A SEMG-angle model based on HMM for human robot interaction

BACKGROUND: An important part of the rehabilitation process using exoskeleton robots has been the creation of a friendly Human Robot Interaction (HRI) system. OBJECTIVE: In order to combine SEMG signal into the HRI system, a SEMG-angle model based on Hidden Markov Model (HMM) was put forward in this paper. METHODS: Feature extraction as a critical issue of signal preprocessing was handled by Principal Component Analysis (PCA) which realized signal data dimension reduction and solved the common problem of redundant features. A comparison study was given to show the different performance of various EMG-angle model separately based on HMM, Back Propagation (BP) neural network and Radial Basis Function (RBF) neural network. RESULTS: The HMM modeling method which with lower calculation complexity can achieve a better modeling performance (average accuracy 93.063%) compared with BP neural network (average accuracy 88.180%) and RBF neural network (average accuracy 88.752%). CONCLUSIONS: SEMG signals have some characteristic properties which is similar to a quasi-stationary filtered white noise stochastic process, the structure of HMMs makes it ideally suited for classification and modeling SEMG signals, and the results of this study show that it can achieve a better performance than the commonly used methods (BP and RBF).