Implementation method for psychological state assessment

Abstract

The invention relates to the technical field of psychological health monitoring, in particular to a psychological state assessment implementation method, a psychological state acquisition module comprises an intelligent bracelet capable of at least acquiring physiological state data and environmental data of a person; the psychological state evaluation module is used for establishing a psychological state evaluation model; the data optimization module is used for performing data screening and data downsampling on the data acquired by the psychological state acquisition module; the intelligent bracelet terminal is used for displaying the final psychological state; wherein the psychological state evaluation model established by the psychological state evaluation module is a model established through a large number of psychological research experiments and data analysis based on a deep neural network and a convolutional neural network, and is used for evaluating and analyzing the obtained psychological state characteristic parameters to determine psychological state data. High continuity of psychological state assessment is realized, and possibility is provided for early intervention; and a deep learning model is used for analysis, so that the complex psychological state can be evaluated more comprehensively and more accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of mental health monitoring, and in particular to a method for implementing mental state assessment. Background Art

[0002] With the rapid development of modern society, people's pace of life is accelerating, and mental health issues are becoming increasingly prominent. According to data from the World Health Organization, about 450 million people in the world suffer from mental health diseases, and this number is still increasing. In this context, how to timely and accurately assess people's mental state has become an urgent problem that needs to be solved.

[0003] Traditional methods of assessing mental status mainly rely on questionnaires and face-to-face interviews. Although these methods are widely used in clinical practice, they have obvious limitations. First, these methods are often intermittent and difficult to capture the dynamic changes in mental status. Second, they rely heavily on the subjective reports of the subjects and are susceptible to social desirability effects and recall bias. In addition, these methods usually require the participation of professionals, making it difficult to achieve large-scale, long-term mental health monitoring.

[0004] In recent years, with the development of wearable devices and artificial intelligence technology, some researchers have begun to try to use physiological data to assess psychological state. Some studies use heart rate variability (HRV) as an indicator of stress level. Although this method achieves a certain degree of objectivity and continuity, it still has some problems. First, a single physiological indicator is difficult to fully reflect the complex psychological state. Second, these methods usually use simple threshold methods or basic machine learning algorithms, which are difficult to capture the complex nonlinear relationship between physiological indicators and psychological state.

[0005] In view of the shortcomings of the prior art, a method for realizing continuous, accurate and non-invasive psychological state assessment is urgently needed. The present invention is proposed to solve this technical problem. Summary of the invention

[0006] In view of the above problems, the present invention proposes a method for implementing psychological state assessment.

[0007] The present invention provides a method for implementing psychological state assessment, comprising: a psychological state acquisition module, comprising a smart bracelet capable of at least collecting physiological state data and environmental data of a person; a psychological state assessment module, used for establishing a psychological state assessment model; a data optimization module, used for performing data screening and data downsampling on data acquired by the psychological state acquisition module; and a smart bracelet terminal, used for displaying a final psychological state; wherein the psychological state assessment model established by the psychological state assessment module is a model established through a large number of psychological research experiments and data analysis based on a deep neural network and a convolutional neural network, and is used for evaluating and analyzing the acquired psychological state characteristic parameters to determine the psychological state data; the evaluation and analysis and data screening processing of the psychological state assessment module and the data optimization module are implemented by a processing device with online and offline data processing capabilities to realize online and offline psychological state evaluation and analysis and data screening processing; the online evaluation and offline big data of the data optimization module are based on a cloud service platform, the online data is distributed and parallel data processed, and the offline data is analyzed based on distributed big data.

[0008] Specifically, the physiological state data collected by the smart bracelet include at least one of skin resistance and / or skin conductance, skin temperature, heart rate, blood pressure, audio signal, voice signal, movement state and / or number of movements; the environmental data obtained include characteristic parameters of room temperature, ambient volume, and light intensity.

[0009] Specifically, the time for acquiring the physiological status data is automatically collected according to the collection interval time set in the smart bracelet, and the collection interval time is determined by the method of acquiring the physiological status data and the evaluation status, and the environmental data is acquired in real time.

[0010] Specifically, after the online and offline psychological state evaluations are completed through the psychological state evaluation module, the smart bracelet terminal receives and evaluates the display data.

[0011] Specifically, the process of establishing the psychological state assessment model includes: using a smart bracelet to collect a person's physiological state data and environmental data; analyzing the corresponding rules of psychological state characteristic parameters of different psychological states in the collected data; determining the input parameters of the psychological state assessment model; using deep neural networks and convolutional neural networks to perform mathematical modeling based on the input parameters to establish a psychological state assessment model; conducting a large number of psychological experiments to collect data, and conducting a large number of verifications and optimizations on the psychological state assessment model based on the collected data; and determining the final psychological state assessment model.

