [Figure 6] 10 shows graphs comparing PPG and IPG data during a baseline event, a vasoconstriction event, and a vasodilation event, according to an exemplary embodiment of the present disclosure. [Figure 7] 10 shows a graph comparing the amplitude of PPG and IPG data before and after a vasoconstriction event for multiple subjects, according to an exemplary embodiment of the present disclosure. [Figure 8] 8 shows a graph comparing changes in peak amplitude of PPG and IPG data due to the vasoconstriction event of FIG. 7, in accordance with an exemplary embodiment of the present disclosure. [Figure 9] FIG. 1 shows a flow diagram of an exemplary, non-limiting computer-implemented method for monitoring the cardiovascular health of a wearer of a wearable computing device, according to an exemplary embodiment of the present disclosure. [Figure 10] 1 illustrates a graph of average heart rate over the course of a day, particularly the nighttime drop, according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0013] Reference numbers repeated among the drawings are intended to identify like features in the various embodiments.

[0014] Reference will now be made in detail to the embodiments of the present invention, one or more examples of which are illustrated in the drawings. Each example is provided by way of explanation of the invention, not as a limitation of the invention. Indeed, it will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the scope or spirit of the invention. For example, features illustrated or described as part of one embodiment can be used with other embodiments to yield still further embodiments. Thus, it is intended that the present invention cover modifications and variations of the present invention provided they come within the scope of the appended claims and their equivalents.

[0015] Generally, the present subject matter is directed to monitoring cardiovascular health using an impedance plethysmography (IPG) sensor in a wearable computing device. For example, the present subject matter is directed to simultaneously monitoring data generated by an IPG sensor in a wearable computing device placed at a peripheral site (e.g., the wrist) and by a photoplethysmography (PPG) sensor to detect episodes of peripheral vasoconstriction or peripheral vasodilation, which are narrowing or widening, respectively, of arteries.

[0016] Peripheral vasoconstriction and vasodilation are directly related to many aspects of cardiovascular health and are affected by diseases including hypertension, atherosclerosis, peripheral arterial disease, Raynaud's phenomenon, diabetes mellitus, heart failure, etc., as well as aging. However, peripheral vasoconstriction and vasodilation are complex physiological processes that are also affected by a wide range of factors, such as temperature changes, posture, hydration, exercise, and stress, which can make them difficult to monitor accurately. For example, PPG sensors are optical sensors used to measure pulsatile blood flow by measuring changes in blood volume in superficial vascular beds. However, PPG signals are prone to degradation during vasoconstriction due to reduced blood flow in superficial tissues and are sensitive to movement and device wear, making it difficult to isolate the effects of vasoconstriction in PPG data.

[0017] The inventors have discovered that IPG sensors, which measure changes in impedance representing pulsatile changes in blood volume due to vascular dilation and contraction with each heartbeat, are less susceptible to vasoconstriction and vasodilation than PPG sensors. For example, when peripheral vasoconstriction occurs, the PPG magnitude decreases relative to the baseline PPG response, while the IPG magnitude remains substantially constant relative to the baseline IPG response. Similarly, when peripheral vasodilation occurs, the PPG magnitude increases relative to the baseline PPG response, while the IPG magnitude remains substantially the same relative to the baseline IPG response. In this manner, the onset of peripheral vasoconstriction or peripheral vasodilation can be detected based on changes in the relative magnitudes of PPG and IPG sensor data. Therefore, by using a combination of PPG and IPG data from a wearable computing device as described herein, a user's myogenic response can be passively monitored over time to monitor the user's cardiovascular health. Furthermore, monitoring how the relative magnitudes of the IPG and PPG signals change over time can provide health / wellness insights into disease progression. For example, if a user had clear PPG and IPG signals, but over time the PPG signal began to decrease relative to the IPG, it could be a sign of changes in peripheral blood flow, such as changes caused by disease(s), and could suggest that further testing is needed.

[0018] In some examples, data from PPG and IPG sensors can be used together or with additional data to determine one or more other cardiovascular health indicators. For example, pulse wave analysis (PWA) derived from PPG and IPG sensor data, and optionally with ballistocardiogram (BCG) data, can provide, or at least improve the accuracy of, an estimate of blood pressure. Similarly, data from PPG and IPG sensors can be used with additional data (e.g., motion data, temperature data, etc.) to determine heat / cold tolerance, exercise initiation / intensity, effects of exercise training, posture, hydration, menstrual cycle, diabetes progression, etc.

[0019] Once certain cardiovascular health indicators are detected from at least the PPG and IPG sensors, it may be useful to communicate this information to the wearer, such as via a notification on the display of the wearable computing device or other computing device (e.g., tablet, mobile phone, laptop, personal computer, etc.), so that the wearer can receive education and / or make lifestyle, dietary, and / or other changes. In some examples, the wearer may be prompted to take active measurements based on the detected cardiovascular health indicators, for example, by using the device to take an electrocardiogram (ECG) reading, measure core body temperature, etc.

[0020] Thus, the disclosed devices, systems, and methods enable passive monitoring of a user's cardiovascular health by using a combination of IPG and PPG data, which can be used to detect the wearer's status with respect to different cardiac and age-related conditions in a non-invasive manner and to make recommendations based on the detected status, so that the wearer can make healthy lifestyle, dietary, and other changes.

[0021] Referring now to the drawings, exemplary embodiments of the present disclosure will be described in more detail.

[0022] Referring to the drawings, FIGS. 1-3 illustrate perspective views of a wearable computing device 102 according to the present disclosure. In particular, as shown in FIG. 1 , the wearable computing device 102 may be worn on the forearm 101 of a user or wearer, similar to a wristwatch. Thus, as shown, the wearable computing device 102 may include a wristband 103 for securing the wearable computing device 102 to the forearm 101 of the user or wearer. However, it should be understood that the wearable computing device 102 may be worn in any other suitable location by a user, such as, for example, on the ankle. It should further be understood that the wearable computing device 102 may include a ring, a band, an earring, a necklace, or any other suitable wearable computing device known by those skilled in the art. Additionally, as shown in FIGS. 1-3 , the wearable computing device 102 has a housing 104 defining an interior volume for housing electronics associated with the wearable computing device 102. Furthermore, the wearable computing device 102 has an outer cover 105 on its upper side for enclosing the interior volume. In one embodiment, outer cover 105 may be constructed of glass, polycarbonate, acrylic, or the like. Additionally, as shown in FIG. 1 , wearable computing device 102 includes an electronic display screen 106 disposed within housing 104 and viewable through outer cover 105. Electronic display screen 106 may cover an electronics package (not shown), which may also be housed within housing 104. Display 106 may be any suitable display, such as a touchscreen, an organic light-emitting diode (OLED), or a liquid crystal display (LCD). Additionally, as shown, wearable computing device 102 may also include one or more buttons 108, which may be implemented to provide mechanisms for activating various features and / or sensors of wearable computing device 102, such as to collect certain health data of the user.The housing 104 of the wearable computing device 102 further defines an underside 110 (FIGS. 2 and 3) configured to contact a user (e.g., the back of the wrist) when worn by the user.

