Device and method for 24-hour ambulatory blood pressure monitoring based on gesture and / or activity detection compensation

Abstract

A device for monitoring a health condition of a person. The device has one or more biometric sensors for periodically measuring biometric data of a person, one or more activity sensors for periodically measuring activity of the person, and circuitry functionally coupled to the one or more biometric sensors and the one or more activity sensors, the circuit has an artificial intelligence (AI) engine configured to analyze a health condition of a person based on measurement data acquired from one or more biometric sensors and one or more activity sensors using a trained AI model.

Description

[0001] Cross - Reference to Related Applications

[0002] This application claims the benefit of U.S. Provisional Patent Application Serial No. 63 / 408,887, filed on September 22, 2022, the entire content of which is incorporated herein by reference. Technical Field

[0003] The present disclosure generally relates to blood pressure monitoring, and more particularly to 24-hour ambulatory blood pressure monitoring with posture and / or activity detection compensation. Background Art

[0004] Blood pressure is closely related to a person's health condition. High blood pressure (also known as "hypertension") or low blood pressure (also known as "hypotension") may cause various health problems. However, a single blood pressure measurement taken in a doctor's office or clinical setting may not be sufficient to confirm hypertension or hypotension, and 24-hour ambulatory blood pressure (24hr ABP) monitoring is often required, in which a person's or patient's blood pressure is monitored periodically (e.g., every 20 to 30 minutes during the day and every hour at night) or continuously for at least 24 hours while also monitoring the heart rate. Summary of the Invention

[0005] According to one aspect of the present disclosure, there is provided a device for monitoring a person's health condition, the device comprising: one or more biometric sensors for periodically measuring a person's biometric data; one or more activity sensors for periodically measuring a person's activity; and a circuit functionally coupled to the one or more biometric sensors and the one or more activity sensors, the circuit including an artificial intelligence (AI) engine configured to: use a trained AI model to analyze the person's health condition based on measurement data collected from the one or more biometric sensors and the one or more activity sensors.

[0006] In some embodiments, the one or more biometric sensors and the one or more activity sensors are configured to periodically perform a set of measurements to periodically measure a person's biometric data and a person's activity; and the one or more activity sensors are configured to: when performing each set of measurements, measure the person's activity at least two of before, during, and after the one or more biometric sensors measure the person's biometric data.

[0007] In some embodiments, the device further comprises: one or more adhesive pads for adhering to a person's body; each of the one or more adhesive pads includes at least one of the one or more biometric sensors and the one or more activity sensors.

[0008] In some embodiments, using a trained AI model to analyze a person's health condition includes: determining whether any of one or more biometric sensors and one or more activity sensors is in error based on measurement data collected by the one or more biometric sensors and the one or more activity sensors; and if none of the one or more biometric sensors and the one or more activity sensors is in error, analyzing the person's health condition based on the measurement data collected by the one or more biometric sensors and the one or more activity sensors.

[0009] In some embodiments, a person's activity includes at least one of a person's movement and a person's posture.

[0010] In some embodiments, the one or more biometric sensors include at least one of one or more blood pressure sensors and one or more heart rate sensors, and the person's biometric data includes the person's blood pressure data and the person's heart rate data.

[0011] In some embodiments, the one or more biometric sensors further include at least one of one or more temperature sensors for measuring a person's body temperature, one or more oxygen saturation sensors for measuring a person's oxygen level, and one or more vibration sensors or one or more microphones for measuring the sound of a person's body.

[0012] According to one aspect of the present disclosure, a computerized method for monitoring a person's health condition is provided, the method including: periodically measuring a person's biometric data using one or more biometric sensors; periodically measuring a person's activity using one or more activity sensors; and analyzing the person's health condition using a trained AI model based on measurement data collected from the one or more biometric sensors and the one or more activity sensors.

[0013] In some embodiments, the periodically measuring a person's biometric data and the periodically measuring a person's activity includes: periodically performing a set of measurements using the one or more biometric sensors and the one or more activity sensors to respectively periodically measure the person's biometric data and the person's activity; and when performing each set of measurements, measuring the person's activity using the one or more activity sensors at least two of before, during, and after the one or more biometric sensors measure the person's biometric data.

[0014] In some embodiments, the computerized method further includes: attaching one or more adhesive pads to a person's body; each of the one or more adhesive pads includes at least one of the one or more biometric sensors and the one or more activity sensors.

[0015] In some embodiments, using the trained AI model to analyze a person's health condition includes: determining whether any of the one or more biometric sensors and the one or more activity sensors has an error based on measurement data collected by the one or more biometric sensors and the one or more activity sensors; and if the one or more biometric sensors and the one or more activity sensors have no error, analyzing the person's health condition based on the measurement data collected by the one or more biometric sensors and the one or more activity sensors.

[0016] In some embodiments, a person's activity includes at least one of a person's movement and a person's posture.

[0017] In some embodiments, the one or more biometric sensors include at least one of one or more blood pressure sensors and one or more heart rate sensors, and a person's biometric data includes a person's blood pressure data and a person's heart rate data.

[0018] In some embodiments, the one or more biometric sensors further include at least one of one or more temperature sensors for measuring a person's body temperature, one or more oxygen saturation sensors for measuring a person's oxygen level, and one or more vibration sensors or one or more microphones for measuring the sound of a person's body.

[0019] According to one aspect of the present disclosure, one or more non-transitory computer-readable storage devices include computer-executable instructions, wherein the instructions, when executed, cause one or more circuits to perform operations, the operations including: periodically measuring a person's biometric data using one or more biometric sensors; periodically measuring a person's activity using one or more activity sensors; and using a trained AI model to analyze the person's health condition based on measurement data collected from the one or more biometric sensors and the one or more activity sensors.

[0020] In some embodiments, the periodically measuring a person's biometric data and the periodically measuring a person's activity include: periodically performing a set of measurements using the one or more biometric sensors and the one or more activity sensors to respectively periodically measure a person's biometric data and a person's activity; and when performing each set of measurements, measuring a person's activity using the one or more activity sensors at least two of before, during, and after the one or more biometric sensors measure a person's biometric data.

