A monitoring device and medium for mental health

Abstract

The present invention provides a monitoring device for mental health. The device includes: a data acquisition module, which is used to obtain basic information of a target individual and corresponding physical health information, and perform desensitization processing; an information recognition module, which collects biometric data of a target person through a sensor, and adds the biometric data of the target person to the biometric data in a known human sample set; a model construction module, which uses a machine learning method to calculate the threshold parameters corresponding to the biometric data, and establishes and trains a mental health assessment model based on the biometric data; a judgment processing module, which uses the threshold parameters determined by the mental health assessment model as the alarm threshold of the corresponding biometric data, compares the biometric data of the target person with the corresponding alarm threshold, and obtains the mental health monitoring result of the target person. The present invention realizes the use of artificial intelligence technology to efficiently and accurately obtain the monitoring results of mental health.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a monitoring device for mental health and a computer-readable storage medium. Background Art

[0002] Currently, worldwide, mental health problems have become the primary cause of individual "disability". Considering its "global", "chronic" and "prevalent" development trends, the existence of mental health problems will not only cause huge consumption and waste of social resources, increase the burden on the entire society, but also directly threaten the individual's daily quality of life and subjective well-being.

[0003] The monitoring methods of mental health have been carried out in many professional fields. The monitoring methods of mental health can be roughly divided into two types: one is based on self-report questionnaires and psychological measurements. Psychological feelings are measured based on cognitive assessments and emotional responses, usually in the form of self-report questionnaires or scales. Although this method is convenient, fast and easy to manage, due to the residual memory of the human body, the test questionnaire form cannot be measured twice in a short period of time. In addition, the accuracy of the collected data will be affected by the subjective factors of the subjects. Because the "questionnaire test" technology enables individuals to play the roles of "observed object" and "observation subject" in the study at the same time, when individuals fill in the scale, their answers will inevitably be affected by social approval psychology and individual cognitive ability, and some invalid data (for example, wrong answers, missed answers, and blind answers) cannot be excluded. The other is psychological stress detection based only on physiological signals. Stress assessment can be achieved by measuring cortisol, plasma catecholamines, electrocardiogram signals, electroencephalogram signals, and electromyogram signals. However, the storage cost of the test samples is high, and the body's stress response may affect the measurement results. The comfort and convenience of the collection cannot be guaranteed, and the details and scale of the collected data are also very limited. In particular, due to the limitations of time, space, and some social conditions, it is difficult to gather sufficient basic data for each experiment, and it is also difficult to track and measure the same group of individuals.

[0004] With the rapid development of computer science, artificial intelligence has penetrated into all areas of life, and machine learning is the core of artificial intelligence. Therefore, compared with the traditional "questionnaire test" technology and the psychological stress detection of a single physiological signal, the use of artificial intelligence can efficiently and accurately obtain the monitoring results of mental health. Summary of the invention

[0005] In order to achieve the technical problem of using artificial intelligence to efficiently and accurately obtain the monitoring results of mental health, the embodiment of the present invention provides a monitoring device for mental health and a computer-readable storage medium. The technical solution is as follows:

[0006] In a first aspect, a device for monitoring mental health is provided, the device comprising: a data acquisition module, for acquiring basic information of a target individual and corresponding physical health status information, and performing desensitization processing; an information recognition module, for collecting biometric data of a target person through a sensor, and adding the biometric data of the target person to the biometric data in a known human sample set; a model construction module, for calculating threshold parameters corresponding to the biometric data using a machine learning method, and establishing and training a mental health assessment model based on the biometric data; a judgment processing module, for comparing the biometric data of the target person with the corresponding alarm threshold based on the threshold parameters determined by the mental health assessment model as the alarm threshold of the corresponding biometric data, and obtaining a mental health monitoring result of the target person.

[0007] Optionally, the information identification module collects biometric data of the target person through a sensor, including: using the sensor to collect biometric data of the target person at least once to generate a biometric data log; performing data preprocessing on the biometric data log to store structured data based on the target individual basic information of the target person.

