Health monitoring device and method, and storage medium

Abstract

The present application relates to the technical field of medical care. Disclosed are a health monitoring device and method, and a storage medium. The device comprises: a microprocessor and multiple types of sensors, wherein: the microprocessor is configured to acquire a health detection instruction and, according to the health detection instruction, detect corresponding physiological indicators by means of one or more of the multiple types of sensors. The solution of the present application integrates multiple types of sensors on one health monitoring device, thereby achieving the effect of accurately detecting multiple physiological indicators with one single device.

Description

Health monitoring device, method and storage medium TECHNICAL FIELD

[0001] The present application relates to the medical technical field, and in particular to a health monitoring device, method and storage medium. BACKGROUND

[0002] In general, a family carries out routine health monitoring, and different physiological indicators are measured by replacing different measuring devices. For example, a thermometer is used to measure body temperature, a sphygmomanometer is used to measure blood pressure, and a blood oxygen meter is used to measure blood oxygen. There are many devices, but each device can only detect one physiological indicator, and the detected physiological indicator is not accurate enough.

[0003] Therefore, how to conveniently and accurately obtain multiple physiological indicators on one device is a problem to be solved at present.

[0004] The disclosure of the above background art content is only used to assist in understanding the inventive concept and technical solutions of the present application, and it does not necessarily belong to the prior art of the present patent application, nor does it necessarily provide technical teaching. It is to provide general background information and does not necessarily constitute prior art. SUMMARY

[0005] The main purpose of the present application is to provide a health monitoring device, method and storage medium, which aims to solve the technical problem of how to conveniently and accurately obtain multiple physiological indicators on one device.

[0006] To achieve the above purpose, the present application provides a health monitoring device, which comprises a microprocessor and multiple sensors, wherein:

[0007] The microprocessor is used to obtain a health detection instruction, and to detect corresponding physiological indicators through one or more of the multiple sensors according to the health detection instruction.

[0008] In an embodiment, the multiple sensors include one or more of a heart-lung sound acquisition module, a PPG acquisition module, an ECG acquisition module, and a body temperature acquisition module.

[0009] The PPG acquisition module is used to acquire human heart rate data and pulse wave envelope.

[0010] The ECG acquisition module is used to acquire human electrocardiogram signals and obtain electrocardiogram data.

[0011] The body temperature acquisition module is used to acquire human body temperature data.

[0012] The heart-lung sound acquisition module is used to acquire human heart sound signals.

[0013] In an embodiment, the health monitoring device further comprises:

[0014] a motion sensor module, configured to collect motion jitter data during a user measurement process, and provide data for jitter noise reduction during the measurement process;

[0015] The microprocessor is further configured to, in combination with the motion sensor module, perform noise reduction processing on the physiological index data detected by the plurality of sensors.

[0016] In an embodiment, the health monitoring device further comprises a display module, a graphics processor, a storage, and a wireless communication module, wherein:

[0017] The graphics processor is configured to draw graphics content and drive the display module to perform graphics display.

[0018] The storage is configured to store various application programs and related data.

[0019] The wireless communication module is configured to connect with an external device or a network.

[0020] In an embodiment, the microprocessor is further configured to, through the wireless communication module, send the detected physiological index to a master device, and perform analysis on the physiological index by the master device, or send the physiological index to a cloud device by the master device.

[0021] In an embodiment, the microprocessor is further configured to, through the wireless communication module, send the detected physiological index to the cloud device, perform analysis on the physiological index by the cloud device, and return the analysis result to the master device.

[0022] In an embodiment, the microprocessor is further configured to receive an operation instruction sent by a user through the master device, detect a corresponding physiological index through one or more of the plurality of sensors according to the operation instruction, or query the detection result stored on the health monitoring device according to the operation instruction.

[0023] In addition, to achieve the above-mentioned purpose, the present application further provides a health monitoring method, comprising:

[0024] obtaining a health detection instruction;

[0025] detecting a corresponding physiological index through one or more of the plurality of sensors according to the health detection instruction.

[0026] In an embodiment, the plurality of sensors comprises one or more of a cardiopulmonary sound collection module, a PPG collection module, an ECG collection module, and a body temperature collection module.

[0027] In an embodiment, if the health detection instruction is a blood pressure detection instruction, the step of detecting the corresponding physiological indicators according to the health detection instruction and through one or more of the plurality of sensors comprises:

[0028] selecting a blood pressure detection mode according to the blood pressure detection instruction;

[0029] synchronously collecting physiological signals through a heart-lung sound acquisition module, a PPG acquisition module, and an ECG acquisition module corresponding to the blood pressure detection mode, wherein the physiological signals comprise heart sound signals, pulse signals, and electrocardiogram signals;

[0030] extracting features of the physiological signals to obtain signal features, wherein the signal features comprise a pre-ejection period duration and a pulse transit time;

[0031] inputting the signal features into a pre-trained blood pressure prediction model, so that the blood pressure prediction model outputs blood pressure physiological indicators.

[0032] In addition, to achieve the above-mentioned purposes, the present application also provides a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the health monitoring method described above are implemented.

[0033] The one or more technical solutions provided by the present application have at least the following technical effects:

[0034] The present application provides a health monitoring device, method, and storage medium. By highly integrating a plurality of sensors in one detection device, a plurality of physiological indicators can be conveniently and accurately obtained on one device. BRIEF DESCRIPTION OF DRAWINGS

[0035] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the present application and, together with the specification, serve to explain the principles of the application.

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0037] FIG. 1 is a structural schematic diagram of a health monitoring device according to the present application;

[0038] FIG. 2 is a schematic diagram of the A-face appearance of the health monitoring device according to the present application;

[0039] FIG. 3 is a schematic diagram of the B-face appearance of the health monitoring device according to the present application;

[0040] Fig. 4 is a schematic diagram of the C-face appearance of the health monitoring device of the present application;

[0041] Fig. 5 is a schematic diagram of the D-face appearance of the health monitoring device of the present application;

[0042] Fig. 6 is a schematic diagram of the posture of a user using the health monitoring device to detect cardiopulmonary sound physiological indicators;

[0043] Fig. 7 is a schematic diagram of the pulse wave transmission time (PWTT) of heart sounds;

[0044] Fig. 8 is a schematic diagram of the flow provided by the first embodiment of the health monitoring method of the present application;

[0045] Fig. 9 is a schematic diagram of the flow provided by the second embodiment of the health monitoring method of the present application;

[0046] Fig. 10 is a schematic diagram of the structure of the health monitoring device of the embodiment of the present application;

[0047] Fig. 11 is a schematic diagram of the device structure of the hardware operating environment involved in the health monitoring method of the embodiment of the present application.

[0048] The object implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0049] It should be understood that the specific embodiments described herein are merely intended to explain the technical solutions of the present application, and are not intended to limit the present application.

[0050] In order to better understand the technical solutions of the present application, the following will be described in detail in combination with the drawings and specific embodiments of the specification.

[0051] The main solution of the embodiment of the present application is to highly integrate multiple sensors in one monitoring device, which can be applied to a family scene, and realize the purpose of quickly and accurately detecting multiple physiological indicators through one monitoring device.

[0052] The embodiment considers that: since general families usually use different detection devices to detect different physiological indicators for routine detection, if multiple physiological indicators are to be detected, different detection devices need to be replaced, which is cumbersome to operate, and the accuracy of the detected physiological indicator results is not high enough.

