CN119818800A - Fatigue intervention method, system, equipment, medium and product - Google Patents
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Abstract
The application discloses a fatigue intervention method, a system, equipment, a medium and a product, which relate to the field of electrocardiosignal processing, wherein the method comprises the steps of collecting electrocardiosignals of a human body; the method comprises the steps of preprocessing an electrocardiosignal by wavelet transformation, extracting features of the preprocessed electrocardiosignal to obtain an initial feature set, screening the initial feature set to obtain a key feature set based on an SHAP algorithm, determining the level of a fatigue state by adopting a fatigue recognition model based on the key feature set, training a random forest model by using a sample key feature set to obtain the fatigue recognition model, processing the sample electrocardiosignal by using the sample key feature set, and performing fatigue intervention by adopting different types of music and time-frequency interference signals with different frequencies based on the level of the fatigue state. The application can identify the level of fatigue status and perform personalized interventions based on the level of fatigue status.
Description
Technical Field
The application relates to the field of electrocardiosignal processing, in particular to a fatigue intervention method, a system, equipment, a medium and a product.
Background
In modern society of rapid development of science and technology and economy, people face a faster life rhythm and greater competitive pressure. This results in that long-term high-intensity brain work becomes common, thereby causing brain fatigue. Not only does brain fatigue lead to fatigue and difficulty in concentrating, long-term accumulation can also induce a series of physiological and psychological problems, such as heart disease, insomnia, anxiety and depression, which seriously threatens the physical and mental health of people.
Disclosure of Invention
The application aims to provide a fatigue intervention method, a system, equipment, a medium and a product, which can identify the grade of a fatigue state and implement personalized intervention according to the grade of the fatigue state.
In order to achieve the above object, the present application provides the following.
In a first aspect, the present application provides a method of fatigue intervention comprising:
collecting electrocardiosignals of a human body;
Preprocessing the electrocardiosignal by adopting wavelet transformation;
extracting features of the preprocessed electrocardiosignals to obtain an initial feature set, wherein the initial feature set comprises heart rate features and heart rate variability features;
screening the initial feature set based on SHAP algorithm to obtain a key feature set;
Determining the grade of the fatigue state by adopting a fatigue recognition model based on the key feature set, wherein the fatigue recognition model is obtained by training a random forest model by using a sample key feature set, and the sample key feature set is obtained by processing a sample electrocardiosignal;
based on the level of the fatigue state, different types of music and different frequency time-frequency interference signals are adopted for fatigue intervention.
In a second aspect, the present application provides a fatigue intervention system comprising:
The electrocardiosignal acquisition module is used for acquiring electrocardiosignals of a human body;
the preprocessing module is used for preprocessing the electrocardiosignals by adopting wavelet transformation;
the device comprises a feature extraction module, a feature extraction module and a processing module, wherein the feature extraction module is used for carrying out feature extraction on the preprocessed electrocardiosignals to obtain an initial feature set, and the initial feature set comprises heart rate features and heart rate variability features;
the feature screening module is used for screening the initial feature set based on an SHAP algorithm to obtain a key feature set;
The fatigue state grade determining module is used for determining the grade of the fatigue state by adopting a fatigue recognition model based on the key feature set, wherein the fatigue recognition model is obtained by training a random forest model by using a sample key feature set, and the sample key feature set is obtained by processing a sample electrocardiosignal;
And the fatigue intervention module is used for performing fatigue intervention by adopting different types of music and time-frequency interference signals with different frequencies based on the level of the fatigue state.
In a third aspect, the application provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the computer program to implement the fatigue intervention method described above.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the fatigue intervention method described above.
In a fifth aspect, the present application provides a computer program product comprising a computer program which, when executed by a processor, implements the fatigue intervention method described above.
According to the specific embodiments provided by the application, the application has the following technical effects.
(1) The application adopts SHAP algorithm to screen heart rate characteristics and heart rate variability characteristics, and can improve the accuracy of fatigue state grade identification and the effectiveness of intervention.
(2) According to the application, fatigue intervention is carried out on fatigue states of different levels through different types of music and time-frequency interference signals of different frequencies, so that the brain can be penetrated into and the human body is stimulated in a non-invasive way, the pain problem possibly caused by traditional direct current stimulation is effectively avoided, and meanwhile, the comfort and tolerance of the intervention are improved, thereby reducing the health and safety risks caused by fatigue to the greatest extent and protecting the physical and mental health of people.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a fatigue intervention method according to an embodiment of the present application.
