A method and system for monitoring mental and physical fatigue
By combining data acquired and analyzed from electromyography (EMG) and heart rate sensors, the problem of traditional fatigue monitoring methods failing to comprehensively reflect fatigue status has been solved, enabling accurate assessment of both physiological and psychological fatigue.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANGHAI AIRCRAFT MFG
- Filing Date
- 2026-02-28
- Publication Date
- 2026-06-05
AI Technical Summary
Traditional fatigue monitoring methods, which rely solely on physiological means, cannot fully reflect the fatigue state of the target, resulting in poor monitoring effectiveness.
By combining electromyography (EMG) and heart rate sensors to acquire EMG and heart rate data of the fatigue monitoring target, physiological fatigue scores and psychological fatigue scores are calculated through time-domain and frequency-domain analysis, and the fatigue monitoring results are determined comprehensively.
It enables precise monitoring of fatigue status, improves the accuracy of fatigue monitoring, and can comprehensively assess the degree of fatigue from both physiological and psychological dimensions.
Smart Images
Figure CN122140205A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of fatigue monitoring technology, and in particular to a method and system for monitoring psychological and physiological fatigue. Background Technology
[0002] Traditional fatigue monitoring methods typically monitor physiological fatigue through physical means, such as acquiring images or videos of the target through a camera and judging fatigue based on facial features and other characteristics of the target captured in the video.
[0003] However, this fatigue monitoring method only monitors physiological fatigue, which cannot fully reflect the fatigue state of the target being monitored, resulting in poor fatigue monitoring results. Summary of the Invention
[0004] This invention provides a method and system for monitoring psychological and physiological fatigue, which can detect physiological fatigue and psychological fatigue to comprehensively determine the fatigue state of the target.
[0005] According to one aspect of the present invention, a method for monitoring psychological and physiological fatigue is provided, the method comprising: Electromyography (EMG) data of the fatigue monitoring target is acquired using an EMG sensor, and heart rate data of the fatigue monitoring target is acquired using a heart rate sensor. The electromyography data were analyzed in the time domain and frequency domain to obtain a physiological fatigue score, and the heart rate data were analyzed in the time domain to obtain a psychological fatigue score. The fatigue monitoring results of the fatigue monitoring target are determined based on the physiological fatigue score and the psychological fatigue score.
[0006] According to another aspect of the present invention, a psychological and physiological fatigue monitoring system is provided, comprising: a fatigue monitoring sensor and a fatigue analysis module; The fatigue monitoring sensor includes an electromyography (EMG) sensor, used to acquire EMG data of the fatigue monitoring target; The fatigue monitoring sensor also includes a heart rate sensor for acquiring heart rate data of the fatigue monitoring target; The fatigue analysis module is used to perform time-domain and frequency-domain analysis on the electromyography data to obtain a physiological fatigue score, and to perform time-domain analysis on the heart rate data to obtain a psychological fatigue score. The fatigue analysis module is also used to determine the fatigue monitoring results of the fatigue monitoring target based on the physiological fatigue score and the psychological fatigue score.
[0007] The technical solution of this application includes: acquiring electromyographic data of a fatigue monitoring target through an electromyography (EMG) sensor and acquiring heart rate data of the fatigue monitoring target through a heart rate sensor; performing time-domain and frequency-domain analysis on the EMG data to obtain a physiological fatigue score, and performing time-domain analysis on the heart rate data to obtain a psychological fatigue score; and determining the fatigue monitoring result of the fatigue monitoring target based on the physiological fatigue score and the psychological fatigue score. This technical solution, by monitoring both physiological and psychological fatigue of the fatigue monitoring target, can determine the fatigue monitoring result of the fatigue monitoring target from both physiological and psychological dimensions, greatly improving the accuracy of fatigue monitoring.
[0008] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description
[0009] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0010] Figure 1 This is a flowchart of a method for monitoring psychological and physiological fatigue according to Embodiment 1 of this application; Figure 2 This is a flowchart of a method for monitoring psychological and physiological fatigue according to Embodiment 2 of this application; Figure 3 This is a flowchart of electromyography data preprocessing according to Embodiment 2 of this application; Figure 4 This is a schematic diagram of a psychological and physiological fatigue monitoring system provided according to Embodiment 3 of this application; Figure 5 This is a flowchart of a psychophysiological integrated fatigue monitoring and early warning system provided according to Embodiment 3 of this application. Detailed Implementation
[0011] To enable those skilled in the art to better understand the present invention, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.
[0012] It should be noted that the terms "first," "second," "target," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0013] Example 1 Figure 1 This document provides a flowchart of a psychological and physiological fatigue monitoring method according to Embodiment 1 of this application. This embodiment is applicable to situations requiring fatigue monitoring, and the method can be executed by a psychological and physiological fatigue monitoring system. Figure 1 As shown, the method includes: S110 acquires electromyographic data of the fatigue monitoring target through an electromyographic sensor and heart rate data of the fatigue monitoring target through a heart rate sensor.
