EMG Discrete Classification Towards a Myoelectric Control of a Robotic Exoskeleton in Motor Rehabilitation

Myoelectric control constitutes a promising interface for robot-aided motor rehabilitation therapies. The development of accurate classifiers and suitable training protocols for this purpose are still challenging. In this study, eight healthy participants underwent electromyography (EMG) recordings while they performed reaching movements in four directions and five different hand movements wearing an exoskeleton on their right upper-limb. We developed an offline classifier based on a back-propagation artificial neural network (ANN) trained with the waveform length as time-domain feature extracted from EMG signals to classify discrete movements. A maximum overall classification performance of 75.54 % ± 5.17 and 67.37 % ± 8.75 were achieved for reaching and hand movements, respectively. We demonstrated that similar or better classification results could be achieved using a small number of electrodes placed over the main muscles involved in the movement instead of a large set of electrodes. This work is a first step towards a discrete decoding-based myoelectric control for a motor rehabilitation exoskeleton.