A Probabilistic Neural Network with Anomaly Detection and Related Application to an EMG-controlled Prosthetic Hand

Abstract

This paper proposes a novel probabilistic neural network and a prosthetic arm control system designed to prevent unintended forearm motions caused by anomalies. The network incorporates a Gaussian mixture model and a one-versus-the-rest classifier model, and can be used to estimate the posterior probability of predefined and undefined classes through training with given data. The control system incorporates the proposed network, thereby preventing unintended prosthetic arm motion in which an unexpected action is performed. In experiments conducted with a forearm amputee and two healthy subjects, electromyogram (EMG) classification ability of the proposed network was demonstrated, including for cases with unlearned motions. The results of the experiments also showed that the system enables smooth prosthetic arm control using EMG signals while preventing malfunctions caused by anomalies.