[0014] The method described in this application embodiment can be executed by a psychological and physiological fatigue monitoring system, and the corresponding data can be obtained through the electromyography sensor and heart rate sensor in the psychological and physiological fatigue monitoring system.
[0015] Electromyography (EMG) data, also known as electromyographic signal data, consists of weak electrical signals generated by muscles during activity. EMG data can be used for fatigue monitoring.
[0016] Specifically, the electromyography (EMG) sensor can be attached to the skin surface of the fatigue monitoring target. While the fatigue monitoring target is performing its current task, the EMG sensor acquires the target's EMG data. Similarly, a heart rate sensor acquires the target's heart rate data. The current task can be related to the fatigue state of the person performing the task (the fatigue detection target), such as a fitness task or a production task.
[0017] S120, perform time-domain and frequency-domain analysis on the electromyography data to obtain a physiological fatigue score, and perform time-domain analysis on the heart rate data to obtain a psychological fatigue score.
[0018] After obtaining electromyography (EMG) data and heart rate data, time-domain and frequency-domain analyses can be performed on the EMG data to determine the physiological fatigue score of the fatigue monitoring target from multiple dimensions. This physiological fatigue score reflects the physiological functional state of the fatigue monitoring target. Similarly, time-domain analysis can be performed on the heart rate data to obtain the heart rate changes of the fatigue monitoring target, thereby determining the psychological state of the fatigue monitoring target, such as emotions and stress, and obtaining a psychological fatigue score.
[0019] It should be noted that the acquisition, storage, use, and processing of data in the technical solution of this application all comply with the relevant provisions of national laws and regulations, and user authorization has been obtained. Taking fitness tasks as an example, users may need to objectively detect their fatigue status in order to adjust their fitness plans. In this case, users can use the psychological and physiological fatigue monitoring system in the embodiments of this application to monitor their fatigue status. Similarly, for work tasks, users may want to maintain a good state to complete the work tasks, but in the process of completing the work tasks, users can only judge fatigue based on their feelings and cannot objectively judge the degree of fatigue. They can use the psychological and physiological fatigue monitoring system in the embodiments of this application to monitor their fatigue status.
[0020] S130, determine the fatigue monitoring result of the fatigue monitoring target based on the physiological fatigue score and the psychological fatigue score.
[0021] In this embodiment, after obtaining the physiological fatigue score and the psychological fatigue score, they can be weighted and summed. Based on the specific numerical value of the calculation result, the overall fatigue level of the fatigue monitoring target can be determined. Alternatively, the degree of physiological fatigue can be judged based on the physiological fatigue score, and the degree of psychological fatigue can be judged based on the psychological fatigue score. The overall fatigue state can then be determined based on both the physiological and psychological fatigue levels.
[0022] The technical solution of this application includes: acquiring electromyographic data of a fatigue monitoring target through an electromyography (EMG) sensor and acquiring heart rate data of the fatigue monitoring target through a heart rate sensor; performing time-domain and frequency-domain analysis on the EMG data to obtain a physiological fatigue score, and performing time-domain analysis on the heart rate data to obtain a psychological fatigue score; and determining the fatigue monitoring result of the fatigue monitoring target based on the physiological fatigue score and the psychological fatigue score. This technical solution, by monitoring both physiological and psychological fatigue of the fatigue monitoring target, can determine the fatigue monitoring result of the fatigue monitoring target from both physiological and psychological dimensions, greatly improving the accuracy of fatigue monitoring.
[0023] Example 2 Figure 2 This is a flowchart of a psychological and physiological fatigue monitoring method provided in Embodiment 2 of this application. This embodiment is an optimization based on the above embodiment.
[0024] like Figure 2 As shown, the method in this embodiment of the application specifically includes the following steps: S210 acquires electromyographic data of the fatigue monitoring target through an electromyographic sensor and acquires heart rate data of the fatigue monitoring target through a heart rate sensor.
[0025] For example, after acquiring electromyographic data of a fatigue monitoring target through an electromyographic sensor, the electromyographic data can be preprocessed, such as... Figure 3 The flowchart shown above illustrates the electromyography (EMG) data preprocessing process. Based on the preprocessed EMG data, parameters such as median EMG frequency, root mean square EMG signal, and muscle unit activation can be calculated.
[0026] S220, perform frequency domain analysis on the electromyography data to obtain the median frequency of the electromyography.
[0027] For example, the median frequency (MF) of electromyography is calculated using frequency domain analysis, as follows: ; Where PSD represents the power spectrum of electromyography, and f is the frequency value corresponding to a sampling point.
