Health monitoring method, system, device and equipment based on human intelligence

Abstract

The invention discloses a health monitoring method, system, device and equipment based on human intelligence. The human intelligence-based health monitoring method comprises the following steps: acquiring physiological data of a user; behavior data of the user in the current environment are obtained, and the behavior data represent information of the current environment where the user is located and behavior operation information of the user in the current environment; inputting the physiological data and the behavior data into a health prediction model for prediction, so that the health prediction model predicts a physiological abnormal state of the user based on the physiological data, and performs health prediction in combination with the behavior data and the physiological abnormal state; the predicted health monitoring result is output, and the health monitoring result represents the health state of the behavior operation executed by the user in the current environment.

Description

Technical Field

[0001] The present invention relates to the technical fields of human intelligence, artificial intelligence, etc., and in particular to a health monitoring method, system, device and equipment based on human intelligence. Background Art

[0002] As the population ages, the contradiction between medical resources and population needs becomes increasingly prominent, which has prompted the need for effective health monitoring technology. Related technologies monitor the user's physiological indicators in order to provide timely warnings when the user's body is abnormal, but the health monitoring methods of related technologies are not smart enough, and the accuracy of the monitoring results is low, making it difficult to provide effective health advice. Summary of the invention

[0003] The embodiments of the present application aim to solve at least one of the technical problems in the related art to a certain extent. To this end, the purpose of the embodiments of the present application is to propose a health monitoring method, system, device, electronic device, storage medium and program product based on human factor intelligence.

[0004] An embodiment of the present application provides a health monitoring method based on human factors intelligence, the method comprising: obtaining physiological data of a user; obtaining behavioral data of the user in a current environment, wherein the behavioral data represents information about the current environment in which the user is located and information about the user's behavioral operations in the current environment; inputting the physiological data and the behavioral data into a health prediction model for prediction, so that the health prediction model predicts the user's abnormal physiological state based on the physiological data, and performs health prediction in combination with the behavioral data and the abnormal physiological state; and outputting a predicted health monitoring result, wherein the health monitoring result represents the health state of the user performing behavioral operations in the current environment.

[0005] Exemplarily, the method further includes: outputting recommendation data based on the health monitoring result.

[0006] Exemplarily, outputting suggestion data based on the health monitoring result includes: when the health monitoring result indicates that the user's health level is less than a preset health level, outputting first suggestion data, wherein the first suggestion data is used to prompt the user to stop performing the current behavior operation.

[0007] Exemplarily, outputting suggestion data based on the health monitoring result includes: when the health monitoring result indicates that the user's health level is greater than or equal to a preset health level, outputting second suggestion data, wherein the second suggestion data is used to prompt that there is a risk in continuing to perform the current behavior operation.

[0008] Exemplarily, the method further includes: when the health monitoring result indicates abnormal health of the user, recording the current environment information and the behavior operation information, so as to output warning information when the current environment information and the behavior operation information are subsequently detected.

[0009] Exemplarily, the health threshold is used as a reference in the process of the health prediction model predicting the health monitoring result; the method also includes: when the health monitoring result indicates that the user's health status has recovered, adjusting the health threshold.

[0010] Exemplarily, the health prediction model includes model parameters, and the method further includes: when the health monitoring results indicate that the user's health status has recovered, obtaining physiological data and behavioral data during the recovery process, and adjusting the model parameters based on the physiological data and behavioral data during the recovery process.

[0011] Exemplarily, the health prediction model includes model parameters, and the method further includes: after outputting the predicted health monitoring results, outputting inquiry information; receiving reply information from the user to the inquiry information, and adjusting the model parameters if the reply information indicates that the health monitoring results do not conform to the user's current health status.

[0012] Exemplarily, the behavior data includes video data, and the video data is collected when the user performs the behavior operation in the current environment.

[0013] Exemplarily, the physiological data includes at least one type of physiological data, and the health monitoring results include results corresponding to different types of health indicators, wherein the health prediction model assigns different weights to different types of physiological data based on the corresponding health indicators when performing health prediction, wherein at least one type of physiological data includes at least one of PPG blood volume pulse wave data, ECG electrocardiogram data, HR / BPM heart rate data, EDA skin conductance data, RESP respiratory data, SPO2 blood oxygen saturation data, BP blood pressure data and nine-axis human body posture data, and the physiological data characterizes at least one of the emotional information, stress information, and physical level information of the user in the current environment.