[0012] Specifically, the data related to the physiological state and the psychological state include at least one of skin resistance, skin conductance, heart rate, respiratory rate, and ambient temperature. The process of evaluating the psychological state evaluation model includes: mathematically converting the acquired characteristic parameters of skin resistance, skin conductance, heart rate, respiratory rate, and ambient temperature; constructing psychological state characteristic parameters and inputting them into a neural network model; constructing a network model for psychological state evaluation; inputting the psychological state characteristic parameters obtained by mathematical conversion into the neural network model; evaluating the correlation between the data and the psychological state; determining whether the characteristic parameters of skin resistance, skin conductance, heart rate, respiratory rate, and ambient temperature are related to the psychological state; and if determined to be related, performing evaluation and analysis.

[0013] Specifically, the smart bracelet includes: an acquisition module capable of collecting environmental data, wherein the acquisition module and the physiological data acquisition module respectively collect environmental data and physiological state data; a wireless transmission module, which is used to transmit data collected by the acquisition module and the physiological state data acquisition module under preset parameters to the smart bracelet in real time; a storage module, which stores preset parameters and various data collected by the acquisition module and the physiological data acquisition module; a processing module, which is intelligently connected to the smart bracelet, receives data, transmits the data to a psychological state assessment module related to deep learning, or controls the acquisition module and the physiological data acquisition module to automatically collect data according to preset parameters.

[0014] Specifically, the smart bracelet terminal includes: a wireless data receiving module for receiving the collected data transmitted remotely by the smart bracelet; a data cache module for storing the received remotely transmitted collected data in the data cache module;

[0015] The data screening module is used to classify and screen data; the data optimization module is used to optimize the screened data in real time; the data output module transmits the final data to the display device of the smart bracelet terminal.

[0016] Specifically, the smart bracelet terminal is a smart device with a display function, which can be a mobile phone, a tablet, a desktop computer and / or a car computer.

[0017] Specifically, the psychological state assessment model includes a deep neural network model and a convolutional neural network model. These two models are established from a large number of psychological state assessment experiments based on the characteristic parameters of psychological state assessment, collection interval time, collection environment decibel number, collection light intensity and skin resistance decibel, heart rate and breathing frequency characteristic parameters. The psychological state assessment model is used to evaluate and analyze the data collected by the smart bracelet, and evaluate and analyze the data; the psychological state assessment model evaluation method includes: calculating the characteristic parameters of psychological data; constructing an input neural network model; constructing a psychological state assessment network model; inputting the calculated psychological data characteristic parameters into the input neural network model; evaluating the correlation between data and psychological state, wherein the correlation is an indicator that measures the correlation between the psychological state and the data characteristic parameters through the correlation coefficient method and the t-test method; if the correlation is determined, evaluation and analysis.

[0018] The present invention proposes an innovative psychological state assessment method, which includes a psychological state acquisition module, a psychological state assessment module, a data optimization module and a smart bracelet terminal. The smart bracelet collects multi-dimensional physiological and environmental data, combines deep learning technology for analysis, and realizes real-time and continuous assessment of the psychological state.

[0019] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0020] First of all, the present invention realizes a high degree of continuity in the assessment of psychological state. Through the all-weather monitoring of the smart bracelet, we can capture subtle changes in the psychological state, providing the possibility for early intervention.

[0021] Secondly, the present invention greatly improves the accuracy of assessment. By collecting multi-dimensional physiological and environmental data and analyzing them using deep learning models, we can more comprehensively and accurately assess complex psychological states.

[0022] Furthermore, the present invention provides a non-invasive assessment method. Users only need to wear a smart bracelet to naturally monitor their psychological state in daily life, which greatly improves user compliance.

[0023] In addition, the data optimization module of the present invention can effectively process a large amount of continuous data, improving the efficiency and reliability of the system. This not only ensures the timeliness of the evaluation results, but also provides the possibility for large-scale mental health data analysis.

[0024] Finally, the method of the present invention has good scalability. With the continuous accumulation of data and continuous optimization of the algorithm, the performance of the system is expected to be further improved. At the same time, the large amount of continuous data generated by this method also provides valuable resources for psychological research.