[0028] S230, perform time-domain analysis on the electromyographic data to obtain the root mean square of electromyography and muscle unit activation.
[0029] For example, the root mean square (RMS) electromyography index is calculated through time-domain analysis, as follows: ; Where N is the number of electromyographic data points, and xi is the electromyographic signal value corresponding to the i-th sampling point in the electromyographic signal.
[0030] Muscle unit activation (a(t)) was calculated using time-domain analysis, as follows: ; ; Where A can be a constant, such as -2, and u(t) represents neural activation. , , is the neural activation coefficient, which can be 0.949, 0.052, or 0.006 for example; d is the time delay, which can be 36 ms for example. t represents the sampling time interval, and t represents the current sampling time point.
[0031] S240, determine the physiological fatigue score based on the median frequency of electromyography, the root mean square of electromyography, and the activation of muscle units.
[0032] After obtaining the median frequency of electromyography (EMG), the root mean square (RMS) of EMG, and the activation of muscle units, the higher the RMS of EMG and the higher the activation of muscle units, the higher the degree of muscle fatigue, while the median frequency of EMG has the opposite effect. Based on this, the physiological fatigue score of the fatigue monitoring target can be determined.
[0033] In this embodiment of the application, optionally, the physiological fatigue score is determined based on the median frequency of electromyography (EMG), the root mean square of EMG, and the activation of muscle units, including: determining the physiological fatigue index corresponding to each sampling time window based on the median frequency of EMG, the root mean square of EMG, and the activation of muscle units for each sampling time window; and determining the physiological fatigue score based on the physiological fatigue index corresponding to each sampling time window within the fatigue score update cycle.
[0034] For example, the time-frequency domain joint analysis method is used to calculate the physiological and psychological fatigue scores based on the above. The window length is five sampling periods and the window interval is 10 sampling periods (that is, the fatigue score update period is 10 sampling periods). Considering the good continuity of the output results and the moderate amount of computation, the window overlap rate can be set to 75%.
[0035] Taking a sensor sampling frequency of 1000Hz as an example, with a sampling period of 1ms and a window length of five sampling periods, the window size is 5ms, the window interval is 10ms (i.e., fatigue score is updated every 10ms), and the window step is window length × (1 - overlap rate), which equals 1.25ms. That is, the fatigue score result is updated every 8 windows, and the update frequency is 100Hz. This method can be adapted to sensors with different sampling frequencies and can meet the real-time requirements of analysis.
[0036] Specifically, within each sampling time window, the physiological fatigue index corresponding to that sampling time window is determined based on the calculated median frequency of electromyography (EMG), root mean square EMG values, and muscle unit activation. This physiological fatigue index is calculated using the following formula: ; in, For the physiological fatigue index, RMS, a(t), and MF use the mean within a sampling time window; weights k1, k2, and k3 are weights. For example, considering the electromyographic time-frequency domain characteristics, k1 is 0.4, k2 is 0.2, and k3 is 0.4.
[0037] The sampling time window is determined by the device's sampling frequency. Five sampling periods can be used as a segment. For example, with a 1000Hz sensor, the sampling period is 1ms, and 5ms of data can be used to calculate the power factor (PF). The higher the PF value, the stronger the user's perceived physiological fatigue.
[0038] In this embodiment of the application, optionally, the physiological fatigue score is determined based on the physiological fatigue index corresponding to each sampling time window within the fatigue score update cycle, including: determining the minimum and maximum physiological fatigue index among the physiological fatigue indices corresponding to each sampling time window within the fatigue score update cycle; normalizing the remaining physiological fatigue indices based on the minimum and maximum physiological fatigue indices to obtain the normalized remaining physiological fatigue index; the remaining physiological fatigue index is the physiological fatigue index other than the minimum and maximum physiological fatigue index among the physiological fatigue indices corresponding to each sampling time window within the fatigue score update cycle; and determining the physiological fatigue score corresponding to the fatigue score update cycle based on the normalized remaining physiological fatigue index.
[0039] For example, for physiological fatigue, the median frequency of electromyography (EMG), root mean square EMG, and muscle unit activation parameters are extracted. The degree of muscle fatigue PF within each sampling time window is calculated. PFmin is the minimum value calculated from n windows within a window interval, and PFmax is the maximum value calculated from n windows within a window interval. The minimum and maximum values are used to standardize the n-2 remaining physiological fatigue indices to a score between 0 and 1. The closer the remaining physiological fatigue index is to 1, the more severe the fatigue. Finally, the average is taken as the current physiological fatigue score PF_SCORE. The specific formula is as follows, where n is the number of windows within a fatigue score update cycle: ; ; in, This represents the remaining physiological fatigue index. This represents the normalized residual physiological fatigue index. The minimum physiological fatigue index. This represents the maximum physiological fatigue index.
