A fatigue intervention method, system, device, medium, and product

Abstract

The application discloses a fatigue intervention method, system, device, medium and product, and relates to the field of electrocardiosignal processing.The method comprises the following steps: collecting a human electrocardiosignal; preprocessing the electrocardiosignal by using a wavelet transform; extracting features from the preprocessed electrocardiosignal to obtain an initial feature set; screening the initial feature set based on a SHAP algorithm to obtain a key feature set; determining the grade of a fatigue state based on the key feature set by using a fatigue recognition model; training a random forest model by using a sample key feature set to obtain the fatigue recognition model; obtaining the sample key feature set by processing a sample electrocardiosignal; and performing fatigue intervention by using different types of music and different frequency time-frequency interference signals based on the grade of the fatigue state.The application can identify the grade of the fatigue state and implement personalized intervention according to the grade of the fatigue state.

Description

Technical Field

[0001] The present application relates to the field of electrocardiogram (ECG) signal processing, and in particular to a fatigue intervention method, system, device, medium, and product. Background Art

[0002] In today's rapidly developing world, people face a faster pace of life and greater competitive pressure. This has led to the prevalence of prolonged, intense mental work, which in turn causes brain fatigue. Brain fatigue not only causes fatigue and difficulty concentrating, but over time, it can also lead to a range of physical and psychological problems, such as heart disease, insomnia, anxiety, and depression, seriously threatening people's physical and mental health. Summary of the Invention

[0003] The purpose of this application is to provide a fatigue intervention method, system, device, medium and product that can identify the level of fatigue status and implement personalized intervention based on the level of fatigue status.

[0004] To achieve the above objectives, this application provides the following solutions.

[0005] In a first aspect, the present application provides a fatigue intervention method, comprising:

[0006] Collect human ECG signals;

[0007] Preprocessing the electrocardiogram signal using wavelet transform;

[0008] Extracting features from the preprocessed ECG signal to obtain an initial feature set; the initial feature set includes heart rate features and heart rate variability features;

[0009] The initial feature set is screened based on the SHAP algorithm to obtain a key feature set;

[0010] Based on the key feature set, a fatigue recognition model is used to determine the level of fatigue status; the fatigue recognition model is obtained by training a random forest model with the sample key feature set; the sample key feature set is obtained by processing the sample electrocardiogram signal;

[0011] Based on the level of the fatigue state, different types of music and time-frequency interference signals of different frequencies are used to perform fatigue intervention.

[0012] In a second aspect, the present application provides a fatigue intervention system, comprising:

[0013] ECG signal acquisition module, used to collect human ECG signals;

[0014] A preprocessing module, configured to preprocess the electrocardiogram signal using wavelet transform;

[0015] A feature extraction module is used to extract features from the preprocessed ECG signal to obtain an initial feature set; the initial feature set includes heart rate features and heart rate variability features;

[0016] A feature screening module is used to screen the initial feature set based on the SHAP algorithm to obtain a key feature set;

[0017] a fatigue state level determination module, configured to determine the level of fatigue state based on the key feature set and using a fatigue recognition model; the fatigue recognition model is obtained by training a random forest model using a sample key feature set; the sample key feature set is obtained by processing a sample electrocardiogram signal;

[0018] The fatigue intervention module is used to perform fatigue intervention based on the level of the fatigue state by using different types of music and time-frequency interference signals of different frequencies.

[0019] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned fatigue intervention method.

[0020] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the above-mentioned fatigue intervention method when executed by a processor.

[0021] In a fifth aspect, the present application provides a computer program product, including a computer program, which implements the above-mentioned fatigue intervention method when executed by a processor.

[0022] According to the specific embodiments provided in this application, this application has the following technical effects.

[0023] (1) This application uses the SHAP algorithm to screen heart rate characteristics and heart rate variability characteristics, which can improve the accuracy of fatigue state level identification and the effectiveness of intervention.

[0024] (2) This application uses different types of music and time-frequency interference signals of different frequencies to intervene in fatigue states of different levels. It can penetrate deep into the brain and provide non-invasive stimulation to the human body, effectively avoiding the pain problems that may be caused by traditional direct current stimulation. At the same time, it improves the comfort and tolerance of the intervention, thereby minimizing the health and safety risks caused by fatigue and protecting people's physical and mental health. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 A flowchart of a fatigue intervention method provided in one embodiment of the present application.

