Sensor Device

Abstract

Various aspects of the present disclosure generally relate to multispectral sensor devices. In some aspects, the sensor device can obtain, from image data collected by the sensor, first image data related to a first measurement location on a measurement target and second image data related to a second measurement location on the measurement target, where the first and second measurement locations are subsurface measurement locations within the measurement target. The sensor device can also determine a pulse transit time measurement based on the first and second image data and provide information identifying the pulse transit time measurement. Numerous other aspects are also provided.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This patent application claims priority to U.S. Provisional Patent Application No. 62 / 923,247, filed October 18, 2019, and entitled "SENSOR DEVICE," and U.S. Nonprovisional Patent Application No. 16 / 949,156, filed October 15, 2020, and entitled "SENSOR DEVICE," which are expressly incorporated by reference herein. [Background technology]

[0002] The sensor device may perform measurements for a variety of purposes. For example, the sensor device may determine a measurement based on an interaction with a target. One example of such a target is the human body, for which the sensor device may perform health-related measurements. Summary of the Invention

[0003] According to some embodiments, a method of the present invention may include the steps of acquiring, by a sensor device, first image data relating to a first measurement location of a measurement target from image data collected by a sensor of the sensor device; acquiring, by the sensor device, second image data relating to a second measurement location of the measurement target from the image data, wherein the first measurement location and the second measurement location are subsurface measurement locations within the measurement target; determining, by the sensor device, a pulse transit time measurement value based on the first image data and the second image data; and providing, by the sensor device, information identifying the pulse transit time measurement value.

[0004] According to some embodiments, a sensor device of the present invention may comprise a sensor and one or more processors operably coupled to the sensor, wherein the processor may be configured to collect image data using the sensor, obtain from the image data first image data relating to a first measurement location of a measurement target, obtain from the image data second image data relating to a second measurement location of the measurement target, where the first measurement location and the second measurement location are subsurface measurement locations within the measurement target, determine a pulse transit time measurement based on the first image data and the second image data, and provide information identifying the pulse transit time measurement.

[0005] According to some embodiments, a non-transitory computer readable medium according to the present invention may store one or more instructions that, when executed by the one or more processors of a sensor device, may cause the one or more processors to obtain first image data relating to a first measurement location of a measurement target and second image data relating to a second measurement location of the measurement target, where the first measurement location and the second measurement location are sub-surface measurement locations within the measurement target and the first image data and the second image data are obtained from a video stream, determine a pulse transit time measurement based on the first image data and the second image data, and provide, by the sensor device, information identifying the pulse transit time measurement. [Brief description of the drawings]

[0006] [Figure 1A] FIG. 1 is a schematic diagram of one or more exemplary implementations described herein. [Figure 1B] FIG. 1 is a schematic diagram of one or more exemplary implementations described herein. [Diagram 2] FIG. 1 is an illustration of an example environment in which the systems and / or methods described herein may be implemented. [Diagram 3] 3 is a block diagram of example components of one or more devices of FIG. 2. [Figure 4] 1 is a flow chart of an exemplary process for pulse transit time determination using an image sensor. [Diagram 5] 1 is a flow chart of an exemplary process for pulse transit time determination using an image sensor. [Figure 6] 1 is a flow chart of an exemplary process for pulse transit time determination using an image sensor. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] The detailed description of the embodiments below refers to the accompanying drawings. The same reference numbers in different drawings may identify the same or similar elements. Some aspects of the following description use a spectrometer as an example. However, the measurement principles, procedures, and methods described herein can be used with any sensor, including but not limited to other optical and spectral sensors.

[0008] The sensor device may be used to perform health-related measurements such as blood pressure measurements, blood oxygen measurements (e.g., peripheral capillary oxygen saturation (SpO2) measurements), glucose measurements, and / or surface measurements (e.g., measurements of skin hydration, skin tone, and / or bilirubin levels, etc.). Many blood pressure measurement approaches use contact means such as wearable devices and / or measuring instruments, which provide blood pressure data at specific contact points.

[0009] It may be beneficial to perform blood pressure measurements at multiple different points (e.g., different spatial points, different time points, and / or different depths in the measurement target). It may also be beneficial to combine blood pressure measurements with other types of health-related measurements, such as those described above. However, determining blood pressure measurements at multiple points using contact means may require large, costly, and complex equipment capable of contacting multiple points on the measurement target, which may be infeasible for some wearable devices or meters. Furthermore, the wearable device or meter may require other contact measurement devices to perform other types of health-related measurements.

[0010] The embodiments described herein provide for measuring blood pressure of a measurement target using a pulse transit time measurement determined using an image sensor of a sensor device. For example, the pulse transit time may be determined with reference to two or more measurement locations on the measurement target, the two or more locations being included in an image or video stream acquired by the image sensor. This may allow for measurement of blood pressure at many different locations on the measurement target. Furthermore, the use of an image sensor may allow for determination of other health related measurements at the locations used to determine the pulse transit time measurement and / or other locations on or below the surface of the measurement target. Thus, the embodiments described herein provide for examination of optical changes in a volume of tissue in which blood flow is contained. By performing such measurements using an image sensor, size, cost, and complexity are reduced compared to devices that perform such measurements using contact means. Furthermore, devices using contact means may need to be at least as large as the distance between the measurement points, so the spacing between the measurement points may be increased compared to devices using contact means. And further, the embodiments described herein may be capable of performing pulse transit time and / or other measurements on multiple measurement targets (e.g., people and / or areas of people) at once, which may not be possible with contact type measurement devices.