[0008] Optionally, the information recognition module adds the biometric data of the target person to the biometric data in the known human sample set, including: obtaining known biometric data by accessing a local database or a network database; and distributing the biometric data of the target person to a corresponding data table.

[0009] Optionally, the biometric data includes facial expression features, and the model building module calculates a threshold parameter corresponding to the biometric data using a machine learning method, and obtains the threshold parameter α of the facial expression feature using the following calculation method:

[0010]

[0011] Where i represents the number of the biometric data in the known human sample set, i = {1, 2, ..., N}, N is the total number of facial expression feature elements in the known human sample set, ω i represents the weight corresponding to the i-th facial expression feature, P(x i ) represents the empirical probability value of the health risk of the ith facial expression feature element, and ξ represents the predetermined compensation coefficient.

[0012] Optionally, the biometric data includes heart rate, blood pressure, and sound frequency; the model building module calculates the threshold parameter corresponding to the biometric data using a machine learning method, and obtains the threshold parameter β of the heart rate, blood pressure, or sound frequency using the following calculation method:

[0013]

[0014] Where i represents the number of biometric data in the known human sample set, i = {1, 2, ..., N}, N is the total number of heart rate, blood pressure, or sound frequency in the known human sample set, ω i represents the weight corresponding to the i-th heart rate, blood pressure, or sound frequency, P(x i ) represents the empirical probability value of the health risk of the ith heart rate, blood pressure, or sound frequency, ξ represents the predetermined compensation coefficient, and δ represents the normalization factor.

[0015] Optionally, the judgment processing module uses the threshold parameters determined by the mental health assessment model as the alarm threshold of the corresponding biometric data, including: based on a semi-supervised learning model, training with the biometric data in a known human sample set as a training set; and verifying the assessment effect of the mental health assessment model on a predetermined verification set to determine the model parameters.

[0016] Optionally, the information identification module performs data preprocessing on the biometric data log, including: cleaning abnormal data in the biometric data log, including: removing erroneous data and redundant data, and standardizing the data format; and aggregating the cleaned biometric data log according to the basic information of the target individual of the target person.

[0017] Optionally, the error data includes: data generated by sensor failure, transmission interference or manual operation error.

[0018] In a second aspect, a method for monitoring mental health is provided, the method comprising: obtaining basic information of a target individual and corresponding physical health information, and performing desensitization processing, wherein the basic information of the target individual comprises: name, gender, age, ID number, mobile phone number, work unit and family information, and the physical health information comprises: historical disease information, genetic disease information and chronic disease information; collecting biometric data of the target person through a sensor, and adding the biometric data of the target person to the biometric data in a known human sample set, wherein the biometric data comprises: heart rate, blood pressure, sound frequency, and facial expression features; calculating threshold parameters corresponding to the biometric data using a machine learning method, establishing and training a mental health assessment model based on the biometric data; using the threshold parameters determined based on the mental health assessment model as the alarm threshold of the corresponding biometric data, comparing the biometric data of the target person with the corresponding alarm threshold, and obtaining a mental health monitoring result of the target person.

[0019] In a third aspect, an electronic device is provided, comprising: a processor; and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the above-mentioned method for monitoring mental health is executed.

[0020] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a program code, and the program code is loaded by a processor and executes the above-mentioned method for monitoring mental health.

[0021] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention at least include: realizing the efficient and accurate acquisition of mental health monitoring results by using artificial intelligence technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 is a schematic block diagram of a device for monitoring mental health provided by an embodiment of the present invention;

[0024] Figure 2 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0026] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a concrete way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" may be both, or it may be one of the two. In the embodiments of the present invention, "image" and "picture" may sometimes be used interchangeably. It should be noted that when the distinction between them is not emphasized, the meanings to be expressed are consistent. In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript form such as W1. When the distinction between them is not emphasized, the meanings to be expressed are consistent.

[0027] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0028] The present invention proposes a monitoring device for mental health, which uses artificial intelligence technology to overcome the fact that the "questionnaire test" technology is affected by social approval psychology and individual cognitive ability, and cannot exclude some invalid data, and can also overcome the defects caused by psychological stress detection based only on physiological signals, such as: the comfort and convenience of collection cannot be guaranteed, and the details and scale of the collected data are also very limited. In addition, the present invention also provides an electronic device and a computer-readable storage medium to achieve the use of artificial intelligence technology to efficiently and accurately obtain mental health monitoring results.