[0023] With particular reference to FIG. 2 , one or more motion sensors 116 may be housed within the housing 104 of the wearable computing device 102 to generate motion data, which may be used to calculate, among other things, step count, pulse rate, etc. The motion sensor(s) 116 may include one or more accelerometers for sensing the motion data. In some embodiments, the motion sensor(s) 116 may include one or more accelerometers for sensing acceleration or other motion data in each of three directions (x, y, and z), which may be orthogonal. For example, the accelerometer may be a three-axis accelerometer. The motion sensor(s) 116 may further include one or more gyroscopes for sensing rotational data. In some embodiments, the motion sensor(s) 116 may include one or more gyroscopes for sensing rotational data, for example, about each of three orthogonal axes. The motion sensor(s) 116 may further include one or more altimeters, such as a pressure altimeter or barometric altimeter. In one or more examples, the motion sensor(s) 116 may include an inertial measurement unit (IMU), which may include a combination of an accelerometer and a gyroscope, etc. In some examples, one or more of the motion sensors 116 (e.g., strain gauges, accelerometers, etc.) may be configured as a ballistocardiogram (BCG) sensor to monitor movement or displacement due to ballistic forces caused by the movement of blood with each heartbeat.

[0024] Additionally, the wearable computing device 102 includes one or more photoplethysmography (PPG) sensors 126 positioned near the underside 110 of the housing 104 of the wearable computing device 102 to make and maintain skin contact with the user when worn by the user on the wrist. The PPG sensor(s) 126 have one or more emitters 127, such as one or more light-emitting diodes (LEDs), for emitting controlled light pulses and one or more detectors 128, such as photodiodes, for generating data indicative of the detected returned light. Some PPG techniques rely on detecting light at a single spatial location, adding signals obtained from two or more spatial locations, or an algorithmic combination thereof. Both of these approaches result in a single spatial measurement from which a heart rate (HR) estimate (or other physiological indicator) is determined. In some embodiments, the PPG sensor(s) 126 use a single emitter 127 (i.e., a single optical path) associated with a single detector 128. Additionally or alternatively, the PPG sensor(s) 126 may use multiple emitters 127 (i.e., two or more optical paths) coupled to a single detector 128 or multiple detectors 128. In other embodiments, the PPG sensor(s) 126 may additionally or alternatively use multiple detectors 128 (i.e., two or more optical paths) coupled to a single optical emitter 127 or multiple emitters 127.

[0025] A processor (e.g., processor 112 of FIG. 4) controls emitter(s) 127 to emit photons that reflect off the wearer's skin, tissue, bone, blood, etc. for detection by detector 128, and the processor converts analog current received from detector(s) 128 into a digital PPG signal. Signal changes associated with peripheral perfusion due to cardiac contractions enable the wearable computing device to use the PPG signal to measure the wearer's pulsatility and heart rate, resting heart rate, beat-to-beat intervals, heart rate variability, etc.

[0026] The PPG sensor(s) 126 can be configured for use with various light wavelengths, such as green (centered at 528 nanometers (nm)), red (centered at 660 nm), and infrared (centered at 940 nm), and the amplitude of reflected light at the green, red, and infrared wavelengths varies with each heartbeat. Typically, when there is a clear pulsatile signal, such as that associated with a heartbeat, the peak-to-peak amplitude of the green PPG signal is greater than the peak-to-peak amplitude of the red and infrared PPG signals. In some cases, the PPG sensor 126 may use a single light source 127 and two or more photodetectors 128, each configured to detect a specific wavelength or wavelength range. In some cases, each detector 128 is configured to detect a different wavelength or wavelength range than the other detectors 128. In other cases, two or more detectors 128 are configured to detect the same wavelength or wavelength range. In still other cases, one or more detectors 128 are configured to detect a different wavelength or wavelength range than one or more other detectors 128. In embodiments using multiple optical paths, the PPG sensor(s) 126 may determine an average of the signals resulting from the multiple optical paths before determining an HR estimate or other physiological indicator. In either case, the PPG sensor(s) 126 can be used to generate noninvasive biometric data related to resting heart rate, heart rate variability, beat-to-beat intervals, etc.

[0027] In addition, the wearable computing device 102 includes a plurality of sensor electrodes 125. For example, the wearable computing device 102 includes one or more pairs of sensor electrodes 125 on the underside 110 of the housing 104 that are configurable to maintain skin contact with the user when worn by the user and to measure the user's electrical impedance at least at the skin contact location (e.g., the dorsum of the wrist), which is associated with electrodermal activity data, etc.

[0028] The wearable computing device 102 further includes one or more IPG sensors 124 for generating IPG data, each including at least four sensor electrodes 125 disposed on the underside 110 of the housing 104, as shown schematically in FIG. 3 . For example, the IPG sensor 124 shown in FIG. 3 includes at least one pair of excitation electrodes 125C and at least one pair of sensing electrodes 125S for providing a stimulation current, and the IPG sensor 124 measures a resulting potential across the pair of voltage sensing electrodes 125S resulting from a stimulation current applied between the excitation electrodes 125C for current injection. In some examples, the sensing electrodes 125S are positioned between the excitation electrodes 125C such that the excitation electrodes 125C are spaced apart by the sensing electrodes 125S. In one or more examples, the IPG sensor 124 includes more than two pairs of sensor electrodes 125C, 125S for generating IPG data. In such examples, the two pairs of sensor electrodes 125C, 125S used to generate the IPG data may be selected based at least in part on the signal quality of the IPG data and / or their proximity to the PPG sensor(s) 126. In some examples, the sensing electrodes 125C, 125S of the IPG sensor(s) 124 are aligned on a common axis A1. For example, in some embodiments, the sensing electrodes 125 of the IPG sensor(s) 124 are centered on a common axis A1 that extends generally parallel to the artery when the wearable computing device 102 is worn. However, in other examples, the sensing electrodes 125C, 125S of the IPG sensor(s) 124 may be arranged in any other suitable pattern.

[0029] In certain examples, to ensure that both the PPG sensor(s) 126 and the IPG sensor(s) 124 are generating data indicative of the same anatomical region, the PPG sensor(s) 126 are positioned in close proximity to the sensor electrodes 125C, 125S of the IPG sensor(s) 124. For example, in one embodiment, the PPG sensor(s) 126 are positioned directly adjacent to one or more of the electrodes 125C, 125S of the IPG sensor(s) 124. In some examples, at least one PPG sensor 126 is positioned between at least a portion of the electrodes 125C, 125S of the IPG sensor(s) 124. For example, the electrodes 125C, 125S of the IPG sensor(s) 124 may be positioned around the PPG sensor(s) 126 such that the PPG sensor(s) 126 is between at least one pair of sensing electrodes 125S of the IPG sensor(s) 124. In alternative embodiments, the various components of the PPG sensor 126 may be arranged around the sensor electrodes 125C, 125S of the IPG sensor(s) 124 and / or in other suitable configurations, such as adjacent to, interspersed with, surrounded by, or underneath the IPG sensor(s) 124 (e.g., when at least a portion of the electrodes 125C, 125S are transparent).