[0011] 1A and 1B are diagrams illustrating an overview of an example sensor device 100 as described herein. As shown, the example sensor device 100 includes an image sensor 105 and a processor 110. Components of the image sensor 105 and the processor 110 are described in more detail in connection with FIGS. 2 and 3. The image sensor 105 and the processor 110 may be associated with a sensor device as described in more detail elsewhere herein. References to the sensor device in the description accompanying FIGS. 1A and 1B may refer to one or more of the image sensor 105, the processor 110, and the user device 155 shown in FIG. 1B.

[0012] As shown, the exemplary sensor device 100 includes a measurement target 115. The measurement target 115 may be tissue (e.g., human tissue, animal tissue, etc.). As further shown, the measurement target 115 may include a blood vessel 120. The sensor device may perform pulse transit time measurements based on the blood vessel 120, as described below.

[0013] As indicated by reference numeral 125, the image sensor 105 may collect image data. For example, the image sensor 105 may generate a signal for an image stream, a video stream, or the like, based on receiving light of one or more wavelengths. In some implementations, the image sensor 105 may be configured to sense multiple different wavelengths (e.g., λ1, λ2, and λ3 in FIG. 1A), which may determine different measurements at different measurement locations (e.g., surface measurement locations or subsurface measurement locations). In some implementations, the image sensor 105 may be configured to sense a single wavelength, which may reduce the complexity and cost of the image sensor 105.

[0014] As shown, the measurement target 115 may be associated with two subsurface measurement locations 130. The sensor device may determine a pulse transit time measurement and / or another type of measurement based on the subsurface measurement locations 130 (e.g., based on information determined using light associated with λ1 and λ2), as described in more detail elsewhere herein. In some implementations, the measurement target 115 may be associated with any number of subsurface measurement locations 130. Use of more subsurface measurement locations 130 may provide additional pulse transit time measurements, and / or blood pressure measurements, etc., and use of fewer subsurface measurement locations 130 may reduce the complexity and processor usage of the pulse transit time determination. As shown, the measurement target 115 may be associated with a surface measurement location 135. The sensor device may perform health-related measurements based on light associated with λ3 to determine bilirubin content, body temperature, skin hydration, or another type of health-related parameter. In some implementations, the sensor device may perform measurements described herein based on non-contact sensing operations. In non-contact sensing operations, the sensor device may not be in contact with the measurement target 115. For example, the sensor device may be any distance from the measurement target 115. Performing measurements using non-contact sensing operations may increase the versatility of the sensor device, allow measurements without contacting the measurement target 115, and may improve the safety and efficiency of performing measurements, as described elsewhere herein.

[0015] In some implementations, the sensor device may identify the locations 130 and / or 135. For example, the sensor device may use computer vision techniques to identify the locations 130 and / or 135 based on information associated with the image captured by the image sensor 105 (e.g., spatial information, and / or a particular wavelength response in the image captured by the image sensor 105, etc.). In some implementations, the sensor device may identify the locations 130 and / or 135 based on which measurement is performed. For example, the sensor device may identify subsurface measurement locations 130 for pulse transit time measurements, measurements of health parameters (e.g., blood oxygen level measurements (e.g., SpO2) or heart rate measurements, among other examples), etc., and may identify surface measurement locations 135 for skin hydration measurements, and / or bilirubin measurements, etc.

[0016] As shown, the subsurface measurement location 130 may be associated with wavelengths λ1 and λ2. In some implementations, λ1 and λ2 may be the same wavelength. In some implementations, λ1 and λ2 may be different wavelengths within the same wavelength range. In some implementations, λ1 and λ2 may be different wavelength ranges. In some implementations, λ1 and / or λ2 may be associated with a near infrared (NIR) range, which may enable measurements to be made at the subsurface measurement location 130. In some implementations, λ1 and / or λ2 may be associated with another wavelength that can penetrate the corresponding subsurface measurement location (e.g., the subsurface measurement location 130).

[0017] As shown, the surface measurement location 135 may be associated with a wavelength λ3. In some implementations, λ3 may be a visible wavelength, which may enable color-based measurements and / or the like. Thus, λ3 may provide visible measurement information about the measurement target. In some implementations, λ3 may be in the same wavelength range as λ1 and / or λ2. In some implementations, λ3 may be in a different wavelength range than λ1 and / or λ2. Measurements using λ3 in a different wavelength range than λ1 and / or λ2 may increase the variety of measurements that can be made using the sensor device, while measurements using λ3 in the same wavelength range as λ1 and / or λ2 may reduce the complexity of the sensor device.

[0018] Although FIG. 1 illustrates a sensor device receiving light at discrete wavelengths for different measurement locations, it should be understood that the sensor device can receive spectral data associated with multiple wavelengths for a given measurement location. For example, the sensor device may receive light in a frequency range (which may include any one or more of λ1, λ2, and / or λ3, depending on the material properties of the measurement target and / or measurement location) for any one or more of the measurement locations shown in FIG. 1. Thus, the sensor device can collect spectrally diverse data measurements associated with multiple different frequencies for a given measurement location. Additionally, the sensor device may collect spatially diverse measurement data for one or more frequencies at multiple different measurement locations. And further, the sensor device may collect temporally diverse measurement data by performing spatially and / or spectrally diverse measurement data measurements over time.

