A LLE-HMM-based framework for recognizing human gait movement from EMG

Recognizing the human gait is an edge research in robotics and rehabilitation. It has been popular to recognize the human gait from kinematic data. However, the recognition from muscle activities, the input of the movement, has not been widely approached. In this paper, we propose a framework to recognize the human walking and running movements by investigating muscle activities through electromyography (EMG). The framework is a Hidden Markov Model (HMM) topology utilizing Locally Linear Embedding (LLE) technique to extract feature vectors. We show that: (1) the high-dimensional EMG data can be embedded into a lower-dimensional space by using a manifold learning algorithm (LLE), primitive components which give meaningful representation of the EMG can be extracted, and (2) our proposed HMM topology whose input are the extracted vectors from EMG can recognize the gait movement at an accuracy rate of over 80%.