DYNAMIC RECURRENT NEURAL NETWORK BASED CLASSIFICATION SCHEME FOR MYOELECTRIC CONTROL OF UPPER LIMB REHABILITATION ROBOT

Abstract

This paper proposes and evaluates the application of a modular dynamic recurrent neural network (DRNN) to classify upper limb motion using myoelectric signals. The DRNN algorithmic issues, including the structure selection, the segmentation of the data and various feature sets such as time-domain features and frequency features, were evaluated experimentally in order to actualize the optimization and configuration of this classification scheme. This was achieved by using a majority vote technique to post-process the output decision stream. The DRNN-based approach was then been compared with two commonly used classification methods: multilayer perceptron (MLP) neural network and linear discriminant analysis (LDA). The DRNN-based motion classification system demonstrated exceptional accuracy and a low computational load for the classification of robust limb motion. The DRNN may also display utility for online training and controlling rehabilitation robots.