[0027] Figure 2 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0029] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0030] In an exemplary embodiment, Figure 1 As shown, a fatigue intervention method is provided, which is executed by a computer device. Specifically, it can be executed by a computer device such as a terminal or a server alone, or it can be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to a server as an example for explanation, including the following steps S1 to S6.

[0031] S1: Collect human ECG signals.

[0032] ECG signals are electrical signals of human heart activity, captured by measuring the action potential of heart cells. Changes in the action potential of heart cells can reflect the physiological state of the heart, and these changes are closely related to an individual's fatigue status.

[0033] S2: Preprocessing the electrocardiogram signal using wavelet transform.

[0034] Wavelet transform is used to preprocess the collected ECG signals to ensure their accuracy and reliability. By decomposing the ECG signals at multiple scales, useful signal components and noise can be effectively separated, removing power frequency interference, high-frequency noise, and baseline drift, which are caused by factors such as power frequency, electromagnetic interference, breathing, and skin impedance. After wavelet transform, useful ECG signal coefficients are retained and the signal is reconstructed, significantly improving the quality of the ECG signal. The characteristics of the ECG signal at different time and scales are analyzed using formula (1).

[0035] (1)

[0036] Among them, X(t) is the original time domain ECG signal. is the time shift parameter, which is used to adjust the position of the wavelet function on the time axis. is a scale parameter used to adjust the width of the wavelet function to analyze signals at different scales. represents the continuous wavelet transform coefficients, Denotes X(t) with respect to the wavelet function The continuous wavelet transform results of the two are essentially different representations of the same transform result, which are used to characterize the ECG signal in different and The following features.

[0037] The ECG signal is decomposed at multiple scales through wavelet transform, the coefficients of useful signals are retained, and finally the signal is reconstructed through the extracted wavelet coefficients, thereby reducing noise interference and facilitating subsequent QRS complex detection.

[0038] S3: Extracting features from the preprocessed ECG signal to obtain an initial feature set, wherein the initial feature set includes heart rate features and heart rate variability features. The heart rate variability features include time domain features, frequency domain features, and nonlinear features.

[0039] Step S3 specifically includes: performing R wave positioning detection based on the preprocessed electrocardiogram signal; and extracting heart rate characteristics and heart rate variability characteristics based on the R wave.

[0040] Heart rate index and heart rate variability index can be used to identify fatigue status. By observing the waveform of the ECG signal, it can be found that the R wave in the QRS complex has a large amplitude, a narrow pulse width, obvious characteristics and is easy to identify. The positioning of the R wave is the most important step in ECG signal recognition. This application uses a method for real-time detection of QRS complexes proposed by Pan and Willis J. Jiapu Tompkins to locate the R wave: the ECG signal that has been denoised is differentially processed to obtain slope information, and then the ECG signal that has been differentially processed is processed using a square operation. In order to remove the negative waveform that may form a double peak after the square operation and affect the detection and positioning of the R wave, the waveform can be smoothed by window sliding integration. After the above processing, the ECG signal is located and detected using an adaptive threshold method. The formulas involved are as follows:

[0041] (2)

[0042] (3)

[0043] (4)

[0044] Formula (2) represents the differential processing of ECG signals. is the original ECG signal of the nth signal sequence, is the signal after differentiation. and represents the value of the signal at different time points, and The difference operation helps extract the slope information of the signal. n represents the sequence number in the signal sequence, which is used to identify the signal values ​​at different times and other related calculations. By performing a difference operation on the signal values ​​corresponding to different n values, the slope information of the ECG signal can be obtained.

[0045] Formula (3) represents the differential signal Performs a square operation. is the squared signal, and T is the sampling period. The squaring operation can enhance the positive waveform of the signal, making the R wave more prominent.

[0046] Formula (4) represents the window sliding integration of the signal to achieve smoothing. Indicates the time period, is the smoothed signal, is the window size, is the original ECG signal at time This integration operation helps reduce the negative waveform that may be generated after the square operation, thereby improving the accuracy of R wave detection.

