Muscle fatigue evaluation system and fatigue evaluation method

Abstract

This invention discloses a muscle fatigue assessment system and method. The muscle fatigue assessment system includes multiple sensing electrodes, a sensing unit, a signal processing module, an analysis and calculation module, and an assessment module. Multiple sensing electrodes are attached to the muscles of the human body, with adjacent sensing electrodes defining a sensing channel, thus defining multiple sensing electrodes as multiple sensing channels. The sensing unit generates multiple electromyographic (EMG) signals through the multiple sensing channels. The signal processing module filters and amplifies the multiple EMG signals to generate multiple processed EMG signals. The analysis and calculation module identifies the highly variable EMG signals from the multiple processed EMG signals and calculates the average vibration and average firing frequency. The assessment module outputs the muscle fatigue assessment result based on the average vibration and average firing frequency.

Claims

1. A muscle fatigue assessment system, comprising: Multiple sensing electrodes are attached to the muscles of a human body, the multiple sensing electrodes are arranged in a grid pattern, and adjacent sensing electrodes can be defined as a sensing channel, so that the multiple sensing electrodes can be defined as multiple sensing channels; a sensing unit is electrically coupled to the multiple sensing electrodes, the sensing unit generates multiple electromyographic signals through the multiple sensing channels; A signal processing module, electrically coupled to the sensing unit, receives multiple electromyographic (EMG) signals and filters and amplifies them to generate multiple processed EMG signals; an analysis and calculation module, electrically coupled to the signal processing module, analyzes the multiple processed EMG signals to identify the EMG signals with high variability from the multiple processed EMG signals, and calculates an average vibration frequency and an average firing frequency from their waveforms; and an evaluation module, electrically coupled to the calculation module, outputs a muscle fatigue evaluation result based on the average vibration frequency and the average firing frequency. The analysis and calculation module further calculates the waveform of the electromyographic signal with large variability to obtain a maximum amplitude and a maximum firing frequency, and calculates the ratio of the average amplitude to the maximum amplitude to obtain an amplitude ratio and the ratio of the average firing frequency to the maximum firing frequency to obtain a firing frequency ratio. The evaluation module has a first fatigue threshold. When both the amplitude ratio and the firing frequency ratio are less than the first fatigue threshold, the muscle fatigue evaluation result output by the evaluation module is a primary fatigue.

2. The muscle fatigue assessment system as described in claim 1, wherein, The evaluation module further includes a second fatigue threshold, which is less than the first fatigue threshold. When both the amplitude ratio and the firing frequency ratio are less than the second fatigue threshold, the muscle fatigue evaluation result output by the evaluation module is a medium-level fatigue.

3. The muscle fatigue assessment system as described in claim 1, wherein, The signal processing module includes: a filtering unit electrically coupled to the sensing unit, the filtering unit receiving multiple electromyographic signals and filtering the multiple electromyographic signals; and a sampling unit electrically coupled to the filtering unit, the sampling unit receiving the filtered multiple electromyographic signals and sampling the multiple electromyographic signals to generate multiple electromyographic sampling signals. And an amplification unit electrically coupled to the sampling unit, the amplification unit receiving multiple electromyographic sampling signals and amplifying the multiple electromyographic sampling signals to generate multiple electromyographic processing signals.

4. The muscle fatigue assessment system as described in claim 3, wherein, The frequency response range of the amplification unit is 10 to 800 Hz, and the gain is set to 60 to 80 dB.

5. The muscle fatigue assessment system as described in claim 1, wherein, The analysis and calculation module includes: an analysis unit electrically coupled to the signal processing module, wherein the analysis unit analyzes multiple electromyographic processing signals to identify the electromyographic processing signals with high variability from the multiple electromyographic processing signals; And a calculation unit electrically coupled to the analysis unit, the calculation unit calculates the average oscillation frequency and the average firing frequency of the electromyographic processing signal with large variability.

6. The muscle fatigue assessment system as described in claim 1, wherein, In the plurality of sensing electrodes, the distance between two adjacent sensing electrodes is 2.50 to 2.60 mm.

7. A method for assessing muscle fatigue, comprising the steps of: attaching a plurality of sensing electrodes to a muscle of a human body, wherein the plurality of sensing electrodes are arranged in a grid pattern and adjacent sensing electrodes can be defined as a sensing channel, so that the plurality of sensing electrodes can be defined as a plurality of sensing channels; A single sensing unit generates multiple electromyographic signals through multiple sensing channels; A signal processing module receives multiple electromyographic (EMG) signals, filters and amplifies them to generate multiple processed EMG signals. An analysis and calculation module analyzes the multiple processed EMG signals, identifies the EMG signals with high variability from them, and calculates an average amplitude and an average firing frequency for their waveforms. An evaluation module outputs a muscle fatigue assessment result based on the average amplitude and the average firing frequency. The analysis and calculation module further calculates the waveform of the highly variable EMG signals to obtain a maximum amplitude and a maximum firing frequency, and calculates the ratio of the average amplitude to the maximum amplitude to obtain an amplitude percentage and the ratio of the average firing frequency to the maximum firing frequency to obtain a firing frequency percentage. The evaluation module has a first fatigue threshold; when both the amplitude percentage and the firing frequency percentage are less than the first fatigue threshold, the muscle fatigue assessment result output by the evaluation module is primary fatigue.

8. The muscle fatigue assessment method as described in claim 7, wherein, The evaluation module further includes a second fatigue threshold, which is less than the first fatigue threshold. When both the amplitude ratio and the firing frequency ratio are less than the second fatigue threshold, the muscle fatigue evaluation result output by the evaluation module is a medium-level fatigue.

Images