Incremental learning of EMG-based control commands using Gaussian Processes

Myoelectric control is the process of controlling a prosthesis or an assistive robot by using electrical signals of the muscles. Pattern recognition in myoelectric control is a challenging field, since the underlying distribution of the signal is likely to change during the application. Covariate shifts, including changes of the arm position or different levels of muscular activation, often lead to significant instability of the control signal. This work tries to overcome these challenges by enhancing a myoelectric human machine interface through the use of the sparse Gaussian Process (sGP) approximation Variational Free Energy and by the introduction of a novel adaptive model based on an unsupervised incremental learning approach. The novel adaptive model integrates an interclass and intraclass distance to improve prediction stability under challenging conditions. Furthermore, it demonstrates the successful incorporation of incremental updates which is shown to lead to a significantly increased performance and higher stability of the predictions in an online user study.