[0025] In general, the present invention provides a new method for assessing mental state, which is significantly superior to the existing technology in terms of accuracy, continuity, non-invasiveness and other aspects. This method can not only help individuals better manage their mental health, but also open up a new path for research and practice in the field of mental health. At a time when mental health problems are becoming increasingly serious, the significance and value of the present invention are self-evident. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of a method for implementing psychological state assessment of the present invention;

[0027] Figure 2 It is a structural schematic diagram of a smart bracelet in a method for implementing psychological state assessment of the present invention;

[0028] Figure 3 It is a structural schematic diagram of a smart bracelet terminal in a method for implementing psychological state assessment of the present invention;

[0029] Figure 4 It is a structural schematic diagram of a psychological state assessment model in a method for implementing psychological state assessment of the present invention. DETAILED DESCRIPTION

[0030] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects thereof are described in detail below in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0031] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0032] Please refer to Figure 1-4 , a method for implementing psychological state assessment, including a psychological state acquisition module 1, a psychological state assessment module 2, a data optimization module 3 and a smart bracelet terminal 4. Among them, the psychological state acquisition module 1 includes a smart bracelet that can at least collect physiological state data and environmental data of a person. The psychological state assessment module 2 is used to establish a psychological state assessment model. The data optimization module 3 is used to perform data screening and data downsampling on the data obtained by the psychological state acquisition module 1. The smart bracelet terminal 4 is used to display the final psychological state.

[0033] Preferably, in one embodiment of the present invention, the psychological state assessment model established by the psychological state assessment module 2 is a model established through a large number of psychological research experiments and data analysis based on deep neural networks and convolutional neural networks. The model is used to evaluate and analyze the acquired psychological state characteristic parameters to determine the psychological state data.

[0034] Furthermore, the evaluation analysis and data screening processing of the psychological state evaluation module 2 and the data optimization module 3 are implemented by a processing device with online and offline data processing capabilities. This enables the system to simultaneously perform online and offline psychological state evaluation analysis and data screening processing.

[0035] Furthermore, the online evaluation and offline big data analysis of the data optimization module 3 are based on the cloud service platform, wherein the online data is processed in a distributed parallel manner, while the offline data is processed based on a distributed big data analysis system.

[0036] In a specific embodiment of the present invention, the physiological state data collected by the smart bracelet includes but is not limited to at least one of skin resistance, skin conductance, skin temperature, heart rate, blood pressure, audio signal, voice signal, exercise state and exercise number. At the same time, the environmental data obtained includes characteristic parameters of room temperature, ambient volume and light intensity. These multi-dimensional data collections provide a solid foundation for the comprehensive assessment of psychological state.

[0037] It is worth noting that the time for acquiring physiological state data is automatically based on the collection interval set in the smart bracelet. This collection interval is not fixed, but is dynamically determined according to the way the physiological state data is acquired and the evaluation state. For example, when the user is under high stress, the system may increase the collection frequency to more closely monitor changes in the user's psychological state. In contrast, environmental data is acquired in real time to ensure that the impact of environmental factors on the psychological state can be reflected in the evaluation results in a timely manner.

[0038] The establishment process of the psychological state assessment model of the present invention includes the following key steps: First, the physiological state data and environmental data of the person are collected using a smart bracelet. Then, the corresponding rules of the psychological state characteristic parameters of different psychological states in the collected data are analyzed. Next, the input parameters of the psychological state assessment model are determined. After that, according to these input parameters, mathematical modeling is performed using deep neural networks and convolutional neural networks to establish a preliminary psychological state assessment model. Finally, data is collected by conducting a large number of psychological experiments, and the psychological state assessment model is extensively verified and optimized based on these collected data, and finally the final psychological state assessment model is determined.

[0039] In the specific application of the model, the data related to physiological state and psychological state mainly include skin resistance, skin conductance, heart rate, respiratory rate and ambient temperature. The evaluation process of the psychological state evaluation model includes the following steps:

[0040] First, the acquired skin resistance, skin conductance, heart rate, respiratory rate and ambient temperature characteristic parameters are mathematically converted. This process involves standardization and normalization of the data to ensure that data of different dimensions can be compared and analyzed on the same scale.

[0041] Then, we construct a neural network model for inputting the psychological state feature parameters. The number of nodes in the input layer of this model corresponds to the number of feature parameters, and the design of the hidden layer is based on empirical values ​​and the results of multiple experiments. For example, for 5 input features, we may choose to use two hidden layers, with 10 nodes in the first layer and 5 nodes in the second layer.

[0042] Next, we build a network model for psychological state assessment. This model is the core of the entire assessment system. It takes the output of the previously constructed input model as input, and finally outputs the assessment results of the psychological state through a complex network structure.

[0043] After that, the psychological state characteristic parameters obtained by mathematical conversion were input into the neural network model. In this process, we adopted a batch processing method, inputting 100 sets of data each time, which can improve the training efficiency and generalization ability of the model.