Fig. 2 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In order that the above-recited objects, features and advantages of the present application will become more readily apparent, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description.
In an exemplary embodiment, as shown in fig. 1, a fatigue intervention method is provided, which is executed by a computer device, specifically, may be executed by a computer device such as a terminal or a server, or may be executed by the terminal and the server together, and in an embodiment of the present application, the method is applied to the server and is described as an example, and includes the following steps S1 to S6.
S1, acquiring electrocardiosignals of a human body.
The electrocardiosignals are electrical signals of the heart activity of a human body and are captured by measuring action potentials of heart cells. Action potential changes in heart cells can reflect the physiological state of the heart, and these changes are closely related to the fatigue state of an individual.
S2, preprocessing the electrocardiosignal by adopting wavelet transformation.
The wavelet transformation is adopted to preprocess the collected electrocardiosignals so as to ensure the accuracy and reliability of the electrocardiosignals. By decomposing the electrocardiographic signals on multiple scales, useful signal components and noise can be effectively separated, and power frequency interference, high-frequency noise and baseline drift, which are caused by factors such as power frequency, electromagnetic interference, respiration, skin impedance and the like, are removed. After wavelet transformation, useful electrocardiosignal coefficients are reserved and signals are reconstructed, so that the quality of electrocardiosignals is remarkably improved. The characteristics of the electrocardiographic signals at different times and scales are analyzed by the formula (1).
(1)
Wherein X (t) is the original time domain electrocardiosignal.Is a time shift parameter for adjusting the position of the wavelet function on the time axis.Is a scale parameter for adjusting the width of the wavelet function so that the signal is analyzed on different scales.Representing successive-wavelet transform (Wavelet Transform) coefficients,Representing X (t) with respect to wavelet functionIs essentially different in representation form of the same transformation result for characterizing electrocardiosignals at different positionsAndThe following features.
The electrocardiosignals are decomposed on a plurality of scales through wavelet transformation, coefficients of useful signals are reserved, and finally, the signals are reconstructed through the extracted wavelet coefficients, so that noise interference is reduced, and subsequent QRS complex detection is facilitated.
And S3, extracting features of the preprocessed electrocardiosignals to obtain an initial feature set, wherein the initial feature set comprises heart rate features and heart rate variability features. The heart rate variability features include time domain features, frequency domain features, and non-linear features.
The step S3 specifically comprises the steps of carrying out positioning detection of R waves based on the preprocessed electrocardiosignals, and extracting heart rate characteristics and heart rate variability characteristics based on the R waves.
Heart rate metrics and heart rate variability metrics may be used for fatigue state identification. By observing the waveform diagram of the electrocardiosignal, the R wave in the QRS wave group has larger amplitude, narrower pulse width, obvious characteristics and easy identification. The positioning of the R-wave is the most important step in the identification of the electrocardiographic signals. The application uses a method for detecting QRS wave group in real time proposed by Pan and WillisJ.Jiapu Tompkins to carry out positioning detection on R waves, namely carrying out differential processing on the denoised electrocardiosignal to obtain slope information, then adopting square operation to process the electrocardiosignal which is subjected to differential processing, and realizing smoothing of the waveforms through window sliding integration in order to remove the detection and positioning of the R waves which are influenced by double peaks formed by negative waveforms possibly caused by square operation. And positioning and detecting the R wave by using an adaptive threshold method after the electrocardiosignal is processed. The formula involved is as follows:
(2)
(3)
(4)
Equation (2) represents differential processing of the electrocardiographic signals, Is the original electrocardiosignal of the nth signal sequence,Is the differential signal. In the formulaAndRepresenting the values of the signal at different points in timeRepresenting the previous differential value, this differential operation helps to extract the slope information of the signal, n representing the sequence number in the signal sequence, for identifying the relevant calculations of signal values etc. at different times. And obtaining slope information of electrocardiosignals and the like by carrying out differential operation on signal values corresponding to different n values.
Equation (3) represents the differential signalAnd performing square operation.Is the squared signal and T is the sampling period. The squaring operation may enhance the forward waveform of the signal so that the R wave is more prominent.