[0030] As described in more detail below, sensors on the underside 110 of the housing 104 of the wearable computing device 102 can enable passive measurements as long as the underside 110 of the housing 104 is placed in close proximity to the user's skin, which can enable passive measurement of different biometrics.

[0031] In one or more examples, the electrodes 125 of the wearable computing device 102 may also include at least one pair of electrodes 125 on an upper side of the wearable computing device 102 for active or on-demand measurement of biometrics (e.g., electrocardiogram (ECG), electrodermal activity (EDA), etc.) of a user wearing the wearable computing device 100. For example, in some implementations, a user may contact (e.g., touch) the electrodes 125 on the upper side of the wearable computing device 102, which, in combination with data from the electrodes 125 on the lower side 110 of the wearable computing device 102, function as an ECG sensor for obtaining on-demand electrocardiogram readings. Alternatively, or additionally, a user may contact (e.g., touch) the electrodes 125 on the upper side of the wearable computing device 102, and data from the electrodes 125 on the upper side of the wearable computing device 102 is used solely as an EDA sensor for obtaining on-demand electrodermal activity readings. In some implementations, the electrodes 125 on the top side of the wearable computing device 102 are spaced apart from one another. In some examples, the electrodes 125 on the top side of the wearable computing device 102 are disposed on the top surface of the cover 105 and / or wrapped around the periphery of the cover 105. Alternatively, or additionally, the sensor electrodes 125 are disposed in any other suitable locations on the wearable computing device 102 such that the sensor electrodes 125 can be selectively (e.g., "actively") contacted to take active measurements while the user is wearing the wearable computing device 102.

[0032] In some embodiments, the wearable computing device 102 may include at least one additional biometric sensor electrode in addition to the PPG sensor(s) 126 and the IPG sensor(s) 124. For example, the wearable computing device 102 may also include one or more temperature sensors 130 (such as an ambient temperature sensor or a skin temperature sensor), humidity sensors, ambient light sensors, pressure sensors, microphones, other optical sensors (e.g., distance sensors), etc.

[0033] 4, components of an exemplary computing system 100 of a wearable computing device 102 that can be utilized in accordance with various embodiments are illustrated. In particular, as shown, the computing system 100 may also include at least one processor 112 communicatively coupled to different portions of the wearable computing device 102, such as the display 106, motion sensor(s) 116, sensor electrodes 125 (e.g., EDA electrodes, electrodes 125C, 125S of an IPG sensor 124, ECG electrodes, etc.), PPG sensor(s) 126, temperature sensor(s) 130 (e.g., ambient or skin), and any other sensors present. Furthermore, in one embodiment, the processor(s) 112 may be a central processing unit (CPU) or a graphics processing unit (GPU) for executing instructions that can be stored in memory 114, such as flash memory or DRAM, among other options. For example, in one embodiment, memory 114 may include RAM, ROM, FLASH memory, or other non-transitory digital data storage, and may include control programs including sequences of instructions that, when loaded from memory 114 and executed using processor(s) 112, cause processor(s) 112 to perform the functions described herein. As will be apparent to one skilled in the art, computing system 100 can include many types of memory, data storage, or computer-readable media, such as data storage for program instructions for execution by any suitable processor. The same or separate storage can be used for images or data, removable memory can be made available for sharing information with other devices, and any number of communication approaches can be utilized for sharing with other devices.

[0034] The computing system 100 also includes one or more power components 208, such as a battery operable to be recharged through a traditional plug-in approach or through other approaches, such as capacitive charging through proximity to a power mat or other such device.

[0035] Computing system 100 may also include one or more wireless components 212 operable to communicate with one or more electronic devices within communication range of a particular wireless channel. The wireless channel may be any suitable channel used to allow devices to communicate wirelessly, such as a Bluetooth, cellular, NFC, ultra-wideband (UWB), or Wi-Fi channel. It should be understood that computing system 100 may have one or more conventional wired communication connections known in the art.

[0036] Computing system 100 may be configured to receive input from display 106 (e.g., if display 106 is a touchscreen) and / or control display 106 to communicate information, and the device may communicate information via audio speakers, a projector, or other means, such as casting the display or streaming data to another device, such as a mobile phone, where an application on the mobile phone displays the data. In further embodiments, computing system 100 may include at least one additional input / output (I / O) component 122 capable of receiving conventional input from a user. This conventional input may include, for example, push buttons, a touchpad, a touchscreen, a wheel, a joystick, a keyboard, a mouse, a keypad, or any other such device or element by which a user may enter commands into computing system 100. In other embodiments, I / O component(s) 122 may also be connected by wireless infrared or Bluetooth or other link in some embodiments. In some embodiments, computing system 100 may also include a microphone or other audio capture element to accept voice or other audio commands. For example, in particular embodiments, computing system 100 may not include any buttons at all, but may be controlled solely through a combination of visual and audio commands, such that a user can control wearable computing device 102 without having to make contact with the wearable computing device. In an embodiment, I / O component 122 may also include one or more of the sensor electrodes 125, optical sensors (e.g., PPG sensor(s) 126), barometric pressure sensors (e.g., altimeters, etc.), temperature sensor(s) 130, etc., described herein.

[0037] It should be appreciated that the emitter 127 and detector 128 of the PPG sensor(s) 126 may be directly or indirectly coupled to the processor 112 using a driver circuit 214 that may enable the processor 112 to control the emitter 127 to emit light and acquire a signal from the detector 128. Similarly, the excitation electrodes 125C of the IPG sensor(s) 124 may be directly or indirectly coupled to the processor 112 to provide current between the excitation electrodes 125C (e.g., from the power component(s) 208).

[0038] Additionally, the server computing system 308 may communicate with the wireless component 212 via one or more networks 310, which may include one or more local area networks, wide area networks, UWB, and / or internetworks using either terrestrial or satellite links. In some embodiments, the server computing system 308 executes control and / or application programs configured to perform some of the functions described herein. Additionally, the network(s) 310 may enable one or more other device(s) to communicate with the wearable computing device 102, such as one or more external data source(s) 312 (e.g., to provide core body temperature data, etc.).

[0039] For example, referring now to FIG. 5 , a schematic diagram of an environment 300 in which aspects of various embodiments may be implemented is shown. In particular, as shown, a user may have several different devices that can communicate using at least one wireless communication protocol. For example, as shown, a user may have a wearable computing device 102, such as a smartwatch or fitness tracker, and the user may want to be able to communicate with other devices, such as a smartphone 304, a tablet computer 306, and one or more external data sources 312 (e.g., a thermometer for measuring core body temperature, a scale for measuring weight, a health record system, etc.). The ability to communicate with multiple devices may enable the user to view information from the smartwatch 302, e.g., data captured using sensors on the smartwatch 302, and any other linked sources (e.g., external data sources 312), using applications installed on either the smartphone 304 or the tablet computer 306. The user may also desire to enable the smartwatch 302 to communicate with a server computing system 308 of a service provider or other such entity that can obtain and process data from the smartwatch and / or any other suitable device (e.g., external data source(s) 312) to provide functionality that may not be available on the smartwatch or individual device installed applications. Additionally, as shown, the smartwatch 302 may be able to communicate with the service provider's server computing system 308 over at least one network 310, such as the Internet or a cellular network, or may communicate via a wireless connection, such as Bluetooth, to one of the individual devices that may in turn communicate over the at least one network.