[0044] Subsequently, the correlation between the data and the psychological state was evaluated. Here we mainly use two methods: correlation coefficient method and t-test method. For the correlation coefficient method, we set a threshold of 0.6, that is, when the correlation coefficient between a feature parameter and the psychological state is greater than 0.6, we believe that there is a significant correlation between them. For the t-test method, we used a significance level of 0.05, that is, when the p value is less than 0.05, we believe that there is a statistically significant correlation between the feature parameter and the psychological state.

[0045] Finally, determine whether the characteristic parameters of skin resistance, skin conductance, heart rate, respiratory rate, and ambient temperature are related to the psychological state. If it is determined to be related, further evaluation and analysis will be carried out.

[0046] In practical applications, the smart bracelet of the present invention includes multiple functional modules, which work together to ensure the efficient operation of the entire system. Specifically:

[0047] First, the smart bracelet is equipped with an environmental data acquisition module and a physiological data acquisition module, which are responsible for collecting environmental data and physiological status data respectively. For example, the environmental data acquisition module may include temperature sensors, light sensors, and sound sensors, while the physiological data acquisition module may include heart rate sensors, skin conductance sensors, etc.

[0048] Secondly, the smart bracelet is also equipped with a wireless transmitter module. The function of this module is to transmit the data collected by the acquisition module and the physiological status data acquisition module under the preset parameters to the smart bracelet terminal in real time. Usually, we will use low-power Bluetooth technology to achieve this function to ensure the stability and durability of data transmission.

[0049] Then, the smart bracelet also includes a storage module. This module not only stores the preset acquisition parameters, but also temporarily stores various data collected by the acquisition module and the physiological data acquisition module. The purpose of this design is to cope with possible network disconnection and ensure the continuity and integrity of the data.

[0050] Finally, the smart bracelet is also equipped with a processing module. This module is intelligently connected to the smart bracelet terminal, can receive data, and transmit the data to the psychological state assessment module with deep learning. In addition, it can also control the acquisition module and the physiological data acquisition module to automatically collect data according to preset parameters. For example, when a sudden increase in the user's heart rate is detected, the processing module may trigger more frequent data collection to better capture changes in the user's psychological state.

[0051] At the same time, the smart bracelet terminal also includes multiple functional modules, which work together to ensure the integrity and reliability of the entire system. Specifically:

[0052] First of all, the smart bracelet terminal is equipped with a wireless data receiving module. This module is responsible for receiving the collected data transmitted remotely by the smart bracelet. In order to ensure the stability and reliability of data transmission, we use advanced wireless communication technologies such as Bluetooth 5.0 or Wi-Fi 6.

[0053] Secondly, the smart bracelet terminal also includes a data cache module. This module can temporarily store the collected data received from remote transmission. The purpose of this design is to cope with possible network fluctuations or data processing delays and ensure the integrity and continuity of the data.

[0054] Then, the smart bracelet terminal is equipped with a data screening module. The main function of this module is to classify and screen the received data. For example, it may classify according to the type of data (such as physiological data or environmental data) or the collection time, so as to prepare for subsequent data processing and analysis.

[0055] Next, the smart bracelet terminal also includes a data optimization module. This module is responsible for real-time optimization of the filtered data. The optimization process may include denoising, smoothing, outlier detection and processing, etc., to improve the quality and reliability of the data.

[0056] Finally, the smart bracelet terminal is equipped with a data output module. The function of this module is to transmit the final processed data to the display device of the smart bracelet terminal. Through this module, users can intuitively see the results of their psychological state assessment.

[0057] It is worth noting that the smart bracelet terminal is not limited to a specific device form factor. It can be a variety of smart devices with display functions, such as mobile phones, tablets, desktop computers, and even car computers. This flexibility allows users to easily view their mental state assessment results in various scenarios.

[0058] The psychological state assessment model of the present invention includes a model based on a deep neural network and a model based on a convolutional neural network. These two models are established by analyzing a large amount of psychological state assessment experimental data. When establishing the model, we considered multiple key factors, including characteristic parameters of psychological state assessment, the interval time of data collection, and characteristic parameters such as ambient decibels, light intensity, skin resistance, heart rate and respiratory rate.

[0059] The main purpose of this psychological state assessment model is to evaluate and analyze the data collected by the smart bracelet. The specific assessment method includes the following steps:

[0060] First, we calculate the psychological data feature parameters. This step involves preprocessing and feature extraction of the raw data. For example, for heart rate data, we will not only calculate the average heart rate, but also analyze more complex indicators such as heart rate variability.

[0061] Then, we construct the input neural network model. The structural design of this model is based on the number and nature of feature parameters. Usually, we use a multi-layer perceptron (MLP) as the basic structure and adjust the number of layers and the number of neurons in each layer according to actual needs.