Equation (4) represents window sliding integration of the signal to achieve smoothing.The time period is indicated as a time period,Is the signal after the smoothing of the signal,Is the size of the window in which the window is to be formed,Is the time of the original electrocardiosignalIs a value of (2). This integration helps to reduce the negative going waveform that may be generated after the squaring operation, thereby improving the accuracy of R-wave detection.
The extraction of heart rate and heart rate variability (HEARTRATE VARIABILITY, HRV) indices based on R-wave localization is a systematic procedure for assessing the autonomic regulation function of the heart. It includes time domain features, frequency domain features, and non-linear features. Time domain features focus mainly on the direct measurement of the heart beat interval, and these indices can reflect the activity of the autonomic nervous system and the adaptability of the heart. The frequency domain features then evaluate the balance of the sympathetic and parasympathetic nervous systems by analyzing the frequency components of the heart rate, these indices helping to identify autonomic nervous system changes in fatigue status. The non-linear features provide a deeper understanding of heart rate complexity that reveals the complexity and regularity of heart rate variability, thereby more accurately assessing fatigue status.
The method comprises the following specific steps:
First, the time interval between two consecutive R-waves, i.e. RR intervals, is calculated, and then these intervals are used to calculate the heart rate, typically by the formula heart rate (bpm) =60/average RR interval (seconds).
Next, heart rate indicators, such as average heart rate, maximum heart rate, minimum heart rate, standard deviation of heart rate, etc., are extracted. The HRV index extraction comprises time domain indexes such as SDNN, RMSSD, NN50 0, frequency domain indexes, total power, LF/HF ratio and the like calculated by analyzing the powers of different frequency bands (such as very low frequency VLF, low frequency LF and high frequency HF) through Fourier transformation, and nonlinear indexes such as Poincare graph analysis, sample entropy (SampEn) and the like.
The meanings of the time domain features and the frequency domain features are shown in tables 1 and 2, respectively.
TABLE 1
TABLE 2
Because of the non-unification of the dimensions and units of the feature indexes, in order to avoid the influence of related factors on the calculation speed of the model, the feature index data must be normalized and mapped between [ -1,1] to obtain an initial feature set.
And S4, screening the initial feature set based on a SHAP algorithm to obtain a key feature set. The method comprises the steps of calculating SHAP values of each initial feature in the initial feature set based on a SHAP algorithm, sequencing the initial features according to the sequence from the large average absolute value to the small average absolute value of the SHAP values, and selecting the first N initial features to construct a key feature set.
In order to remove redundant features, avoid multiple co-linearity problems and improve the overall quality of the data, this step will be the feature selection. The SHAP (SHAPLEY ADDITIVE exPlanations) algorithm evaluates the importance of features by calculating the contribution of each feature to the model's predictions. First, the SHAP value of each initial feature in the initial feature set obtained in step S3 is calculated. The average impact of each initial feature on model predictions can be analyzed by SHAP values. In general, a SHAP value with a large absolute value means that the feature has a large impact on model prediction, the initial features are ranked according to the average absolute value of the SHAP values, and the first N initial features with the largest impact on model prediction are selected to construct the key feature set.
S5, determining the grade of the fatigue state by adopting a fatigue recognition model based on the key feature set, wherein the fatigue recognition model is obtained by training a random forest model by using a sample key feature set, and the sample key feature set is obtained by processing a sample electrocardiosignal.
The random forest model improves the accuracy and stability of the overall model by constructing a plurality of decision trees and combining the prediction results of the decision trees. The random forest model is advantageous in processing high dimensional data in that it does not require feature selection because each tree is trained on a randomly selected set of sample key features. The random forest model designs four output nodes corresponding to a mild fatigue state, a moderate fatigue state, a severe fatigue state, and a non-fatigue state, respectively. In the training and predicting process of the random forest model, the sample key feature set is input into the random forest model. The sample key feature sets integrate heart rate indexes and Heart Rate Variability (HRV) indexes, and provide powerful physiological basis for evaluating the fatigue state of an individual. The random forest model can accurately classify the sample key feature set into the four fatigue states by virtue of the strong pattern recognition capability, so that the classification of the fatigue state levels is realized.
In the training process of the random forest model, the sample key feature set is continuously subjected to iterative improvement, and the key feature set which enables the model performance to be optimal is found.