[0053] Therefore, the present application provides a solution to highly integrate sensors for detecting multiple physiological indicators in one health monitoring device, so that only one detection device can be used to accurately detect multiple physiological indicators.

[0054] Referring to FIG. 1, which is a structural schematic diagram of the health monitoring device, an embodiment of the present application provides a health monitoring device, which comprises a microprocessor and a plurality of sensors.

[0055] The microprocessor is configured to acquire a health detection instruction, and detect a corresponding physiological index according to the health detection instruction and one or more of the plurality of sensors.

[0056] It should be noted that the microprocessor is a central processing unit (CPU) with reduced frequency and specifications, and the memory, timer, USB, A / D converter, UART, PLC, DMA, and other peripheral interfaces are integrated on a single chip to form a completely independent microcomputer. The microprocessor performs pre-programmed automation tasks in a single device. In the health monitoring device, the microprocessor can acquire and execute health detection instructions, and exchange information with external memory and logic components. It is the operation control part of the health monitoring device.

[0057] After the health monitoring device is powered on, the microprocessor undergoes a power-on reset. During this process, all registers and memories of the microprocessor are initialized to the default state. The microprocessor has a unique reset vector address, which points to a special location in the memory. After receiving a health detection instruction from the outside, the microprocessor automatically jumps to this special location and starts executing the health detection instruction.

[0058] The code that the microprocessor starts executing from the reset vector address is called the bootloader. The bootloader is fixed in the read-only memory unit of the microprocessor. The main task of the bootloader is to initialize the hardware resources of the microprocessor to ensure that the microprocessor can run normally. Once the hardware resources are initialized, the microprocessor starts loading the main program, which is a pre-compiled application program.

[0059] It can be understood that the main program corresponding to different health detection instructions is inconsistent, and the microprocessor can load different main programs according to different health detection instructions. After the main program is loaded into the microprocessor, the bootloader transfers the control right to the main program, and the microprocessor starts to execute the data acquisition and data processing tasks in the main program, and completes the detection of the physiological indicators of the human body through various sensors.

[0060] In the embodiment, the microprocessor detects the physiological indicators through various sensors, and the microprocessor processes the data collected by the sensors. For example, when measuring the body temperature, the microprocessor also considers the influence of the environmental temperature, so that the detection accuracy of the health monitoring device can be improved.

[0061] The various sensors can be set according to different physiological indicator checking requirements.

[0062] In a possible implementation, the various sensors include one or more of a heart-lung sound acquisition module, a PPG acquisition module, an ECG acquisition module, and a body temperature acquisition module.

[0063] The PPG acquisition module is configured to acquire human heart rate data and pulse wave envelope.

[0064] The microprocessor can calculate the data collected by the PPG acquisition module, so that the heart rate and blood oxygen physiological indicators of the human body can be obtained.

[0065] It should be noted that PPG refers to the use of photoplethysmography technology to detect the heart rate of the human body, and the change of the blood vessel volume in the cardiac cycle is recorded by detecting the difference in reflected light intensity after the human blood and tissue absorption, so that the heart rate is obtained. The image drawn according to the heart rate characteristics is called pulse wave envelope, and the pulse signal can be obtained according to the peak value and period of the pulse wave envelope image.

[0066] In the process of detecting the heart rate and blood oxygen physiological indicators by the health monitoring device, after the microprocessor receives the health detection instruction for detecting the heart rate and blood oxygen of the human body, the main program for detecting the heart rate and blood oxygen is started, and the health monitoring device enters the heart rate and blood oxygen detection mode.

[0067] Further, please refer to FIG. 2, which provides a schematic view of the A face of the health monitoring device.

[0068] Specifically, as shown in FIG. 2, the A surface is a hand holding surface, which is directly contacted with the palm, and the optical window of the sensor of the PPG acquisition module is located on the A surface. When the user holds the device, the sensor window of the PPG acquisition module is contacted with the palm, and the sensor of the PPG acquisition module can detect the pulse wave data of the palm position. When the health monitoring device detects the heart rate and blood oxygen physiological indicators, the user holds the device with one hand, and the palm is in close contact with the A surface of the device. The microprocessor collects the PPG data of the preset time length in the main program through the PPG acquisition module, and then calculates the heart rate and blood oxygen physiological indicators according to the collected PPG data. Thus, the detection of the heart rate and blood oxygen physiological indicators is completed.

[0069] The ECG acquisition module is configured to acquire a human body electrocardiogram signal and obtain electrocardiogram data.

[0070] It should be noted that the cell membrane of the human myocardial cell is a semi-permeable membrane, and ions can pass through the myocardial cell membrane. During the activity of the human myocardial cell, sodium ions and potassium ions pass through the cell membrane, resulting in a change in the potential inside and outside the membrane. By monitoring the change in the potential inside and outside the membrane, the specific activity of the heart can be known. The high and low potentials represent different activities of the heart. The electrode sheet is used to acquire the human body electrocardiogram signal, and the microprocessor obtains the ECG physiological indicators according to the potential level and potential change period of the electrocardiogram signal.

[0071] During the detection of the ECG physiological indicators by the health monitoring device, after receiving the health detection instruction for detecting the ECG of the human body, the microprocessor starts the main program of the ECG detection, and the health monitoring device enters the ECG detection mode.

[0072] Further, please refer to FIG. 3, FIG. 4 and FIG. 5, wherein FIG. 3 is a schematic view of the B surface of the health monitoring device, specifically, the B surface is a surface facing the user when the device is held by the hand, and the ECG electrode 3 is located on the B surface; FIG. 4 is a schematic view of the C surface of the health monitoring device, specifically, the C surface is a surface on which the thumb is placed when the device is held by the hand, and the ECG electrode 1 is located on the C surface, and the user holds the device with the thumb pressing the ECG electrode 1; and FIG. 5 is a schematic view of the D surface of the health monitoring device, specifically, the D surface is a surface on which the other four fingers are placed when the device is held by the hand, and the ECG electrode 2 is located on the D surface, and the user holds the device with the index finger or the middle finger pressing the ECG electrode 2.

[0073] The health monitoring device will prompt the measurement posture in the ECG detection mode.

[0074] Specifically, the right-handed user is prompted to contact the palm of the right hand with the A face of the device, press the ECG electrode 1 on the C face with the right thumb, and press the ECG electrode 2 on the D face with the right index finger or middle finger. The left thumb or other fingers of the left hand press the ECG electrode 3 on the B face. For a left-handed user, the left-handed user is prompted to contact the palm of the left hand with the A face of the device, press the ECG electrode 2 on the D face with the left thumb, and press the ECG electrode 1 on the C face with the left index finger or middle finger. The right thumb or other fingers of the right hand press the ECG electrode 3 on the B face. By prompting the user to adopt a standard measurement posture, the accuracy of the collected ECG data can be improved, and the accuracy of the detected physiological indicators can be ensured.

[0075] After the ECG data of the preset duration is collected by the ECG collection module, the microprocessor obtains the ECG physiological indicators according to the collected ECG data.

[0076] The body temperature collection module is configured to collect body temperature data of a human body.

