Surface Electromyogram Feature Set Optimization for Lower Limb Activity Classification

Abstract

Surface electromyogram (SEMG) shows the vital information of human motor activity. This information can identify the motor activity and controlling assistive and rehabilitative devices. However, its efficient extraction and interpretation plays a very critical role. The present study is focused on lower limb activity classification by selecting the efficient feature vector of the lower limb SEMG signal. Here, the SEMG signal-dependent continuous locomotion mode classification method has been proposed along with a dual-stage Feature Selection Algorithm (FSA). To evaluate the proposed method, SEMG signals of fifteen subjects were recorded from two lower limb muscles (Fibularis longus and Biceps Femoris) for five lower limb activities. The performance of six different classifiers was compared for intuitive feature vectors and FSA. The study analysed the performance of the proposed model for a single muscle approach and a dual muscle approach of activity classification. The time domain features were found more effective over other features, whereas Biceps Femoris muscle came out as a dominant muscle. The FSA enhanced the performance of classification model with fewer features as compared to intuitive feature subsets. Moreover, the dual muscle approach outperformed (p-value<0.05) over the single muscle approach. The best performance, for the dual muscle approach, was achieved 97.73 ± 2.47%, 98.99 ± 1.26% and 96.33 ± 2.70% for Neural Network, Linear Discriminant Analysis and Support Vector Machine classifiers, respectively (p-value > 0.05).