[0062] Next, we build a psychological state assessment network model. This model is the core of the entire assessment system. It takes the output of the previously built input model as input, and finally outputs the psychological state assessment result through a complex network structure. In this model, we usually use some advanced deep learning techniques, such as attention mechanism and residual connection, to improve the performance of the model.

[0063] After that, the calculated psychological data feature parameters are input into the neural network model. In this process, we will use batch processing to input a certain number of data samples each time to improve the training efficiency and generalization ability of the model.

[0064] Then, we evaluate the correlation between the data and the psychological state. Here we mainly use two methods: correlation coefficient method and t-test method. For the correlation coefficient method, we usually set a threshold, such as 0.6. When the correlation coefficient between a feature parameter and the psychological state is greater than this threshold, we believe that there is a significant correlation between them. For the t-test method, we generally use a significance level of 0.05. In other words, when the p-value is less than 0.05, we believe that there is a statistically significant correlation between the feature parameter and the psychological state.

[0065] Finally, if a correlation is determined, we conduct further evaluation and analysis, which may include deeper statistical analysis, pattern recognition, or machine learning algorithms to produce a final psychological state assessment result.

[0066] In general, the implementation method of the psychological state assessment provided by the present invention realizes the continuous and accurate assessment of human psychological state by combining the multi-dimensional data collection capability of the smart bracelet and advanced deep learning technology. This not only provides a powerful tool for personal mental health management, but also provides valuable data support for research in related fields. In the future, with the further development of technology and the continuous accumulation of data, we have reason to believe that the accuracy and reliability of this assessment method will be further improved, making greater contributions to the cause of human mental health.

[0067] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0068] like Figure 1 As shown, a method for implementing psychological state assessment provided by the present invention mainly includes the following steps:

[0069] Step S1: Data collection. This step is mainly completed by the psychological state collection module 1. Specifically, the smart bracelet will continuously collect the user's physiological state data and environmental data. Physiological state data may include but are not limited to indicators such as skin resistance, skin conductance, heart rate, and blood pressure. For example, the normal range of skin conductance is usually between 2-20μS, and the normal range of heart rate is between 60-100 times / minute. Environmental data may include room temperature, ambient volume, and light intensity. For example, a comfortable room temperature is usually between 20-26℃, and a suitable ambient volume should not exceed 60 decibels.

[0070] Step S2: Data preprocessing. This step is completed by the data optimization module 3. First, the collected raw data is screened to remove obvious outliers. For example, if the detected heart rate exceeds 200 beats / minute, it is likely caused by equipment failure or improper wearing and should be screened out. Then, the screened data is downsampled. The downsampling ratio may be dynamically adjusted according to the type of data and the current processing capacity, usually between 1:2 and 1:10.

[0071] Step S3: Feature extraction. This step is the first step of the psychological state assessment module 2. In this step, we will extract features that can reflect the psychological state from the preprocessed data. For example, we may calculate the heart rate variability (HRV), which is an important indicator of the activity of the autonomic nervous system. Under normal circumstances, the SDNN (standard deviation) value of HRV should be between 20-220ms.

[0072] Step S4: Model evaluation. This is the core part of the mental state evaluation module 2. In this step, we will input the extracted features into the pre-trained deep neural network and convolutional neural network models. The structures of these models are as follows Figure 4 As shown, it usually includes multiple hidden layers, and the number of neurons in each layer may range from 64 to 1024, depending on the number and complexity of the input features.

[0073] Step S5: Result output. Finally, the evaluation result will be displayed to the user through the smart bracelet terminal 4. The result may include multiple dimensions, such as stress level (low, medium, high), emotional state (positive, neutral, negative), etc.

[0074] Now, let's take a closer look at the structure and functions of smart bracelets and smart bracelet terminals.

[0075] like Figure 2 As shown in the figure, the smart bracelet mainly includes the following parts:

[0076] 1. Data acquisition module: This module is responsible for collecting the user's physiological data and environmental data. It includes a variety of sensors, such as heart rate sensor, skin conductance sensor, temperature sensor, etc. The sampling frequency of these sensors can be adjusted as needed, usually between 0.1Hz and 100Hz.

[0077] 2. Wireless transmission module: This module is responsible for transmitting the collected data to the smart bracelet terminal in real time. It usually uses Bluetooth low energy (BLE) technology, and the transmission distance can reach about 10 meters.

[0078] 3. Storage module: When real-time data transmission is not possible (for example, out of Bluetooth range), this module temporarily stores the collected data. Typically, its storage capacity is between 4GB and 16GB, which is enough to store data for several days to several weeks.