[0047] Based on R-wave localization, the extraction of heart rate and heart rate variability (HRV) metrics is a systematic process used to assess the heart's autonomic regulatory function. This process includes time-domain, frequency-domain, and nonlinear features. Time-domain features focus on direct measurements of heartbeat intervals; these metrics reflect the activity of the autonomic nervous system and the adaptability of the heart. Frequency-domain features analyze the frequency components of the heart rate to assess the balance between the sympathetic and parasympathetic nervous systems. These metrics help identify changes in the autonomic nervous system during fatigue. Nonlinear features provide a deeper understanding of the complexity of heart rate and can reveal the complexity and regularity of heart rate variability, leading to more accurate assessment of fatigue.

[0048] The specific steps are as follows:

[0049] First, the time interval between two consecutive R waves, the RR interval, is calculated, and then these intervals are used to calculate the heart rate, usually calculated using the formula heart rate (bpm) = 60 / average RR interval (seconds).

[0050] Next, heart rate metrics are extracted, such as average heart rate, maximum heart rate, minimum heart rate, and standard deviation of heart rate. HRV metrics extraction includes time-domain metrics such as SDNN, RMSSD, and NN50; frequency-domain metrics, such as Fourier transform analysis of power in different frequency bands (such as very low frequency (VLF), low frequency (LF), and high frequency (HF)) to calculate total power and LF / HF ratio; and nonlinear metrics such as Poincare plot analysis and sample entropy (SampEn).

[0051] The meanings of time domain features and frequency domain features are shown in Table 1 and Table 2 respectively.

[0052] Table 1

[0053]

[0054] Table 2

[0055]

[0056] Due to the inconsistency of the dimensions and units of each feature index, in order to avoid the impact of related factors on the calculation speed of the model, the feature index data must be normalized and mapped to [-1,1] to obtain the initial feature set.

[0057] S4: Filtering the initial feature set based on the SHAP algorithm to obtain a key feature set. This includes: calculating the SHAP value of each initial feature in the initial feature set based on the SHAP algorithm; sorting the initial features in descending order of the average absolute value of the SHAP values, and selecting the top N initial features to construct the key feature set.

[0058] To remove redundant features, avoid multicollinearity, and improve overall data quality, feature selection is performed in this step. The SHAP (SHapley Additive exPlanations) algorithm assesses feature importance by calculating each feature's contribution to model predictions. First, the SHAP value is calculated for each initial feature in the initial feature set obtained in step S3. The SHAP value can be used to analyze the average impact of each initial feature on model predictions. Generally, SHAP values ​​with larger absolute values ​​indicate a greater impact on model predictions. Initial features are ranked according to their average absolute SHAP values, and the top N initial features with the greatest impact on model predictions are selected to form the key feature set.

[0059] S5: Based on the key feature set, a fatigue recognition model is used to determine the level of fatigue status; the fatigue recognition model is obtained by training a random forest model with the sample key feature set; the sample key feature set is obtained by processing the sample electrocardiogram signal.

[0060] The random forest model improves the accuracy and stability of the overall model by constructing multiple decision trees and combining their predictions. The advantage of the random forest model when processing high-dimensional data is that it does not require feature selection, as each tree is trained on a randomly selected set of sample key features. The random forest model is designed with four output nodes, corresponding to mild fatigue, moderate fatigue, severe fatigue, and a non-fatigue state. During the training and prediction process of the random forest model, the sample key feature sets are input into the random forest model. These sample key feature sets combine heart rate indicators and heart rate variability (HRV) indicators, providing a strong physiological basis for assessing an individual's fatigue state. With its powerful pattern recognition capabilities, the random forest model can accurately classify the sample key feature sets into the four fatigue states mentioned above, thereby achieving a classification of fatigue levels.

[0061] During the training process of the random forest model, the key feature set of samples is continuously iteratively improved to find the key feature set that optimizes the model performance.

[0062] S6: Based on the level of the fatigue state, different types of music and time-frequency interference signals of different frequencies are used to perform fatigue intervention.

[0063] When the level of the fatigue state is a non-fatigue state, no fatigue intervention is performed.

