Effect of muscle length changes on classification of EMG for prosthesis control

Significant advances in the use of pattern classification-based Electromyography (EMG) control for prosthesis have not resulted in clinical solutions. This paper explores whether muscle length-dependent changes in EMG characteristics play a significant part in this. We show, theoretically and experimentally, that the EMG frequency spectrum is affected by fibre length change. Further, we use this EMG in conjunction with a clustering algorithm to show that this length dependence has a profound effect on the classification of EMG. The clustering is done by extracting the wavelet feature from the EMG and using the k-means clustering algorithm. Various ways of compensating for this muscle length dependence of the EMG are suggested.

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