[0077] In the process of detecting the body temperature physiological indicators by the health monitoring device, after receiving the health detection instruction of the body temperature detection, the microprocessor starts the main program of the body temperature detection, and the health monitoring device enters the body temperature detection mode.

[0078] Further, referring to FIGS. 2 and 3, the body temperature module is located on the B face. The body temperature detection main program will prompt the user to adopt different postures for collecting body temperature data according to the sensor type in the body temperature collection module. For example, when the sensor is an infrared temperature sensor, the measurement posture is that the user holds the device with one hand. The palm contacts the A face of the device, and the B face body temperature module faces the forehead. When the sensor is a contact temperature sensor, the measurement posture is that the user holds the device with one hand. The palm contacts the A face of the device, and the B face body temperature module is closely attached to the forehead to measure the forehead temperature. Or the palm is directly attached to the B face body temperature module to measure the palm temperature.

[0079] After the body temperature data of the preset duration is collected by the body temperature collection module, the microprocessor calculates the body temperature value of the user according to the collected body temperature data and the environmental temperature, and obtains the body temperature physiological indicators. The microprocessor eliminates the influence of the environmental temperature on the body temperature, and ensures that the accurate body temperature physiological indicators are obtained.

[0080] The heart-lung sound collection module is configured to collect heart sound signals of a human body.

[0081] In the process of detecting the heart-lung sound physiological indicators by the health monitoring device, after receiving the health detection instruction of the heart-lung sound detection, the microprocessor starts the main program of the heart-lung sound detection, and the health monitoring device enters the heart-lung sound detection mode.

[0082] Further, please refer to FIG. 6, which provides a posture schematic diagram of a user using the home health monitoring device to detect a cardiopulmonary sound physiological indicator. The user places the health monitoring device on the chest, and the microprocessor acquires cardiopulmonary sound data of a preset time length through the cardiopulmonary sound acquisition module to obtain the cardiopulmonary sound physiological indicator.

[0083] It should be noted that the conventional blood pressure measurement method mainly estimates blood pressure through pulse transmission time.

[0084] Please refer to FIG. 7, which provides a schematic diagram of a heart sound pulse wave transmission time PWTT, wherein S1 represents a first heart sound (S1), which is a sign of the beginning of the systole period of a cardiac cycle; S2 represents a heart sound generated during diastole, which is the sound of the arterial valve closing after the blood in each diastolic chamber of the heart is emptied, representing the completion of a cardiac cycle; and P represents a pulse wave main peak point. The pulse transmission time (PWTT) is defined as the time required for the cardiac ejection to reach the distal point from the proximal point at the same time. The method for obtaining the pulse transmission time is usually to synchronously acquire an electrocardiogram signal and a pulse wave signal, to take the R wave peak of the electrocardiogram signal as the starting point and to take the pulse wave feature point as the end point, and the time difference of this segment is the pulse transmission time. However, in fact, the R wave peak is not the time when the heart starts to contract, and there is a preparation time before the heart starts to contract, which is called the preejection period (PEP). Due to the existence of the preejection period, the blood pressure result estimated based on the pulse transmission time is unreliable, and an accurate blood pressure physiological indicator needs to be obtained through joint analysis of the heart sound signal, the pulse signal, and the electrocardiogram signal. Therefore, after the microprocessor receives the health detection instruction of blood pressure detection, the main program of blood pressure detection is started, the health monitoring device enters the blood pressure detection mode, and the microprocessor needs to synchronously acquire the heart sound signal, the pulse signal, and the electrocardiogram signal through the cardiopulmonary sound acquisition module, the PPG acquisition module, and the ECG acquisition module. After the microprocessor extracts the features of the acquired physiological signals, the extracted features are input into the pre-trained blood pressure prediction model to obtain the blood pressure physiological indicator.

[0085] In this embodiment, the heart sound signal, the electrocardiogram signal, the temperature data, and a series of data of a human body are acquired through the cardiopulmonary sound acquisition module, the PPG acquisition module, the ECG acquisition module, and the temperature acquisition module, and the accuracy of the acquired data is ensured by reminding the user to maintain a specified measurement posture during the acquisition process. The microprocessor comprehensively considers the influence of multiple factors when processing the acquired data to ensure that more accurate physiological indicators are detected.

[0086] The above is only one feasible implementation provided by the present embodiment, and the present embodiment does not specifically limit the specific implementation of the multiple sensors in the health monitoring device.

[0087] The embodiment provides a health monitoring device, which comprises a microprocessor and a plurality of sensors, wherein the microprocessor is used for acquiring a health detection instruction, detecting a corresponding physiological index according to the health detection instruction and through one or more of the plurality of sensors. The effect of detecting a plurality of physiological indexes by one device is achieved through a device highly integrated with a plurality of sensors. Further, the microprocessor processes the data collected by the plurality of sensors, so as to ensure the accuracy of the obtained physiological index.

[0088] Based on the first embodiment, the second embodiment is provided. In the second embodiment, the same or similar contents as the above-mentioned first embodiment can be referred to the above description, and will not be described in detail. On this basis, the health monitoring device further comprises:

[0089] The motion sensor module is used for collecting motion jitter data in the measurement process of the user, so as to provide data for jitter noise reduction in the measurement process.

[0090] It can be understood that the health monitoring device provided by the application can be applied in a family scene, and most of the applicable population are the elderly. In the process of measuring the physiological index by the health monitoring device, physiological jitter and the like are inevitable, which leads to inaccurate measurement results. The motion sensor module can convert the human motion data into an electrical signal, provide the microprocessor with jitter data in the measurement process, and help the microprocessor to perform noise reduction processing on the detected physiological index. The motion sensor module comprises an acceleration sensor, a gyroscope, an altimeter and the like electromechanical sensor, which can record the motion jitter data of the human body, provide data for the noise reduction of the physiological index by the microprocessor, and thus improve the accuracy of the physiological index.

[0091] In a feasible implementation manner, the microprocessor performs noise reduction processing on the heart rate, electrocardio and heart sound signals collected by the plurality of sensors by using an adaptive filtering method according to the motion jitter data collected by the motion sensor.

[0092] The motion sensor module sends the collected motion jitter data in the measurement process of the user to the microprocessor. For example, the microprocessor collects heart and lung sound data for a preset time length through the heart and lung sound collection module in the process of detecting the heart and lung sound. In the process of collecting the heart and lung sound, the health monitoring device needs to be placed on the chest. Since the heart and lung sound data for a certain time length need to be collected, the human body will jitter in the collection process, and it is difficult to ensure that the human body maintains the same detection posture for a long time. The motion sensor collects the motion jitter data in the measurement process, and sends the motion jitter data to the microprocessor. After the microprocessor ends the heart and lung sound data collection through the heart and lung sound collection module, two data are obtained, one is the heart and lung sound data collected through the heart and lung sound collection module, and the other is the motion jitter data collected through the motion sensor.

[0093] The microprocessor obtains the cardiopulmonary sound physiological indicators after processing the cardiopulmonary sound data collected by the cardiopulmonary sound collection module, and performs adaptive filtering and noise reduction processing on the cardiopulmonary sound physiological indicators by using the motion jitter data. Based on the statistical characteristics of the cardiopulmonary sound physiological indicators and the minimum mean square error criterion, the parameters of the adaptive filter are constantly adjusted by using the motion jitter data, so that the cardiopulmonary sound physiological indicators achieve the effect of noise reduction.