[0064] When the fatigue level is mild, fatigue intervention is performed by playing soothing music and weak high-frequency (1000Hz) time-frequency interference signals to keep the mind clear and able to concentrate on completing work.

[0065] When the fatigue state is moderate, fatigue intervention is performed by playing rock music with a stronger sense of rhythm and applying a stronger high-frequency (2000 Hz) time-frequency interference signal.

[0066] When the fatigue state is severe, fatigue intervention is performed by applying a time-frequency interference signal with a frequency of 3000 Hz. Increasing the intensity of the time-frequency interference signal can help regain the necessary energy and attention for work.

[0067] This application utilizes time-domain interferometry (TI) technology for fatigue intervention. This technology is a non-invasive deep brain stimulation method that is achieved by placing two electrodes on the scalp and applying two pairs of high-frequency stimulation signals with similar but slightly different frequencies. The interference of these signals in specific areas within the brain forms a strong overlapping electric field and generates a low-frequency envelope wave. This low-frequency envelope wave can penetrate deep into the brain and regulate brain activity. Through precise control of the electric field, it is possible to deeply regulate brain activity and improve fatigue symptoms.

[0068] In the fatigue intervention method of the present application, the parameters of the time-frequency interferometry (TI) signal are precisely adjusted according to the severity of the individual's fatigue state to achieve personalized intervention. This includes adjusting the intensity of the stimulation by changing the current intensity delivered by the electrodes to achieve effective intervention in severe fatigue states; changing the frequency difference between the two pairs of high-frequency stimulation signals to change the frequency of the low-frequency envelope wave, thereby affecting the synchronization of the neural network and achieving different neuromodulation effects; in addition, combining different styles of music to further adjust the intensity of neural stimulation to meet the needs of different fatigue levels. This multi-dimensional adjustment strategy ensures the flexibility and effectiveness of the intervention measures, aiming to optimize the intervention effect and maintain and promote the physical and mental health of individuals.

[0069] The fatigue intervention method provided in this application has the following advantages.

[0070] 1. The electric field stimulation generated by the applied electric field in this application can more precisely target specific brain regions. Compared to traditional current stimulation, its targeting is more accurate and can act more deeply on the brain. This precise stimulation method can more effectively regulate brain activity and provide more significant fatigue intervention effects, while reducing unnecessary stimulation of non-target areas and lowering potential adverse reactions.

[0071] 2. This application introduces feature selection techniques to optimize the performance of fatigue recognition models. By using SHAP values ​​to quantify the contribution of each feature to model predictions, we can identify and select the most critical features for fatigue assessment. This feature selection step not only improves the accuracy and efficiency of the model but also enhances its interpretability, making fatigue intervention more precise and personalized.

[0072] Based on the same inventive concept, embodiments of the present application also provide a system for implementing the aforementioned fatigue intervention method. The solution provided by this system is similar to the solution described in the aforementioned method. Therefore, the specific limitations in one or more fatigue intervention system embodiments provided below can be found in the limitations of the fatigue intervention method described above and will not be further elaborated here.

[0073] In an exemplary embodiment, a fatigue intervention system is provided, comprising the following modules.

[0074] The ECG signal acquisition module is used to collect human ECG signals.

[0075] The preprocessing module is used to preprocess the electrocardiogram signal using wavelet transform.

[0076] The feature extraction module is used to extract features from the preprocessed ECG signal to obtain an initial feature set; the initial feature set includes heart rate features and heart rate variability features.

[0077] The feature screening module is used to screen the initial feature set based on the SHAP algorithm to obtain a key feature set.

[0078] The fatigue state level determination module is used to determine the level of the fatigue state based on the key feature set using a fatigue recognition model; the fatigue recognition model is obtained by training a random forest model with a sample key feature set; the sample key feature set is obtained by processing a sample electrocardiogram signal.

[0079] The fatigue intervention module is used to perform fatigue intervention based on the level of the fatigue state by using different types of music and time-frequency interference signals of different frequencies.

[0080] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-mentioned method embodiments. The computer device can be a server or a terminal, and its internal structure can be as shown in FIG. Figure 2As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store data to be processed. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a fatigue intervention method is implemented.