And S6, performing fatigue intervention by adopting different types of music and time-frequency interference signals with different frequencies based on the level of the fatigue state.
When the grade 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 carried out by playing soothing music and a weak high-frequency (frequency is 1000 Hz) time-frequency interference signal, so that the mind is kept awake, and the work can be concentrated.
When the level of the fatigue state is a moderate fatigue state, fatigue intervention is performed by playing rock music with stronger rhythm sense and applying stronger high-frequency (frequency is 2000 Hz) time-frequency interference signals.
When the fatigue state is in a severe fatigue state, fatigue intervention is performed by applying a time-frequency interference signal with the frequency of 3000Hz, and the necessary energy and attention in the work can be recovered by improving the intensity of the time-frequency interference signal.
The application uses time domain interferometry (Temporal Interference, TI) technology for fatigue intervention, which is a non-invasive deep brain stimulation means 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 inside the brain creates a strong overlapping electric field and creates a low frequency envelope wave. The low frequency envelope wave can penetrate into deep brain layer to regulate brain activity. Through accurate electric field control, brain activities can be deeply regulated, and fatigue symptoms can be improved.
In the fatigue intervention method, parameters of time-frequency interference (TI) signals are accurately adjusted according to the severity of individual fatigue states so as to realize personalized intervention. The method comprises the steps of adjusting the intensity of stimulation through changing the intensity of current transmitted by the electrodes so as to realize effective intervention on a heavy fatigue state, changing the frequency of a low-frequency envelope wave through adjusting the frequency difference of two pairs of high-frequency stimulation signals so as to influence the synchronism of a neural network and realize different nerve adjusting effects, and further adjusting the intensity of nerve stimulation by combining with different types of music so as to adapt to the requirements of different fatigue grades. This multidimensional adjustment strategy ensures the flexibility and effectiveness of interventions aimed at optimizing the intervention effect, maintaining and promoting the physical and mental health of the individual.
The fatigue intervention method provided by the application has the following advantages.
1. The application can locate the specific area of brain more accurately by the electric field stimulation generated by the external electric field, and compared with the traditional current stimulation, the application has more accurate location and can act on the brain more deeply. The accurate stimulation mode can effectively regulate brain activities, provide more remarkable fatigue intervention effect, reduce unnecessary stimulation to non-target areas and reduce potential adverse reactions.
2. The application introduces feature selection techniques to optimize the performance of the fatigue recognition model. Features most critical to fatigue state assessment are identified and selected by quantifying the contribution of each feature to model predictions using SHAP values. The feature selection step not only improves the accuracy and efficiency of the model, but also enhances the interpretation capability of the model, so that the fatigue intervention is more accurate and personalized.
Based on the same inventive concept, the embodiment of the application also provides a system for realizing the fatigue intervention method. The implementation of the solution provided by the system is similar to that described in the above method, so the specific limitations in one or more embodiments of the fatigue intervention system provided below may be referred to above as limitations of the fatigue intervention method, and will not be repeated here.
In one exemplary embodiment, a fatigue intervention system is provided, comprising the following modules.
And the electrocardiosignal acquisition module is used for acquiring human electrocardiosignals.
And the preprocessing module is used for preprocessing the electrocardiosignals by adopting wavelet transformation.
The feature extraction module is used for carrying out feature extraction on the preprocessed electrocardiosignals to obtain an initial feature set, wherein the initial feature set comprises heart rate features and heart rate variability features.
And the feature screening module is used for screening the initial feature set based on the SHAP algorithm to obtain a key feature set.
The fatigue state grade determining module is used for determining the grade of the fatigue state by adopting a fatigue recognition model based on the key feature set, wherein the fatigue recognition model is obtained by training a random forest model by using a sample key feature set, and the sample key feature set is obtained by processing a sample electrocardiosignal.
And the fatigue intervention module is used for performing fatigue intervention by adopting different types of music and time-frequency interference signals with different frequencies based on the level of the fatigue state.
In an exemplary embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor performing the steps of the method embodiments described above when the computer program is executed. The computer device may be a server or a terminal, and its internal structure may be as shown in fig. 2. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured 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, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing data to be processed. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a fatigue intervention method.
It will be appreciated by persons skilled in the art that the architecture shown in fig. 2 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor performing the steps of the method embodiments described above when the computer program is executed.
