EMG Channel Selection for Improved Hand Gesture Classification

Electromyographic activities (EMG) from upper limb muscles exhibit distinct patterns associated with different hand gestures. To capture these EMG activities, an appropriate number of sensors (channels) affixed at right locations is needed. This paper presents a novel approach to automatically identify EMG channels most salient to classifying different hand gestures. The proposed approach is based on a regularized generative-discriminative encoding of time-series EMG data. The proposed approach 1) encodes each time-series channel of multi-channel EMG observations into a generative hidden Markov model (HMM), 2) constructs a shared probabilistic embedding space, where EMG observations are represented in terms of pair-wise distances between the channel-specific HMMs, 3) weighs different dimensions of the shared probabilistic space using multinomial logistic regression with group least absolute shrinkage and selection operator (group Lasso) penalty, with each group corresponding to channel-specific distances (i.e., distances between a channel and the rest of the channels), 4) determines salient channels based on the weighted parameters, and 5) classifies gestures based on the identified salient channels. The performance of the proposed approach was evaluated using the NinaPro2 dataset. The identified salient EMG channels improved classification accuracies as high as 11{% compared with the case where all the channels were used.