[0079] 4. Processing module: This module is responsible for controlling the work of the entire smart bracelet. It can adjust the frequency and method of data collection according to preset parameters. For example, when it detects that the user is in a high-stress state (such as a heart rate that continues to exceed 100 beats / minute), it may increase the frequency of data collection.

[0080] like Figure 3 As shown, the structure of the smart bracelet terminal is more complex, mainly including:

[0081] 1. Wireless data receiving module: This module is responsible for receiving data from the smart bracelet. It supports multiple wireless communication protocols, such as Bluetooth, Wi-Fi, etc., to ensure stable data reception in various environments.

[0082] 2. Data Cache Module: The received data will be stored in this module first. Usually, its capacity is between 32GB and 128GB, which can store data for several months.

[0083] 3. Data screening module: This module will perform preliminary processing on the cached data to remove noise and outliers. For example, if the detected skin conductance value suddenly jumps from 5μS to 100μS, this is likely caused by the sensor being temporarily separated from the skin and should be screened out.

[0084] 4. Data optimization module: This module will further process the filtered data, such as standardization and normalization. For example, the basic heart rate of different people may vary greatly, and we may need to standardize the heart rate data to between 0 and 1 for subsequent analysis.

[0085] 5. Data output module: This module is responsible for presenting the processed data to the user in an intuitive way. It may use various charts and indicators, such as stress index (standard score of 0-100), emotion curve, etc.

[0086] Finally, let’s take a closer look at the structure and workings of the mental state assessment model. Figure 4 As shown in Figure 3, this model mainly consists of two parts: deep neural network (DNN) and convolutional neural network (CNN).

[0087] The DNN part is mainly used to process discrete feature data, such as heart rate, skin conductance, etc. It usually contains 3 to 5 hidden layers, and the number of neurons in each layer may range from 64 to 1024. The activation function is usually ReLU, because it can effectively alleviate the gradient vanishing problem.

[0088] The CNN part is mainly used to process continuous time series data, such as electrocardiogram, electroencephalogram, etc. It usually contains multiple convolutional layers and pooling layers. The size of the convolution kernel may vary from 3x3 to 7x7, and the pooling layer usually uses a maximum pooling of 2x2.

[0089] The outputs of these two parts will be merged at the back end of the network, and then pass through several fully connected layers to finally output the evaluation results of the psychological state.

[0090] In the process of model training, we used a large amount of psychological research experimental data. These data include objective physiological indicators and subjective psychological evaluations. We use the cross-validation method to evaluate the performance of the model, usually dividing the data set into 80% training set and 20% test set.

[0091] The model is trained using batch gradient descent, and the batch size is usually set to 32 or 64. The learning rate starts at 0.01 and gradually decreases as the training progresses. To prevent overfitting, we also use the dropout technique, and the dropout rate is usually set to 0.5.

[0092] In practical applications, this model can achieve near real-time psychological state assessment. Usually, the entire process from data input to result output takes less than 1 second. This allows us to capture changes in the user's psychological state in a timely manner, providing the possibility for timely intervention.

[0093] In general, the psychological state assessment method provided by the present invention realizes accurate and continuous assessment of human psychological state by combining advanced hardware equipment and complex algorithm models. This not only provides a powerful tool for personal mental health management, but also provides valuable data support for psychological research. With the continuous advancement of technology and the continuous accumulation of data, we have reason to believe that the accuracy and reliability of this assessment method will be further improved, making greater contributions to the cause of human mental health.

[0094] In order to verify the superiority of the present invention, we conducted a series of comparative experiments. The specific conditions of the embodiments and comparative examples will be described in detail below, and the corresponding test results and analysis will be given.

[0095] Embodiment 1: Using the psychological state assessment method of the present invention

[0096] In this embodiment, we used a smart bracelet equipped with multiple sensors, including heart rate sensor, skin conductance sensor, temperature sensor, etc. The data acquisition frequency was 10Hz, and the acquisition time was 7 consecutive days. The evaluation model used a combination of deep neural network and convolutional neural network. The number of hidden layers of the model was 5 layers, and the number of neurons in each layer was 512. The data optimization module used an adaptive downsampling algorithm to dynamically adjust the downsampling ratio according to the data change rate.

[0097] Comparative Example 1: Traditional Questionnaire Method

[0098] This method uses standardized mental health questionnaires, such as the Beck Depression Inventory (BDI) and the Self-Rating Anxiety Scale (SAS), which participants fill out once a day for seven days.

