Forearm High-Density Electromyography Data Visualization and Classification with Machine Learning for Hand Prosthesis Control.

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Electromyography offers a way to interface an amputee's resilient muscles to control a bionic prosthesis. While myoelectric prostheses are promising, user acceptance of these devices remain low due to a lack of intuitiveness and ease-of-use. Using a low-cost wearable flexible electrodes array, the proposed system leverages high-density surface electromyography (HD-EMG) and deep learning techniques to classify forearm muscle contractions. These techniques allow for increased intuitiveness and ease-of-use of a myoelectric control scheme with a single easy-to-install electrodes apparatus. This paper proposes a flexible electrodes array construction using standard printed circuit board manufacturing processes for low-cost and quick design-to-production cycles. HD-EMG dataset visualization with t-distributed Stochastic Neighbor Embedding (t-SNE) is introduced, and offline classification results of the wearable gesture recognition system for hand prosthesis control are validated on a group of 8 able-bodied subjects. Using a majority vote on 5 successive inferences, a median recognition accuracy of 98.61 % was obtained across the group for an 8 gestures set. For a 6 gestures set containing commonly used prosthesis positions, the median accuracy reached 99.57 % with the majority vote.