[0029] Specifically, an embodiment of the present invention provides a monitoring device for mental health. Figure 1 The schematic block diagram of the monitoring device 100 for mental health shown in FIG. 1 may include:

[0030] The data acquisition module 102 is used to acquire basic information of the target individual and corresponding physical health information, and perform desensitization processing.

[0031] The types of personal information are based on the characteristics of the data openness scenarios and the personal information protection requirements of relevant laws and regulations, especially name, gender, age, ID number, mobile phone number, work unit and family information. The physical health information includes: historical disease information, genetic disease information and chronic disease information. Set personal information desensitization rules, and set different information granularity to be retained and desensitization forms such as hiding or replacing for each type of personal information identified. For example, each type of personal information can be appropriately processed to remove the sensitivity of the information while retaining its data structure characteristics and data statistical characteristics; the basic personal information desensitization rules can retain common surnames in personal names, retain the first three digits of mobile phone numbers, and replace the subsequent numbers with asterisks.

[0032] The information recognition module 104 collects the biometric data of the target person through sensors, and adds the biometric data of the target person to the biometric data in the known human sample set.

[0033] In some exemplary embodiments of the present invention, the information identification module, which collects biometric data of a target person through a sensor, may include: using a sensor to collect biometric data of the target person at least once to generate a biometric data log; performing data preprocessing on the biometric data log, and storing structured data based on basic information of the target individual of the target person.

[0034] In some other exemplary embodiments of the present invention, the information identification module adds the biometric data of the target human body to the biometric data in the known human body sample set, which may include: obtaining known biometric data by accessing a local database or a network database; and distributing the biometric data of the target human body to corresponding data tables.

[0035] In some other exemplary embodiments of the present invention, the information identification module performs data preprocessing on the biometric data log, including: cleaning abnormal data in the biometric data log, which may include: removing erroneous data and redundant data, and standardizing the data format; and aggregating the cleaned biometric data log according to the basic information of the target individual of the target human body.

[0036] The model building module 106 uses machine learning methods to calculate threshold parameters corresponding to biometric data, and establishes and trains a mental health assessment model based on biometric data.

[0037] In some exemplary embodiments of the present invention, the biometric data includes facial expression features, and the model building module calculates the threshold parameter corresponding to the biometric data using a machine learning method. The following calculation method can be used to obtain the threshold parameter α of the facial expression feature:

[0038]

[0039] Where i represents the number of the biometric data in the known human sample set, i = {1, 2, ..., N}, N is the total number of facial expression feature elements in the known human sample set, ω i represents the weight corresponding to the i-th facial expression feature, P(x i ) represents the empirical probability value of the health risk of the ith facial expression feature element, and ξ represents the predetermined compensation coefficient.

[0040] In some other exemplary embodiments of the present invention, the biometric data includes heart rate, blood pressure, and sound frequency; the model building module calculates the threshold parameter corresponding to the biometric data using a machine learning method, and the threshold parameter β of the heart rate, blood pressure, or sound frequency can be obtained by the following calculation method:

[0041]

[0042] Where i represents the number of biometric data in the known human sample set, i = {1, 2, ..., N}, N is the total number of heart rate, blood pressure, or sound frequency in the known human sample set, ω i represents the weight corresponding to the i-th heart rate, blood pressure, or sound frequency, P(x i ) represents the empirical probability value of the health risk of the ith heart rate, blood pressure, or sound frequency, ξ represents the predetermined compensation coefficient, and δ represents the normalization factor.

[0043] The judgment processing module 108 uses the threshold parameters determined by the mental health assessment model as the alarm threshold of the corresponding biometric data, compares the biometric data of the target person with the corresponding alarm threshold, and obtains the mental health monitoring result of the target person.