[0099] Comparative Example 2: Single Physiological Index Monitoring Method

[0100] This method only uses heart rate variability (HRV) as an evaluation indicator. An ordinary heart rate monitoring bracelet is used, and the data collection frequency is 1Hz for 7 consecutive days. The evaluation model uses a simple threshold method, that is, when the SDNN is lower than a certain threshold, it is judged as a stress state.

[0101] Comparative Example 3: Simple Machine Learning Method

[0102] This method uses a wristband with heart rate and skin conductance sensors. The data is collected at a frequency of 1Hz for 7 consecutive days. The evaluation model uses a support vector machine (SVM) and only uses two features, heart rate and skin conductance, for classification.

[0103] Testing standards and methods:

[0104] 1. Accuracy: The diagnosis results of professional psychologists are used as the gold standard, and the consistency between the evaluation results of each method and the gold standard is calculated.

[0105] 2. Real-time: Calculate the average time from data input to evaluation results.

[0106] 3. User experience: Through questionnaire surveys, evaluate users’ acceptance and convenience of each method.

[0107] 4. Continuity: The proportion of time that each method can provide valid evaluation results within 7 days.

[0108] The test results are shown in the following table:

[0109] Evaluation Methodology Accuracy (%) Real-time (seconds) User experience (1-10 points) Continuity (%) Example 1 92％ 0.5 9 98 Comparative Example 1 78％ 3600 6 14 Comparative Example 2 65％ 5 8 95 Comparative Example 3 80％ 2 7 96

[0110] It can be seen from the test results that the psychological state assessment method of the present invention (Example 1) has shown obvious advantages in all indicators.

[0111] First, in terms of accuracy, Example 1 reached 92%, which is much higher than other methods. This is mainly due to our use of multi-dimensional physiological data collection and advanced deep learning models. The combination of multiple physiological indicators can more comprehensively reflect the state of the human body, and the deep learning model can effectively capture the complex relationship between these indicators, thereby making a more accurate assessment.

[0112] Secondly, in terms of real-time performance, Example 1 only takes 0.5 seconds to give the evaluation results. This near-real-time feedback is essential for timely discovery and intervention of potential psychological problems. In contrast, the traditional questionnaire method (Comparative Example 1) requires users to spend a lot of time filling out questionnaires, making it difficult to achieve real-time monitoring.

[0113] In terms of user experience, Example 1 also performed well, scoring 9 points. This is mainly because our method does not require active operation by the user, and can be monitored naturally in daily life without bringing additional burden to the user. In contrast, although the questionnaire method is accurate, it requires users to spend time filling it out every day, and it is difficult to persist in the long term.

[0114] Finally, in terms of continuity, Example 1 can achieve effective monitoring for 98% of the time, which means that we can grasp the changes in the user's psychological state almost around the clock. In contrast, the questionnaire method has the worst continuity, only 14%, due to the limitation of filling frequency.

[0115] It is worth noting that although the single physiological indicator monitoring method (Comparative Example 2) and the simple machine learning method (Comparative Example 3) performed well in terms of continuity and real-time performance, their accuracy was significantly lower than that of our method. This shows that it is difficult to fully and accurately evaluate complex psychological states by relying on only a single or a few physiological indicators.

[0116] In summary, the psychological state assessment method of the present invention has shown obvious advantages in accuracy, real-time, user experience and continuity. This method can not only provide more accurate and timely psychological state assessment, but also achieve all-weather monitoring in a user-friendly way. This is of great significance for early detection of mental health problems and timely intervention. At the same time, the large amount of continuous data generated by this method also provides valuable resources for psychological research, which helps us to understand the dynamic changes of psychological state more deeply.

[0117] Nevertheless, we also recognize that the assessment of mental state is an extremely complex issue, and relying solely on physiological indicators may not be comprehensive enough. In future research, we plan to further integrate multi-source information such as behavioral data and social data to achieve a more comprehensive and accurate assessment of mental state.

[0118] It should be noted that 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 principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for implementing psychological state assessment, characterized in that: include: A psychological state collection module, including a smart bracelet capable of collecting at least physiological state data and environmental data of a person; A psychological state assessment module, used to establish a psychological state assessment model; A data optimization module is used to perform data screening and data downsampling on the data acquired by the psychological state acquisition module; a smart bracelet terminal is used to display the final psychological state; wherein, the psychological state assessment model established by the psychological state assessment module is a model established based on deep neural networks and convolutional neural networks through a large number of psychological research experiments and data analysis, and is used to evaluate and analyze the acquired psychological state characteristic parameters to determine the psychological state data; the evaluation and analysis and data screening processing of the psychological state assessment module and the data optimization module are realized by a processing device with online and offline data processing capabilities to realize online and offline psychological state evaluation and analysis and data screening processing; the online evaluation and offline big data of the data optimization module are based on a cloud service platform, the online data is distributed and parallel data processing is performed, and the offline data is based on distributed big data analysis.

