REAL-TIME EMG ACQUISITION AND FEATURE EXTRACTION FOR REHABILITATION AND PROSTHESIS

Electromyogram signals have been used for various applications in the healthcare sector for developing various methodologies and techniques in rehabilitation and prosthetics. This paper focuses on the use of EMG signals of transradial amputees for developing a myoelectric lower limb prosthesis capable of individual finger movement. The aim of this work is to develop proper hardware and software systems for real-time EMG classification. An improved double thresholding method for onset and offset detections has been developed to ensure its applicability in real-time. The proposed algorithm has been tested with real-time patient EMG signals using a three-lead electrode system from flexor digitalis region of the hand. Around 3000 samples of usable data corresponding to the flexion of each finger (Thumb-553, Index-655, Middle-723, Ring-720, Little-655) were acquired from 10 healthy subjects. The resultant extracted features were classified using various classifiers (KNN, KNN with PCA and LDA) and a comparison was done between the accuracies acquired from a commonly shared dataset against a subject-specific dataset. A robust onset signal processing algorithm enabled the real-time classification of EMG in noisy environments.