[0040] This scheme is designed to achieve real-time updates of physiological fatigue scores. Because the sampling time windows within the fatigue score update cycle overlap, the calculated physiological fatigue index is continuous and related to the muscle fatigue state (physiological fatigue state changes continuously, not suddenly fatigued and suddenly not fatigued). This avoids the problem of outliers and can accurately reflect the degree of physiological fatigue. Therefore, the physiological fatigue score calculated based on the remaining physiological fatigue index can accurately reflect the degree of physiological fatigue.
[0041] S250, perform time-domain analysis on the heart rate data to obtain a psychological fatigue score.
[0042] In this embodiment of the application, optionally, time-domain analysis is performed on the heart rate data to obtain a psychological fatigue score, including: calculating the heart rate variability corresponding to each sampling time window based on the heart rate data corresponding to each sampling time window within the fatigue score update period; determining the minimum and maximum heart rate variability among the heart rate variability corresponding to each sampling time window within the fatigue score update period; normalizing the remaining heart rate variability based on the minimum and maximum heart rate variability to obtain normalized remaining heart rate variability; the remaining heart rate variability is the heart rate variability within the fatigue score update period excluding the minimum and maximum heart rate variability; and determining the psychological fatigue score corresponding to the fatigue score update period based on the normalized remaining heart rate variability.
[0043] For example, similarly, within the sampling time window, the corresponding heart rate variability is calculated based on the collected heart rate data.
[0044] Heart rate variability (HRV) is calculated using time-domain analysis. The calculation method is as follows: Where RR stands for inter-heart interval. This represents the i-th RR interval. is the average RR interval, and N is the total number of RR intervals.
[0045] Mental fatigue (MF) is characterized by the HRV (Human Risk Value) index, specifically as follows: Among them, available This represents the result of averaging within a sampling window. The sampling time is determined by the sampling frequency of the device. Five sampling periods can be used as a segment. For example, for a 1000Hz sensor, the sampling period is 1ms, and 5ms of data can be used to calculate MF.
[0046] To address mental fatigue, heart rate variability was analyzed, and the mental fatigue score MF_SCORE (a lower HRV indicates a higher degree of fatigue) was derived using the method described above. The formula is as follows: ; ; Where n is the number of sampling windows within the fatigue score update cycle. For residual heart rate variability, For normalized residual heart rate variability, For minimum heart rate variability, This refers to maximum heart rate variability.
[0047] S260, determine the fatigue monitoring result of the fatigue monitoring target based on the physiological fatigue score and the psychological fatigue score.
[0048] In this embodiment of the application, optionally, determining the fatigue monitoring result of the fatigue monitoring target based on the physiological fatigue score and the psychological fatigue score includes: in each fatigue score update cycle, performing a weighted summation of the physiological fatigue score and the psychological fatigue score corresponding to the fatigue score update cycle to obtain the fatigue monitoring result of the fatigue monitoring target corresponding to the fatigue score update cycle.
[0049] For example, the fatigue monitoring result, Fatigue_score, can be calculated using a weighted average method, as shown in the following formula: ; The weights in the formula can be set to initial values k1=0.5, k2=0.5, and the parameters can be dynamically adjusted according to the usage scenario and requirements, but the constraint k1+k2=1 must be satisfied.
[0050] This algorithm can perform multi-dimensional quantitative assessment of individual fatigue and determine the user's overall fatigue status under the current task.
[0051] In this embodiment of the application, optionally, after determining the fatigue monitoring result of the fatigue monitoring target based on the physiological fatigue score and the psychological fatigue score, the method further includes: displaying the physiological fatigue score curve, the current psychological fatigue score, and / or fatigue adjustment information determined based on the fatigue monitoring result in a graphical user interface.
[0052] For example, to help users understand their fatigue state, a real-time fatigue status display can be provided through a graphical user interface. Users can view their fatigue status through the graphical user interface, including data such as muscle fatigue curves, muscle fatigue warning values, heart rate variability, and subjective stress levels.
[0053] Specifically, the muscle fatigue curve can be obtained using the aforementioned physiological fatigue state index (PF). The curve of the physiological fatigue state index is displayed in real time, allowing users to easily perceive their own physical state. When the physiological fatigue state index exceeds a specified threshold, the current fatigue state index value is displayed on the interface in real time, reminding the user to rest or stop the task. Similarly, heart rate variability and subjective stress state (i.e., psychological fatigue state index) can also be displayed in real time on the visualization interface, providing user-friendly interactivity and reminder functions. The interface is shown in the following figure: In practical applications, users can view this data through a mobile app or tablet application. The application provides suggestions based on the user's fatigue level. For example, when muscle fatigue is at a critical level, the system will suggest that the user take a short rest or adjust their posture; when heart rate fluctuations are too large and mental fatigue increases, the system will prompt the user to perform mental adjustment, such as deep breathing or listening to soothing music.