[0040] There may be several other types or reasons for communication in various embodiments. For example, the user or wearer may want to allow system 100 to access health data from an external health record system (e.g., for a health provider). For example, the health data may include the user's or wearer's existing health data in the form of an electronic health record, which may include biomarker data, age, existing health conditions, etc. The biomarker data may have been previously collected by invasive or non-invasive methods and may include biomarkers from blood tests, which may relate to a complete blood count, a comprehensive metabolic panel, insulin levels, blood glucose levels, total cholesterol levels, HDL cholesterol levels, LDL cholesterol levels, triglyceride levels, HbA1c levels, high-sensitivity C-reactive protein levels, gamma glutamyltransferase levels, testosterone levels, blood urea nitrogen levels, creatinine levels, estimated glomerular filtration rate (eGFR), sodium levels, potassium levels, chloride levels, carbon dioxide levels, calcium levels, total protein levels, albumin levels, globulin levels, albumin / globulin ratio, total bilirubin levels, alkaline phosphatase (ALP) levels, aspartate aminotransferase (AST) levels, alanine aminotransferase (ALT) levels, or combinations thereof.

[0041] In addition to being able to communicate, a user or wearer may also want devices to be able to communicate in some way or using a particular manner. For example, a user or wearer may want communications between devices to be secure, especially if the data may include personal health data or other such communications. Device or application providers may also be required to protect this information in at least some circumstances. A user may want devices to be able to communicate with each other simultaneously, rather than sequentially. This may be particularly true when pairing may be required, as a user may prefer that each device be paired at most once so that manual pairing is not necessary. A user may also want communications to be as standards-based as possible, not only so that little manual intervention is required on the user's part, but also so that the device can communicate with as many other types of devices as possible, which is often not the case with various proprietary formats. Thus, a user may want to walk into a room with one device and have such device automatically communicate with other target devices with little or no effort on the user's part. In various conventional approaches, devices utilize communication technologies such as Wi-Fi to communicate with other devices using wireless local area networking (WLAN). Smaller or lower-volume devices, such as many Internet of Things (IoT) devices, instead utilize communication technologies such as Bluetooth®, particularly Bluetooth Low Energy (BLE), which consumes very little power.

[0042] 5 allows for data to be captured, processed, and displayed in several different ways. For example, data may be captured using sensors on the smartwatch 302, but due to limited resources on the smartwatch 302, the data may be transferred to the smartphone 304 or a service provider's server computing system 308 (or cloud resources) for processing, and the results of that processing may then be presented back to the user on other such devices associated with the user, such as the smartwatch 302, smartphone 304, and / or tablet computer 306. In at least some embodiments, the user may also be able to use an interface on any of these devices to provide input, such as health data from external data sources, which may then be considered when making that decision.

[0043] As described above, data collected from the IPG sensor(s) 124 and the PPG sensor(s) 126 may be utilized to passively detect one or more cardiovascular health responses of a wearer of the wearable computing device 102 (e.g., without the user having to actively position themselves to measure). More specifically, data generated by the IPG sensor(s) 124 and by the PPG sensor(s) 126 of the wearable computing device 102 positioned at a peripheral site (e.g., wrist, leg, finger, etc.) may be simultaneously monitored to detect episodes of peripheral vasoconstriction or peripheral vasodilation, which are narrowing or dilation of arteries, respectively. In general, the IPG sensor(s) 124, which measure changes in impedance representing pulsatile changes in blood volume, are less susceptible to vasoconstriction and vasodilation compared to the PPG sensor(s) 126.

[0044] For example, referring to FIG. 6 , a graph 400 is shown comparing PPG data 402 generated by the PPG sensor 126 with IPG data 404 generated by the IPG sensor(s) 124 during a baseline event, a vasoconstriction event, and a vasodilation event, according to an example embodiment of the present disclosure. The magnitude of the PPG data 402C during the vasoconstriction event is decreased by a first amount D1 compared to the magnitude of the PPG data 402B during the baseline event, while the magnitude of the IPG data 404C during the vasoconstriction event is substantially the same as the magnitude of the IPG data 404B during the baseline event. Similarly, the magnitude of the PPG data 402D during the vasodilation event is increased by a second amount D2 compared to the magnitude of the PPG data 402B during the baseline event, while the magnitude of the IPG data 404D during the vasodilation event is substantially the same as the magnitude of the IPG data 404B during the baseline event. In this manner, an onset of peripheral vasoconstriction or peripheral vasodilation can be detected based on the relative magnitudes in the PPG and IPG sensor data. Thus, a vasoconstriction event may be determined to have occurred when the magnitude of the data from the PPG sensor(s) 126 decreases without a significant, respective change in magnitude in the data from the IPG sensor(s) 124, or conversely, a vasodilation event may be determined to have occurred when the magnitude of the data from the PPG sensor(s) 126 increases without a significant, respective change in magnitude in the data from the IPG sensor(s) 124. In some embodiments, a myogenic response (e.g., vasoconstriction or vasodilation) is determined only if the magnitude of the PPG data 402 changes by a threshold amount (e.g., a first amount D1 or a second amount D2) relative to the change (or lack thereof) in the magnitude of the IPG data 404.

[0045] 7 and 8 show further examples illustrating the relationship between PPG data and IPG data. For example, FIG. 7 shows a graph comparing the peak amplitudes of PPG data and IPG data before (during a baseline event) and after (after an intervention) a vasoconstriction event for multiple subjects, and FIG. 8 shows a graph comparing changes in peak amplitudes of PPG data and IPG data due to a vasoconstriction event, according to an exemplary embodiment of the present disclosure. In graph 406 of FIG. 7, the mean amplitude 406B of the IPG data for the baseline event was essentially the same as the mean amplitude 406C of the IPG data after the intervention (after the vasoconstriction event was induced). Meanwhile, as shown in graph 408 of FIG. 7, the mean amplitude 408C of the PPG data after the intervention changed (i.e., decreased) from the mean amplitude 408B of the PPG data during baseline. Correspondingly, in the graph 410 of PPG data in FIG. 8 , the average change in amplitude 412 of the PPG data was approximately minus 20 millivolts [mV], while the average change in amplitude 411 of the IPG data was approximately 0.002 ohms. It should be understood that the values ​​provided in FIGS. 7 and 8 are for illustrative purposes based on an exemplary sample set to illustrate that PPG data is more sensitive (or more sensitive) to myogenic events than IPG data. The relative change in IPG data and PPG data during use may vary depending on many factors, including, but not limited to, the severity of changes in environmental temperature, exercise intensity, cardiovascular status, etc., as described in more detail below. Corresponding statistical data regarding the amplitude of the PPG data, the change in amplitude of the PPG data, the amplitude of the IPG data, and the change in amplitude of the IPG data are shown in Table 1 below.

