Optimal Elbow Angle for Extracting sEMG Signals During Fatiguing Dynamic Contraction

Abstract

Surface electromyographic (sEMG) activity of the biceps muscle was recorded from 13 subjects. Data was recorded while subjects performed dynamic contraction until fatigue and the signals were segmented into two parts (Non-Fatigue and Fatigue). An evolutionary algorithm was used to determine the elbow angles that best separate (using Davies-Bouldin Index, DBI) both Non-Fatigue and Fatigue segments of the sEMG signal. Establishing the optimal elbow angle for feature extraction used in the evolutionary process was based on 70% of the conducted sEMG trials. After completing 26 independent evolution runs, the best run containing the optimal elbow angles for separation (Non-Fatigue and Fatigue) was selected and then tested on the remaining 30% of the data to measure the classification performance. Testing the performance of the optimal angle was undertaken on nine features extracted from each of the two classes (Non-Fatigue and Fatigue) to quantify the performance. Results showed that the optimal elbow angles can be used for fatigue classification, showing 87.90% highest correct classification for one of the features and on average of all eight features (including worst performing features) giving 78.45%.

Effect of Two Adjacent Muscles of Flexor and Extensor on Finger Pinch and Hand Grip Force

“…EMG pattern classification has attracted significant interest in current research activities due to its consolidation with the human machine controls. Noticeable research has been conducted in this area and some have reported improvements in the human machine controls such as prosthetic hand [4], [5], [6], [7]. Lots of studies have reported that the real time accuracy is generally within 90% to 97%.…”