[0054] The application interface uses simple charts and numerical displays, allowing users to intuitively see their fatigue status and warning messages. For example, muscle fatigue is displayed as a curve, and the curve's changes reflect the accumulation of muscle fatigue; heart rate variability and subjective stress levels are displayed as bar charts, making it easy for users to understand their current mental state. Through this real-time feedback mechanism, users can adjust their exertion methods and work pace at any time during task execution, avoiding mistakes or injuries caused by accumulated fatigue.
[0055] In addition, it provides a historical data review function, allowing users to view changes in their fatigue status over a period of time. By comparing data from different time periods, users can analyze the patterns of fatigue changes and provide a reference for future work or exercise arrangements.
[0056] Optionally, in this embodiment of the application, the method further includes: determining the fatigue level conditions satisfied by the fatigue monitoring results, and performing fatigue intervention based on the fatigue level conditions.
[0057] For example, after obtaining the fatigue monitoring results, a fatigue level can be preset, the fatigue level conditions that the fatigue monitoring results meet can be determined, and fatigue intervention can be carried out according to the fatigue level conditions.
[0058] Optionally, in this embodiment of the application, the method further includes: if the psychological fatigue score of the fatigue monitoring target meets the first psychological fatigue condition, then obtaining the first duration of the fatigue monitoring target's current task execution; determining the second duration based on the first duration and the preset total duration of the current task; the second duration being less than the first duration; and providing the fatigue monitoring target with a progress reminder for the current task execution based on the second duration.
[0059] Specifically, the first psychological fatigue condition can be determined based on the actual situation, such as a psychological fatigue score exceeding a preset value. The first duration is the duration during which the fatigue monitoring target performs the current task.
[0060] Specifically, when the psychological fatigue score meets the first psychological fatigue condition, the first duration of the fatigue monitoring target's current task execution is obtained. If the first duration is less than the preset total duration of the current task, the first duration is multiplied by a reduction coefficient (which can be less than 1) to obtain the second duration. The resulting second duration is less than the first duration. Based on the second duration, the fatigue monitoring target is reminded of the current task's execution progress. Clearly, since the second duration is less than the first duration, the duration used to remind the fatigue monitoring target of the task's execution progress is a "false" duration, less than the actual first duration. This makes the fatigue monitoring target realize that the task execution time is short and not yet very tiring, thus reducing the fatigue level. Taking a fitness task as an example, the fatigue monitoring target can set a total fitness time of 1 hour. If a high level of psychological fatigue is detected after 40 minutes, it can announce that half an hour of fitness has been completed, thus encouraging the fatigue monitoring target to regain their focus. It should be noted that when the task execution time equals the preset total duration of the current task, the current task has been completed, and the task can be ended. This setting ensures that the total task execution time for fatigue monitoring remains unchanged, while mitigating the effects of fatigue when psychological fatigue occurs.
[0061] Example 3 Figure 4 This is a schematic diagram of a psychological and physiological fatigue monitoring system provided in Embodiment 3 of this application. This system can execute the psychological and physiological fatigue monitoring method provided in any embodiment of this invention, and possesses the corresponding functional modules and beneficial effects for executing the method. For example... Figure 4 As shown, the system includes: a fatigue monitoring sensor 310 and a fatigue analysis module 320; The fatigue monitoring sensor 310 includes an electromyography sensor 311, used to acquire electromyography data of the fatigue monitoring target; The fatigue monitoring sensor 310 also includes a heart rate sensor 312, used to acquire the heart rate data of the fatigue monitoring target; The fatigue analysis module 320 is used to perform time-domain and frequency-domain analysis on the electromyography data to obtain a physiological fatigue score, and to perform time-domain analysis on the heart rate data to obtain a psychological fatigue score. The fatigue analysis module 320 is also used to determine the fatigue monitoring results of the fatigue monitoring target based on the physiological fatigue score and the psychological fatigue score.
[0062] This application first integrates psychological and physiological fatigue information to capture individual fatigue changes in real time, avoiding assessment errors caused by data lag, quantitatively analyzing the current fatigue state, and predicting future fatigue trends, thus realizing a fatigue monitoring system that combines psychological and physiological assessment. Second, based on this monitoring method, a real-time visual feedback system for muscle fatigue and fatigue stress perception is implemented, providing users with references for exertion and work frequency. Finally, based on the monitored user fatigue level, a subjective and objective fatigue early warning and intervention system is designed, employing multiple intervention methods to intervene in the user's subjective and objective fatigue and psychological fatigue state, thereby improving production safety and work efficiency.