[0046] [Table 1]

[0047] As shown in Table 1 above, the intervention (e.g., the cause of the vasoconstriction event from Figures 7-8) was statistically significant (p-value less than 0.05) for PPG amplitude (p-value 0.001) and therefore for the change in PPG amplitude (p-value 0.001), but not for the IPG data (p-value 0.677) or the change in IPG data (p-value 0.284). Because the change in PPG amplitude was statistically significant but the change in IPG amplitude was not statistically significant (in other words, the IPG amplitude remained substantially constant), it can be determined that a myogenic event occurred. It should be understood that the values ​​provided in Table 1 are not intended to be limiting but are examples based on the values ​​in Figures 7 and 8, and therefore simply serve as an example showing that PPG data is more sensitive (or more sensitive) to myogenic events than IPG data.

[0048] In this manner, a user's myogenic response can be passively detected by using a combination of PPG and IPG data from a wearable computing device, such as wearable computing device 102, as described herein. Additionally, a user's myogenic response can be passively monitored over longer periods of time (e.g., days, weeks, months, years, etc.) to monitor the user's cardiovascular health. For example, monitoring how the relative magnitudes of the IPG and PPG signals change over a longer period of time can provide health / wellness insights regarding disease progression. For example, if a user had clear PPG and IPG signals but the PPG signal began to decrease relative to the IPG over a longer period of time, this could be a sign of changes in peripheral blood flow, such as those caused by long-term causes such as disease(s) and / or age, and could suggest the need for further testing.

[0049] In some examples, data from the PPG and IPG sensors 126, 124 may be used together or with additional data to determine one or more cardiovascular health indicators. For example, data from the PPG and IPG sensors 126, 124 may be used with motion data, temperature data, etc. to determine further indicators of health status.

[0050] For example, vasoconstriction may occur when a user is exposed to a cold environment, and vasodilation may occur when a user is exposed to a warm environment, among other things. To determine whether a myogenic event detected based on a combination of IPG data and PPG data was at least partially attributable to one or more of the environmental exposures, data from an environmental and / or skin temperature sensor (e.g., the temperature sensor 130 of the wearable computing device 102) may be used to estimate whether the user is exposed to a change in environmental temperature. For example, if a change in the user's skin temperature exceeds a threshold change and a myogenic response occurs, the myogenic response can be determined to be at least partially due to the environmental exposure. If a user frequently experiences a myogenic response when exposed to changes in environmental temperature, a specific condition such as Raynaud's phenomenon may be present, depending on the severity of the myogenic response and / or the severity of the change in environmental temperature. While the myogenic response is typically early and sustained in response to changes in environmental temperature, the myogenic response diminishes with age. For example, older users tend to have a delayed myogenic response when exposed to changes in environmental temperature compared to younger users. Thus, age-related thermoregulation may be detected if a user experiences a delayed myogenic response when exposed to changes in environmental temperature.

[0051] Vasoconstriction often occurs when a user begins to exercise to redistribute blood from inactive tissues to active muscles. As a user continues to exercise and their body begins to warm up, vasodilation may occur to help cool the user. Generally, the higher the intensity of exercise (e.g., the more energy the user expends), the higher the internal or core body temperature must be before vasodilation begins. Thus, workout intensity can be estimated by monitoring the time between vasoconstriction and subsequent vasodilation when exercise is detected (e.g., based on heart rate exceeding a heart rate threshold, motion data exceeding a motion threshold, etc.). Furthermore, physically active people who are trained in exercise have a faster and more reactive skin blood flow response (to body temperature) compared to untrained and / or sedentary people. Tracking the change in the time between the start of a user's exercise and vasoconstriction and / or the change in the time between vasoconstriction and vasodilation can be monitored over time (e.g., over weeks, months, years, etc.) to assess the user's training or fitness level. In some examples, core body temperature readings may also be taken (e.g., using an external data source 312 such as a core body temperature sensor), in which case changes in core body temperature at which a myogenic response occurs during exercise may be tracked over time to determine the user's training level. For example, a decrease in the temperature at which vasoconstriction occurs over a period (e.g., months) of training may be associated with a successful training effect (e.g., resulting in an increase in training or fitness level).

[0052] The vasoconstriction level can be used to estimate the number of daily stressors. Daily exposure to psychosocial stressors adversely affects the vasoconstrictor function of microvessels, regardless of the perceived severity or emotional consequences of the stressor exposure. Generally, the more stressor events within a short period (e.g., one day), the greater the degree of vasoconstriction (less blood flow). In this way, the instantaneous stress algorithm can use the measured vasoconstriction level as a variable in addition to other variables (e.g., heart rate, etc.), and the greater the vasoconstriction, the higher the stress estimate. The degree of vasoconstriction ("vasoconstriction level") can be determined at least in part based on the change in PPG amplitude relative to the change (or lack thereof) in IPG amplitude. For example, multiple vasoconstriction levels may be established (e.g., mild vasoconstriction, moderate vasoconstriction, severe vasoconstriction, extreme vasoconstriction), each of the multiple vasoconstriction levels being associated with a respective change in PPG amplitude for a detected vasoconstriction event (e.g., a 50% decrease from baseline PPG amplitude, a 60% decrease in constriction from baseline PPG amplitude, a 70% decrease from baseline PPG amplitude, and an 80% decrease from baseline PPG amplitude, respectively).

[0053] Peripheral vasoconstriction and increased heart rate occur when a user is in an upright (orthostatic) position to help move blood to the brain. Thus, vasoconstriction detection can be used in conjunction with heart rate monitoring to detect whether a user is in an upright position. When the wearable computing device 102 is on a distal limb (e.g., an arm), a change in limb position relative to the user's core (e.g., raising the arm) causes a hydrostatic effect on underlying vascular pressure. Thus, motion data (e.g., inertial measurement unit readings from the motion sensor(s) 116, etc.) can be additionally used in conjunction with vasoconstriction and heart rate monitoring to detect a user's posture.

[0054] Detection of vasoconstriction can be used to estimate hydration status (e.g., hydration or dehydration). For example, while a typical user is dehydrated, temporary vasoconstriction occurs to maintain blood pressure, which in turn maintains tissue perfusion pressure, particularly cerebral perfusion pressure. Furthermore, during hypovolemic and hyperosmolar conditions, there is a delayed onset of vasodilation associated with increased internal temperature. Thus, monitoring a user's core body temperature (e.g., using a core body temperature sensor) in relation to the degree of vasoconstriction and when vasodilation occurs can be used to determine whether a severe hydration problem may exist.

[0055] Users with type 2 diabetes have a reduced heat tolerance. Similar to users experiencing dehydration, users with type 2 diabetes have a delayed internal temperature threshold for the onset of vasodilation during whole-body warming compared to users without type 2 diabetes. Thus, monitoring the progression of delayed vasodilation over time (e.g., weeks, months, years, etc.) can be used to estimate the progression of type 2 diabetes and / or for scoring overall metabolic health. Users with diabetic neuropathy may experience delayed onset of vasoconstriction (e.g., in response to sympathetic activation, which normally causes vasoconstriction and / or increased heart rate) compared to users without diabetic neuropathy. Thus, similarly, monitoring the progression of delayed onset of vasoconstriction over time (e.g., weeks, months, years, etc.) can be used to estimate the progression of diabetes and / or for scoring overall metabolic health.

