EMG Physical Action Detection using Recurrence Plot Approach

Abstract

Electromyography (EMG) is used to identify neuromuscular illnesses, motor issues, nerve damage, and degenerative ailments. EMG signals are difficult to accurately classify because of their complexity, nonlinearity, and time-variable nature; therefore, proper feature extraction and classification algorithms must be used. The proposed study uses the recurrence plot (RP) approach combined with deep learning (DL) techniques for the purpose of normal and aggressive EMG physical action (PA) classification. The transfer learning (TL) approach Inception-ResNet-v2 is utilized for feature extraction here. The Inception-ResNet-v2 model’s results demonstrate the excellent generalizability of the proposed method. Using only a few of the eight leads in the original EMG data, the model demonstrated the best accuracy (0.9841) and F1 score (0.99). These results show that the 2D RP-based technique has a significant clinical potential for classifying PA while requiring fewer EMG leads.