摘要
Surface Electromyography (sEMG) activities of the four muscles were studied from twelve healthy subjects to analyze muscle fatigue. Data were recorded while subjects performed isometric exercises for a period of time until fatigue. The signal was segmented with 5000 samples to enable the evolutionary process. Based on the mean power spectrum and Median Frequency (MDF) of each segment, we developed a methodology that is able to detect the signal into a meaningful sequence of Non-Fatigue to Transition-to-Fatigue. By identifying this transitional fatigue stage, it is possible to predict when fatigue will occur, which provides the foundation of the automated system that has the potential to aid in many applications of our lives, including sports, rehabilitation and ergonomics.
Surface Electromyography (sEMG) activities of the four muscles were studied from twelve healthy subjects to analyze muscle fatigue. Data were recorded while subjects performed isometric exercises for a period of time until fatigue. The signal was segmented with 5000 samples to enable the evolutionary process. Based on the mean power spectrum and Median Frequency (MDF) of each segment, we developed a methodology that is able to detect the signal into a meaningful sequence of Non-Fatigue to Transition-to-Fatigue. By identifying this transitional fatigue stage, it is possible to predict when fatigue will occur, which provides the foundation of the automated system that has the potential to aid in many applications of our lives, including sports, rehabilitation and ergonomics.