[0056] Detection of vasodilation can be used to track the menstrual cycle. For example, core body temperature rises by approximately 0.5 degrees Celsius during the mid-luteal phase and drops slightly during the pre-ovulatory phase. Accordingly, thermoregulatory control of skin blood flow reflects these changes in core body temperature. For example, the threshold core temperature for the onset of vasodilation and sweating is higher during the mid-luteal phase than during the pre-ovulatory phase. Thus, by monitoring core temperature (e.g., using a core body temperature sensor) and / or sweating (e.g., using electrodes 125 of an EDA sensor, such as on the underside 110 for continuous monitoring), in relation to when vasodilation occurs over time, cyclical patterns that can be associated with different menstrual phases can be determined.

[0057] Peripheral vasoconstriction occurs during obstructive sleep apnea events. Thus, monitoring peripheral vasoconstriction during sleep based on a comparison of PPG data and IPG data can be used to detect potential sleep apnea events. Furthermore, by monitoring the degree or level of severity of vasoconstriction, the severity of a sleep apnea event can be detected. PPG data and IPG data can be used in combination with heart rate data, movement data, etc. to detect when sleep is occurring and more reliably determine when a sleep apnea event will occur.

[0058] PPG data is used for functions such as estimating heart rate, passive atrial fibrillation detection, and pulse loss detection. However, due to many confounding factors (e.g., movement and device wear), there are moments when the PPG data has a degraded signal, which leads to false positives of heart rate, atrial fibrillation, and pulse loss. Because IPG data can be used to detect pulses, IPG data can be used as a complementary signal to PPG data to reduce false positives. The IPG signal can also be used as a reference signal for PPG to compensate for motion artifacts or pressure applied to the sensor.

[0059] Combining IPG and PPG data can be used to provide more accurate blood pressure estimation. For example, changes in arterial stiffness occur due to coronary artery disease, aging, metabolic disease, sleep disorders, smoking, poor cardiovascular health, etc. Augmentation index values ​​are determined by pulse wave analysis (PWA) and used as an indicator of arterial stiffness. For example, augmentation index values ​​are typically determined as the ratio of late systolic pressure to early systolic pressure or the difference between late systolic pressure and early systolic pressure. There is a strong correlation between augmentation index values ​​obtained from IPG and blood pressure, and different correlations between augmentation index values ​​and blood pressure have been established for different groups (e.g., groups with coronary artery disease, groups without confirmed coronary artery disease, etc.). Thus, determining augmentation index values ​​from PWA derived from both IPG and PPG data allows for more accurate blood pressure estimation based on known correlations.

[0060] Co-located and simultaneously acquired BCG and IPG data, and optionally pulse transit time (PTT) derived from PPG data, can similarly provide improved blood pressure estimation. For example, PTT is the time delay for a pressure wave to travel between two arterial sites and is estimated based on the timing between proximal and distal arterial waveforms, and PTT is often inversely proportional to BP. By determining PTT based on IPG data in addition to BCG and / or PPG data, PTT can be more accurately determined because IPG data is less sensitive to other factors (e.g., movement, myogenic response, etc.) compared to BCG and PPG data.

[0061] When certain cardiovascular health indicators are detected from at least the PPG and IPG sensors, it may be useful to notify the wearer of this information via notifications on the display 106 and / or through other output devices on the wearable computing device 102 or other computing device (e.g., tablet, mobile phone, laptop, personal computer, etc.) so that the wearer can be educated and / or make lifestyle, dietary, and / or other changes, such as notifications indicating myogenic development tracking, environmental heat / cold tolerance (and optionally potential underlying causes, Raynaud's phenomenon, age, etc.), exercise tracking, fitness level and / or the impact of exercise on fitness level, daily stress estimation, hydration status, type 2 diabetes risk, menstrual cycle tracking (e.g., estimated menstrual cycle phase), number and / or severity of sleep apnea events (and an optional recommendation to consult a physician if the number and / or severity exceeds a threshold), heart rate, atrial fibrillation detection, pulse loss detection, blood pressure (and optionally blood pressure reading and / or meaning of changes), etc. In some examples, a user can interact with a conversational interface (e.g., a chatbot) that may access an artificial intelligence (AI) model (e.g., a machine-learned, large-scale language model, etc.) via one or more user interfaces (e.g., a wearable computing device 102, a laptop, a tablet, etc.) for the user to ask questions about their cardiovascular health indicators and receive feedback from the interface. In examples where an AI model is accessible, the feedback may be more personalized based on the user's personal indicators, depending on the user's permission settings.

[0062] In some examples, the notification may request the user to take an active measurement, for example, by taking an electrocardiogram (ECG) reading with the wearable computing device 102, measuring core body temperature with a core body temperature sensor, etc., based on the detected cardiovascular health indicators. For example, as described above, an ECG on the wearable computing device 102 requires active user intervention to perform the measurement (i.e., placing the wearer's fingertips or palm on the electrodes 125 on the upper side of the wearable computing device 102) to facilitate a measurement that includes the heart. There is an evolving relationship between ECG readings and IPG signal morphology, correlating cardiac electrical processes with cardiac mechanical processes. For example, while IPGs primarily measure waveforms that represent mechanical aspects of cardiovascular health, if there are electrical changes in the heart (e.g., due to disease), these changes are also reflected in the peripheral mechanical signals detected by the IPG. Because IPG is a passive measurement, if a morphological change is passively detected in the IPG data (alone or in combination with other data, such as PPG data), the user may be prompted to take a spot-check ECG reading to confirm the morphological change. In this way, the use of the IPG can be used to prompt an ECG at critical times, making the ECG more useful than random spot-checks by the user.

[0063] Referring now to FIG. 9 , a flow diagram of one embodiment of a method 500 for monitoring cardiovascular health is provided. Generally, the method 500 is described herein with reference to the wearable computing device 102 described in FIGS. 1-5 and the graphs of FIGS. 6-8 . However, it should be understood that the disclosed method 500 may be implemented in any other suitable wearable computing device having any other suitable configuration. Additionally, while FIG. 9 depicts steps performed in a particular order for purposes of illustration and explanation, the methods described herein are not limited to any particular order or arrangement. Those skilled in the art will understand, using the disclosure provided herein, that elements of any of the methods described herein may be omitted, rearranged, combined, added, and / or adapted in various ways without departing from the scope of the present disclosure.

[0064] At step (502), method 500 may include receiving impedance plethysmography (IPG) data generated by an IPG sensor disposed on the underside 110 of the housing 104 of the wearable computing device. For example, as described above, computing system 100 may receive IPG data generated by an IPG sensor 124 disposed on the underside 110 of the housing 104 of the wearable computing device 102, the IPG sensor 124 having a pair of excitation electrodes 125C and a pair of sensing electrodes 125D, the IPG data indicative of a voltage applied across the pair of excitation electrodes 125C and passed between the pair of sensing electrodes 125S due to current conducted through the user.