[0094] In this embodiment, the motion jitter data and the physiological indicators detected by the multiple sensors are processed by using adaptive filtering to achieve noise reduction. The correlation between the physiological indicators and the motion jitter data is used to constantly update the parameters of the filter through an iterative process, so that the filter can better adapt to the characteristics of the collected signals, thereby achieving the effect of noise reduction and ensuring the accuracy of the obtained physiological indicators.

[0095] The above is only one feasible implementation provided by the embodiment, and the embodiment does not specifically limit the specific implementation of the noise reduction processing of the motion jitter data collected by the motion sensor module in the health monitoring device.

[0096] The embodiment provides a health monitoring device, which further comprises a motion sensor module configured to collect motion jitter data during a measurement process of a user, and a microprocessor configured to perform noise reduction processing on physiological indicators detected by multiple sensors in combination with the motion jitter data collected by the motion sensor, so as to obtain more accurate physiological indicators.

[0097] Based on the first embodiment and / or the second embodiment of the present application, the third embodiment of the present application is proposed. In the third embodiment of the present application, the same or similar contents as those in the first embodiment and the second embodiment can be referred to the above description, and will not be described hereinafter.

[0098] On this basis, as shown in FIG. 1, the health monitoring device further comprises a display module, a graphics processor, a storage and a wireless communication module, wherein:

[0099] The graphics processor is configured to draw graphics content and drive the display module to perform graphics display.

[0100] In order to more intuitively display the detected ECG physiological indicators, the microprocessor needs to display the ECG on the health monitoring device when running the main program of the ECG detection instruction. The graphics processor can draw graphic content according to the obtained ECG physiological indicators and drive the display module to display graphics. After receiving the transmitted ECG physiological indicators from the microprocessor, the graphics processor performs transformation, projection, clipping and other operations on the ECG physiological indicators through the parallel processing unit (SM, Streaming Multiprocessors) of the graphics processor, generates screen space coordinates of geometric primitives, and the graphics processor performs rasterization processing on the screen space coordinates to obtain the graphics of the ECG and displays it through the display module. Users can more intuitively obtain the ECG physiological indicators.

[0101] a storage for storing various application programs and related data.

[0102] The microprocessor receives different health detection instructions and needs to run different main programs. In order to comprehensively understand the user's physical condition changes in a period of time, the user's historical health data detected before also needs to be stored, so a storage is needed to store various programs and the user's historical data detected before. The storage is a collection of many storage units arranged according to unit numbers, which can store different types of data without interfering with each other, ensuring that the corresponding main program can be accurately accessed when receiving different health detection instructions, so as to ensure the accuracy of physiological indicator detection.

[0103] a wireless communication module for connecting with external devices or networks.

[0104] In the first feasible implementation, the microprocessor sends the detected physiological indicators to the host device through the wireless communication module, and the host device analyzes the physiological indicators.

[0105] It can be understood that the health monitoring device proposed in the present application can be applied in a home scenario, and the users using the health monitoring device in the home scenario do not necessarily have professional medical knowledge, so it is difficult for non-medical professional users to accurately know their physical condition according to the detected physiological indicators. The physiological indicators detected by the wireless communication module are sent to the external device, and the user's physical condition is judged according to the analysis result returned by the external device, helping the user to understand the real-time physical health condition. The host device performs preliminary disease risk assessment on the physiological indicators sent by the microprocessor through the wireless communication module, helping the user to understand their physical condition.

[0106] In this embodiment, the health monitoring device is connected to the external host device through the wireless communication module, and the physiological indicators detected by the health monitoring device are sent to the host device. The host device performs preliminary disease risk assessment analysis based on the physiological indicators, helps the user to perform early screening and intervention, and has important clinical application value.

[0107] In a second possible implementation, the microprocessor sends the detected physiological indicators to the cloud device through the wireless communication module. The cloud device analyzes the physiological indicators and returns the analysis results to the host device.

[0108] The microprocessor sends the physiological indicators and the user's historical health data to the cloud device through the wireless communication module. The cloud device can integrate and analyze the physiological indicators and the user's historical health data to obtain more complete disease risk assessment results. At the same time, by connecting the cloud device through the wireless communication module, a remote consultation program can be established, and doctors can also make a more comprehensive assessment of the user's physical condition based on the health data stored in the cloud device.

[0109] In this embodiment, the health monitoring device detects the physiological indicators of the user and sends the physiological indicators to the remote cloud device through the wireless communication module. The cloud device combines historical health data to analyze the physiological indicators to obtain more complete assessment results. Doctors can also learn about the user's physical condition at any time to help the user truly understand their own health status.

[0110] The following will specifically illustrate the detection process of the cardiopulmonary sound physiological indicators, blood pressure physiological indicators, ECG electrocardiogram physiological indicators, heart rate and oxygen physiological indicators, and body temperature physiological indicators:

[0111] Specifically, as a possible implementation, the detection process of the cardiopulmonary sound physiological indicators using the health monitoring device is as follows:

[0112] The user issues a cardiopulmonary sound detection instruction. After receiving the cardiopulmonary sound detection instruction, the microprocessor starts the cardiopulmonary sound detection main program, and the device enters the cardiopulmonary sound detection mode. The user places the health monitoring device on the chest. The microprocessor collects cardiopulmonary sound data for a predetermined time through the cardiopulmonary sound collection module to obtain cardiopulmonary sound physiological indicators. The microprocessor sends the cardiopulmonary sound physiological indicators to the host device through the wireless transmission module. The host device analyzes and processes the cardiopulmonary sound physiological indicators, performs preliminary disease risk assessment, obtains preliminary disease risk assessment results, and uploads the cardiopulmonary sound physiological indicators to the cloud device. The cloud device integrates and analyzes the cardiopulmonary sound physiological indicators and the historical user health data stored in the cloud to obtain more complete disease risk assessment results. The cloud device can also establish a remote consultation program, and doctors can diagnose diseases based on the health data stored in the cloud.

[0113] Specifically, as a feasible implementation, the detection process of blood pressure physiological indicators using the health monitoring device is as follows:

[0114] The user issues a blood pressure detection instruction, and the microprocessor starts a blood pressure detection main program after receiving the blood pressure detection instruction, and the device enters a blood pressure detection mode. The user places the health monitoring device on the chest, and the microprocessor collects heart sound signals, pulse signals, and ECG signals of a preset time length through the heart-lung sound acquisition module, the PPG acquisition module, and the ECG acquisition module. The microprocessor extracts features from the collected physiological signals to obtain feature signals, and inputs the feature signals into a blood pressure prediction model to obtain blood pressure physiological indicators. The microprocessor sends the blood pressure physiological indicators and the collected physiological signals to the host device through the wireless transmission module, the host device performs preliminary disease risk assessment based on the blood pressure physiological indicators and the collected physiological signals to obtain a preliminary risk assessment result, and uploads the blood pressure physiological indicators to the cloud device. The cloud device integrates and analyzes the blood pressure physiological indicators and the historical user health data stored in the cloud to obtain a more complete disease risk assessment result. The cloud device can also establish a remote consultation program, and doctors can diagnose diseases based on the health data stored in the cloud.