[0014] Exemplarily, each of the health indicators is associated with at least one dimension; for a health indicator and a corresponding dimension, the health indicator and the dimension have a corresponding correlation coefficient; weights are assigned to the health indicators in different dimensions according to the correlation coefficient, and the weights of the health indicators in each dimension are different.

[0015] Another embodiment of the present application provides a health monitoring system based on human factors intelligence, the system comprising: a collection device and a prediction device, wherein the collection device is used to collect the user's physiological data and behavioral data, and the prediction device is used to execute the steps of the method described in any of the above embodiments.

[0016] Another embodiment of the present application provides a health monitoring device based on human factors intelligence, the device comprising: a first acquisition module, used to acquire the physiological data of the user; a second acquisition module, used to acquire the behavioral data of the user in the current environment, wherein the behavioral data represents the current environment information of the user and the behavioral operation information of the user in the current environment; a prediction module, used to input the physiological data and the behavioral data into a health prediction model for prediction, so that the health prediction model predicts the abnormal physiological state of the user based on the physiological data, and performs health prediction in combination with the behavioral data and the abnormal physiological state; an output module, used to output the predicted health monitoring result, wherein the health monitoring result represents the health state of the user performing behavioral operations in the current environment.

[0017] Another embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method described in any one of the above embodiments are implemented.

[0018] Another embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method described in any one of the above embodiments are implemented.

[0019] Another embodiment of the present application provides a computer program product, which includes instructions. When the instructions are executed by a processor of a computer device, the computer device can perform the steps of the method described in any of the above embodiments.

[0020] In the above implementation, the physiological data of the user is obtained; the behavioral data of the user in the current environment is obtained, wherein the behavioral data characterizes the current environment information of the user and the behavioral operation information of the user in the current environment; the physiological data and the behavioral data are input into the health prediction model for prediction, so that the health prediction model predicts the abnormal physiological state of the user based on the physiological data, and performs health prediction in combination with the behavioral data and the abnormal physiological state; and the predicted health monitoring result is output, wherein the health monitoring result characterizes the health state of the user performing the behavioral operation in the current environment. The human factors intelligence-based health monitoring method of the present invention can combine the physiological data of the user and the behavioral data in the current environment for health monitoring, realize intelligent monitoring, and have high monitoring accuracy, so as to facilitate timely early warning or output of health suggestions based on the health monitoring results. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic diagram of a process for a health monitoring method based on human factors intelligence provided in an embodiment of the present application;

[0022] Figure 2 A schematic diagram of a process of a health monitoring method based on human factors intelligence provided by another embodiment of the present application;

[0023] Figure 3 A schematic diagram of a health monitoring system based on human factors intelligence provided by an embodiment of the present application;

[0024] Figure 4 A schematic diagram of a health monitoring device based on human factors intelligence provided in an embodiment of the present application;

[0025] Figure 5 A block diagram of an electronic device provided for an embodiment of the present application. DETAILED DESCRIPTION

[0026] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0027] With the aging of the population, the contradiction between medical resources and population needs has become increasingly prominent, which has prompted the demand for effective health monitoring technology. The relevant technology monitors the user's physiological indicators in order to provide timely warnings when the user's body is abnormal. However, the health monitoring method of the relevant technology is not smart enough, and the accuracy of the monitoring results is low, making it difficult to give effective health advice.

[0028] In view of this, the implementation mode of the present application provides an optimized health monitoring method based on human factors intelligence, which can combine the user's physiological data and behavioral data in the current environment to perform health monitoring, realize intelligent monitoring, and have high monitoring accuracy, so as to facilitate timely early warning or output of health recommendations based on the health monitoring results.

[0029] Figure 1 A flowchart of a health monitoring method based on human factors intelligence provided in an implementation manner of the present application.

[0030] like Figure 1 As shown, the human factor intelligence-based health monitoring method 100 provided in an embodiment of the present application includes, for example, steps S110-S140.

[0031] Step S110, obtaining the user's physiological data.

[0032] Step S120, obtaining the behavior data of the user in the current environment, where the behavior data represents the current environment information of the user and the behavior operation information of the user in the current environment.

[0033] Step S130, inputting the physiological data and the behavioral data into the health prediction model for prediction, so that the health prediction model predicts the abnormal physiological state of the user based on the physiological data, and performs health prediction in combination with the behavioral data and the abnormal physiological state.