[0065] Method 500 may further include, at step (504), receiving PPG data generated by a photoplethysmography (PPG) sensor disposed on the underside 110 of the housing and proximate to the IPG sensor. As described above, computing system 100 may receive PPG data generated by PPG sensor 126 disposed on the underside 110 of housing 104 and proximate to IPG sensor 124, PPG sensor 126 including emitter 127 configured to emit light and detector 128 configured to detect light emitted from emitter 127, and the PPG data indicative of the amount of light detected by detector 128.

[0066] Furthermore, method 500 may further include, at step (506), determining a myogenic response based at least in part on a comparison between the PPG data and the IPG data. For example, as described above, computing system 100 may determine a myogenic response (e.g., vasoconstriction or vasodilation) based at least in part on a comparison between the PPG data and the IPG data. For example, a myogenic response is determined when the amplitude of the PPG data changes significantly from the baseline PPG amplitude, but the amplitude of the IPG data at a corresponding time does not change significantly from the corresponding IPG amplitude.

[0067] Additionally, method 500 may further include, at step (508), controlling a user interface based at least in part on the myogenic response. For example, as described above, computing system 100 may control a user interface, such as electronic display screen 106 of wearable computing device 102, a screen of another device (e.g., smartphone 304, tablet device 306, etc.), or other output element 122 (e.g., speaker, light, etc.), based at least in part on the myogenic response. For example, computing system 100 may control a user interface to indicate the myogenic response or a report related to the monitored myogenic response, such as exercise initiation and / or intensity, heat / cold tolerance, diabetes risk, menstrual cycle tracking, sleep apnea detection, cardiovascular health (e.g., arteriosclerosis estimation), etc., and / or to indicate a request to perform an action, such as taking an ECG measurement using wearable computing device 102.

[0068] According to further aspects of the present subject matter, heart rate and blood pressure can be monitored (together or separately) for assessment of nocturnal dip. Nocturnal dip is a physiological phenomenon in which blood pressure decreases by more than about 10% compared to daytime values. In some examples, heart rate may exhibit a similar nocturnal dip as blood pressure. For example, as shown in FIG. 10 , a graph 600 of heart rates over a day for multiple subjects is shown. As shown in graph 600, the average heart rates 602 for multiple subjects are plotted in beats per minute (BPM) along with standard deviation plots 604, 606 over the entire day (e.g., time 0 refers to midnight, time 12 refers to noon, etc.). As shown in graph 600, the average heart rates 602 (and corresponding standard deviation plots 604, 606) are lowest or “dip” during the early morning hours at time 608, between approximately 4 AM and 5 AM. Heart rate 602 at time 608 may be approximately 60 BPM, while the average during the day (e.g., between approximately 7 AM and 9 PM) may be approximately 71 BPM, which corresponds to an overnight drop in heart rate of over 10% compared to daytime values.

[0069] However, some people may be of the drop phenotype, experiencing such a nocturnal drop, while others may be of the non-drop phenotype, not experiencing such a nocturnal drop. For example, some users may be of the double drop nocturnal phenotype, experiencing both a drop in nighttime heart rate and nighttime blood pressure. Some users may be of the single drop nocturnal phenotype, experiencing a drop in heart rate without a drop in blood pressure, or a drop in blood pressure without a drop in heart rate. Some users may be of the non-drop type, experiencing neither a drop in heart rate nor a drop in blood pressure. Double drop users have better health outcomes than single drop users, who generally have better health outcomes than non-drop users. For example, double drop users may live longer than single drop users, who may live longer than non-drop users. Furthermore, non-drop phenotypes may be more likely to experience sleep disorders such as obstructive sleep apnea and narcolepsy.

[0070] Thus, it may be useful to monitor blood pressure and / or heart rate for nocturnal dips. Similar to PPG readings, nocturnal blood pressure dips may be affected in the short term by changes in circadian rhythm, autonomic nervous system responses (e.g., stress, illness, etc.), water and sodium regulation (e.g., hydration levels), etc. Thus, monitoring nocturnal dips over several weeks may enable more accurate classification of a user's phenotype. For example, in some embodiments, heart rate may be monitored based on PPG data (e.g., from PPG sensor 126 of FIGS. 2-4 ) over a period of time (e.g., two weeks or more) to ascertain whether a user experiences a nocturnal dip in heart rate. In some examples, when PPG data is low (e.g., as discussed above with respect to vasoconstriction and vasodilation) and / or when reduced visible light is desirable (e.g., at night), IPG data (e.g., from IPG sensor(s) 124 of FIGS. 3-4 ) may be used instead or in addition to monitoring heart rate. Additionally or alternatively, blood pressure may be monitored over a similar period based on PPG data, IPG data, and / or BCG data (e.g., from the motion sensor 116 of FIG. 4 ) to ascertain whether the user experiences a nocturnal drop in blood pressure. If changes in nocturnal drops in heart rate and / or blood pressure occur over time, there may be significant changes in the user's health, particularly cardiovascular health. In some examples, the computing system 100 may control a user interface associated with the wearable computing device 102 to present or report a nocturnal drop phenotype (e.g., double drop, single drop, no drop, etc.), information related to the phenotype, changes in nocturnal drop patterns, information related to changes in nocturnal drop patterns, etc., and / or recommendations based on the phenotype or changes in the phenotype (e.g., consult a physician, etc.).

[0071] Additional Disclosures The technology described herein refers to servers, databases, software applications, and other computer-based systems, as well as actions performed on and information sent to and received from such systems. The inherent flexibility of computer-based systems allows for a wide variety of possible configurations, combinations, and divisions of tasks and functions among components. For example, the processes described herein can be implemented using a single device or component, or multiple devices or components working in combination. Databases and applications can be implemented on a single system or distributed across multiple systems. Distributed components can operate sequentially or in parallel.

[0072] Additionally, although the figures and descriptions show and describe steps being performed in a particular order for purposes of illustration and explanation, the methods described herein are not limited to any particular order or arrangement. Those skilled in the art will, using the disclosure provided herein, understand that various steps of the methods described herein may be omitted, rearranged, combined, added, and / or adapted in various ways without departing from the scope of the present disclosure.

[0073] In addition to the above, the systems, programs, or features described herein may provide controls to the user or wearer that allow the user or wearer to make choices about both whether and when collection of user information (e.g., information about the user's health data, activities, social networks, social behavior, occupation, user preferences, or the user's current location) may be enabled, as well as whether content or communications are sent from the server to the user. Furthermore, certain data may be processed in one or more ways to remove personally identifiable information before being stored or used. For example, the user's identifying information may be processed so that personally identifiable information about the user cannot be determined, or, if location information is obtained (e.g., to the city, zip code, or state level), the user's geographic location may be generalized so that the user's specific location cannot be determined. Thus, the wearer or user may control what information is collected about the user, how that information is used, and what information is provided to the user.

