Extraction and Classification of Multichannel Electromyographic Activation Trajectories for Hand Movement Recognition.

This paper proposes a system for hand movement recognition using multichannel electromyographic (EMG) signals obtained from the forearm surface. This system can be used to control prostheses or to provide inputs for a wide range of human computer interface systems. In this work, the hand movement recognition problem is formulated as a multi-class distance based classification of multi-dimensional sequences. More specifically, the extraction of multi-channel EMG activation trajectories underlying hand movements, and classifying the extracted trajectories using a metric based on multi-dimensional dynamic time warping are investigated. The developed methods are evaluated using the publicly available NINAPro database comprised of 40 different hand movements performed by 40 subjects. The average movement error rate obtained across the 40 subjects is 0.09±0.047. The low error rate demonstrates the efficacy of the proposed trajectory extraction method and the discriminability of the utilized distance metric.