Muscle redistribution surgery based capacitive sensing for upper-limb motion recognition: Preliminary results

In this paper, we present a muscle redistribution surgery strategy based capacitive sensing method to recognize upper-limb motion modes. To better obtain explicit muscle shape changes during movements, we propose a new surgery to redistribute upper-limb muscles in amputation. The designed capacitive sensing system can record capacitive signals of the residual forearm. We carry out several pilot studies to evaluate the proposed method. One forearm amputee was taken the surgery and participated in the experiment. The subject finished five upper-limb motions by motor imagery, including gripping, wrist flexion, wrist extension, fingers flexion, and fingers extension. We used LDA classifier and QDA classifier to recognize different motion modes by using capacitive signals. The proposed method obtained 97.27%(LDA) and 100%(QDA) average recognition accuracy. The preliminary results indicate that the proposed muscle redistribution surgery strategy based capacitive sensing is a promising solution for upper-limb prosthesis control.