[0019] As shown in FIG. 1B and by reference numeral 140, the processor 110 may determine a pulse transit time using λ1 and λ2. For example, the processor 110 may determine a pulse transit time based on measurements at the subsurface measurement location 130. In some implementations, the processor 110 may sample image data or video stream captured by the image sensor 105 (e.g., multiple times per second, etc.) and may identify a pulse at the first subsurface measurement location 130 and may identify a pulse at the second subsurface measurement location 130. Based on the time difference (e.g., number of samples) between identifying a pulse at the first subsurface measurement location 130 and identifying a pulse at the second subsurface measurement location 130, the processor 110 may determine a pulse transit time. In some implementations, the processor 110 may determine a blood pressure value based on the pulse transit time. For example, the processor 110 may determine a blood pressure value based on a relationship between pulse transit time and blood pressure.

[0020] As indicated by reference numeral 145, the processor 110 may determine another measurement using light associated with λ3 (e.g., at the surface measurement location 135). In some implementations, the processor 110 may determine other measurements simultaneously with determining the pulse transit time measurement. This may allow for determining time-correlated health-related measurements that may provide therapeutic benefits, and / or accuracy benefits, etc. Time-correlated health-related measurements may be difficult to capture using two or more different sensor devices configured to perform respective health-related measurements, such as due to different delays associated with the two or more different sensor devices and / or difficulties in coordinating the operation of the two or more sensor devices. In an exemplary implementation sensor device 100, the other measurement is a bilirubin measurement (e.g., based on a color associated with the surface measurement location 135, such as skin color), although the other measurement may include any health-related measurement that can be captured via imaging.

[0021] In some implementations, the processor 110 may determine multiple pulse transit time values ​​based on the image data. For example, the processor 110 may obtain additional image data relating to one or more other measurement locations and may determine multiple pulse transit time values ​​based on the additional image data. For example, the multiple pulse transit times may be associated with different regions of the measurement target 115, different blood vessels 120, etc. This may also allow for differential measurement of pulse transit times and may allow for detection of inconsistencies in pulse transit times and / or blood pressures, etc. at different locations on the measurement target. Determining the pulse transit time values ​​based on the image data may also allow for measurement of pulse transit times of multiple different measurement targets (e.g., multiple different people) using a single image sensor since multiple different measurement targets may be captured in a single image, which may conserve resources associated with implementing multiple different sensor devices.

[0022] As indicated by reference numeral 150, the processor 110 may provide information identifying the measurements determined in connection with reference numerals 140 and 145 to a user device 155. In some implementations, the user device 155 may be a sensor device. For example, the processor 110 and the image sensor 105 may be components of the user device 155. In some implementations, the user device 155 may be separate from the sensor device.

[0023] As indicated by reference numeral 160, the user device 155 may provide a visual interface for health-related measurements determined by the sensor device. Here, the visual interface is indicated as a health interface. As indicated by reference numeral 165, the visual interface indicates a blood pressure measurement determined based on the pulse transit time. Additionally, the visual interface indicates a subsurface measurement location (e.g., the subsurface measurement location 130 shown in FIG. 1A) used to determine the pulse transit time. As indicated by reference numeral 170, the visual interface indicates that the bilirubin measurement may be abnormal (e.g., based on the color of the measurement target 115 at the surface measurement location 135 of FIG. 1A). Additionally, the visual interface indicates a surface measurement location (e.g., the surface measurement location 135 shown in FIG. 1A) used to determine the bilirubin measurement.

[0024] In some implementations, the user device 155 may update the visual interface. For example, the user device 155 may update the blood pressure measurement based on images captured over time, and / or provide additional measurements determined based on images captured by the sensor device, etc. In some implementations, the user device 155 may provide information based on an interaction with the visual interface. For example, the user device 155 may provide additional details about the blood pressure measurement (e.g., pulse transit time, heart rate associated with the pulse transit time, additional pulse transit time and / or blood pressure for different measurement locations of the measurement target 115, and / or the like) based on receiving an interaction (e.g., user interaction, etc.) with the visual representation of the blood pressure measurement. As another example, the user device 155 may modify the measurement location based on the interaction (e.g., an interaction that moves the visual representation of the measurement location and / or an interaction that specifies a new location for the measurement location, etc.). As yet another example, the user device 155 or the processor 110 may perform a measurement based on the interaction. For example, the interaction may select a measurement to be performed (e.g., from a menu of available measurements) or may specify a location for the measurement. The user device 155 and / or the processor 110 may perform measurements at the location and provide information indicative of the results of the measurements.

[0025] In some implementations, the processor 110 may perform a measurement based on the results of another measurement. For example, the processor 110 may determine that a blood pressure measurement or a heart rate measurement meets a threshold and may perform another measurement (e.g., a blood oxygen measurement, a temperature measurement, and / or a skin hydration measurement, etc.) based on the blood pressure measurement or the heart rate measurement meeting a threshold. In some implementations, the processor 110 may perform a measurement without user interaction (e.g., automatically), thereby conserving processor resources used in association with manually triggering a measurement. In some implementations, the processor 110 may provide information identifying the measurement (e.g., via a visual interface and / or as a notification or alert, etc.). In some implementations, the processor 110 may trigger an action to be performed based on the measurement (e.g., dispatching a nurse, administering medication, and / or providing a notification for the user to perform an activity, etc.).