[0021] In some embodiments, the operations further include: pasting one or more adhesive pads onto a person's body; each of the one or more adhesive pads includes at least one of the one or more biometric sensors and the one or more activity sensors.

[0022] In some embodiments, using the trained AI model to analyze a person's health condition includes: determining whether any of the one or more biometric sensors and the one or more activity sensors have errors based on measurement data collected by the one or more biometric sensors and the one or more activity sensors; and if the one or more biometric sensors and the one or more activity sensors have no errors, analyzing the person's health condition based on the measurement data collected by the one or more biometric sensors and the one or more activity sensors.

[0023] In some embodiments, a person's activity includes at least one of a person's movement and a person's posture.

[0024] In some embodiments, the one or more biometric sensors include at least one sensor of the one or more blood pressure sensors and the one or more heart rate sensors, and the person's biometric data includes the person's blood pressure data and the person's heart rate data.

[0025] In some embodiments, the one or more biometric sensors further include at least one sensor of the one or more temperature sensors for measuring a person's body temperature, the one or more oxygen saturation sensors for measuring a person's oxygen level, and the one or more vibration sensors or the one or more microphones for measuring the sound of a person's body. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] To understand the present disclosure more comprehensively, please refer to the following description and drawings, wherein:

[0027] Figure 1 is a schematic diagram of a health monitoring and analysis system according to some embodiments of the present disclosure;

[0028] Figure 2 shows according to some embodiments of the present disclosure Figure 1 a schematic diagram of the simplified hardware structure of the client computing device and the server computer of the health monitoring and analysis system shown;

[0029] Figure 3 shows according to some embodiments of the present disclosure Figure 1 a schematic diagram of the simplified software structure of the client computing device and the server computer of the health monitoring and analysis system shown;

[0030] Figure 4 shows according to some embodiments of the present disclosure Figure 1 a schematic diagram of the simplified hardware structure of the health measurement device of the health monitoring and analysis system shown;

[0031] Figure 5is a schematic diagram showing the functional structure of a health monitoring and analysis system according to some embodiments of the present disclosure; Figure 1 as shown;

[0032] Figure 6 is a flowchart showing the steps of a blood pressure monitoring and analysis program executed by a health monitoring and analysis system according to some embodiments of the present disclosure; Figure 1 as shown;

[0033] Figure 7 is a schematic diagram of a health monitoring and analysis system according to some other embodiments of the present disclosure;

[0034] Figure 8 is a schematic diagram of a health monitoring and analysis system according to some yet other embodiments of the present disclosure;

[0035] Figure 9 is a schematic diagram showing the health measurement device of a health monitoring and analysis system according to some embodiments of the present disclosure; Figure 1 as shown;

[0036] Figure 10 is a schematic cross-sectional view of a gasket of a health measurement device according to some embodiments of the present disclosure; Figure 9 as shown;

[0037] Figure 11 is a schematic diagram showing a blood pressure sensor coupled to a gasket according to some embodiments of the present disclosure; Figure 10 as shown;

[0038] Figure 12 is a schematic diagram showing the health measurement device of a health monitoring and analysis system according to some embodiments of the present disclosure; Figure 1 as shown;

[0039] Figure 13 is a schematic diagram showing the health measurement device of a health monitoring and analysis system according to some other embodiments of the present disclosure; Figure 1 as shown; and

[0040] Figure 14 is a schematic diagram showing the health measurement device of a health monitoring and analysis system according to some still other embodiments of the present disclosure; and Figure 1 as shown; and

[0041] Figure 15 is a schematic diagram showing the health measurement device of a health monitoring and analysis system according to some still yet other embodiments of the present disclosure. Figure 1 as shown; Detailed Description

[0042] The embodiments disclosed herein relate to blood pressure monitoring, and more particularly, to 24-hour ambulatory blood pressure (24hr ABP) monitoring.

[0043] Generally, a blood pressure reading (or simply "blood pressure") includes (1) systolic blood pressure, which is the blood pressure when the heart beats, and (2) diastolic blood pressure, which is the blood pressure when the heart is at rest between two beats. Blood pressure is typically expressed as "(systolic reading) / (diastolic reading)". For example, a blood pressure of 120 / 80 millimeters of mercury (mm Hg) means that the systolic reading is 120 mm Hg and the diastolic reading is 80 mm Hg.

[0044] The normal blood pressure values for a healthy person are below 120 mm Hg and below 80 mm Hg, respectively.

[0045] If a person's systolic blood pressure is between 120 mm Hg and 129 mm Hg and the diastolic blood pressure is below 80 mm Hg, then the person may have a risk of elevated blood pressure, a health condition.

[0046] If the systolic blood pressure is between 130 mm Hg and 139 mm Hg or the diastolic blood pressure is between 80 mm Hg and 89 mm Hg, then the person can be diagnosed with stage 1 hypertension.

[0047] If the systolic blood pressure is greater than 140 mm Hg or the diastolic blood pressure is greater than 90 mm Hg, then the person can be diagnosed with stage 2 hypertension.

[0048] If the systolic blood pressure is greater than 180 mm Hg and / or the diastolic blood pressure is greater than 120 mm Hg, then the person can be diagnosed with hypertensive crisis.

[0049] Stage 1 hypertension, stage 2 hypertension, and hypertensive crisis are generally collectively referred to as "hypertension" or "high blood pressure".

[0050] On the other hand, low blood pressure can be diagnosed as "hypotension", and hypotension generally includes absolute hypotension and orthostatic hypotension. If the resting blood pressure (i.e., the blood pressure when a person is at rest) is below 90 / 60 mm Hg, then the person can be diagnosed with absolute hypotension. If the blood pressure drops within three minutes after a person gets up from a sitting position, where the systolic blood pressure drops by more than 20 mm Hg and the diastolic blood pressure drops by more than 10 mm Hg, then the person can be diagnosed with orthostatic hypotension (also known as "postural hypotension").

