Abstract
A key challenge associated with myoelectric prosthesis limbs is the acquisition of a good quality gesture intent signal from the residual anatomy of an amputee. In this study, the authors aim to overcome this limitation by observing the classification accuracy of the fusion of wearable electromyography (EMG) and near-infrared (NIR) to classify eight hand gesture motions across 12 able-bodied participants. As part of the study, they investigate the classification accuracy across a multi-layer perceptron neural network, linear discriminant analysis and quadratic discriminant analysis for different sensing configurations, i.e. EMG-only, NIR-only and EMG-NIR. A separate offline ultrasound scan was conducted as part of the study and served as a ground truth and contrastive basis for the results picked up from the wearable sensors, and allowed for a closer study of the anatomy along the humerus during gesture motion. Results and findings from the work suggest that it could be possible to further develop transhumeral prosthesis using affordable, ergonomic and wearable EMG and NIR sensing, without the need for invasive neuromuscular sensors or further hardware complexity.
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