Recognition of walking movement from EMG using a framework combining LLE and HMM

Abstract

An understanding of muscle activities can reveal the mechanism of producing locomotion, helping to improve the performance of assistive robots in supporting the users' movement. This paper proposes a framework to recognize the human walking movement by investigating electromyography (EMG), which has not been widely approached yet. The framework is a combination of a Hidden Markov Model (HMM) and Locally Linear Embedding (LLE) technique. First, we show that using LLE algorithm we could reduce the dimensionality of the high-dimensional EMG dataset. The extracted primitive components gave a meaningful representation of the EMG. Second, we demonstrate that the HMMs trained by these components could recognize the movement intention at a high rate of accuracy.