[0051] As is understood by those skilled in the art, blood pressure is generally related to a person's posture and activities. In addition, a single blood pressure measurement taken in a doctor's office or clinical setting may not be sufficient to diagnose hypertension or hypotension. Therefore, 24-hour ambulatory blood pressure (24hr ABP) monitoring is often used, in which a person's or patient's blood pressure is monitored periodically (e.g., every 20 to 30 minutes during the day and every hour at night) or continuously for at least 24 hours while also monitoring the heart rate.

[0052] Using the blood pressure data collected during 24Hr ABP, a blood pressure profile of a person in their daily environment can be obtained without the stress that a person might experience in a clinical setting. Such a blood pressure profile can enable accurate blood pressure analysis and diagnosis of blood pressure-related health problems.

[0053] As is understood by those skilled in the art, a person's blood pressure can change or vary according to a person's daily activities and sleep patterns. Therefore, the analysis of 24Hr ABP measurements may need to take into account a person's daily activities and sleep patterns.

[0054] According to one aspect of the present disclosure, a health monitoring and analysis system is disclosed. The health monitoring and analysis system includes a health measurement device that can be attached to a person. The health measurement device includes a plurality of sensors, such as one or more biometric sensors (such as one or more blood pressure sensors 202 and / or one or more heart rate sensors 204, oxygen saturation sensors, CO2 saturation sensors, temperature sensors, vibration sensors, other audio, optical, thermal, pressure sensors, etc.) for periodically measuring a person's biometric data (such as blood pressure, heart rate, respiratory rate, pulse rate, electrocardiogram (ECG), photoplethysmography (PPG), oxygen level, CO2 level, respiratory sounds, body mass index (BM) / weight / height, etc.), and one or more activity sensors. The health measurement device uses the plurality of sensors to collect measurement data of a person's blood pressure, heart rate, posture, and activities, and communicates with a client computing device and / or a server computer to analyze the person's health condition based on the collected measurement data using an artificial intelligence (AI) engine and a trained AI model.

[0055] In some embodiments, the health measurement device provides a user-friendly interface and device settings that allow patients and doctors to operate through simple instructions. When the health measurement device is carried by a person (such as a patient) or otherwise attached to a person, the health measurement device can measure and monitor a person's blood pressure, heart rate, and other information at regular intervals over a continuously extended period of time, such as 48 hours (e.g., every 20 to 30 minutes during the day and every hour at night). In some embodiments, the measurement frequency and time can be easily programmed.

[0056] In some embodiments, the activity sensor may collect posture and activity measurement data during the time intervals when the blood pressure sensor and the heart rate sensor collect their measurement data.

[0057] In other embodiments, the activity sensor may collect posture and activity measurement data (such as acceleration and angular velocity) before, during, and / or after the time intervals when the blood pressure sensor and the heart rate sensor collect their measurement data. For example, the activity sensor may collect posture and activity measurement data once before each blood pressure and / or heart rate measurement, approximately 10 to 15 times during each measurement, and once after each measurement (so each blood pressure and / or heart rate measurement generates approximately 12 to 17 sensor data sets), and this data can be used to identify a person's activities (such as standing upright, walking, running, resting, sleeping, etc.) and create a working graph for more accurately analyzing the blood pressure and / or heart rate measurement data.

[0058] In still other embodiments, the activity sensor may continuously collect posture and activity measurement data throughout the 24-hour ABP monitoring period (or a longer ABP monitoring period as needed).

[0059] The measurement data may be stored in a health measurement device, a client computing device, and / or a server computer (such as in a person's personal cloud account).

[0060] Thus, the collected measurement data includes a person's blood pressure data, heart rate data, posture data, and activity data during the person's daily activities (including sleep). More specifically, the collected measurement data can be classified into:

[0061] · Blood pressure measurement data when the person is active;

[0062] · Heart rate measurement data when the person is active;

[0063] · Blood pressure measurement data when the person is at rest; and

[0064] · Heart rate measurement data when the person is at rest.

[0065] By analyzing the collected measurement data, the health monitoring and analysis system can provide an accurate health measurement report of the person, such as the person's blood pressure status, heart rate status, posture curve, activity curve (e.g., active or resting), etc.

[0066] By using an AI engine and a trained AI model to analyze a person's health condition, a health monitoring and analysis system can accurately identify fluctuations in a person's blood pressure and / or heart rate caused by activities and / or postures. For example, the health monitoring and analysis system can accurately identify that a person's elevated blood pressure at night is caused by the person getting out of bed. As another example, the health monitoring and analysis system can accurately identify that a person's high blood pressure during night-time sleep is caused by the person snoring. The health monitoring and analysis system can also accurately identify fluctuations in a person's blood pressure caused by a change in the person's posture (e.g., from lying down to standing up).

[0067] By using an AI engine and a trained AI model to analyze a person's health condition, the health monitoring and analysis system can also identify sensor errors that may cause irregularities in the data samples of the collected blood pressure and / or heart rate measurements.

[0068] Now turning to Figure 1 , there is shown a health monitoring and analysis system in accordance with some embodiments of the present disclosure and generally designated by reference numeral 100. The health monitoring and analysis system 100 includes a health measurement device 102 that can be attached to a person. The health measurement device 102 wirelessly communicates with a client computing device 104 via a suitable wireless or wired communication technology such as (BLUETOOTH is a registered trademark of Bluetooth Sig Inc., Kirkland, Washington, USA), Bluetooth Low Energy (BLE), (WI-FI is a registered trademark of Wi-Fi Alliance, Austin, Texas, USA), Ethernet, Z-Wave, Long Range (LoRa), (ZIGBEE is a registered trademark of ZigBee Alliance Corp., San Ramon, California, USA), wireless broadband communication technologies such as Global System for Mobile Communications (GSM), Code Division Multiple Access (CDMA), Universal Mobile Telecommunications System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX), CDMA2000, Long Term Evolution (LTE), 3GPP, Fifth Generation New Radio (5G NR), Sixth Generation (6G) wireless networks, etc. The client computing device 104 wirelessly or wiredly communicates with one or more server computers 106 via a network 108.