[0063] Figure 5 This is a flowchart of a psychological and physiological integrated fatigue monitoring and early warning system.
[0064] To address the problem that traditional fatigue monitoring methods, which focus only on a single dimension (physiological or psychological fatigue), cannot comprehensively reflect a user's fatigue state, this application integrates comprehensive monitoring indicators. It uses an electromyography (EMG) sensor to collect physiological fatigue information and a heart rate sensor to collect psychological fatigue information. Employing a real-time acquisition and analysis device, it achieves instant information capture and analysis, extracting features such as median EMG frequency, root mean square EMG, muscle unit activation, and heart rate variability for joint time-frequency domain analysis. This allows for the calculation of muscle fatigue state (i.e., physiological) and stress state (i.e., psychological), while also predicting future fatigue trends. This provides a more comprehensive and accurate fatigue assessment, helping to more accurately understand the complexity and multidimensional characteristics of fatigue.
[0065] This function is particularly important for people who need to maintain high levels of concentration or physical exertion for extended periods (such as athletes, drivers, and medical personnel), allowing them to take preventative measures to mitigate or avoid impending fatigue peaks, thus improving work efficiency and safety. To facilitate widespread application, the device features an easy-to-wear and simple-to-operate design, ensuring users can easily learn and continuously use it. Because it comprehensively considers both psychological and physiological fatigue monitoring, this invention has broad application prospects and can be applied to various fields such as sports training, industrial production, aerospace, transportation, and healthcare, providing personalized fatigue management solutions for professionals in different industries.
[0066] This application employs a combination of multiple sensors to achieve simultaneous monitoring of psychological and physiological information. Physiological fatigue is monitored using an electromyography (EMG) sensor, which can acquire muscle signals in real time and extract features such as median EMG frequency, root mean square EMG value, and muscle unit activation. Psychological fatigue is monitored using a heart rate sensor, which collects heart rate and heart rate variability data and combines this with heart rate fluctuations to assess stress levels.
[0067] Specifically, the system's hardware includes wearable electromyography (EMG) and heart rate sensor modules. These sensor modules can be directly attached to the user's skin to collect EMG and heart rate signals. The sensors transmit the data to a host computer via a wireless transmission module. The host computer can be a laptop, industrial PC, or other computing resource with storage and processing capabilities.
[0068] In the host computer, raw data is stored and analyzed in real time, including electromyography data preprocessing, median frequency of electromyography, root mean square of electromyography, muscle unit activation, and heart rate variability characteristic calculation. The processing software is PyCharm, and the language is Python.
[0069] Optionally, in this embodiment of the application, the system further includes: a real-time visual feedback system for psychophysiological fatigue. To achieve a user-friendly human-computer interface and facilitate users' understanding of fatigue status and indicators, a real-time visual feedback system for psychophysiological fatigue was implemented. A GUI interface was designed to display muscle fatigue status curves, muscle fatigue warning values, real-time heart rate and heart rate variability, and calculated subjective fatigue stress status in real time, providing athletes, operators, and others with references for exertion and work frequency.
[0070] Through a GUI interface, complex physiological and psychological fatigue data is transformed into easily understandable charts and values, enabling real-time visualization of multi-dimensional data. This intuitive data display not only improves users' awareness of their own fatigue state but also enhances their sense of participation and control during use. Based on real-time feedback data, personalized exertion and work frequency references are provided for specific occupational groups such as athletes and operators. By monitoring and analyzing users' fatigue status, the system intelligently adjusts the suggested exertion intensity and work rhythm to help users optimize work performance and reduce errors and injuries caused by fatigue.
[0071] Optionally, in this embodiment, the system further includes: a subjective and objective fatigue early warning and intervention system. When the system detects that neither the user's muscles nor their mind are showing signs of fatigue, it does not react but continues monitoring. When the system detects muscle fatigue, it prompts the user to rest. If further analysis reveals significant muscle fatigue, the system issues an early warning to force the user to rest. When the system detects increased psychological fatigue or stress while the muscles show no fatigue and the user continues to exert expected effort, the system issues an early warning about the user's psychological state and controls the on-site timer, causing a subjective working time error (the user cannot perceive the change in time). This intervention improves the user's subjective experience by intervening in the user's time feedback. Simultaneously, predefined music is played in a small area on the production floor to intervene in the user's psychological state through sound feedback, ensuring production safety.
[0072] The system has the ability to accurately identify increased psychological fatigue and stress in users before their muscles show signs of fatigue. This differentiated identification technology allows the system to provide early warnings and interventions for users' psychological state before their muscles reach their limits, avoiding the limitations of traditional methods that rely solely on muscle fatigue as a criterion. At the same time, it allows users to exert their maximum effort.