[0034] Step S140: output the predicted health monitoring result, where the health monitoring result represents the health status of the user performing the behavior operation in the current environment.

[0035] For example, for long-term monitoring of users with chronic diseases, wearable devices can be used to collect users' physiological data and behavioral data. Wearable devices include necklaces, bracelets, etc., which have the function of collecting physiological data and behavioral data. Of course, physiological data can also be collected through professional medical equipment. Behavioral data includes image data, video data, etc. For example, wearable devices have the function of taking pictures or recording videos. After obtaining user authorization, behavioral data of users who need health monitoring can be collected in real time.

[0036] Physiological data include, for example, heart rate data, electrocardiogram data, blood pressure data, blood oxygen data, etc. Taking the behavior data as video data as an example, the video data is collected when the user performs a behavior operation in the current environment. The video data includes the user's current environment and the user's behavior operation in the environment. The behavior operation includes, for example, what the user is currently doing.

[0037] After collecting physiological data and behavioral data, the physiological data and behavioral data are obtained, and the obtained physiological data and behavioral data are input into a health prediction model for health prediction. The health prediction model includes, for example, an artificial intelligence model, specifically a machine learning model, a deep learning model, and the like.

[0038] When the health prediction model makes predictions based on physiological data and behavioral data, it can predict the user's abnormal physiological state based on the physiological data. The abnormal physiological state is an abnormality caused by the user performing behavioral operations in the current environment. The behavioral data and the abnormal physiological state can be combined to further predict the user's health status and obtain the final health monitoring result. The health monitoring result characterizes the health status of the user when performing behavioral operations in the current environment.

[0039] For example, when the health prediction model predicts that the user's physiological abnormality is normal or less abnormal based on physiological data, if the behavioral data indicates that the patient is currently running on the playground, it means that although the physiological abnormality is normal or less abnormal, there is a greater risk in performing the behavioral operation in the current environment. Therefore, the health monitoring results given by the health prediction model in combination with the physiological abnormality and behavioral data may also be the result of health risks. It can be seen that combining the user's physiological data and behavioral data in the current environment for health monitoring can realize intelligent monitoring with high monitoring accuracy, and can predict potential health risks, so as to facilitate timely warning or output of health suggestions based on health monitoring results.

[0040] Figure 2 A flowchart of a health monitoring method based on human factors intelligence provided in another embodiment of the present application.

[0041] like Figure 2 As shown, a health monitoring method 200 based on human factors intelligence provided in another embodiment of the present application includes, for example, steps S210-S250.

[0042] Step S210, obtaining the user's physiological data.

[0043] Step S220, obtaining the behavior data of the user in the current environment, where the behavior data represents the current environment information of the user and the behavior operation information of the user in the current environment.

[0044] Step S230, inputting the physiological data and the behavioral data into the health prediction model for prediction, so that the health prediction model predicts the abnormal physiological state of the user based on the physiological data, and performs health prediction in combination with the behavioral data and the abnormal physiological state.

[0045] Step S240: output the predicted health monitoring result, where the health monitoring result represents the health status of the user performing the behavior operation in the current environment.

[0046] Step S250: outputting recommended data based on the health monitoring results.

[0047] Exemplarily, steps S210 - S240 are the same as or similar to the above steps S110 - S140 and are not described in detail here.

[0048] In this example, after the health monitoring result of the user is predicted by the health prediction model, recommendation data can also be output based on the health monitoring result.

[0049] In one example, when the health monitoring result indicates that the user's health level is less than the preset health level, the first suggestion data is output, and the first suggestion data is used to prompt the user to stop performing the current behavior operation. Among them, if the health monitoring result indicates that the user's health level is less than the preset health level, it means that the user currently has a greater health risk, and further indicates that the behavior operation performed by the user in the current environment will cause greater damage to health. At this time, the user can be advised to stop performing the current behavior operation in the current environment. For example, the first suggestion data can be output based on the health test results. For example, when the health test results indicate that the patient's heart rate is too high, the first suggestion data can be used to remind the patient to stop the current action to perform breathing training, slow down the heart rate or seek medical treatment in time.