[0069] In these embodiments, the health measurement device 102 is used to measure and monitor a person's blood pressure (such as 24Hr ABP monitoring), as well as monitor the person's daily activities and sleep patterns. The health measurement device 102 reports the measurement data of the person's blood pressure, activities, and sleep patterns to the client computing device 104. The client computing device 104 uses the data received from the health measurement device 102 to analyze the person's health condition and can display the analysis result on its screen. The client computing device 104 can use the services provided by the server computer 106 to facilitate its health condition analysis and / or store the data received from the health measurement device 102 and the analysis result to the server computer 106.

[0070] The client computing device 104 can be a portable or non-portable computing device, such as a smartphone, a tablet computer, a personal digital assistant (PDA), a laptop computer, a desktop computer, etc.

[0071] The server computer 106 can be a computing device specifically designed to be a server, or a general-purpose computing device that acts as a server computer and is also available for various users.

[0072] Generally, the client computing device 104 has a similar hardware structure to the server computer 106, such as Figure 2 the hardware structure shown. As shown in the figure, the computing device 104 / 106 includes a processing structure 122, a control structure 124, one or more non-transitory computer-readable memories or storage devices 126, a network interface 128, an input interface 130, and an output interface 132, which are functionally interconnected through a system bus 138. The computing device 104 / 106 may also include other components 134 coupled to the system bus 138.

[0073] The processing structure 122 can be one or more single-core or multi-core computing processors, such as a microprocessor (INTEL is a registered trademark of Intel Corp. in Santa Clara, California, USA), a microprocessor (AMD is a registered trademark of Advanced Micro Devices Inc. in Sunnyvale, California, USA), a microprocessor (ARM is a registered trademark of Arm Ltd. in Cambridge, UK), which is manufactured by different manufacturers (such as Qualcomm in San Diego, California, USA) and is under an architecture, etc. When the processing structure 122 includes multiple processors, its processors can cooperate via a dedicated circuit (such as a dedicated bus) or via the system bus 138.

[0074] The processing structure 122 may also include one or more real-time processors, programmable logic controllers (PLCs), microcontroller units (MCUs), μ-controllers (UCs), dedicated / custom processors, and / or controllers using, for example, field programmable gate array (FPGA) or application specific integrated circuit (ASIC) technologies, etc.

[0075] Generally, each processor of the processing structure 122 includes the necessary circuitry implemented using technologies such as electrical and / or optical hardware components for performing one or more processes according to the implementation purpose and / or possible use cases to execute various tasks. In many embodiments, one or more processes may be implemented as firmware and / or software stored in the memory 126. Those skilled in the art will understand that in these embodiments, one or more processors of the processing structure 122 are generally useless without meaningful firmware and / or software.

[0076] Of course, those skilled in the art will understand that processors can be implemented using other technologies such as analog technologies.

[0077] The control structure 124 includes one or more control circuits, such as a graphics controller, input / output chipset, etc., for coordinating the operations of various hardware components and modules of the computing device 104 / 106.

[0078] The memory 126 includes one or more non-transitory computer-readable storage devices or media accessible by the processing structure 122 and the control structure 124 for reading and / or storing instructions for execution by the processing structure 122, and for reading and / or storing data, which includes input data and data generated by the processing structure 122 and the control structure 124. The memory 126 can be volatile and / or non-volatile, non-removable or removable memory, such as RAM, ROM, EEPROM, solid state memory, hard disk, CD, DVD, flash memory, etc. In use, the memory 126 is generally divided into multiple parts for different purposes. For example, a part of the memory 126 (represented herein as storage memory) can be used for long-term data storage, e.g., for storing files or databases. Another part of the memory 126 can be used as system memory (represented herein as working memory) for storing data during processing.

[0079] The network interface 128 includes one or more network modules for connecting to other computing devices or networks via the network 108 using suitable wired and / or wireless communication technologies. In some embodiments, parallel ports, serial ports, USB connections, optical connections, etc. can also be used to connect to other computing devices or networks, although they are generally considered input / output interfaces for connecting input / output devices.

[0080] The input interface 130 includes one or more input modules for one or more users to input data via, for example, a touch-sensitive screen, a touch-sensitive whiteboard, a touchpad, a keyboard, a computer monitor, a trackball, a microphone, a scanner, a camera, etc. The input interface 130 can be a physically integrated part of the computing device 104 / 106 (e.g., the touchpad of a laptop computer or the touch-sensitive screen of a tablet computer), or can be a device that is physically separated from other components of the computing device 104 / 106 (e.g., a computer mouse) but is functionally coupled. In some implementations, the input interface 130 can be integrated with the display output to form a touch-sensitive screen or a touch-sensitive whiteboard.

[0081] The output interface 132 includes one or more output modules for outputting data to the user. Examples of output modules include displays (such as monitors, LCD displays, LED displays, projectors, etc.), speakers, printers, virtual reality (VR) headsets, augmented reality (AR) glasses, etc. The output interface 132 can be a physically integrated part of the computing device 104 / 106 (e.g., the display of a laptop computer or a tablet computer), or can be a device that is physically separated from other components of the computing device 104 / 106 (e.g., the monitor of a desktop computer) but is functionally coupled.

[0082] The computing device 104 / 106 can also include other components 134, such as one or more positioning modules, temperature sensors, barometers, inertial measurement units (IMUs), etc. Examples of positioning modules can be one or more global navigation satellite system (GNSS) components (e.g., one or more components that operate with the Global Positioning System (GPS) of the United States, the Global Satellite Navigation System (GLONASS) of Russia, the Galileo positioning system of the European Union, and / or the Beidou system of China).

[0083] The system bus 138 interconnects the various components 122 to 134 so that they can send and receive data and control signals from each other.

