Hand Gesture Classification using sEMG Data:Combining Gesture Detection and Cross-Validation

Abstract

Prosthetic hands play a vital role in the rehabilitation of upper limb amputees. Gesture recognition using surface electromyography (sEMG) data has emerged as an excellent option for controlling such prosthetic devices since one does not require invasive methods to obtain these data. In order to improve gesture recognition, we must extract the muscle activity from the raw data before classification, as each gesture has its own patterns. In this paper, we use an artificial neural network classifier with individualized data segmentation based on gesture detection to identify six hand movements. We used data from ten healthy volunteers. By combining data segmentation and crossvalidation, we were able to refine the amplitude thresholds used to determine the beginning and end of muscle contractions for each person. We designed several experiments using different types of cross-validation. The performance achieved by the proposed model using 4-fold cross-validation was (93.6 ± 0.7)%, which represents 3.5% more than the mean accuracy of the baseline model, in which there is a single arbitrarily-chosen segmentation threshold for all volunteers.