[0115] Specifically, as a feasible implementation, the detection process of ECG physiological indicators using the health monitoring device is as follows:

[0116] The user issues an ECG detection instruction, and the microprocessor starts an ECG detection main program after receiving the ECG detection instruction, and the device enters an ECG detection mode. The user holds the device with both hands to perform ECG detection, and the ECG acquisition module collects ECG data of a certain time length to obtain ECG physiological indicators. The microprocessor sends the ECG physiological indicators to the host device through the wireless transmission module. The host device analyzes and processes the ECG physiological indicators to obtain a preliminary disease risk assessment result, and uploads the ECG data to the cloud device. The cloud device integrates and analyzes the ECG physiological indicators and the historical user health data stored in the cloud to obtain a more complete disease risk assessment result. The cloud device establishes a remote consultation program, and doctors can also diagnose diseases based on the health data stored in the cloud device.

[0117] Specifically, as a feasible implementation, the detection process of heart rate and blood oxygen physiological indicators using the health monitoring device is as follows:

[0118] The user issues a heart rate and blood oxygen detection instruction, and the microprocessor starts a heart rate and blood oxygen detection main program after receiving the heart rate and blood oxygen detection instruction, and the device enters a heart rate and blood oxygen detection mode. The PPG collection module collects PPG data for a certain time length, the microprocessor calculates the heart rate and blood oxygen value of the user based on the PPG data, and displays the heart rate and blood oxygen value through the display module. The microprocessor sends the heart rate and blood oxygen physiological indicators and the PPG data to the host device through the wireless transmission module. The host device analyzes and processes the PPG data and the heart rate and blood oxygen physiological indicators to obtain a preliminary disease risk assessment result, and uploads the PPG data and the heart rate and blood oxygen physiological indicators to the cloud device. The cloud device integrates and analyzes the PPG data, the heart rate and blood oxygen physiological indicators, and the historical health data of the user stored in the cloud to obtain a more complete disease risk assessment result. The cloud device can establish a remote consultation program, and doctors can also diagnose diseases based on the health data stored in the cloud device.

[0119] Specifically, as a feasible implementation manner, the detection process of the temperature physiological indicator using the health monitoring device is as follows:

[0120] The user issues a temperature detection instruction, and the microprocessor starts a temperature detection main program after receiving the temperature detection instruction, and the device enters a temperature detection mode. The temperature module collects temperature data and environmental temperature data for a certain time length, the microprocessor calculates the temperature physiological indicator of the user based on the temperature data and the environmental temperature data, and displays the temperature physiological indicator through the display module. The microprocessor sends the temperature physiological indicator, the temperature data and the environmental temperature data to the host device through the wireless transmission module. The host device analyzes and processes the temperature physiological indicator and the environmental temperature data to obtain a preliminary disease risk assessment result, and uploads the temperature physiological indicator, the environmental temperature data and the temperature data to the cloud device. The cloud device integrates and analyzes the temperature data, the environmental temperature data and the temperature physiological indicator with the historical health data of the user stored in the cloud to obtain a more complete disease risk assessment result. The cloud device can establish a remote consultation program, and doctors can also diagnose diseases based on the health data stored in the cloud device.

[0121] The embodiment provides a health monitoring device which further has a display module, a graphics processor, a memory and a wireless communication module, and can be connected with external devices through the wireless communication module. The memory is used for storing main programs corresponding to different health detection instructions, so that the main programs corresponding to the health detection instructions can be smoothly run when different health detection instructions are acquired, and physiological indexes corresponding to the health detection instructions are ensured to be detected. The display module driven by the graphics processor displays the acquired physiological indexes, so that the user can more intuitively acquire the physiological indexes. The microprocessor sends physiological indexes and physiological signals such as heart sounds, electrocardiograms and heart rates collected by the sensor to a master control device through the wireless communication module, so as to provide data for disease risk analysis and evaluation of the master control device. The master control device can help the user to understand the health condition of the user through the physiological indexes. The master control device can also send the physiological indexes and the physiological signals such as the heart sounds, the electrocardiograms and the heart rates collected by the sensor to a cloud device, the cloud device combines historical health data of the user stored in the cloud to perform more complete analysis and obtain more complete disease risk evaluation results. The cloud device can establish a remote diagnosis program to assist doctors in remote diagnosis. The wireless communication module is connected with the external cloud device and the master control device, the disease risk evaluation results of the cloud device and the master control device are acquired, and the user can understand the health condition of the user through the physiological indexes detected by the health monitoring device.

[0122] In addition, the application further provides a health monitoring method, referring to FIG. 8, which is a flowchart of a first embodiment of the health monitoring method of the application.

[0123] In the embodiment, the method is applied to a health monitoring device which is integrated with multiple sensors, and the health monitoring method comprises steps S10-S20.

[0124] In step S10, a health detection instruction is acquired.

[0125] It should be noted that the health detection instruction refers to a detection instruction for a physiological index of a human body, and the health detection instruction comprises one or more of a heart-lung sound detection instruction, a blood pressure detection instruction, an electrocardiogram detection instruction, a heart rate and blood oxygen detection instruction and a body temperature detection instruction. It can be understood that the health detection instruction can be a detection instruction for only one physiological index, or a detection instruction for multiple physiological indexes.

[0126] In step S20, a corresponding physiological index is detected according to the health detection instruction and through one or more of the multiple sensors.

[0127] After obtaining the health detection instruction, the corresponding detection mode is entered according to the health detection instruction, and after entering the detection mode corresponding to the health detection instruction, the sensor corresponding to the detection mode is selected to detect the physiological index.

[0128] It should be noted that the heart-lung sound detection mode is entered according to the heart-lung sound detection instruction, and the heart sound signal of the human body is collected by using the heart-lung sound collection module in the heart-lung sound detection mode; the blood pressure detection mode is entered according to the blood pressure detection instruction, and the electrocardiogram signal, pulse signal and heart sound signal of the human body are collected by using the heart-lung sound collection module, PPG collection module and ECG collection module in the blood pressure detection mode; the ECG electrocardiogram detection mode is entered according to the electrocardiogram detection instruction, and the electrocardiogram signal of the human body is collected by using the ECG collection module in the ECG electrocardiogram detection mode to obtain electrocardiogram data; the heart rate blood oxygen detection mode is entered according to the heart rate blood oxygen detection instruction, and the heart rate data of the human body is collected by using the PPG collection module in the heart rate blood oxygen detection mode; the body temperature detection mode is entered according to the body temperature detection instruction, and the body temperature data of the human body is collected by using the body temperature collection module in the body temperature detection mode. After the corresponding physiological signals and data are collected, the microprocessor processes the collected physiological signals and data to obtain the corresponding physiological index.

[0129] In the first feasible implementation, step S20 can include: according to the health detection instruction of heart-lung sound detection, detecting the heart-lung sound physiological index by using the corresponding sensor.

[0130] After receiving the instruction of detecting the heart-lung sound, the health monitoring device enters the heart-lung sound detection mode, and in the heart-lung sound detection mode, the health monitoring device will prompt the user to place the device on the chest through the loudspeaker module. The heart-lung sound collection module collects heart-lung sound data for a certain time length to obtain the heart-lung sound physiological index.

[0131] It should be noted that, in order to improve the accuracy of collecting physiological signals, the time length of collecting heart-lung sound data by the heart-lung sound collection module is a pre-set time length, and the time length collected by the corresponding collection module in different detection modes is not consistent.

