Hand Movement Classification with Four Channel EMG Signals for Underactuated Hand Prosthesis Test Platform

People who undergo upper extremity amputation for various reasons face a dramatic decrease in their quality of life. Prostheses designed to remedy this situation, at least partially, are becoming more successful day by day. In this study, the “Nvidia Jetson“ card-supported experiment platform, designed especially for the development of artificial intelligence procedures in under-actuated myoelectric hand prostheses, is presented. The system, in which electromyography (EMG) signals are processed with artificial intelligence procedures and converted into control signals, is designed modularly so that different methods and equipment can be tested. The platform, where all the subunits that form the whole system are combined and their interactions are ensured, was tested with a support vector machine (SVM) based motion classification procedure selected as an example and the results were examined. In the design output, it was seen that hand control was achieved with 92.5% accuracy in five hand movements. Using this platform, different algorithms will be tested, and performance will be increased with the hardware and training used