Pattern recognition of EMG signals: Towards adaptive control of robotic arms

Abstract

Given the importance of hands as dexterous instruments to execute daily life tasks and the huge disability people suffer from when losing their limbs, this paper uses the electrical activity of the muscles (EMG) as control signals in a pattern recognition control system to manipulate the movement of a motorized 3D printed robotic arm. A system to acquire the electromyography signals is first designed and tested. Some features are then extracted from the EMG signal to build a support vector machine classification model. The obtained results indicate the fidelity of acquired signals, the efficiency of the exoskeleton, and the accuracy of the classification process in achieving a robust myoelectric control system. The final results compose a backbone for a future development of a robust classification system to fulfill a complete prototype.