[0084] Figure 3 A simplified software structure of the computing device 104 or 106 is shown. The software structure 160 includes an application layer 162, an operating system 166, a logical input / output (I / O) interface 168, and a logical memory 172. The application layer 162, the operating system 166, and the logical I / O interface 168 are generally implemented as computer-executable instructions or code that are stored in the logical memory 172 in the form of a software program or a firmware program and can be executed by the processing structure 122.

[0085] The application layer 162 includes one or more application programs 164 that are executed or carried out by the processing structure 122 for performing various tasks.

[0086] The operating system 166 manages various hardware components of the computing device 104 or 106 via the logical I / O interface 168, manages the logical memory 172, and manages and supports the application programs 164. The operating system 166 also communicates with other computing devices (not shown) via the network 108 to allow the application programs 164 to communicate with programs running on other computing devices. As will be understood by those skilled in the art, the operating system 166 can be any suitable operating system, such as (MICROSOFT and WINDOWS are registered trademarks of Microsoft Corp. of Redmond, Washington, USA)、 OS X、 iOS (APPLE is a registered trademark of Apple Inc. of Cupertino, California, USA), Linux, (ANDROID is a registered trademark of Google Inc. of Mountain View, California, USA), etc. The computing devices 104 or 106 of the computer network system 100 can all have the same operating system, or can have different operating systems.

[0087] The logical I / O interface 168 includes one or more device drivers 170 for communicating with the corresponding input interface 130 and output interface 132 to receive data from and send data to them. The received data can be sent to the application layer 162 for processing by one or more application programs 164. The data generated by the application programs 164 can be sent to the logical I / O interface 168 for output to various output devices (via the output interface 132).

[0088] The logical memory 172 is a logical mapping of the physical memory 126 to facilitate access by the application programs 164. In this embodiment, the logical memory 172 includes a storage memory area that can be mapped to non-volatile physical memory, such as a hard disk, solid state disk, flash drive, etc., generally for long-term data storage therein. The logical memory 172 also includes a working memory area that is generally mapped to high-speed and volatile (in some implementations) physical memory (such as RAM), generally for the application programs 164 to temporarily store data during program execution. For example, the application programs 164 can load data from the storage memory area into the working memory area and can store the data generated during their execution into the working memory area. The application programs 164 can also store some data into the storage memory area as needed or in response to a user's command.

[0089] In server computer 106, application layer 162 generally includes one or more server - side applications 164 that provide server functions for managing network communications with client computing device 104 (and other computing devices connected to network 108) and facilitating collaboration between server computer 106 and client computing device 104. In this document, the term "server" can refer to server computer 106 from a hardware perspective or a logical server from a software perspective, depending on the context.

[0090] In these embodiments, health measurement device 102 is a small, portable machine configured to measure a person's blood pressure, heart rate, and other information at regular intervals over a continuously extended period of time, such as 48 hours. Figure 4 FIG. [FIG. number] is a schematic diagram showing the hardware structure of health measurement device 102 according to some embodiments of the present disclosure.

[0091] As shown, health measurement device 102 includes one or more biometric sensors (such as one or more blood pressure sensors 202 and / or one or more heart rate sensors 204 for periodically measuring a person's blood pressure data and heart rate data) for periodically measuring a person's biometric data, one or more activity sensors 206, a user input / output (I / O) interface 208, a memory 210, and a communication module 212, all of which are connected to a control circuit or controller 214.

[0092] Blood pressure sensor 202 can be in any suitable form, such as an upper arm cuff, a finger cuff, an optical plethysmography (PPG) optical sensor for positioning on the wrist, etc. Blood pressure sensor 202 can use any suitable technique to measure blood pressure, such as auscultation methods, oscillometric techniques, ultrasonic techniques, finger cuff methods, optical techniques, etc.

[0093] Heart rate sensor 204 can use any suitable technique to measure heart rate, such as an electrical sensor that measures heartbeats by detecting electrical signals associated with the dilation and contraction of heart chambers (e.g., an electrocardiogram (ECG) sensor), an optical sensor that measures heart rate by measuring changes in blood volume in blood vessels (e.g., a PPG optical sensor), etc.

[0094] Activity sensor 206 can be any sensor suitable for detecting a person's posture and activity, such as an accelerometer, a gyroscope, an inertial measurement unit (IMU), an inclinometer, etc.

[0095] The user input / output (I / O) interface 208 may include any suitable I / O interface components for receiving user input (such as one or more buttons, a keyboard, a computer mouse, a trackball, a touch screen, a digital pen, a microphone, etc.) and for outputting information to the user (such as a screen, a monitor, a light-emitting diode (LED) panel, a speaker, etc.).

[0096] The memory 210 may be any suitable non-transitory, computer-readable, volatile and / or non-volatile storage device or medium similar to the memory 126 described above.

[0097] The communication module 212 may be any suitable module for communicating with the client computing device 104 or other computing devices using wireless and / or wired communication technologies. For example, in some embodiments, the communication module 212 may be module.

[0098] The controller 214 may be a processing unit or circuit, such as an integrated circuit (IC) chip, which is functionally coupled to the modules 202 to 212 to control the operation of these modules, receive measurement data from the blood pressure sensor 202, the heart rate sensor 204, and one or more activity sensors 206, receive user input from the user I / O interface 208, display information to the user through the user I / O interface 208, receive data and / or instructions from other computing devices (such as the client computing device 104), and store various received data and instructions in the memory 210. In some embodiments, the controller 214 may process the received data (such as the measurement data received from the blood pressure sensor 202, the heart rate sensor 204, and one or more activity sensors 206), analyze the measurement data, and send the received data and / or analysis results to the client computing device 104. In some other embodiments, the controller 214 may not analyze the measurement data. Instead, the analysis of the measurement data may be performed by the client computing device 104 and / or the server computer 106.