[0081] Those skilled in the art will understand that Figure 2 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application and does not constitute a limitation on the computer device to which the solution of the present application is applied. A specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps of the above-mentioned method embodiments when executing the computer program.

[0082] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0083] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0084] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0085] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0086] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0087] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0088] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A fatigue intervention method, characterized in that: include: Collect human ECG signals; Preprocessing the electrocardiogram signal using wavelet transform; Perform feature extraction on the preprocessed ECG signal to obtain an initial feature set; The initial feature set includes heart rate features and heart rate variability features; The heart rate variability characteristics include: time domain characteristics, frequency domain characteristics and nonlinear characteristics; The initial feature set is screened based on the SHAP algorithm to obtain a key feature set, specifically comprising: calculating a SHAP value of each initial feature in the initial feature set based on the SHAP algorithm; sorting the initial features in descending order according to the average absolute value of the SHAP value, and selecting the first N initial features to construct a key feature set; Based on the key feature set, a fatigue recognition model is used to determine the level of fatigue state; the fatigue state levels include: non-fatigue state, mild fatigue state, moderate fatigue state and severe fatigue state; the fatigue recognition model is obtained by training a random forest model with the sample key feature set; the sample key feature set is obtained by processing the sample electrocardiogram signal; Based on the level of the fatigue state, different types of music and time-frequency interference signals of different frequencies are used for fatigue intervention; specifically, when the level of the fatigue state is a non-fatigue state, no fatigue intervention is performed; when the level of the fatigue state is a mild fatigue state, fatigue intervention is performed by playing soothing music and applying a time-frequency interference signal with a frequency of 1000Hz; when the level of the fatigue state is a moderate fatigue state, fatigue intervention is performed by playing rock music and applying a time-frequency interference signal with a frequency of 2000Hz; when the level of the fatigue state is a severe fatigue state, fatigue intervention is performed by applying a time-frequency interference signal with a frequency of 3000Hz.

2. The fatigue intervention method according to claim 1, characterized in that: Perform feature extraction on the preprocessed ECG signal to obtain the initial feature set, which specifically includes: Perform R wave location detection based on preprocessed ECG signals; Heart rate features and heart rate variability features are extracted based on the R wave.

3. A fatigue intervention system, characterized in that: include: ECG signal acquisition module, used to collect human ECG signals; A preprocessing module, configured to preprocess the electrocardiogram signal using wavelet transform; The feature extraction module is used to extract features from the preprocessed ECG signal to obtain an initial feature set; The initial feature set includes heart rate features and heart rate variability features; The heart rate variability characteristics include: time domain characteristics, frequency domain characteristics and nonlinear characteristics; A feature screening module is used to screen the initial feature set based on the SHAP algorithm to obtain a key feature set, specifically comprising: calculating the SHAP value of each initial feature in the initial feature set based on the SHAP algorithm; sorting the initial features in descending order according to the average absolute value of the SHAP value, and selecting the first N initial features to construct a key feature set; a fatigue state level determination module, configured to determine the fatigue state level based on the key feature set using a fatigue recognition model; the fatigue recognition model is obtained by training a random forest model using a sample key feature set; the sample key feature set is obtained by processing sample electrocardiogram signals; the fatigue state levels include: non-fatigue state, mild fatigue state, moderate fatigue state, and severe fatigue state; The fatigue intervention module is used to perform fatigue intervention based on the level of the fatigue state using different types of music and time-frequency interference signals of different frequencies; specifically, when the level of the fatigue state is a non-fatigue state, no fatigue intervention is performed; when the level of the fatigue state is a mild fatigue state, fatigue intervention is performed by playing soothing music and applying a time-frequency interference signal with a frequency of 1000Hz; when the level of the fatigue state is a moderate fatigue state, fatigue intervention is performed by playing rock music and applying a time-frequency interference signal with a frequency of 2000Hz; when the level of the fatigue state is a severe fatigue state, fatigue intervention is performed by applying a time-frequency interference signal with a frequency of 3000Hz.

4. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the fatigue intervention method according to any one of claims 1 to 2.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the fatigue intervention method according to any one of claims 1 to 2 is implemented.

6. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the fatigue intervention method according to any one of claims 1 to 2 is implemented.

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