[0132] In this embodiment, different detection modes are entered by obtaining different health detection instructions, and different time lengths of data are collected according to the sensors corresponding to the detection modes to obtain more accurate physiological indexes.

[0133] In the second feasible implementation, step S20 can include: according to the health detection instruction of body temperature detection, detecting the body temperature physiological index by using the corresponding sensor.

[0134] After receiving the instruction of detecting the body temperature, the health monitoring device enters the body temperature detection mode, in which the body temperature collection module collects body temperature data and environmental temperature data for a certain time length, and the microprocessor calculates the body temperature physiological index according to the body temperature data and the environmental temperature data.

[0135] In this embodiment, the body temperature physiological index is calculated more accurately by collecting the body temperature data and the environmental temperature data, and the influence of the environmental temperature is excluded.

[0136] In a third possible implementation, step S20 can include: according to the health detection instruction of blood pressure detection, detecting the blood pressure physiological index by the corresponding sensor.

[0137] Since the influence of PEP on PWTT needs to be considered when detecting blood pressure, when detecting the blood pressure physiological index, the heart-lung sound collection module, the PPG collection module and the ECG collection module are used to collect heart sound signals, pulse signals and electrocardiogram signals, so as to exclude the influence of PEP on PWTT and obtain a more accurate blood pressure physiological index.

[0138] In this embodiment, the heart-lung sound collection module, the PPG collection module and the ECG collection module are used to collect heart sound signals, pulse signals and electrocardiogram signals, so as to exclude the influence of PEP on PWTT and improve the measurement accuracy of blood pressure.

[0139] The above are only three possible implementations of step S20 provided by the present embodiment, and the present embodiment does not specifically limit the specific implementation of step S20.

[0140] The present embodiment provides a health monitoring method, which first enters a corresponding detection mode by obtaining a health detection instruction, collects physiological signals and data by using a sensor corresponding to the detection mode in the detection mode, processes the collected physiological signals and data by a microprocessor to obtain a corresponding physiological index, and realizes detection of multiple physiological indexes on the same device. In addition, the microprocessor processes different physiological signals and data in different ways to obtain more accurate physiological indexes.

[0141] Based on the first embodiment of the method, the second embodiment of the method is proposed. The second embodiment of the method takes measuring the blood pressure physiological index as an example to describe the method of measuring blood pressure by combining heart sound and pulse.

[0142] In the second embodiment of the method, the same or similar contents as the above first embodiment can be referred to the above description, and will not be described in detail hereinafter.

[0143] Specifically, please refer to FIG. 9, step S20, according to the health detection instruction, and through one or more of the plurality of sensors, the corresponding physiological indicators can include steps S21-S24:

[0144] Step S21, according to the blood pressure detection instruction, select the blood pressure detection mode;

[0145] According to the blood pressure detection instruction, the device enters the blood pressure detection mode. In the blood pressure detection mode, the user is reminded to place the device on the chest through the speaker module and the display module.

[0146] Step S22, according to the heart and lung sound acquisition module, PPG acquisition module, ECG acquisition module corresponding to the blood pressure detection mode, synchronously acquire physiological signals, wherein the physiological signals include electrocardiogram signals, pulse signals and heart sound signals;

[0147] It should be noted that the traditional blood pressure measurement method mainly estimates blood pressure through pulse transmission time. Pulse wave transmission time (PWTT) is defined as the time required for the heart to eject blood from the proximal point to the distal point at the same time. The method of obtaining pulse wave transmission time is to synchronously acquire electrocardiogram signals and pulse wave signals, taking the R wave peak of the electrocardiogram signal as the starting point and the pulse wave feature point as the end point. The time difference between the two points is the pulse wave transmission time. However, in fact, the R wave peak is not the time when the heart starts to contract. There is a preparation time before the heart starts to contract, which is called the preejection period (PEP). Due to the existence of the preejection period, the blood pressure result estimated based on the pulse wave transmission time is unreliable. By synchronously acquiring electrocardiogram signals, pulse signals and heart sound signals through the heart and lung sound acquisition module, PPG acquisition module and ECG acquisition module, the influence of PEP on PWTT is considered, so as to obtain more accurate blood pressure physiological indicators.

[0148] In the first feasible implementation, the human body jitter data collected by the motion sensor module is combined with the noise reduction processing of the collected electrocardiogram signals, pulse signals and heart sound signals by the microprocessor to improve the signal-to-noise ratio of the electrocardiogram signal acquisition.

[0149] In this embodiment, the microprocessor performs noise reduction processing on the heart sound signals, electrocardiogram signals and pulse signals by combining the human body jitter data collected by the motion sensor module to obtain more accurate physiological indicators.

[0150] In the second feasible implementation, the physiological signals are collected simultaneously at the same time to determine the relationship between the electrocardiogram signals, pulse signals and heart sound signals and time, so as to ensure the strict synchronism of the physiological signals. And the real-time time after the correction can also be used to record the time when the data starts to be collected.

[0151] In this embodiment, the electrocardiosignal, the pulse signal and the heart sound signal are collected at the same time, the influence of PEP on PWTT is reduced, and the accuracy of the blood pressure physiological index is ensured.

[0152] In step S23, the physiological signal is subjected to feature extraction to obtain a signal feature, wherein the signal feature includes a PEP duration and a pulse transit time.

[0153] Further, to improve the accuracy of the extracted signal feature, the physiological signal can be subjected to signal processing, such as filtering, noise reduction, segmentation and the like, before the signal feature of the physiological signal is extracted. The signal feature is obtained based on the processed physiological signal.

[0154] Specifically, the process of extracting the signal feature from the physiological signal includes the following steps. First, the physiological signal is subjected to signal preprocessing to filter out noise caused by motion artifacts, thereby obtaining a clean high-quality physiological signal. Second, the signal duration of the preprocessed physiological signal is determined, and the preprocessed physiological signal is subjected to signal segmentation processing to obtain multiple sub physiological signals, and the sub signal feature of each sub signal is extracted. Finally, the mean signal feature of all the sub signal features is determined, and the mean signal feature is taken as the signal feature of the physiological signal.

[0155] It should be noted that the mean signal features include but are not limited to the pre-ejection period duration, pulse transit time, pulse arrival time (PAT), pulse signal and heart sound signal systolic duration ratio, pulse signal and heart sound signal diastolic duration ratio, time domain features, frequency domain features, time-frequency features, statistical features. Among them, the time domain features include but are not limited to the following features: R-R interval (RR) of electrocardiogram signal, R-R standard deviation (SDNN), root mean square difference (RMSSD) and the like; PP interval (PP) of pulse signal, half-width pulse width (PW50), systolic time, diastolic time, rise time, fastest rise area, peak height, rising slope and the like; first zero-crossing time of VPG signal (signal obtained by first-order difference processing of pulse signal), last inflection point time, peak value and first zero-crossing slope, peak slope, peak area and the like; APG signal (signal obtained by second-order difference processing of pulse signal) minimum point time, peak point and minimum point slope, first zero-crossing and minimum point slope; first heart sound duration, second heart sound duration, systolic duration, diastolic duration and the like in heart sound signal. The frequency domain features include but are not limited to the following features: electrocardiogram signal power spectral density; first component frequency and its amplitude of pulse signal, second component frequency and its amplitude, third component frequency and its amplitude; S1 main component frequency of heart sound signal, S2 main component frequency and the like. The time-frequency features include but are not limited to the following features: wavelet coefficients, Hilbert-Huang transform coefficients, mel-frequency cepstral coefficients, linear prediction coefficient features and the like. The statistical features include but are not limited to the following features: kurtosis factor, skewness factor, standard deviation of characteristic sequence and the like.