[0044] In some exemplary embodiments of the present invention, the judgment processing module uses the threshold parameters determined by the mental health assessment model as the alarm threshold of the corresponding biometric data, which may include: training based on a semi-supervised learning model with the biometric data in a known human sample set as a training set; and verifying the assessment effect of the mental health assessment model on a predetermined validation set to determine the model parameters.

[0045] In some other exemplary embodiments of the present invention, the error data includes: data generated by sensor failure, transmission interference or human operation error.

[0046] The present invention also provides a method for monitoring mental health, the method comprising: obtaining basic information of a target individual and corresponding physical health information, and performing desensitization processing, wherein the basic information of the target individual includes: name, gender, age, ID number, mobile phone number, work unit and family information, and the physical health information includes: historical disease information, genetic disease information and chronic disease information; collecting biometric data of the target person through a sensor, and adding the biometric data of the target person to the biometric data in a known human sample set, wherein the biometric data includes: heart rate, blood pressure, sound frequency, and facial expression features; using a machine learning method to calculate threshold parameters corresponding to the biometric data, and establishing and training a mental health assessment model based on the biometric data; using the threshold parameters determined based on the mental health assessment model as the alarm threshold of the corresponding biometric data, comparing the biometric data of the target person with the corresponding alarm threshold, and obtaining the mental health monitoring result of the target person.

[0047] Figure 2 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 2 As shown, the electronic device 300 may include a processor 3001 and a memory 3002. Optionally, the electronic device 300 may further include a transceiver 3003. The processor 3001, the memory 3002 and the transceiver 3003 may be connected, for example, via a communication bus. The memory 3002 stores computer-readable instructions, which, when executed by the processor 3001, implement the above-mentioned monitoring method for mental health.

[0048] In a specific implementation, as an embodiment, the processor 3001 may include one or more CPUs, such as Figure 2 CPU0 and CPU1 are shown in FIG.

[0049] In a specific implementation, as an embodiment, the electronic device 300 may also include multiple processors, such as Figure 2 3001 and processor 3004 are shown in FIG. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). A processor herein may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0050] The memory 3002 is used to store the software program for executing the solution of the present invention, and the execution is controlled by the processor 3001. The specific implementation method can refer to the above method embodiment, which will not be repeated here.

[0051] The transceiver 3003 is used to communicate with a network device or a terminal device.

[0052] Optionally, the transceiver 3003 may include a receiver and a transmitter, wherein the receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0053] Optionally, the transceiver 3003 may be integrated with the processor 3001 , or may exist independently and be coupled to the processor 3001 via an interface circuit of the electronic device 300 , which is not specifically limited in the embodiment of the present invention.

[0054] It should be noted that Figure 2 The structure of the electronic device 300 shown in the figure does not constitute a limitation on the electronic device. The actual electronic device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. In addition, the technical effects of the electronic device 300 can refer to the technical effects of the above-mentioned method embodiment, which will not be repeated here.

[0055] In an exemplary embodiment, the present invention further provides a computer-readable storage medium, wherein a program code is stored in the computer-readable storage medium, and the program code is loaded by a processor and executes the above-mentioned monitoring method for mental health. For example, the computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0056] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0057] References in the specification to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments", etc. indicate that the described embodiments may include a particular feature, structure, or characteristic, but not every embodiment may include the particular feature, structure, or characteristic. In addition, when a particular feature, structure, or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the relevant art to implement such feature, structure, or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0058] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0059] In the present invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0060] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0061] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0062] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0063] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0064] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0065] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0066] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A device for monitoring mental health, characterized in that: The device comprises: The data acquisition module is used to obtain the basic information of the target individual and the corresponding physical health information, and perform desensitization processing; An information recognition module collects biometric data of a target person through a sensor and adds the biometric data of the target person to the biometric data in a known human sample set; The model building module uses machine learning methods to calculate the threshold parameters corresponding to biometric data, and builds and trains a mental health assessment model based on biometric data; A judgment processing module, based on the threshold parameter determined by the mental health assessment model as the alarm threshold of the corresponding biometric data, compares the biometric data of the target person with the corresponding alarm threshold to obtain the mental health monitoring result of the target person; The biometric data includes facial expression features, and the model building module uses a machine learning method to calculate a threshold parameter corresponding to the biometric data, and uses the following calculation method to obtain the threshold parameter α of the facial expression feature: Where i represents the number of the biometric data in the known human sample set, i = {1, 2, ..., N}, N is the total number of facial expression feature elements in the known human sample set, ω i represents the weight corresponding to the i-th facial expression feature, P(x i ) represents the empirical probability value of the health risk of the ith facial expression feature element, and ξ represents the predetermined compensation coefficient.