[0026] In some implementations, the processor 110 or user device 155 may determine blood pressure based on pulse transit time. For example, the processor 110 or user device 155 may determine blood pressure based on an estimated pressure difference between measurement locations 130 based on pulse wave velocity (e.g., by dividing the distance traveled between measurement locations 130 by the pulse transit time). In some implementations, the processor 110 or user device 155 may determine blood pressure based on pulse transit time using a different technique than that described above.

[0027] In this manner, a pulse transit time measurement using the image sensor 105 is performed. Furthermore, additional measurements using the image sensor 105 may be determined in conjunction with (e.g. contemporaneously with) the pulse transit time measurement, thereby allowing for temporal correlation of such measurements. Thus, the complexity of the sensor device is reduced and the flexibility of measurements is improved. Furthermore, pulse transit times may be performed for any two or more measurement locations at any suitable interval from each other, thereby improving the usefulness of the pulse transit time data and reducing the mechanical complexity of the sensor device compared to sensor devices having contact means for determining pulse transit times at adjustable intervals.

[0028] In some implementations, the image sensor 105 and / or the processor 110 may be included in a sensor device, such as the sensor device 210 described in connection with FIG. 2 below. The sensor device 210 may be capable of sampling spectra across multiple points in a scene to provide images in which features and locations can be identified to provide multiple points for spectral comparison. Thus, the sensor device may perform pulse transit time based blood pressure measurements and / or one or more other measurements described herein. Additionally, the sensor device 210 may provide more flexibility than devices employing sensors at different points in space. For example, the sensor device 210 may be capable of performing measurements in a non-contact manner for multiple users, including users not wearing the sensor device 210. Additionally, the sensor device 210 may be more resilient to suboptimal sensor placement than devices employing respective sensors at different points in space. For example, sensor device 210 may be capable of capturing images associated with a field of view (FOV) and may analyze multiple objects within the FOV, which may be particularly beneficial in health care environments such as care homes where sensor device 210 may instantly recognize and monitor personal health emergencies while moving within a common use space. Additionally, in some implementations, sensor device 210 may perform the operations described herein in a non-contact manner (e.g., without touching the measurement target of sensor device 210) and may provide spectral data at multiple points (e.g., all points, multiple points) in a scene within the FOV of sensor device 210.

[0029] As noted above, Figures 1A and 1B are provided merely as one or more examples, and other examples may differ from those described with respect to Figures 1A and 1B.

[0030] Figure 2 is a diagram of an example environment 200 in which the systems and / or methods described herein may be implemented. As shown in Figure 2, environment 200 may include user devices 240, a network 250, and a sensor device 210, which may include a processor 220 and an image sensor 230. The devices of environment 200 may be interconnected via wired connections, wireless connections, or a combination of wired and wireless connections.

[0031] The sensor device 210 may include one or more devices capable of performing sensor measurements on an object, and / or an optical device capable of storing, processing, and / or routing information related to a sensor determination. For example, the sensor device 210 may include a spectroscopic device performing spectroscopy, such as a spectroscopic sensor device (e.g., a binary multispectral sensor device performing vibrational spectroscopy, such as a near-infrared (NIR) spectrometer, a mid-infrared spectrometer (mid-IR), and / or a Raman spectrometer). For example, the sensor device 210 may perform health parameter monitoring determinations, pulse transit time determinations, biometric authentication determinations, and / or activity detection determinations, etc. In this case, the sensor device 210 may utilize the same wavelengths, different wavelengths, and / or a combination of the same and different wavelengths for such determinations. In some implementations, the sensor device 210 may be incorporated into a user device 240, such as a wearable spectrometer and / or the like. In some implementations, the sensor device 210 may receive information from and / or transmit information to another device in the environment 200, such as the user device 240.

[0032] In some implementations, the sensor device 210 may include a spectroscopic imaging camera. A spectroscopic imaging camera is a device capable of capturing an image of a scene. The spectroscopic imaging camera (or a processor 220 associated with the spectroscopic imaging camera) may be capable of determining the spectral content or changes in spectral content at different points within the image of the scene, such as any point within the image of the scene.

[0033] In some implementations, the sensor device 210 may include a spectral imaging camera capable of performing hyperspectral imaging. For example, the sensor device 210 may include a spectral filter array (e.g., a tiled spectral filter array). In some implementations, the spectral filter array may be disposed on the image sensor 230. In some implementations, the sensor device 210 may include a diffuser. For example, the diffuser may be configured to diffuse light on its way to the image sensor 230. Each point in an image captured by the sensor device 210 may be mapped to a unique pseudo-random pattern on the spectral filter array that encodes the multiplexed spatiotemporal spectral information. Thus, a hyperspectral volume with sub-superpixel resolution may be recovered by solving a sparsity-constrained inverse problem. The sensor device 210 may include continuous or non-continuous spectral filters, which may be selected for a given application. The use of diffusers and computational approaches to determine hyperspectral volumes at sub-superpixel resolution may improve sampling of spectral content, thereby enabling imaging using spectral filters such as hyperspectral filter arrays. Thus, the manufacture of the sensor device 210 is simplified relative to the manufacture of a filter on the order of the dimensions of each pixel. In some implementations, the sensor device 210 may comprise a lens.

[0034] The sensor device 210 may include a processor 220. The processor 220 is described in more detail in connection with FIG.