[0156] And PEP is the delay between the start of cardiac electrical activity and the start of mechanical ventricular ejection, PEP needs to be detected by accurately measuring and extracting a large number of features from heart sound signals, pulse signals and electrocardiogram signals. The present application considers the influence of PEP on PWTT by extracting a large number of signal features from multiple sub-physiological signals, and ensures the accuracy of blood pressure physiological indicators.

[0157] Step S24, inputting the signal features into the pre-trained blood pressure prediction model, so that the blood pressure prediction model outputs blood pressure physiological indicators.

[0158] It should be noted that the pre-trained blood pressure prediction model can be a blood pressure prediction model trained based on a database, and the database at least includes real blood pressure data and signal feature data corresponding to each real blood pressure data. The database can be obtained by pre-collecting real blood pressure data and signal feature data.

[0159] Further, in order to improve the accuracy of the blood pressure prediction model and realize personalized prediction of blood pressure, the real blood pressure values and physiological signals of the detection object can be collected multiple times in advance, an individual data set is established, the individual data set is used as a database for training the prediction model, and the training of the blood pressure prediction model is completed.

[0160] Specifically, as a feasible implementation manner, the pre-training process of the blood pressure prediction model can be:

[0161] S1, selecting features from the multi-category feature library through an existing database;

[0162] S2, calculating mutual information between two features, wherein p(x) is the probability of occurrence of x, p(y) is the probability of occurrence of y, and p(x, y) is the probability of simultaneous occurrence of x and y, i.e., the joint probability. The higher the mutual information, the higher the dependence between the two features. Features lower than the mutual information threshold are removed to obtain a new feature subset;

[0163] S3, calculating the correlation coefficient between the new feature subset and blood pressure, wherein the higher the correlation coefficient, the higher the linear correlation between the feature and blood pressure;

[0164] S4, sorting the feature subset according to the correlation coefficient from high to low to obtain a sorted feature subset;

[0165] S5, dividing the sorted feature subset into a training set and a test set, and the ratio of the training set to the test set is 8:2;

[0166] S6, for the training set, using ten-fold cross-validation and backward feature selection to select the number of features to obtain a feature subset S with the lowest RMSE (Root Mean Squared Error, Root Mean Squared Error);

[0167] S7, using the final feature subset to train a multiple linear regression model: wherein:

[0168] BP is a specific blood pressure value, S i is the optimal feature subset, and it is to be noted that in the case of i = 1, the optimal feature subset is the feature subset S with the lowest RMSE, K i is a multiple linear regression model fitting coefficient, and n is the dimension of the optimal feature subset.

[0169] Based on the pre-trained blood pressure prediction model, the signal features can be input into the pre-trained blood pressure prediction model: In the embodiment, the feature selection is not performed from the multi-category feature library in the existing database, but is performed from the large amount of signal features extracted from the physiological signals. The blood pressure prediction model calculates the mutual information between the features, and removes the features lower than the mutual information threshold to obtain a new feature subset. According to the correlation coefficient obtained in the pre-training process of the blood pressure prediction model, the feature subset is sorted from high to low according to the correlation coefficient to obtain a sorted feature subset. The training set is divided into a training set and a test set according to a ratio of 8:2. The ten-fold cross-validation and the backward feature selection are performed on the training set to select the number of features, and the feature subset S with the lowest RMSE is obtained and substituted into wherein: BP is a specific blood pressure value, S i is the optimal feature subset, and it is to be noted that, in the case of i = 1, the optimal feature subset is the feature subset S with the lowest RMSE, K i is a fitting coefficient of a multiple linear regression model, and n is the dimension of the optimal feature subset. Finally, the specific blood pressure value BP, i.e., the blood pressure physiological index, is obtained.

[0170] In addition, the personalized features such as the age, gender, height, weight, and BMI (Body Mass Index) of the user can be collected, and the signal feature data is subjected to model training to obtain a pre-trained personalized blood pressure prediction model. After the signal features are extracted from the physiological signals, the personalized features such as the age, gender, height, weight, and BMI (Body Mass Index) of the tester are detected. The signal features and the personalized features are input into the pre-trained personalized blood pressure prediction model to output the blood pressure physiological index. The influence of the factors such as the age, gender, height, weight, and BMI (Body Mass Index) on the blood pressure is considered, and the accuracy of the blood pressure physiological index can be further improved.

[0171] In the embodiment, the health detection instruction is a blood pressure detection instruction. The blood pressure detection mode is selected according to the blood pressure detection instruction, and the physiological index is detected according to the sensors required to be used in the blood pressure detection mode. The physiological signals are collected by the cardiopulmonary sound acquisition module, the PPG acquisition module, and the ECG acquisition module. The physiological signals include the electrocardiogram signal, the pulse signal, and the heart sound signal. The signal features obtained after the feature extraction of the physiological signals are input into the pre-trained blood pressure prediction model to obtain the blood pressure physiological index. The influence of the PEP on the PWTT is considered, and the measurement accuracy of the blood pressure is improved.

[0172] The application further provides a health monitoring device. Please refer to FIG. 10. The health monitoring device comprises:

[0173] The instruction acquisition module 10 is configured to acquire a health detection instruction.

[0174] Obtaining a health detection instruction issued by a user, and identifying the detection instruction.

[0175] A detection module 20 is configured to detect a corresponding physiological index according to the health detection instruction and through one or more sensors of the plurality of sensors.

[0176] According to the health detection instruction, the microprocessor enters different detection modes, and in different detection modes, the microprocessor starts a main program corresponding to the detection mode, collects physiological signals such as heart sound signals and heart rate signals through one or more sensors of the heart-lung sound collection module, the PPG collection module, the ECG collection module, and the body temperature collection module, and processes the collected physiological signals to obtain accurate physiological indexes. Further, the detection module includes a wireless communication module capable of connecting with external devices and networks, transmitting the detected physiological indexes, and helping the user to know the health condition of the user through the physiological indexes.

[0177] The health monitoring device provided in the present application adopts the health monitoring method in the above embodiments, and can solve the technical problem of health monitoring. Compared with the prior art, the health monitoring device provided in the present application has the same beneficial effects as the health monitoring method provided in the above embodiments, and other technical features in the health monitoring device are the same as the features disclosed in the above embodiments, which will not be repeated here.

[0178] The present application provides a health monitoring device, which comprises at least one processor and a memory connected with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the health monitoring method in Embodiment I.

[0179] Reference is made to FIG. 11, which shows a structural schematic diagram of a health monitoring device suitable for implementing the embodiments of the present application. The health monitoring device in the embodiments of the present application can include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), and vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and fixed terminals such as digital TVs and desktop computers. The health monitoring device shown in FIG. 11 is only an example, and should not impose any limitation on the functions and use range of the embodiments of the present application.

