EMG signals based gait phases recognition using hidden Markov models

The application of hidden Markov model (HMM) to recognize gait phase using electromyographic (EMG) signals is described. Four time-domain features are extracted within a time segment of each channel of EMG signals to preserve pattern structure. According to the division of the gait cycle, the structure of HMM is determined, in which each state is associated with a gait phase. A modified Baum-Welch algorithm is used to estimate the parameter of HMM. And Viterbi algorithm achieves the phase recognition by finding the best state sequence to assign corresponding phases to the given segments. The feature set and data segmentation manner yielded high rate of accuracy are ascertained through evaluation experiments.