[0073] When the system detects that the user's psychological state needs adjustment, it innovatively uses a controlled on-site timer to create a subjective error in working time, making it impossible for the user to accurately perceive the passage of time. This time feedback intervention mechanism cleverly utilizes human sensitivity to time perception, reducing the user's psychological stress and fatigue by altering their perception of time, thereby improving their subjective experience and task execution efficiency.
[0074] The system also incorporates sound feedback intervention, specifically playing music in a small, controlled environment. This music not only meets safety requirements but is also personalized based on the user's psychological state to achieve relaxation, stress reduction, or motivational effects. The music provides users with a more comfortable and harmonious working environment, helping to further alleviate their psychological stress and fatigue.
[0075] To further ensure user safety during high-intensity work or exercise, this invention designs a subjective and objective fatigue early warning and intervention system. This system can issue different levels of fatigue warnings based on the user's psychological and physiological fatigue state and take corresponding intervention measures.
[0076] Specifically, when the system detects a significant increase in muscle fatigue in the user, approaching the fatigue limit (physiological fatigue index greater than 0.8), the system will prompt the user to stop working and take a forced rest via vibration alerts or audible and visual alarms. If the user continues to ignore the prompts, the system will forcibly terminate the task execution of the fatigue monitoring target to ensure the user receives sufficient recovery time. Similarly, when psychological fatigue increases and stress rises significantly (when the psychological fatigue index is greater than 0.5), the system will intervene psychologically by adjusting the work rhythm (e.g., adjusting the metronome rhythm) and playing soothing music; when psychological stress also approaches its limit (when the psychological fatigue index is greater than 0.8), the system will remind the user to stop working and take a forced rest.
[0077] In terms of psychological fatigue intervention, this invention innovatively proposes a method to alleviate psychological stress by intervening in the user's perception of time. When the system detects significant psychological fatigue in the user, it will remind the user of their current working time (this working time is less than the actual working time, a false time, calculated as 90% of the actual time) through a prompting sound, thus shortening the user's perceived working time. Furthermore, it guides the frequency of task execution for fatigue monitoring targets by playing a rhythmic beat. Through this control of subjective time error, the user will feel that their working or exercising time has decreased, thereby reducing psychological stress and improving work efficiency and comfort. This method does not affect the actual calculation of working hours.
[0078] In addition, the system further intervenes in the user's psychological state by playing music. Different types of music are played according to the user's psychological state. For example, soothing music is played when the user feels too stressed to help them relax; motivational music is played when the user is fatigued but the task has not yet been completed to stimulate their fighting spirit and endurance.
[0079] The greatest advantage of this subjective and objective fatigue early warning and intervention system lies in its ability to accurately distinguish between psychological and physiological fatigue, and to provide targeted intervention measures based on different fatigue states. For example, when a user's muscles show obvious fatigue, the system mainly intervenes physiologically, prompting rest or adjustments to exertion patterns; while when a user experiences significant psychological fatigue, the system intervenes psychologically by adjusting time perception and playing music. This multi-dimensional and personalized fatigue management approach can effectively reduce the accumulation of fatigue during long-term task execution, improving work safety and efficiency.
[0080] The system described in this application has broad application prospects, especially suitable for people who need to maintain high concentration or high physical output for extended periods, such as athletes, drivers, medical personnel, and production line workers. Through fatigue monitoring and intervention by this system, users can achieve timely physiological and psychological recovery without affecting the completion of their work tasks, thereby reducing the risks and errors caused by fatigue.
[0081] In summary, the embodiments of this application, through a combined psychophysiological assessment of fatigue status, combined with real-time visual feedback and personalized intervention methods, can significantly improve users' fatigue management capabilities and provide practical and effective solutions for practitioners in different industries.
[0082] This application combines time-feedback intervention with music intervention to form a comprehensive psychological state intervention strategy. This strategy not only focuses on the user's current psychological state but also uses multiple methods to achieve comprehensive regulation and optimization of the user's psychological state.
[0083] Through timely psychological state early warning and comprehensive intervention strategies, the embodiments of this application help reduce the risk of accidents caused by psychological fatigue and increased stress on fatigue monitoring targets, thereby improving safety and stability. Simultaneously, by improving the subjective feelings and work efficiency of fatigue monitoring targets, it can also enhance overall productivity and quality.
[0084] The psychological and physiological fatigue monitoring system provided in this application embodiment can execute the psychological and physiological fatigue monitoring method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.
[0085] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.
[0086] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.
