Statistical Class Separation Using sEMG Features Towards Automated Muscle Fatigue Detection and Prediction

• Published in: 2009 2nd International Congress on Image and Signal Processing pp. 1 - 5
• Main Authors: Al-Mulla, M.R., Sepulveda, F., Colley, M., Al-Mulla, F.
• Format: Conference Proceeding
• Language: English; Japanese
• Published: IEEE 01.10.2009
• Subjects: Band pass filters; Data mining; Elbow; Electromyography; Fatigue; Frequency; Goniometers; Muscles; Signal processing; Testing

Surface Electromyography (sEMG) activity of the biceps muscle was recorded from ten subjects. Data were recorded while subjects performed isometric contraction until fatigue. The signals were segmented into three parts (Non-Fatigue, Transition-to-Fatigue and Fatigue), assisted by a fuzzy classifier...