[0035] The sensor device 210 may include an image sensor 230. The image sensor 230 includes a device capable of sensing light. For example, the image sensor 230 may include an image sensor, a multispectral sensor, and / or a spectral sensor, etc. In some implementations, the image sensor 230 may include a charge-coupled device (CCD) sensor, and / or a complementary metal-oxide semiconductor (CMOS) sensor, etc. In some implementations, the image sensor 230 may include a front-side illumination (FSI) sensor, and / or a back-side illumination (BSI) sensor, etc. In some implementations, the image sensor 230 may be included in a camera of the sensor device 210 and / or the user device 240.

[0036] User device 240 includes one or more devices that can receive, generate, store, process, and / or provide information related to sensor determinations. For example, user device 240 may include a communication and / or computing device, such as a mobile phone (e.g., a smart phone, and / or a wireless phone), a computer (e.g., a laptop computer, a tablet computer, and / or a handheld computer), a gaming device, a wearable communication device (e.g., a smart watch, and / or a smart glasses), or a similar type of device. In some implementations, user device 240 may receive information from and / or transmit information to another device in environment 200, such as sensor device 210.

[0037] Network 250 includes one or more wired and / or wireless networks. For example, network 250 may include a cellular network (e.g., a Long Term Evolution (LTE) network, a Code Division Multiple Access (CDMA) network, a 3G network, a 4G network, a 5G network, and / or another type of next generation network, etc.), a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network (e.g., the Public Switched Telephone Network (PSTN)), a private network, an ad-hoc network, an intranet, the Internet, an optical fiber-based network, a cloud computing network, etc., and / or a combination of these or other types of networks.

[0038] The number and arrangement of devices and networks shown in FIG. 2 are provided as examples. In practice, there may be additional devices and / or networks, fewer devices and / or networks, different devices and / or networks, or different arrangements of devices and / or networks than those shown in FIG. 2. Furthermore, two or more devices shown in FIG. 2 may be embodied within a single device, or a single device shown in FIG. 2 may be embodied as multiple distributed devices. For example, although sensor device 210 and user device 240 are described as separate devices, sensor device 210 and user device 240 may be embodied as a single device. Additionally or alternatively, a set of devices (e.g., one or more devices) of environment 200 may perform one or more functions described as being performed by another set of devices of environment 200.

[0039] 3 is a diagram of example components of a device 300. The device 300 may correspond to the sensor device 210 and the user device 240. In some implementations, the sensor device 210 and / or the user device 240 may include one or more of the devices 300 and / or one or more components of the devices 300. As shown in FIG. 3, the device 300 may include a bus 310, a processor 320, a memory 330, a storage component 340, an input component 350, an output component 360, and a communication interface 370.

[0040] The bus 310 includes components that allow communication between the multiple components of the device 300. The processor 320 is implemented in hardware, firmware, and / or a combination of hardware and software. The processor 320 is a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), a microprocessor, a microcontroller, a digital signal processor (DSP), a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), or other type of processing component. In some implementations, the processor 320 includes one or more processors that can be programmed to perform functions. The memory 330 includes a random access memory (RAM), a read only memory (ROM), and / or another type of dynamic or static storage device (e.g., flash memory, magnetic memory, and / or optical memory) that stores information and / or instructions for use by the processor 320.

[0041] Storage component 340 stores information and / or software related to the operation and use of device 300. For example, storage component 340 may include a hard disk (e.g., a magnetic disk, an optical disk, and / or a magneto-optical disk), a solid-state drive (SSD), a compact disk (CD), a digital versatile disk (DVD), a floppy disk, a cartridge, a magnetic tape, and / or another type of non-transitory computer-readable medium along with a corresponding drive.

[0042] Input components 350 include components that enable device 300 to receive information, such as via user input (e.g., a touchscreen display, a keyboard, a keypad, a mouse, buttons, switches, and / or a microphone). Additionally or alternatively, input components 350 may include components for determining position (e.g., a global positioning system (GPS) component) and / or sensors (e.g., an accelerometer, a gyroscope, an actuator, and / or another type of position or environmental sensor, etc.). Output components 360 include components that provide output information from device 300 (e.g., via a display, a speaker, a haptic feedback component, and / or an audio or visual indicator, etc.).

[0043] The communications interface 370 includes transceiver-like components (e.g., a transceiver, another receiver, and / or another transmitter, etc.) that enable the device 300 to communicate with other devices, such as via a wired connection, a wireless connection, or a combination of wired and wireless connections. The communications interface 370 may enable the device 300 to receive information from and / or provide information to other devices. For example, the communications interface 370 may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, a Wi-Fi interface, and / or a cellular network interface, etc.

[0044] The device 300 may perform one or more of the processes described herein. The device 300 may perform these processes based on the processor 320 executing software instructions stored by a non-transitory computer-readable medium, such as the memory 330 and / or the storage component 340. As used herein, the term "computer-readable medium" refers to a non-transitory memory device. A memory device includes memory space within a single physical storage device or memory space spanning multiple physical storage devices.

[0045] Software instructions may be loaded into memory 330 and / or storage component 340 from another computer-readable medium or from another device via communication interface 370. When executed, the software instructions stored in memory 330 and / or storage component 340 may cause processor 320 to perform one or more operations described herein. Additionally, or alternatively, hardware circuitry may be used in place of, or in combination with, software instructions to perform one or more operations described herein. Thus, implementations described herein are not limited to any specific combination of hardware circuitry and software.

[0046] The number and arrangement of components shown in Figure 3 are provided as an example. In practice, device 300 may include additional, fewer, different, or differently arranged components than those shown in Figure 3. Additionally or alternatively, a set of components (e.g., one or more components) of device 300 may perform one or more functions that are described as being performed by another set of components of device 300.