[0099] Figure 5 is a schematic diagram showing the functional structure of the health monitoring and analysis system 100 according to some embodiments of the present disclosure. As shown, the health monitoring and analysis system 100 includes a data acquisition module 242 for data acquisition, a data analysis module 244 having an artificial intelligence (AI) engine (such as a machine learning engine) for analyzing the acquired data using a trained AI model 246, and a reporting module 248 for reporting the analysis results. In these embodiments, the AI model 246 may be any suitable AI model, such as a machine learning model, a deep neural network model, a clustering model, a convolutional neural network, etc., and may be trained using historical measurement data collected from multiple individuals.

[0100] In this document, the data acquisition module 242 is generally implemented in the health measurement device 102. In some embodiments, the data analysis module 244, the AI model 246, and the reporting module 248 are implemented in the client computing device 104. In some other embodiments, some of the data analysis module 244, the AI model 246, and the reporting module 248 may be implemented in the server computer 106.

[0101] Figure 6 is a flowchart showing the steps of a blood pressure monitoring and analysis program 300 executed by the health monitoring and analysis system 100.

[0102] When the program 300 starts (step 302), the health monitoring and analysis system 100 uses the data acquisition module 242 to obtain measurement data of blood pressure, heart rate, and the person's posture and activity from the blood pressure sensor 202, the heart rate sensor 204, and one or more activity sensors 206 (step 304). Then, the acquired measurement data is sent to the data analysis module 244 for analysis.

[0103] At step 306, the data analysis module 244 uses the AI engine and the trained AI model 246 to identify sensor errors based on the acquired measurement data. As understood by those skilled in the art, a person's blood pressure and heart rate generally have a corresponding relationship. For example, a higher heart rate generally corresponds to a higher blood pressure. In addition, a person's blood pressure and heart rate also generally have a corresponding relationship with the person's posture and activity. For example, a person in vigorous activity usually has a higher heart rate and a higher blood pressure.

[0104] Those skilled in the art will also understand that such relationships are generally complex, and the instantaneous measurement of a person's blood pressure and / or heart rate may not be consistent with such relationships and may seem to deviate from them. There are various reasons that may cause such measurement deviations, such as due to so-called "noise", data errors (e.g., caused by sensor errors), sudden changes in a person's health condition, etc. Therefore, it is necessary to correctly identify the cause of the measurement deviation and take corresponding measures. For example, if the cause of the measurement deviation is noise, the noise needs to be eliminated and a corrected measurement is obtained. If the cause of the measurement deviation is a sensor error, the faulty sensor needs to be replaced. If the cause of the measurement deviation is due to a sudden change in a person's health condition, appropriate advice needs to be provided to the person (such as visiting a family doctor or going to the hospital, calling an emergency number, etc.). However, due to the complex nature of the relationship between a person's blood pressure / heart rate and a person's posture / activity, it is impossible or at least very difficult to correctly identify such causes manually.

[0105] By using the trained AI model 246, the AI engine of the data analysis module 244 can identify measurement data samples that deviate from such relationships and determine that the identified measurement data samples are incorrect data samples, which are likely caused by sensor failures in collecting these incorrect data samples.

[0106] For example, if the blood pressure rises but the heart rate remains low, the blood pressure sensor or the heart rate sensor may be faulty. Then, if the activity sensor reports that the person is in a highly active situation, the blood pressure sensor is operating correctly and the heart rate sensor is faulty.

[0107] If, at step 308, the data analysis module 244 identifies any sensor errors, the data analysis module 244 reports the sensor errors using any suitable method (step 310), such as triggering an alarm beep, displaying an error message on the screen of the client computing device 104, sending a message to the user (e.g., a caregiver), etc. Then, the program 300 ends (step 318).

[0108] If, at step 308, the data analysis module 244 does not identify any sensor errors, the data analysis module 244 uses the AI engine and the trained AI model 246 to identify the person's posture and / or activity pattern (including the sleep pattern) (step 312). For example, when the activity sensor 206 reports that the person is in an upright position and the heart rate sensor 204 reports an increased heart rate, the data analysis module 244 can identify that the person is in an active state rather than a sleeping state. In some embodiments, the data analysis module 244 can identify the person's posture and / or activity pattern as one of upright, walking, running, resting, and sleeping.

[0109] At step 314, the data analysis module 244 uses the AI engine and the trained AI model 246 to analyze the person's health condition based on the identified pattern and the measurement data. As described above, the measurement data can be various biometric data, such as blood pressure, heart rate, respiratory rate, pulse rate, ECG, PPG, oxygen level, CO2 level, breathing sound, BM / weight / height, etc. Some biometric data can be measured or otherwise obtained from one or more biometric sensors, some biometric data can be obtained or corrected by checking against related other biometric data, some biometric data can be pre-stored biometric data, and some biometric data can be obtained using suitable prediction algorithms (such as suitable AI methods).

[0110] At step 316, the analysis results are sent from the data analysis module 244 to the reporting module 248 for reporting, such as storing the analysis results in a database (such as in a person's personal cloud account), displaying the analysis results to the user, transmitting the analysis results to another computing device, such as another client computing device or server 106, etc. In some embodiments, the reported analysis results may include:

[0111] · Activity classification: such as no movement, mild movement, or strong movement;

[0112] · Posture report: such as standing upright, lying down, sleeping, etc.;

[0113] · Blood pressure classification: such as low blood pressure, normal, or high blood pressure; and

[0114] · Heart rate classification: high, normal, or low.

[0115] In some embodiments, if the data analysis module 244 determines that the blood pressure, heart rate, posture, and activity measurement data are not correctly correlated and no sensor error is identified, the data analysis module 244 may instruct the reporting module 248 to report the risk of heart disease or failure and trigger an alert to the person.

[0116] After reporting, the program 300 then ends (step 318).

[0117] Those skilled in the art will understand that various embodiments are readily available. For example, as Figure 7 shown, in some embodiments, the health monitoring and analysis system 100 may include multiple client computing devices 104, and the health measurement device 102 may be directly connected to the network 108 via suitable wireless and / or wired communication technologies.

