Causes of Performance Degradation in Non-invasive Electromyographic Pattern Recognition in Upper Limb Prostheses - Kyranou, Iris; Vijayakumar, Sethu; Erden, Mustafa Suphi (Frontiers in Neurorobotics, 2018)
Electromyography (EMG) pattern classification has been investigated over the past years as a means of controlling upper limb prostheses. Despite the very good reported performance of myoelectric controlled prosthetic hands in lab conditions, real-time performance in everyday life conditions is not as robust and reliable, explaininng the limited clinical use of pattern recognition control; at the time of writing of this paper only Coapt is providing such control. The main reason behind the instability of myoelectric pattern recognition control is that EMG signals are non-stationary in real-life environments and present a lot of variability over time and across subjects. This can be the result of one or many combined changes, such as muscle fatigue, electrode displacement, difference in arm posture, user adaptation on the device over time and inter-subject singularity. In this paper a literature review is performed to present the causes of the drift of EMG signals and possible methods to counteract for their effects.