[0050] In another example, when the health monitoring result indicates that the user's health level is greater than or equal to the preset health level, the second suggestion data is output, and the second suggestion data is used to prompt that there is a risk in continuing to perform the current behavior operation. Among them, if the health monitoring result indicates that the user's health level is greater than or equal to the preset health level, it means that the user currently has a small health risk, but if the user continues to perform the behavior operation in the current environment, risks may arise later, so the user can be prompted that there is a risk in continuing to perform the current behavior operation in the current environment. For example, if the patient is currently walking fast on the playground, although the current health monitoring result indicates that the patient currently has a small health risk, if the patient walks fast on the playground for a long time, there may be risks such as increased heart rate and increased blood pressure later, so the user can be prompted that there is a risk in continuing to perform the current behavior operation in the current environment.

[0051] In another example, when the health monitoring result indicates that the user's health is abnormal, the current environment information and behavior operation information are recorded so that when the current environment information and behavior operation information are detected later, warning information can be output. For example, when the next prediction is made, if it is detected that the user currently has similar behavior operations or is in a similar environment, a warning can be issued in time to remind the user that there may be health risks.

[0052] In another example, the health threshold is used as a reference in the process of predicting the health monitoring results by the health prediction model, and the health threshold can be adjusted when the health monitoring results indicate that the user's health status has recovered. For example, when the user's physical health is not good at the beginning, a more stringent health threshold can be set so that when the user's health status fluctuates slightly, it can also be easier to trigger health risk reminders to maximize health monitoring. When the long-term health monitoring results indicate that the user has recovered his health to a large extent, it means that the user's current physical condition can adapt to many environments, and the probability of health risks is relatively small. At this time, the original health threshold can be adjusted to a more relaxed threshold, and the health risk reminder will be triggered only when the user's health status fluctuates greatly, thereby improving the health monitoring experience.

[0053] In another example, the health prediction model includes model parameters. The present application can also obtain physiological data and behavioral data during the recovery process when the health monitoring results indicate that the user's health status has been restored, and adjust the model parameters based on the physiological data and behavioral data during the recovery process. For example, in two different states of poor health and restored health, the health prediction model may have different strictness in predicting risks. For example, when the user is in poor health, it is hoped that the health prediction model can strictly identify potential risks when predicting risks. When the user recovers health, it is hoped that the health prediction model can be more relaxed when predicting risks to avoid considering any small fluctuations in the user's physical condition as risks. Therefore, in the process of the user's health recovery, the physiological data and behavioral data during the recovery process can be obtained, and the physiological data and behavioral data during the recovery process can be input into the health prediction model. The health prediction model adjusts the model parameters based on the physiological data and behavioral data. The adjusted health prediction model is more adaptable to the user's current health condition and can achieve better health prediction results.

[0054] In another example, the health prediction model includes model parameters, and after outputting the predicted health monitoring results, it outputs query information; receives the user's reply information to the query information, and adjusts the model parameters when the reply information indicates that the health monitoring results do not match the user's current health status. For example, the health prediction model can be customized, and the physiological data range of the patient in a normal state and the physiological data range in special scenarios (such as exercise) are input. The input data range is, for example, a health threshold. After outputting the health results, the patient can be asked whether it is consistent with the current state. If not, the model parameters are adapted in time.

[0055] In another example, the physiological data includes at least one type of physiological data, and the at least one type of physiological data includes at least one of heart rate data, electrocardiogram data, blood pressure data, and blood oxygen data. For example, the at least one type of physiological data includes at least one of PPG blood volume pulse wave data, ECG electrocardiogram data, HR / BPM heart rate data, EDA skin electricity data, RESP breathing data, SPO2 blood oxygen saturation data, BP blood pressure data, and nine-axis human body posture data, and the physiological data characterizes at least one of the emotional information, stress information, and body level information of the user in the current environment.

[0056] The health monitoring results include the results corresponding to different types of health indicators, such as blood pressure indicators, heart rate indicators, and so on. When the health prediction model performs health prediction, different weights are assigned to different types of physiological data based on the corresponding health indicators. For example, when performing health prediction for blood pressure indicators, the weight of blood pressure data in the physiological data can be larger. When performing health prediction for heart rate indicators, the weights of heart rate indicators and electrocardiogram data in the physiological data can be larger. Assigning different weights to different types of physiological data based on different health indicators can improve the accuracy of health prediction.

[0057] In a preferred embodiment, multi-dimensional predictions can be performed based on at least three types of physiological data. Generally, a single indicator can be used to perform predictions in one dimension. For example, emotional state can be predicted based on EDA skin electrode indicators, and stress state can be predicted based on blood pressure indicators. In this example, multi-dimensional predictions can be performed based on at least three types of physiological data to improve prediction accuracy.