[0074] While the present subject matter has been described in detail with reference to various specific exemplary embodiments thereof, each example is provided for purposes of illustration and not limitation of the present disclosure. Those skilled in the art, upon understanding the foregoing, may readily create modifications, variations, and equivalents to such embodiments. Accordingly, the disclosure of the subject matter does not exclude the inclusion of such modifications, variations, and / or additions to the subject matter as would be readily apparent to one skilled in the art. For example, features illustrated or described as part of one embodiment can be used with other embodiments to yield still further embodiments. Accordingly, the present disclosure is intended to cover such modifications, variations, and equivalents.

Claims

1. 1. A system for monitoring cardiovascular health, the system comprising: a housing for a wearable computing device, the housing having an upper side and a lower side, the lower side facing the skin of a user when the wearable computing device is worn by the user, the system further comprising: an impedance plethysmography (IPG) sensor disposed on the underside of the housing, the IPG sensor having a pair of excitation electrodes and a pair of sensing electrodes configured to contact the skin of the user, the IPG sensor configured to generate IPG data indicative of a voltage passing between the pair of sensing electrodes due to a current applied to the pair of excitation electrodes; a photoplethysmography (PPG) sensor disposed on the underside of the housing and proximate to the IPG sensor, the PPG sensor including an emitter configured to emit light and a detector configured to detect the light emitted from the emitter, the PPG sensor configured to generate PPG data indicative of an amount of light detected by the detector, the system further comprising: a computing system, the computing system comprising: receiving the IPG data; receiving the PPG data; A system configured to determine a myogenic response based at least in part on a comparison of the PPG data and the IPG data.

2. The system of claim 1 , wherein the computing system is configured to determine the myogenic response when the magnitude of the PPG data changes by a threshold amount relative to a change in magnitude of the IPG data.

3. 2. The system of claim 1, wherein the computing system is configured to determine the myogenic response by determining vasoconstriction when the magnitude of the PPG data decreases while the magnitude of the IPG data remains substantially constant, and determining vasodilation when the magnitude of the PPG data increases while the magnitude of the IPG data remains substantially constant.

4. The system of claim 3 , wherein the computing system is further configured to determine stress of the user based at least in part on determining that the myogenic response includes the vasoconstriction.

5. The system of claim 1 , wherein the computing system is further configured to determine a long-term cause when the magnitude of the PPG data decreases relative to the magnitude of the IPG data over a period of time.

6. The computing system includes: receiving temperature data generated by a temperature sensor and indicative of at least one of an environmental temperature or a skin temperature; The system of claim 1 , further configured to determine whether the myogenic response was at least partially attributable to one or more of the environmental exposures based at least in part on the temperature data.

7. 7. The system of claim 6, wherein the computing system is configured to determine that the myogenic response was at least partially due to the environmental exposure when the change in the temperature of the user exceeds a threshold change.

8. further comprising a motion sensor within the housing, the motion sensor configured to generate motion data indicative of motion of the housing; The computing system includes: receiving the motion data; The system of claim 1 , further configured to determine an onset of movement when the myogenic response includes vasoconstriction and the movement data exceeds a movement threshold.

9. 9. The system of claim 8, wherein the computing system is further configured to determine the intensity of the exercise based at least in part on a time period during which the myogenic response includes vasodilation following the vasoconstriction.

10. The computing system includes: receiving core body temperature data of the user associated with the wearable computing device; 10. The system of claim 1, further configured to determine at least one of the user's hydration status, menstrual cycle, or training effect based at least in part on the myogenic response and the core body temperature data.

11. further comprising a motion sensor within the housing, the motion sensor configured to generate motion data indicative of motion of the housing; The computing system includes: receiving the motion data; The system of claim 1 , further configured to determine at least one of a sleep apnea event or a posture of the user based at least in part on the myogenic response and the movement data.

12. The system of claim 1 , wherein the pair of excitation electrodes are spaced apart by the pair of sensing electrodes.

13. The system of claim 1 , wherein the PPG sensor is positioned between the pair of sensing electrodes of the IPG sensor.

14. 1. A wearable computing device, comprising: a housing having an upper side and a lower side, the lower side facing the skin of a user when the wearable computing device is worn by the user, the wearable computing device further comprising: an impedance plethysmography (IPG) sensor disposed on the underside of the housing, the IPG sensor having a pair of excitation electrodes and a pair of sensing electrodes configured to contact the skin of the user, the IPG sensor configured to generate IPG data indicative of a voltage passing between the pair of sensing electrodes due to a current applied to the pair of excitation electrodes; and the wearable computing device further comprising: a photoplethysmography (PPG) sensor disposed on the underside of the housing and proximate to the IPG sensor, the PPG sensor including an emitter configured to emit light and a detector configured to detect the light emitted from the emitter, the PPG sensor configured to generate PPG data indicative of an amount of light detected by the detector, the wearable computing device further comprising: a computing system, the computing system comprising: receiving the IPG data; receiving the PPG data; A wearable computing device configured to determine a cardiovascular response based at least in part on a comparison of the IPG data and the PPG data.

15. 15. The wearable computing device of claim 14, wherein the cardiovascular response includes at least one of a myogenic response, blood pressure, heart rate, atrial fibrillation, pulselessness, or nocturnal descent phenotype.

16. 1. A method for monitoring cardiovascular health, said method comprising: receiving, at a computing device, impedance plethysmography (IPG) data generated by an IPG sensor disposed on an underside of a housing of a wearable computing device, the underside of the housing facing the skin of a user when the wearable computing device is worn by the user, the IPG sensor having a pair of excitation electrodes and a pair of sensing electrodes configured to contact the skin of the user, the IPG sensor configured to generate IPG data indicative of a voltage passing between the pair of sensing electrodes due to a current applied to the pair of excitation electrodes, the method further comprising: receiving, at the computing device, photoplethysmography (PPG) data generated by a PPG sensor disposed on the underside of the housing and proximate to the IPG sensor, the PPG sensor including an emitter configured to emit light and a detector configured to detect the light emitted from the emitter, the PPG data indicating an amount of light detected by the detector, the method further comprising: determining, at the computing device, a myogenic response based at least in part on a comparison of the PPG data and the IPG data; and controlling, at the computing device, a user interface based at least in part on the myogenic response.

17. 17. The method of claim 16, wherein determining the myogenic response comprises determining the myogenic response when the magnitude of the PPG data changes by a threshold amount relative to a change in magnitude of the IPG data.

18. 17. The method of claim 16, wherein determining the myogenic response comprises determining the myogenic response by determining vasoconstriction when the magnitude of the PPG data decreases while the magnitude of the IPG data remains substantially constant, and determining vasodilation when the magnitude of the PPG data increases while the magnitude of the IPG data remains substantially constant.

19. 17. The method of claim 16, further comprising determining, at the computing device, a long-term cause when the magnitude of the PPG data decreases relative to the magnitude of the IPG data over a period of time.

20. 20. The method of claim 19, wherein controlling the user interface includes controlling the user interface to display a recommendation to take an electrocardiogram (ECG) measurement with an ECG sensor of the wearable computing device.

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