A Proposal for Wearable Controller Device and Finger Gesture Recognition using Surface Electromyography

Hand and finger motion is very complicated and achieved by intertwining forearm part (extrinsic) and finger part (intrinsic) muscles. We created a wearable finger-less glove controller using dry electrodes of sEMG(surface Electromyography) and only intrinsic hand muscles were sensed. Our wearable interface device is easy to wear and light-weighted. In offline analysis, we identified the tapping motion of fingers using the wearable glove. Totally eleven features were extracted, and linear discriminant analysis (LDA) was used as a classifier. The average of the discrimination result of in-tersubject analysis was 88.61±3.61%. In online analysis, we created a demo that reflects actual movement in the virtual space by Unity. Our demo showed a prediction of finger motions, and realized the motions in the virtual space.