[0058] For example, each of the health indicators is associated with at least one dimension; for a health indicator and a corresponding dimension, the health indicator and the dimension have a corresponding correlation coefficient; weights are assigned to health indicators in different dimensions according to the correlation coefficient, and the weights of health indicators in each dimension are different. Specifically, during the model training process, algorithm training for a single indicator and a single dimension can be performed first to calculate the correlation coefficient, and then single-dimensional models for different indicators can be performed, and weights of different indicators can be assigned according to the correlation coefficient, and the weights of indicators in each dimension are different.

[0059] In addition, the application can also customize the health prediction model for different users, so that the health prediction model matches different users. When the health monitoring results indicate that the user is at health risk, an early warning can also be issued, such as informing or warning the user himself and notifying his guardian when the user's body is in an abnormal state.

[0060] This application collects behavioral data by combining the environment and behavior, and predicts the user's abnormal physiological state based on physiological data. It combines the patient's environment and the things to be done to intelligently predict whether the user's current physical condition meets the physical conditions for the current things to be done, and outputs suggestions in a timely manner.

[0061] This application performs health monitoring based on multiple parameters, including behavioral data, heart rate data, electrocardiogram data, blood pressure data, blood oxygen data, etc. Through a variety of sensors and devices, multiple physiological parameters can be monitored in real time, providing comprehensive health monitoring to help users fully understand their health status. Big data analysis and machine learning technology are used to analyze and pattern recognize the user's physiological data and behavioral data, provide personalized health management suggestions and warnings, and help users detect and prevent potential health problems early.

[0062] Figure 3 A schematic diagram of a health monitoring system based on human factors intelligence provided in an embodiment of the present application.

[0063] like Figure 3 As shown, the health monitoring system 300 based on human factor intelligence provided by the embodiment of the present application includes a collection device 310 and a prediction device 320. The collection device 310 is used to collect the physiological data and behavior data of the user, and the prediction device 320 is used to execute the above-mentioned health monitoring method based on human factor intelligence.

[0064] The collection device 310 includes, for example, a variety of sensors for collecting physiological data and devices for collecting behavioral data. The collection device 310 includes, for example, a wearable device, such as a necklace, a physiological bracelet, etc. The necklace and the physiological bracelet may also have image or video collection functions.

[0065] The prediction device 320 includes a data acquisition module and a storage module. The prediction device 320 includes an AI data processing model deployment model training module (AI chip), an early warning module, a data recording module, and the like.

[0066] The acquisition module 310 and the prediction module 320 may be integrated into one device, for example, the acquisition module 310 and the prediction module 320 are both integrated into a necklace, a physiological bracelet, or other device.

[0067] Figure 4 A schematic diagram of a health monitoring device based on human factors intelligence provided in an embodiment of the present application.

[0068] like Figure 4 As shown, the human factor intelligence-based health monitoring device 400 provided in an embodiment of the present application includes: a first acquisition module 410 , a second acquisition module 420 , a prediction module 430 and an output module 440 .

[0069] A first acquisition module 410 is used to acquire physiological data of a user;

[0070] The second acquisition module 420 is used to acquire the behavior data of the user in the current environment, wherein the behavior data represents the current environment information of the user and the behavior operation information of the user in the current environment;

[0071] Prediction module 430, used to input physiological data and behavioral data into the health prediction model for prediction, so that the health prediction model predicts the abnormal physiological state of the user based on the physiological data, and combines the behavioral data and the abnormal physiological state to perform health prediction;

[0072] The output module 440 is used to output the predicted health monitoring result, wherein the health monitoring result represents the health status of the user performing the behavior operation in the current environment.

[0073] Exemplarily, the health monitoring device 400 based on human factors intelligence further includes: a suggestion data output module, which is used to output suggestion data based on the health monitoring results.

[0074] Exemplarily, the suggestion data output module is also used to: when the health monitoring result indicates that the user's health level is less than a preset health level, output first suggestion data, wherein the first suggestion data is used to prompt the user to stop performing the current behavior operation.

[0075] Exemplarily, the suggestion data output module is also used to: when the health monitoring result indicates that the user's health level is greater than or equal to a preset health level, output second suggestion data, wherein the second suggestion data is used to prompt that there is a risk in continuing to perform the current behavior operation.

[0076] Exemplarily, the health monitoring device 400 based on human factors intelligence also includes: a recording module, which is used to record current environmental information and behavioral operation information when the health monitoring results indicate that the user's health is abnormal, so as to output warning information when the current environmental information and behavioral operation information are detected subsequently.