[0047] 4 is a flow chart of an example process 400 for pulse transit time determination using an image sensor. In some implementations, one or more process blocks of FIG. 4 may be performed by a sensor device (e.g., sensor device 210 and / or a sensor device described in connection with FIG. 1, etc.). In some implementations, one or more process blocks of FIG. 4 may be performed by a device or group of devices separate from the sensor device, such as a user device (e.g., user device 155 and / or user device 240, etc.).

[0048] 4, process 400 may include acquiring first image data for a first measurement location of the measurement target (block 410). For example, the sensor device (e.g., using the processor 320, memory 330, and / or communication interface 370, etc.) may acquire the first image data for the first measurement location of the measurement target, as described above.

[0049] 4, process 400 may include acquiring second image data for a second measurement location of the measurement target, where the first measurement location and the second measurement location are subsurface measurement locations within the measurement target (block 420). For example, the sensor device (e.g., using the processor 320, memory 330, and / or communication interface 370, etc.) may acquire the second image data for the second measurement location of the measurement target, as described above. In some implementations, the first measurement location and the second measurement location are subsurface measurement locations within the measurement target.

[0050] 4, process 400 may include determining a pulse transit time measurement based on the first image data and the second image data (block 430). For example, the sensor device (e.g., using the processor 320, memory 330, and / or communication interface 370, etc.) may determine a pulse transit time measurement based on the first image data and the second image data, as described above.

[0051] 4, process 400 may include providing information identifying a pulse transit time measurement (block 440). For example, the sensor device (e.g., using the processor 320, memory 330, and / or communication interface 370, etc.) may provide information identifying a pulse transit time measurement, as described above.

[0052] Process 400 may include additional implementations, such as any single implementation or any combination of implementations described below and / or implementations in conjunction with one or more other processes described elsewhere herein.

[0053] In a first embodiment, the sensor comprises an image sensor of a camera of the sensor device.

[0054] In a second embodiment, alone or in combination with the first embodiment, step 400 includes determining the blood pressure value using pulse transit time measurements.

[0055] In a third embodiment, alone or in combination with one or more of the first and second embodiments, the image data comprises multispectral image data.

[0056] In a fourth embodiment, alone or in combination with one or more of the first through third embodiments, the process 400 includes using the image data to determine measurements other than pulse transit time measurements.

[0057] In a fifth embodiment, either alone or in combination with one or more of the first to fourth embodiments, other measurements are performed using the visibility range information.

[0058] In a sixth embodiment, alone or in combination with one or more of the first to fifth embodiments, the further measurement relates to the skin colour of the measurement target.

[0059] In a seventh embodiment, alone or in combination with one or more of the first to sixth embodiments, the further measurements comprise health parameters.

[0060] In an eighth embodiment, alone or in combination with one or more of the first to seventh embodiments, the health parameters comprise at least one of a heart rate measurement, or an SpO2 measurement.

[0061] In a ninth embodiment, alone or in combination with one or more of the first to eighth embodiments, the first image data and the second image data are associated with wavelengths transmitted through the measurement target at the first measurement location and the second measurement location, respectively.

[0062] Although Figure 4 illustrates example blocks of process 400, in some implementations, process 400 may include additional, fewer, different, or differently arranged blocks than those depicted in Figure 4. Additionally, or alternatively, two or more of the blocks of process 400 may be performed in parallel.

[0063] 5 is a flow chart of an example process 500 for pulse transit time determination using an image sensor. In some implementations, one or more process blocks of FIG. 5 may be performed by a sensor device (e.g., sensor device 210, and / or a sensor device described in connection with FIG. 1, etc.). In some implementations, one or more process blocks of FIG. 5 may be performed by a device or group of devices separate from the sensor device, such as a user device (e.g., user device 155, and / or user device 240, etc.).

[0064] 5, process 500 may include collecting image data using a sensor (block 510). For example, a sensor device (e.g., using processor 320, memory 330, communication and / or communications interface 370, etc.) may collect image data using the sensor, as described above.

[0065] 5, process 500 may include obtaining, from the image data, first image data related to a first measurement location of the measurement target (block 520). For example, the sensor device (e.g., using the processor 320, memory 330, and / or communication interface 370, etc.) may obtain, from the image data, the first image data related to the first measurement location of the measurement target, as described above.

[0066] 5, process 500 may include obtaining, from the image data, second image data for a second measurement location of the measurement target, where the first measurement location and the second measurement location are subsurface measurement locations within the measurement target (block 530). For example, the sensor device may obtain (e.g., using the processor 320, memory 330, and / or communication interface 370, etc.) from the image data, the second image data for the second measurement location of the measurement target, as described above. In some implementations, the first measurement location and the second measurement location are subsurface measurement locations within the measurement target.

[0067] 5, process 500 may include determining a pulse transit time measurement based on the first image data and the second image data (block 540). For example, the sensor device (e.g., using the processor 320, memory 330, and / or communication interface 370, etc.) may determine a pulse transit time measurement based on the first image data and the second image data, as described above.

[0068] 5, process 500 may include providing information identifying a pulse transit time measurement (block 550). For example, the sensor device (e.g., using processor 320, memory 330, and / or communications interface 370, etc.) may provide information identifying a pulse transit time measurement, as described above.