In an exemplary embodiment, a computer-readable storage medium is provided, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method embodiments described above.
In an exemplary embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are both information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data are required to meet the related regulations.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magneto-resistive random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (PHASE CHANGE Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic RandomAccess Memory, DRAM), etc.
The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The principles and embodiments of the present application have been described herein with reference to specific examples, which are intended to facilitate an understanding of the principles and concepts of the application and are to be varied in scope and detail by persons of ordinary skill in the art based on the teachings herein. In view of the foregoing, this description should not be construed as limiting the application.
Claims (10)
1. A method of fatigue intervention, comprising:
collecting electrocardiosignals of a human body;
Preprocessing the electrocardiosignal by adopting wavelet transformation;
extracting features of the preprocessed electrocardiosignals to obtain an initial feature set, wherein the initial feature set comprises heart rate features and heart rate variability features;
screening the initial feature set based on SHAP algorithm to obtain a key feature set;
Determining the grade of the fatigue state by adopting a fatigue recognition model based on the key feature set, wherein the fatigue recognition model is obtained by training a random forest model by using a sample key feature set, and the sample key feature set is obtained by processing a sample electrocardiosignal;
based on the level of the fatigue state, different types of music and different frequency time-frequency interference signals are adopted for fatigue intervention.
2. The fatigue intervention method according to claim 1, wherein the feature extraction is performed on the preprocessed electrocardiographic signals to obtain an initial feature set, and specifically comprises:
Performing positioning detection of R waves based on the preprocessed electrocardiosignals;
heart rate features and heart rate variability features are extracted based on the R-waves.
3. The method of claim 2, wherein the heart rate variability features include time domain features, frequency domain features, and non-linear features.
4. The fatigue intervention method according to claim 1, wherein the initial feature set is screened based on a SHAP algorithm to obtain a key feature set, comprising:
calculating a SHAP value of each initial feature in the initial feature set based on a SHAP algorithm;
and sequencing the initial features according to the sequence from the large average absolute value to the small average absolute value of the SHAP value, and selecting the first N initial features to construct a key feature set.
5. The method of claim 1, wherein the fatigue status comprises a non-fatigue status, a mild fatigue status, a moderate fatigue status, and a severe fatigue status.
6. The method according to claim 5, characterized in that based on the level of the fatigue state, different types of music and different frequencies of time-frequency interference signals are used for fatigue intervention, comprising in particular:
when the grade 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, performing fatigue intervention by playing soothing music and applying a time-frequency interference signal with the frequency of 1000 Hz;
When the level of the fatigue state is a moderate fatigue state, performing fatigue intervention by playing rock music and applying a time-frequency interference signal with the frequency of 2000 Hz;
When the level of fatigue state is a severe fatigue state, fatigue intervention is performed by applying a time-frequency interference signal with a frequency of 3000 Hz.
7. A fatigue intervention system, comprising:
The electrocardiosignal acquisition module is used for acquiring electrocardiosignals of a human body;
the preprocessing module is used for preprocessing the electrocardiosignals by adopting wavelet transformation;
the device comprises a feature extraction module, a feature extraction module and a processing module, wherein the feature extraction module is used for carrying out feature extraction on the preprocessed electrocardiosignals to obtain an initial feature set, and the initial feature set comprises heart rate features and heart rate variability features;
the feature screening module is used for screening the initial feature set based on an SHAP algorithm to obtain a key feature set;
The fatigue state grade determining module is used for determining the grade of the fatigue state by adopting a fatigue recognition model based on the key feature set, wherein the fatigue recognition model is obtained by training a random forest model by using a sample key feature set, and the sample key feature set is obtained by processing a sample electrocardiosignal;
And the fatigue intervention module is used for performing fatigue intervention by adopting different types of music and time-frequency interference signals with different frequencies based on the level of the fatigue state.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor executes the computer program to implement the fatigue intervention method of any of claims 1-6.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the fatigue intervention method according to any of claims 1-6.
10. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements the fatigue intervention method of any of claims 1-6.