[0077] Exemplarily, the health threshold is used as a reference in the process of the health prediction model predicting the health monitoring result. The health monitoring device 400 based on human factors intelligence also includes: a first adjustment module, which is used to adjust the health threshold when the health monitoring result indicates that the user's health status has recovered.

[0078] Exemplarily, the health prediction model includes model parameters, and the health monitoring device 400 based on human factors intelligence also includes: a second adjustment module, which is used to obtain physiological data and behavioral data during the recovery process when the health monitoring results indicate that the user's health status has recovered, and adjust the model parameters based on the physiological data and behavioral data during the recovery process.

[0079] Exemplarily, the health prediction model includes model parameters, and the health monitoring device 400 based on human factors intelligence also includes: an inquiry information output module, which is used to output inquiry information after outputting the predicted health monitoring results; a reply information receiving module, which is used to receive the user's reply information to the inquiry information, and adjust the model parameters when the reply information indicates that the health monitoring results do not conform to the user's current health status.

[0080] Exemplarily, the behavior data includes video data, and the video data is collected when the user performs the behavior operation in the current environment.

[0081] Exemplarily, the physiological data includes at least one type of physiological data, and the health monitoring results include results corresponding to different types of health indicators, wherein the health prediction model assigns different weights to different types of physiological data based on the corresponding health indicators when performing health prediction, wherein at least one type of physiological data includes at least one of PPG blood volume pulse wave data, ECG electrocardiogram data, HR / BPM heart rate data, EDA skin conductance data, RESP respiratory data, SPO2 blood oxygen saturation data, BP blood pressure data and nine-axis human body posture data, and the physiological data characterizes at least one of the emotional information, stress information, and physical level information of the user in the current environment.

[0082] Exemplarily, each health indicator is associated with at least one dimension; for a health indicator and a corresponding dimension, the health indicator and the dimension have a corresponding correlation coefficient; weights are assigned to health indicators in different dimensions according to the correlation coefficient, and the weights of health indicators in each dimension are different.

[0083] It can be understood that the specific implementation process of the health monitoring device 400 based on human factors intelligence can refer to the implementation process of the health monitoring method based on human factors intelligence mentioned above, and will not be repeated here.

[0084] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method in any one of the above embodiments are implemented.

[0085] One embodiment of the present application provides a computer program product, which includes instructions. When the instructions are executed by a processor of a computer device, the computer device can perform the steps of the method of any of the above embodiments.

[0086] Figure 5 A block diagram of an electronic device provided for an embodiment of the present application.

[0087] An embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned health monitoring method based on human factors intelligence when executing the computer program.

[0088] like Figure 5 As shown, for ease of understanding, the embodiment of the present application shows a specific electronic device 500.

[0089] The electronic device 500 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0090] like Figure 5 As shown, the device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0091] Multiple components in the electronic device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a disk, an optical disk, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the electronic device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0092] The computing unit 501 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 501 performs the various methods described above. For example, in some embodiments, any one or more of the various methods described above may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, any one or more of the various methods described above may be executed. One or more steps. Alternatively, in other embodiments, the computing unit 501 may be configured to perform any one or more of the various methods described above in any other appropriate manner (e.g., by means of firmware).

[0093] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this application, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing in a suitable manner if necessary, and then stored in a computer memory.

[0094] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0095] In the description of the present application, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In the present application, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0096] In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0097] In addition, the terms "first", "second", etc. used in the embodiments of the present application are only used for descriptive purposes and should not be understood as indicating or implying relative importance, or implicitly indicating the number of technical features indicated in the present embodiment. Therefore, the features defined by the terms "first", "second", etc. in the embodiments of the present application can explicitly or implicitly indicate that at least one of the features is included in the embodiment. In the description of the present application, the word "multiple" means at least two or two or more, such as two, three, four, etc., unless otherwise clearly and specifically defined in the embodiments.

[0098] In this application, unless otherwise clearly specified or limited in the embodiments, the terms "installed", "connected", "connected" and "fixed" etc. appearing in the embodiments should be understood in a broad sense. For example, the connection can be a fixed connection, a detachable connection, or an integrated connection. It can be understood that it can also be a mechanical connection, an electrical connection, etc.; of course, it can also be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal connection of two elements, or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to the specific implementation situation.

