ANN-based EMG classification for myoelectric control
This work presents a new neural network model related to EMG signal classification for myoelectric control. The aim of this work is to develop a more accurate method for pattern recognition and intention interpretation of five human forearm hand gestures: grasping, extension/flexion, and ulna/radial deviation. A sum of 750 signals that incorporated all the selected hand movements were acquired from five volunteers, preprocessed, and then time and time-series domain features were extracted. Classification model in MATLAB platform is then utilised for classification purposes. The neural network classifier achieved an average accuracy up to 96.7%. The system overall average validation parameters calculated for the five movements were: sensitivity of 96.9%, specificity of 99.0%, PPV of 96.9%, and NPV = 99.1%.