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Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108765876A (en) * | 2018-05-31 | 2018-11-06 | 东北大学 | Driving fatigue depth analysis early warning system based on multimode signal and method |
| US20190001127A1 (en) * | 2017-06-30 | 2019-01-03 | Lungpacer Medical Inc. | Devices and methods for prevention, moderation, and/or treatment of cognitive injury |
| CN113080986A (en) * | 2021-05-07 | 2021-07-09 | 中国科学院深圳先进技术研究院 | Method and system for detecting exercise fatigue based on wearable equipment |
| CN113288168A (en) * | 2021-05-21 | 2021-08-24 | 天津工业大学 | Wearable fatigue monitoring of intelligence and early warning system |
| US20210267523A1 (en) * | 2018-05-01 | 2021-09-02 | Wyss Center For Bio And Neuro Engineering | Neural Interface System |
| CN114343642A (en) * | 2021-10-22 | 2022-04-15 | 厦门大学 | Fatigue driving detection method and system based on heart rate variability index |
| CN115067962A (en) * | 2022-04-29 | 2022-09-20 | 清华大学 | Electrocardiosignal classification method and device |
| WO2023003501A1 (en) * | 2021-07-23 | 2023-01-26 | Frigg Ab | Device and method for stimulating a target area |
| CN115969383A (en) * | 2023-02-16 | 2023-04-18 | 北京科技大学 | Human body physiological fatigue detection method based on electrocardiosignals and respiratory signals |
| CN116327211A (en) * | 2023-02-23 | 2023-06-27 | 南京信息工程大学 | ECG signal classification device based on sustainable learning shallow recurrent neural network |
| CN118643416A (en) * | 2024-05-28 | 2024-09-13 | 桂林电子科技大学 | A self-learning mental fatigue quantification method based on scalable adaptive weighted data fusion |
| CN119014881A (en) * | 2024-08-16 | 2024-11-26 | 天津工业大学 | A dual-channel EEG fatigue detection method, device, equipment, medium and product |
| CN119312213A (en) * | 2024-09-27 | 2025-01-14 | 重庆科技大学 | Driver fatigue state recognition method, system and storage medium |
| CN119494026A (en) * | 2024-09-27 | 2025-02-21 | 东南大学 | An intelligent arrhythmia classification device based on deep learning |
-
2025
- 2025-03-17 CN CN202510308501.4A patent/CN119818800B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190001127A1 (en) * | 2017-06-30 | 2019-01-03 | Lungpacer Medical Inc. | Devices and methods for prevention, moderation, and/or treatment of cognitive injury |
| US20210267523A1 (en) * | 2018-05-01 | 2021-09-02 | Wyss Center For Bio And Neuro Engineering | Neural Interface System |
| CN108765876A (en) * | 2018-05-31 | 2018-11-06 | 东北大学 | Driving fatigue depth analysis early warning system based on multimode signal and method |
| CN113080986A (en) * | 2021-05-07 | 2021-07-09 | 中国科学院深圳先进技术研究院 | Method and system for detecting exercise fatigue based on wearable equipment |
| CN113288168A (en) * | 2021-05-21 | 2021-08-24 | 天津工业大学 | Wearable fatigue monitoring of intelligence and early warning system |
| WO2023003501A1 (en) * | 2021-07-23 | 2023-01-26 | Frigg Ab | Device and method for stimulating a target area |
| CN114343642A (en) * | 2021-10-22 | 2022-04-15 | 厦门大学 | Fatigue driving detection method and system based on heart rate variability index |
| CN115067962A (en) * | 2022-04-29 | 2022-09-20 | 清华大学 | Electrocardiosignal classification method and device |
| CN115969383A (en) * | 2023-02-16 | 2023-04-18 | 北京科技大学 | Human body physiological fatigue detection method based on electrocardiosignals and respiratory signals |
| CN116327211A (en) * | 2023-02-23 | 2023-06-27 | 南京信息工程大学 | ECG signal classification device based on sustainable learning shallow recurrent neural network |
| CN118643416A (en) * | 2024-05-28 | 2024-09-13 | 桂林电子科技大学 | A self-learning mental fatigue quantification method based on scalable adaptive weighted data fusion |
| CN119014881A (en) * | 2024-08-16 | 2024-11-26 | 天津工业大学 | A dual-channel EEG fatigue detection method, device, equipment, medium and product |
| CN119312213A (en) * | 2024-09-27 | 2025-01-14 | 重庆科技大学 | Driver fatigue state recognition method, system and storage medium |
| CN119494026A (en) * | 2024-09-27 | 2025-02-21 | 东南大学 | An intelligent arrhythmia classification device based on deep learning |
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