[0099] In the present application, unless otherwise clearly specified and limited, a first feature being “above” or “below” a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being “above”, “above”, and “above” a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being “below”, “below”, and “below” a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0100] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A health monitoring method based on human factor intelligence, characterized in that: The method comprises: Obtaining the user's physiological data; Acquire the user's behavior data in the current environment, wherein the behavior data represents the user's current environment information and the user's behavior operation information in the current environment; Inputting the physiological data and the behavioral data into a health prediction model for prediction, so that the health prediction model predicts the abnormal physiological state of the user based on the physiological data, and performs health prediction in combination with the behavioral data and the abnormal physiological state; The predicted health monitoring result is output, wherein the health monitoring result represents the health status of the user performing the behavior operation in the current environment.

2. The method according to claim 1, characterized in that The method further comprises: Based on the health monitoring results, advice data is output.

3. The method according to claim 2, characterized in that The outputting of suggestion data based on the health monitoring result includes: When the health monitoring result indicates that the user's health level is less than a preset health level, first suggestion data is output, wherein the first suggestion data is used to prompt the user to stop performing the current behavior operation.

4. The method according to claim 2, characterized in that: The outputting of suggestion data based on the health monitoring result includes: When the health monitoring result indicates that the user's health level is greater than or equal to a preset health level, second suggestion data is output, wherein the second suggestion data is used to prompt that there is a risk in continuing to perform the current behavior operation.

5. The method according to claim 1, characterized in that: The method further comprises: When the health monitoring result indicates that the user's health is abnormal, the current environment information and the behavior operation information are recorded so that when the current environment information and the behavior operation information are detected later, warning information can be output.

6. The method according to claim 1, characterized in that The health threshold is used as a reference in the process of obtaining the health monitoring result predicted by the health prediction model; The method further comprises: When the health monitoring result indicates that the user's health status has recovered, the health threshold is adjusted.

7. The method according to claim 1, characterized in that The health prediction model includes model parameters, and the method further includes: In the case where the health monitoring result indicates that the user's health status has recovered, the physiological data and behavioral data during the recovery process are acquired, and the model parameters are adjusted based on the physiological data and behavioral data during the recovery process.

8. The method according to claim 1, characterized in that: The health prediction model includes model parameters, and the method further includes: After outputting the predicted health monitoring results, query information is output; Receive reply information from the user to the inquiry information, and adjust the model parameters if the reply information indicates that the health monitoring result does not conform to the current health status of the user.

9. The method according to claim 1, characterized in that: The behavior data includes video data, and the video data is collected when the user performs a behavior operation in a current environment.

10. The method according to claim 1, characterized in that The physiological data includes at least one type of physiological data, and the health monitoring results include results corresponding to different types of health indicators, wherein the health prediction model assigns different weights to different types of physiological data based on the corresponding health indicators when performing health prediction, Among them, at least one type of physiological data includes at least one of PPG blood volume pulse wave data, ECG electrocardiogram data, HR / BPM heart rate data, EDA skin conductance data, RESP breathing data, SPO2 blood oxygen saturation data, BP blood pressure data and nine-axis human body posture data, and the physiological data represents at least one of the emotional information, stress information and physical level information of the user in the current environment.

11. The method according to claim 10, characterized in that Each of the health indicators is associated with at least one dimension; for a health indicator and a corresponding dimension, the health indicator and the dimension have a corresponding correlation coefficient; The health indicators in different dimensions are weighted according to the correlation coefficient, and the weights of the health indicators in each dimension are different.

12. A health monitoring system based on human factor intelligence, characterized in that: The system comprises: a collection device and a prediction device, wherein the collection device is used to collect physiological data and behavioral data of the user, and the prediction device is used to execute the steps of any one of the methods of claims 1-11.

13. A health monitoring device based on human factors intelligence, characterized in that: The device comprises: A first acquisition module, used to acquire the user's physiological data; A second acquisition module is used to acquire the behavior data of the user in the current environment, wherein the behavior data represents the current environment information of the user and the behavior operation information of the user in the current environment; A prediction module, used for inputting the physiological data and the behavioral data into a health prediction model for prediction, so that the health prediction model predicts the abnormal physiological state of the user based on the physiological data, and performs health prediction in combination with the behavioral data and the abnormal physiological state; The output module is used to output the predicted health monitoring result, wherein the health monitoring result represents the health status of the user performing the behavioral operation in the current environment.

14. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 11 are implemented.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 11 are implemented.

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