[0118] In some other embodiments, as Figure 8 shown, some sensors, such as the activity sensor 206, may not be included in the health measurement device 102. Instead, these sensors may be implemented as separate sensor devices. In these embodiments, the health measurement device 102 and the activity sensor 206 may be connected to the network 108 via one or more access points 402.

[0119] In some embodiments, the health measurement device 102 may not include any heart rate sensors 204 and thus does not monitor a person's heart rate.

[0120] In some embodiments, the health measurement device 102 may not monitor a person's posture, and thus the activity sensor 206 is only used to monitor a person's movement.

[0121] In some embodiments, as Figure 9As shown, the health measurement device 102 may include one or more adhesive pads 402 for attachment to a suitable location on a user, such as the user's chest, wrist, elbow area, ankle area, etc. Each pad 402 includes a blood pressure sensor 202 that is connected to a central unit 404, which is attached to, for example, a belt worn by the user. In these embodiments, the central unit 404 may include other modules, such as an activity sensor 206, a user I / O interface 208, a memory 210, a communication module 212, and a controller 214. In these embodiments, the heart rate sensor 204 may be a separate component attachable to the user (such as a separate pad suitable for attachment to the user's body).

[0122] Figure 10 FIG. 4 is a schematic cross-sectional view of a pad 402 according to some embodiments of the present disclosure. As shown, the pad 402 includes a sensor layer 412 having a blood pressure sensor 202, an attachment layer 414 coupled to the front side of the sensor layer 412 and having a suitable adhesive material for attachment to a person's skin, and a protective layer 416 coupled to the rear side of the sensor layer 412.

[0123] Figure 11 FIG. 8 is a schematic diagram showing a blood pressure sensor 202 (coupled to the pad 402) according to some embodiments of the present disclosure. As shown, the blood pressure sensor 202 includes a light emitter 412 (such as a pulsed light emitting diode) and a light sensor 414. When the blood pressure sensor 202 is coupled to the pad 402, the attachment layer 414 may include a transparent adhesive material and / or may include an opening at a location corresponding to the location of the blood pressure sensor 202 to allow light to pass through the attachment layer 414.

[0124] In operation, the light emitter 412 emits two light beams of two different wavelengths towards the user's skin. The light beams are reflected by the peripheral arteries under the user's skin and captured by the light sensor 414. Then, the controller 214 may use photoplethysmography (PPG) to analyze the captured light beams to obtain the user's blood pressure.

[0125] In some embodiments, each activity sensor 206 may be in the form of an adhesive pad.

[0126] In some embodiments, at least one pad 402 may include two or more of the blood pressure sensor 202, the heart rate sensor 204, and the activity sensor 206.

[0127] In the above-described embodiments, one or more blood pressure sensors 202 and one or more heart rate sensors 204 serve as biometric sensors for periodically measuring a person's biometric data. In some embodiments, the biometric sensors may further include one or more temperature sensors for measuring a person's body temperature at one or more locations, one or more oxygen saturation sensors for measuring a person's oxygen level, one or more vibration sensors or microphones for measuring sounds of different body parts (such as a person's lungs, throat, etc.). In some embodiments, the adhesive pad may include a single sensor among the sensors described above. In some other embodiments, the adhesive pad may include a plurality of the sensors described above.

[0128] The measured temperature, oxygen level, and sound can be combined with the measured blood pressure and / or heart rate and the measured activity to determine a person's health condition using an AI engine. For example, if the measured activity indicates that the person is walking or running but the measured temperature drops and / or the oxygen level drops, a risk of heart failure can be determined. Similarly, the measured sound can indicate sleep apnea caused by snoring, which alone or in combination with the measured heart rate can indicate a risk of heart failure. The measured sound can also indicate the state of the lungs (e.g., the lungs being filled with fluid is an indication of cancer risk), which can be combined with other sensor measurements to more accurately determine a person's health condition.

[0129] Those skilled in the art will understand that the health measurement device 102 can be in any suitable form. For example, Figure 12 is a schematic diagram showing a health measurement device 102 according to some embodiments of the present disclosure. As shown, the health measurement device 102 is in the form of an adhesive pad and includes all components (such as blood pressure sensors 202, heart rate sensors 204, vibration sensors, controller 214, etc.) for pasting onto a person's chest.

[0130] In some embodiments, as Figure 13 shown, the health measurement device 102 is in the form of a cuff for positioning around a user's elbow and includes all components (such as blood pressure sensors 202, heart rate sensors 204, controller 214, etc.).

[0131] In some embodiments, as Figure 14 shown, the health measurement device 102 includes a cuff 422 for positioning around a user's elbow and includes all components except vibration sensors. The health measurement device 102 further includes a pair of vibration sensors 424 in the form of adhesive pads for pasting around the left and right lungs on the chest, respectively. The pads 424 are connected to the cuff 422 using a suitable wired or wireless method.

[0132] In some embodiments, asFigure 15 As shown, the health measurement device 102 includes a first adhesive pad 432 and a second adhesive pad 432 for respectively adhering around the chests of the left and right lungs. All components (such as a blood pressure sensor 202, a heart rate sensor 204, a controller 214, etc.) are included in the first adhesive pad 432, and may or may not include a vibration sensor. The second adhesive pad 434 includes a vibration sensor and is connected to the first pad 432 using a suitable wired or wireless method.

[0133] Although the embodiments have been described above with reference to the accompanying drawings, those skilled in the art will understand that variations and modifications can be made without departing from the scope of the present disclosure defined by the appended claims.

Claims

1. A device for monitoring a person's health condition, the device comprising: One or more biometric sensors for periodically measuring a person's biometric data; One or more activity sensors for periodically measuring a person's activity; And A circuit functionally coupled to the one or more biometric sensors and the one or more activity sensors, the circuit including an artificial intelligence (AI) engine configured to: Analyze a person's health condition using a trained AI model based on measurement data collected from the one or more biometric sensors and the one or more activity sensors.