2. The device for monitoring mental health according to claim 1, characterized in that: The information recognition module collects the target person's biometric data through sensors, including: Using the sensor to collect biometric data of the target person at least once, and generate a biometric data log; The biometric data log is preprocessed to generate structured data based on the target individual basic information of the target person.

3. The device for monitoring mental health according to claim 1, characterized in that: The information recognition module adds the target person's biometric data to the biometric data in the known human sample set, including: Obtain known biometric data by accessing a local database or a network database; The biometric data of the target human body is distributed to corresponding data tables.

4. The device for monitoring mental health according to claim 1, characterized in that: The biometric data includes heart rate, blood pressure, and sound frequency; the model building module uses a machine learning method to calculate the threshold parameter corresponding to the biometric data, and uses the following calculation method to obtain the threshold parameter β of the heart rate, blood pressure, or sound frequency: Where i represents the number of biometric data in the known human sample set, i = {1, 2, ..., N}, N is the total number of heart rate, blood pressure, or sound frequency in the known human sample set, ω i represents the weight corresponding to the i-th heart rate, blood pressure, or sound frequency, P(x i ) represents the empirical probability value of the health risk of the ith heart rate, blood pressure, or sound frequency, ξ represents the predetermined compensation coefficient, and δ represents the normalization factor.

5. The device for monitoring mental health according to claim 1, characterized in that: The judgment processing module uses the threshold parameter determined by the mental health assessment model as the alarm threshold of the corresponding biometric data, including: Based on the semi-supervised learning model, the biometric data in the known human sample set is used as the training set for training; On a predetermined validation set, the evaluation effect of the mental health assessment model is verified to determine the model parameters.

6. The device for monitoring mental health according to claim 2, characterized in that: The information identification module performs data preprocessing on the biometric data log, including: Cleaning abnormal data in the biometric data log, including removing erroneous data and redundant data, and standardizing the data format; The cleaned biometric data logs are aggregated according to the target individual basic information of the target human body.

7. A method for monitoring mental health, characterized in that: The method comprises: Used to obtain basic information of the target individual and the corresponding physical health information, and perform desensitization processing, wherein the basic information of the target individual includes: name, gender, age, ID number, mobile phone number, work unit and family information, and the physical health information includes: historical disease information, genetic disease information and chronic disease information; Collecting biometric data of a target human body through a sensor, and adding the biometric data of the target human body to the biometric data in a known human body sample set, wherein the biometric data includes: heart rate, blood pressure, sound frequency, and facial expression features; Use machine learning methods to calculate threshold parameters corresponding to biometric data, and establish and train a mental health assessment model based on biometric data; Based on the threshold parameter determined by the mental health assessment model as the alarm threshold of the corresponding biometric data, the biometric data of the target person is compared with the corresponding alarm threshold to obtain the mental health monitoring result of the target person; The biometric data includes facial expression features, and the model building module uses a machine learning method to calculate the threshold parameter corresponding to the biometric data, and the following calculation method is used to obtain the threshold parameter α of the facial expression feature: Where i represents the number of the biometric data in the known human sample set, i = {1, 2, ..., N}, N is the total number of facial expression feature elements in the known human sample set, ω i represents the weight corresponding to the i-th facial expression feature, P(x i ) represents the empirical probability value of the health risk of the ith facial expression feature element, and ξ represents the predetermined compensation coefficient.

8. An electronic device, characterized in that: The electronic device comprises: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method for monitoring mental health as claimed in claim 7 is executed.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program code, and the program code is loaded by the processor and executed by the method for monitoring mental health as described in claim 7.

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