[0180] As shown in FIG. 11, the health monitoring device can include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes according to programs stored in a read only memory (ROM) 1002 or loaded from a storage device 1003 into a random access memory (RAM) 1004. Various programs and data required for operation of the health monitoring device are also stored in the RAM 1004. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; the storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the health monitoring device to communicate wirelessly or by wire with other devices to exchange data. Although the health monitoring device with various systems is shown in the figure, it should be understood that all of the systems shown are not required to be implemented or possessed. More or fewer systems can be alternatively implemented or possessed.

[0181] In particular, the processes described above with reference to the flowcharts can be implemented as computer software programs according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network through the communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the methods of embodiments of the present disclosure are performed.

[0182] The health monitoring device provided by the present disclosure adopts the health monitoring method in the above embodiments, and can solve the technical problem of health monitoring. Compared with the prior art, the health monitoring device provided by the present disclosure has the same beneficial effects as the health monitoring method provided by the above embodiments, and other technical features in the health monitoring device are the same as the features disclosed in the above embodiments, which will not be repeated here.

[0183] It should be understood that various aspects of the disclosure can be implemented in hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any appropriate manner in any one or more embodiments or examples.

[0184] The above description is merely illustrative of the application and is not intended to limit the scope of the application. Any variations and modifications that can be made by any person skilled in the art within the spirit and scope of the application are intended to be encompassed by the application. Therefore, the scope of the application should be determined by the appended claims.

[0185] The application provides a computer readable storage medium having stored thereon computer readable program instructions (i.e., a computer program) for performing the health monitoring method in the above embodiments.

[0186] The computer readable storage medium provided by the application may, for example, be a U disk, but is not limited to an electric, magnetic, optical, electromagnetic, infrared or semiconductor system, system or device, or any combination of the above. More specific examples of the computer readable storage medium can include, but are not limited to, an electric connection with one or more conductive wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present embodiment, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system or device. The program code contained on the computer readable storage medium can be transmitted by any appropriate medium, including but not limited to an electric wire, an optical cable, an RF (Radio Frequency), etc., or any suitable combination of the above.

[0187] The above computer readable storage medium can be included in the health monitoring device; or can exist separately without being assembled into the health monitoring device.

[0188] The computer readable storage medium described above carries one or more programs, when the one or more programs are executed by the health monitoring device, the health monitoring device: acquires a health detection instruction; and detects a corresponding physiological index according to the health detection instruction and through one or more of the plurality of sensors.

[0189] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0190] The flow diagrams and the block diagrams in the drawings are illustrations of architectures, functionalities, and operations of possible implementations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flow diagrams or block diagrams can represent a module, a segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flow diagrams, and combinations thereof, can be implemented by special purpose hardware-based systems that perform the specified functions or operations, or combinations of special purpose hardware and computer instructions.

[0191] The modules involved in the embodiments of the present application can be implemented in the form of software or in the form of hardware. In some cases, the name of the module does not constitute a limitation on the module itself.

[0192] The readable storage medium provided by the application is a computer readable storage medium, which stores computer readable program instructions (i.e. computer programs) for executing the health monitoring method described above, and can solve the technical problem of health monitoring. Compared with the prior art, the beneficial effects of the computer readable storage medium provided by the application are the same as those of the health monitoring method provided by the above-mentioned embodiments, and are not described here.

[0193] The application also provides a computer program product comprising a computer program, which, when executed by a processor, implements the steps of the health monitoring method as described above.

[0194] The computer program product provided by the application can solve the technical problem of health monitoring. Compared with the prior art, the beneficial effects of the computer program product provided by the application are the same as those of the health monitoring method provided by the above-mentioned embodiments, and are not described here.

[0195] The above-mentioned only some embodiments of the application, not therefore limit the patent scope of the application, any equivalent structural transformation made by the application specification and the content of the drawings, or directly / indirectly applied in other related technical fields under the technical concept of the application are included in the patent protection scope of the application.

Claims

1. A health monitoring device, characterized by The health monitoring device comprises a microprocessor and a plurality of sensors, wherein: The microprocessor is configured to acquire a health detection instruction, and detect corresponding physiological indicators according to the health detection instruction and through one or more of the plurality of sensors.

2. The health monitoring device of claim 1, wherein, The plurality of sensors comprises one or more of a heart-lung sound acquisition module, a PPG acquisition module, an ECG acquisition module, and a body temperature acquisition module, wherein: The PPG acquisition module is configured to acquire human heart rate data and pulse wave envelope; The ECG acquisition module is configured to acquire human electrocardiogram signals and obtain electrocardiogram data; The body temperature acquisition module is configured to acquire human body temperature data; The heart-lung sound acquisition module is configured to acquire human heart sound signals.

3. The health monitoring device of claim 2, wherein, The device further comprises: A motion sensor module configured to acquire motion jitter data during user measurement, and provide data for jitter noise reduction during measurement; The microprocessor is further configured to perform noise reduction processing on physiological indicator data detected by the plurality of sensors in combination with the motion sensor module.

4. The health monitoring device of claim 3, wherein, The device further comprises a display module, a graphics processor, a storage, and a wireless communication module, wherein: The graphics processor is configured to draw graphic content and drive the display module to display graphics; The storage is configured to store various application programs and related data; The wireless communication module is configured to connect with external devices or networks.

5. The health monitoring device of claim 4, wherein, The microprocessor is further configured to send detected physiological indicators to a master device through the wireless communication module, and perform analysis on the physiological indicators by the master device, or send the physiological indicators to a cloud device by the master device.

6. The health monitoring device of claim 5, wherein, The microprocessor is further configured to send detected physiological indicators to the cloud device through the wireless communication module, perform analysis on the physiological indicators by the cloud device, and return the analysis results to the master device.

7. The health monitoring device of claim 6, wherein, The microprocessor is further configured to receive operation instructions sent by a user through the master device, detect corresponding physiological indicators according to the operation instructions and through one or more of the plurality of sensors, or query the detection results stored on the health monitoring device according to the operation instructions.

8. A health monitoring method characterized by, The method is applied to a health monitoring device integrated with a plurality of sensors, and the method comprises: Acquiring a health detection instruction; Detecting corresponding physiological indicators according to the health detection instruction and through one or more of the plurality of sensors; the plurality of sensors comprises one or more of a heart-lung sound acquisition module, a PPG acquisition module, an ECG acquisition module, and a body temperature acquisition module.

9. The method of claim 8, wherein, If the health detection instruction is a blood pressure detection instruction, the step of detecting corresponding physiological indicators according to the health detection instruction and through one or more of the plurality of sensors comprises: Selecting a blood pressure detection mode according to the blood pressure detection instruction; Synchronously acquiring physiological signals according to the heart-lung sound acquisition module, the PPG acquisition module, and the ECG acquisition module corresponding to the blood pressure detection mode, wherein the physiological signals comprise heart sound signals, pulse signals, and electrocardiogram signals; extracting a signal feature from the physiological signal, wherein the signal feature comprises a pre-ejection period duration and a pulse transit time; inputting the signal feature into a pre-trained blood pressure prediction model, so as to output a blood pressure physiological index by the blood pressure prediction model.

10. A storage medium, characterized by The storage medium is a computer-readable storage medium, and the storage medium stores a computer program. The computer program is executed by the processor to implement the steps of the health monitoring method according to any one of claims 8 to 9.

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