2. A method for implementing psychological state assessment as claimed in claim 1, characterized in that: The physiological state data collected by the smart bracelet include at least one of skin resistance and / or skin conductance, skin temperature, heart rate, blood pressure, audio signal, voice signal, movement state and / or number of movements; the environmental data obtained include characteristic parameters of room temperature, ambient volume, and light intensity.

3. A method for implementing psychological state assessment as claimed in claim 2, characterized in that: The time for acquiring the physiological state data is automatically acquired according to the acquisition interval time set in the smart bracelet. The acquisition interval time is determined by the method for acquiring the physiological state data and the evaluation state. The environmental data is acquired in real time.

4. A method for implementing psychological state assessment according to claim 1, characterized in that: After completing the online and offline psychological state evaluation through the psychological state evaluation module, the smart bracelet terminal receives and evaluates the displayed data.

5. A method for implementing psychological state assessment as claimed in claim 1, characterized in that: The process of establishing the psychological state assessment model includes: using a smart bracelet to collect a person's physiological state data and environmental data; analyzing the corresponding rules of psychological state characteristic parameters of different psychological states in the collected data; determining the input parameters of the psychological state assessment model; using deep neural networks and convolutional neural networks to perform mathematical modeling based on the input parameters to establish a psychological state assessment model; conducting a large number of psychological experiments to collect data, and conducting a large number of verifications and optimizations on the psychological state assessment model based on the collected data; and determining the final psychological state assessment model.

6. A method for implementing psychological state assessment as claimed in claim 5, characterized in that: The data related to the physiological state and the psychological state include at least one of skin resistance, skin conductance, heart rate, respiratory rate, and ambient temperature. The process of evaluating the psychological state evaluation model includes: mathematically converting the acquired characteristic parameters of skin resistance, skin conductance, heart rate, respiratory rate, and ambient temperature; constructing the psychological state characteristic parameters and inputting them into the neural network model; constructing the network model for psychological state evaluation; inputting the psychological state characteristic parameters obtained by mathematical conversion into the neural network model; evaluating the correlation between the data and the psychological state; judging whether the characteristic parameters of skin resistance, skin conductance, heart rate, respiratory rate, and ambient temperature are related to the psychological state; and if it is determined to be related, performing evaluation and analysis.

7. A method for implementing psychological state assessment as claimed in claim 1, characterized in that: The smart bracelet includes: a collection module capable of collecting environmental data, wherein the collection module and the physiological data collection module collect environmental data and physiological state data respectively; a wireless transmission module, which is used to transmit data collected by the collection module and the physiological state data collection module under preset parameters automatically to the smart bracelet in real time; a storage module, which stores preset parameters and various data collected by the collection module and the physiological data collection module; a processing module, which is intelligently connected to the smart bracelet, receives data, transmits the data to a psychological state assessment module related to deep learning, or controls the collection module and the physiological data collection module to automatically collect data according to preset parameters.

8. A method for implementing psychological state assessment as claimed in claim 1, characterized in that: The smart bracelet terminal includes: a wireless data receiving module, which receives the collected data transmitted remotely by the smart bracelet; a data caching module, which can store the received remotely transmitted collected data in the data caching module; a data screening module, which is used to classify and screen the data; a data optimization module, which is used to optimize the screened data in real time; and a data output module, which transmits the final data to the display device of the smart bracelet terminal.

9. A method for implementing psychological state assessment as claimed in claim 1, characterized in that: The smart bracelet terminal is a smart device with a display function, which can be a mobile phone, a tablet, a desktop computer and / or a car computer.

10. A method for implementing psychological state assessment according to claim 1, characterized in that: The psychological state assessment model includes a deep neural network model and a convolutional neural network model. The two models are based on a large number of psychological state assessment experiments, and are based on the characteristic parameters of psychological state assessment, collection interval time, collection environment decibel number, collection light intensity and skin resistance decibel, heart rate and breathing frequency. The psychological state assessment model is used to evaluate and analyze the data collected by the smart bracelet, and to evaluate and analyze the data; The psychological state evaluation model evaluation method includes: calculating characteristic parameters of psychological data; constructing an input neural network model; constructing a psychological state evaluation network model; inputting the calculated psychological data characteristic parameters into the input neural network model; evaluating the correlation between data and psychological state, wherein the correlation is an indicator of the correlation between the psychological state and the data characteristic parameters through the correlation coefficient method and the t-test method; if the correlation is determined, evaluating and analyzing.

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