EMG signals recognition for continuous prosthetic arm control purpose

Abstract

To be functional in a practical sense for real-time control of assistive devices, a myoprocessor must successfully integrate both detection and estimation systems. This paper describes an approach for classifying electromyographic (EMG) signals using a multilayer perceptrons (MLPs) and hidden Markov models (HMMs) hybrid classifier and force estimation. Instead of using MLPs as probability generators for HMMs the authors propose to use MLPs as the second classifiers to increase discrimination rates of myoelectric patterns. This strategy is proposed to overcome weak discrimination and to consider dynamic properties of EMG signals. Two discrimination strategies (HMM, and HMM with three subnet MLPs) for discriminating signals representative of 6 primitive class of motions are described and compared. The proposed strategy increase the discrimination results considerably. Results are presented to support this approach.