2. The device according to claim 1, wherein The one or more biometric sensors and the one or more activity sensors are configured to periodically perform a set of measurements to periodically measure a person's biometric data and a person's activity; And Wherein, the one or more activity sensors are configured to: during each set of measurements, Measure the person's activity at least two of before, during, and after the one or more biometric sensors measure the person's biometric data.

3. The device according to claim 1 or 2, further comprising: One or more adhesive pads for sticking on a person's body; Wherein each of the one or more adhesive pads includes at least one of the one or more biometric sensors and the one or more activity sensors.

4. The apparatus according to any one of claims 1 to 3, wherein, The analyzing a person's health condition using a trained AI model includes: Determining whether any of the one or more biometric sensors and the one or more activity sensors has an error based on measurement data collected from the one or more biometric sensors and the one or more activity sensors; and If none of the one or more biometric sensors and the one or more activity sensors has an error, analyzing a person's health condition based on measurement data collected from the one or more biometric sensors and the one or more activity sensors.

5. The device according to any one of claims 1 to 4, wherein The person's activity includes at least one of the person's movement and the person's posture.

6. The device according to any one of claims 1 to 5, wherein, The one or more biometric sensors include at least one of one or more blood pressure sensors and one or more heart rate sensors; and Wherein, the person's biometric data includes the person's blood pressure data and the person's heart rate data.

7. The device according to claim 6, wherein The one or more biometric sensors include at least one of one or more temperature sensors for measuring a person's body temperature, one or more oxygen saturation sensors for measuring a person's oxygen level, and one or more vibration sensors or one or more microphones for measuring the sound of a person's body.

8. A computerized method for monitoring a person's health condition, the method comprising: Periodically measuring a person's biometric data using one or more biometric sensors; Periodically measuring a person's activity using one or more activity sensors; And Using a trained AI model to analyze a person's health condition based on measurement data collected from the one or more biometric sensors and the one or more activity sensors.

9. The computerized method according to claim 8, wherein, The periodically measuring a person's biometric data and the periodically measuring a person's activity include: Periodically performing a set of measurements using one or more biometric sensors and one or more activity sensors to respectively periodically measure a person's biometric data and a person's activity; and wherein, when performing each set of measurements, Measuring a person's activity using the one or more activity sensors at least two of before, during, and after the one or more biometric sensors measure the person's biometric data.

10. The computerized method according to claim 8 or 9, further comprising: Pasting one or more adhesive pads onto a person's body; wherein each of the one or more adhesive pads includes at least one sensor of the one or more biometric sensors and the one or more activity sensors.

11. The computerized method according to any one of claims 8 to 10, wherein, The using a trained AI model to analyze a person's health condition includes: Determining whether any of the one or more biometric sensors and the one or more activity sensors has an error based on measurement data collected from the one or more biometric sensors and the one or more activity sensors; and If none of the one or more biometric sensors and the one or more activity sensors has an error, analyzing a person's health condition based on measurement data collected from the one or more biometric sensors and the one or more activity sensors.

12. The computerized method according to any one of claims 8 to 11, wherein The person's activity includes at least one of a person's movement and a person's posture.

13. The computerized method according to any one of claims 8 to 12, wherein, The one or more biometric sensors include at least one sensor of one or more blood pressure sensors and one or more heart rate sensors; and wherein the person's biometric data includes the person's blood pressure data and the person's heart rate data.

14. The computerized method according to claim 13, wherein, The one or more biometric sensors further include at least one sensor of one or more temperature sensors for measuring a person's body temperature, one or more oxygen saturation sensors for measuring a person's oxygen level, and one or more vibration sensors or one or more microphones for measuring the sound of a person's body.

15. One or more non-transitory computer-readable storage devices including computer-executable instructions, wherein, The instructions, when executed, cause one or more circuits to perform actions, the actions including: Periodically measuring a person's biometric data using one or more biometric sensors; Periodically measuring a person's activity using one or more activity sensors; and Using a trained AI model to analyze a person's health condition based on measurement data collected from the one or more biometric sensors and the one or more activity sensors.

16. The one or more non-transitory computer-readable storage devices according to claim 15, wherein, The periodically measuring a person's biometric data and the periodically measuring a person's activity include: Periodically performing a set of measurements using one or more biometric sensors and one or more activity sensors to respectively periodically measure a person's biometric data and a person's activity; and wherein, when performing each set of measurements, Measure the person's activities using the one or more activity sensors at least two of before, during, and after the one or more biometric sensors measure the person's biometric data.

17. One or more non-transitory computer-readable storage devices according to claim 15 or 16, wherein, The actions further include: Sticking one or more adhesive pads to the person's body; wherein each of the one or more adhesive pads includes at least one of the one or more biometric sensors and the one or more activity sensors.

18. One or more non-transitory computer-readable storage devices according to any one of claims 15 to 17, wherein, The using the trained AI model to analyze the person's health condition includes: Determining whether any of the one or more biometric sensors and the one or more activity sensors is in error based on the measurement data collected by the one or more biometric sensors and the one or more activity sensors; and If none of the one or more biometric sensors and the one or more activity sensors is in error, analyzing the person's health condition based on the measurement data collected by the one or more biometric sensors and the one or more activity sensors.

19. One or more non-transitory computer-readable storage devices according to any one of claims 15 to 18, wherein, The person's activities include at least one of the person's movement and the person's posture.

20. One or more non-transitory computer-readable storage devices according to any one of claims 15 to 19, wherein, The one or more biometric sensors include at least one of one or more blood pressure sensors and one or more heart rate sensors; and wherein the person's biometric data includes the person's blood pressure data and the person's heart rate data.

21. The one or more non-transitory computer-readable storage devices according to claim 13, wherein, The one or more biometric sensors further include at least one of one or more temperature sensors for measuring the person's body temperature, one or more oxygen saturation sensors for measuring the person's oxygen level, and one or more vibration sensors or one or more microphones for measuring the sound of the person's body.

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