[0069] Process 500 may include additional implementations, such as any single implementation or any combination of implementations described below and / or implementations in conjunction with one or more other processes described elsewhere herein.

[0070] In a first embodiment, the first image data and the second image data are based on light generated by a sensor device.

[0071] In a second embodiment, alone or in combination with the first embodiment, the first image data and the second image data relate to the near infrared spectral range.

[0072] In a third embodiment, alone or in combination with one or more of the first and second embodiments, process 500 includes obtaining additional image data relating to one or more other measurement locations and determining one or more other pulse transit time values ​​based on the additional image data.

[0073] In a fourth embodiment, alone or in combination with one or more of the first to third embodiments, the input of the sensor is filtered by a filter based on binary multispectral techniques.

[0074] In a fifth embodiment, alone or in combination with one or more of the first to fourth embodiments, the first image data is associated with a first wavelength passed to the sensor and the second image data is associated with a second wavelength passed to the sensor.

[0075] Although Figure 5 illustrates example blocks of process 500, in some implementations, process 500 may include additional, fewer, different, or differently arranged blocks than those depicted in Figure 5. Additionally, or alternatively, two or more of the blocks of process 500 may be performed in parallel.

[0076] 6 is a flow chart of an example process 600 for pulse transit time determination using an image sensor. In some implementations, one or more process blocks of FIG. 6 may be performed by a sensor device (e.g., sensor device 210, and / or a sensor device described in connection with FIG. 1, etc.). In some implementations, one or more process blocks of FIG. 6 may be performed by a device or group of devices separate from the sensor device, such as a user device (e.g., user device 155, and / or user device 240, etc.).

[0077] As shown in FIG. 6, process 600 may include acquiring first image data for a first measurement location of a measurement target and second image data for a second measurement location of the measurement target, where the first measurement location and the second measurement location are subsurface measurement locations within the measurement target, and the first image data and the second image data are obtained from a video stream (block 610). For example, the sensor device may acquire (e.g., using processor 320, memory 330, and / or communication interface 370, etc.) the first image data for the first measurement location of the measurement target and the second image data for the second measurement location of the measurement target, as described above. In some implementations, the first measurement location and the second measurement location are subsurface measurement locations within the measurement target. In some implementations, the first image data and the second image data are obtained from a video stream.

[0078] 6, process 600 may include determining a pulse transit time measurement based on the first image data and the second image data (block 620). For example, the sensor device (e.g., using the processor 320, memory 330, and / or communication interface 370, etc.) may determine a pulse transit time measurement based on the first image data and the second image data, as described above.

[0079] 6, process 600 may include providing information identifying a pulse transit time measurement (block 630). For example, the sensor device (e.g., using processor 320, memory 330, and / or communications interface 370, etc.) may provide information identifying a pulse transit time measurement, as described above.

[0080] Process 600 may include additional implementations, such as any single implementation or any combination thereof described below and / or implementations in conjunction with one or more other processes described elsewhere herein.

[0081] In a first embodiment, the sensor device includes a smartphone.

[0082] In a second embodiment, alone or in combination with the first embodiment, process 600 includes providing a visual representation of the video stream having information identifying a pulse transit time value or information determined based on the pulse transit time.

[0083] In a third embodiment, the process 600 includes using the video stream, alone or in combination with one or more of the first and second embodiments, to determine another measurement other than the pulse transit time measurement.

[0084] Although Figure 6 illustrates example blocks of process 600, in some implementations, process 600 may include additional, fewer, different, or differently arranged blocks than those depicted in Figure 6. Additionally, or alternatively, two or more of the blocks of process 600 may be performed in parallel.

[0085] The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit the embodiments to the precise forms disclosed. Modifications and variations may be made in light of the above disclosure or may be acquired from practice of the embodiments.

[0086] As used herein, the term "component" is intended to be interpreted broadly as hardware, firmware, and / or a combination of hardware and software.

[0087] As used herein, meeting a threshold may refer to a value being greater than the threshold, more than the threshold, higher than the threshold, greater than or equal to the threshold, less than the threshold, lower than the threshold, or the same as the threshold, depending on the context.

[0088] Certain user interfaces are described herein and / or illustrated in the figures. The user interfaces may include graphical user interfaces, non-graphical user interfaces, and / or text-based user interfaces, etc. The user interfaces may provide information for display. In some implementations, a user may interact with the information, such as by providing input via an input component of the device that provides the user interface for display. In some implementations, the user interface may be configurable by the device and / or the user (e.g., the user may change the size of the user interface, the information provided through the user interface, the location of the information provided through the user interface, etc.). Additionally or alternatively, the user interface may be pre-configured into a standard configuration, a specific configuration based on the type of device on which the user interface is displayed, and / or a set of configurations based on the capabilities and / or specifications associated with the device on which the user interface is displayed.

[0089] It will be apparent that the systems and / or methods described herein may be implemented in different forms of hardware, firmware, or a combination of hardware and software. The actual specialized control hardware or software code used to implement these systems and / or methods is not intended to limit the embodiments. Thus, the operation and behavior of the systems and / or methods are described herein without reference to specific software code - it will be understood that software and hardware can be designed to implement the systems and / or methods based on the description herein.

[0090] Although particular combinations of features are recited in the claims and / or disclosed in the specification, these combinations are not intended to limit the disclosure of the various embodiments. Indeed, many of these features can be combined in ways not specifically recited in the claims and / or disclosed herein. Although each dependent claim set forth below may depend directly on only one claim, the disclosure of the various embodiments includes each dependent claim in combination with all other claims in the claim set.

