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

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.