Claims
1. A method for monitoring psychological and physiological fatigue, characterized in that, include: Electromyography (EMG) data of the fatigue monitoring target is acquired using an EMG sensor, and heart rate data of the fatigue monitoring target is acquired using a heart rate sensor. The electromyography data were analyzed in the time domain and frequency domain to obtain a physiological fatigue score, and the heart rate data were analyzed in the time domain to obtain a psychological fatigue score. The fatigue monitoring results of the fatigue monitoring target are determined based on the physiological fatigue score and the psychological fatigue score.
2. The method according to claim 1, characterized in that, Time-domain and frequency-domain analyses were performed on the electromyography data to obtain a physiological fatigue score, including: Frequency domain analysis was performed on the electromyographic data to obtain the median frequency of the electromyography. Time-domain analysis was performed on the electromyographic data to obtain the root mean square of electromyography and muscle unit activation. Physiological fatigue scores are determined based on the median frequency of electromyography, the root mean square of electromyography, and the activation of muscle units.
3. The method according to claim 2, characterized in that, Based on the median frequency of electromyography (EMG), root mean square EMG, and muscle unit activation, a physiological fatigue score is determined, including: Based on the median frequency of electromyography, root mean square of electromyography, and muscle unit activation in each sampling time window, the physiological fatigue index corresponding to that sampling time window is determined. The physiological fatigue score is determined based on the physiological fatigue index corresponding to each sampling time window within the fatigue score update cycle.
4. The method according to claim 3, characterized in that, The physiological fatigue score is determined based on the physiological fatigue index corresponding to each sampling time window within the fatigue score update cycle, including: Within the fatigue score update cycle, determine the minimum and maximum physiological fatigue indices among the physiological fatigue indices corresponding to each sampling time window; The remaining physiological fatigue index is normalized based on the minimum and maximum physiological fatigue indices to obtain the normalized remaining physiological fatigue index. The remaining physiological fatigue index is the physiological fatigue index other than the minimum and maximum physiological fatigue indices among the physiological fatigue indices corresponding to each sampling time window within the fatigue score update cycle. Based on the normalized residual physiological fatigue index, the physiological fatigue score corresponding to the fatigue score update cycle is determined.
5. The method according to claim 1, characterized in that, Time-domain analysis was performed on the heart rate data to obtain a psychological fatigue score, including: Based on the heart rate data corresponding to each sampling time window within the fatigue score update cycle, calculate the heart rate variability corresponding to each sampling time window. Determine the minimum and maximum heart rate variability in the heart rate variability corresponding to each sampling time window within the fatigue score update cycle; The residual heart rate variability is normalized based on the minimum and maximum heart rate variability to obtain the normalized residual heart rate variability; the residual heart rate variability is the heart rate variability excluding the minimum and maximum heart rate variability during the fatigue score update cycle. The psychological fatigue score corresponding to the fatigue score update cycle is determined based on the normalized residual heart rate variability.
6. The method according to claim 4 or 5, characterized in that, The fatigue monitoring results for the fatigue monitoring target are determined based on the physiological fatigue score and the psychological fatigue score, including: In each fatigue score update cycle, the physiological fatigue score and psychological fatigue score corresponding to that fatigue score update cycle are weighted and summed to obtain the fatigue monitoring result of the fatigue monitoring target corresponding to that fatigue score update cycle.
7. The method according to claim 1, characterized in that, After determining the fatigue monitoring results of the fatigue monitoring target based on the physiological fatigue score and the psychological fatigue score, the method further includes: The graphical user interface displays a physiological fatigue score curve, a current psychological fatigue score, and / or fatigue regulation information determined based on the fatigue monitoring results.
8. The method according to claim 1, characterized in that, The method further includes: Determine the fatigue level conditions that the fatigue monitoring results meet, and conduct fatigue intervention based on the fatigue level conditions.
9. The method according to claim 1, characterized in that, The method further includes: If the psychological fatigue score of the fatigue monitoring target meets the first psychological fatigue condition, then the first duration of the fatigue monitoring target performing the current task is obtained; The second duration is determined based on the first duration and the preset total duration of the current task; the second duration is less than the first duration. Based on the second duration, the fatigue monitoring target is reminded of the current task execution progress.
10. A psychological and physiological fatigue monitoring system, characterized in that, include: Fatigue monitoring sensors and fatigue analysis modules; The fatigue monitoring sensor includes an electromyography (EMG) sensor, used to acquire EMG data of the fatigue monitoring target; The fatigue monitoring sensor also includes a heart rate sensor for acquiring heart rate data of the fatigue monitoring target; The fatigue analysis module is used to perform time-domain and frequency-domain analysis on the electromyography data to obtain a physiological fatigue score, and to perform time-domain analysis on the heart rate data to obtain a psychological fatigue score. The fatigue analysis module is also used to determine the fatigue monitoring results of the fatigue monitoring target based on the physiological fatigue score and the psychological fatigue score.