[0091] No element, act, or instruction used herein should be construed as critical or essential unless expressly described as such. Also, as used herein, the indefinite articles "a" and "an" are intended to include one or more items and may be used interchangeably with "one or more." Additionally, as used herein, the definite article "the" is intended to include one or more items referenced in connection with the definite article "the" and may be used interchangeably with "one or more." Additionally, as used herein, the term "set" is intended to include one or more items (e.g., related items, unrelated items, combinations of related and unrelated items, etc.) and may be used interchangeably with "one or more." When only one item is intended, the phrase "only one" or similar language is used. Also, as used herein, the terms "have," "have," "having," and the like are intended to be open-ended terms. Additionally, the phrase "based on" is intended to mean "based at least in part on," unless expressly stated otherwise. Also, as used herein, the term "or" when used in a series is intended to be inclusive and can be used interchangeably with "and / or" unless expressly stated otherwise (e.g., when used in combination with "either" or "either one only").

Claims

1. identifying a plurality of measurement locations based on whether a type of measurement made by the sensor device is associated with a subsurface type measurement location or a surface type measurement location; identifying a first measurement location and a second measurement location of the plurality of measurement locations based on the type of measurement to be performed being associated with the subsurface type measurement location; acquiring first image data by a sensor device using a wavelength associated with the near infrared region transmitted to the first measurement location; acquiring, by the sensor device, second image data relating to the second measurement location, the first measurement location and the second measurement location are different subsurface measurement locations within the same blood vessel; providing, by the sensor device, information identifying a pulse transit time measurement based on the first image data and the second image data; A method comprising:

2. The method of claim 1 , wherein the sensor comprises an image sensor of a camera of the sensor device.

3. 2. The method of claim 1 , determining a blood pressure value using said pulse transit time measurements; The method further comprises:

4. The method of claim 1 , wherein the steps of acquiring the first image data and acquiring the second image data are based on a non-contact sensing operation.

5. 2. The method of claim 1 , determining measurements other than said pulse transit time measurements using light associated with a third wavelength; The method further comprises:

6. The method of claim 5 , wherein the other measurement value relates to a skin color of the measurement target.

7. The method of claim 5 , wherein the other measurements are determined using visibility range information.

8. The method of claim 5 , wherein the other measurements comprise health parameters.

9. 9. The method of claim 8, wherein the health parameter comprises at least one of a heart rate measurement or an SpO2 measurement.

10. 2. The method of claim 1, wherein the first image data and the second image data are associated with wavelengths transmitted to the first measurement location and the second measurement location, respectively.

11. identifying a plurality of measurement locations on the measurement target based on whether a type of measurement to be performed is associated with a surface of the measurement target or below the surface of the measurement target; wherein a first measurement location and a second measurement location of the plurality of measurement locations are identified based on a type of the measurement to be performed; acquiring first image data using a wavelength associated with a near infrared region transmitted to the first measurement location; acquiring second image data relating to the second measurement location of the measurement target; wherein the first measurement location and the second measurement location are subsurface measurement locations within the same blood vessel; and providing information identifying a pulse transit time measurement based on the first image data and the second image data. A sensor device comprising one or more processors configured to:

12. The sensor device according to claim 11 , wherein the first image data and the second image data are data based on light generated by the sensor device.

13. 12. The sensor device of claim 11, wherein the image data and the second image data are data relating to the near infrared spectral range.

14. 12. The sensor device of claim 11, wherein the one or more processors further comprise: acquiring additional image data relating to one or more other measurement locations; and determining one or more other pulse transit time values ​​based on said additional image data; A sensor device configured as follows:

15. 12. The sensor device of claim 11, wherein the sensor input is configured to be filtered by a spectral filter array.

16. 16. The sensor device of claim 15, configured such that the first image data is associated with a first wavelength passed to the sensor and the second image data is associated with a second wavelength passed to the sensor.

17. A non-transitory computer-readable medium storing instructions, the instructions comprising: The one or more instructions, when executed by one or more processors of a sensor device, cause the one or more processors to: identifying a plurality of measurement locations on the measurement target based on whether a type of measurement to be made is associated with a surface of the measurement target or below the surface; wherein a first measurement location and a second measurement location of the plurality of measurement locations are identified based on a type of the measurement to be performed; acquiring first image data using a wavelength associated with a near infrared region transmitted to the first measurement location; acquiring second image data relating to a second measurement position of the measurement target; wherein the first measurement location and the second measurement location are subsurface measurement locations within the same blood vessel; the first image data and the second image data are obtained from a video stream; and configured to cause the sensor device to provide information identifying the pulse transit time measurement based on the first image data and the second image data. Non-transitory computer-readable medium.

18. 20. The non-transitory computer readable medium of claim 17, wherein the sensor device comprises a smartphone.

19. 20. The non-transitory computer-readable medium of claim 17, wherein the one or more instructions, when executed by the one or more processors, cause the one or more processors to: and causing a visual representation of the video stream to be provided having information identifying the pulse transit time measurement or information determined based on the pulse transit time. Non-transitory computer-readable medium.

20. 20. The non-transitory computer-readable medium of claim 17, wherein the one or more instructions, when executed by the one or more processors, cause the one or more processors to: configured to use the video stream to determine another measurement other than the pulse transit time measurement. Non-transitory computer-readable medium.

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