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In recent years, researchers have conducted many studies on the design and control of prosthesis devices that take the place of a missing limb. Functional ability of prosthesis hands thatmimic biological hand functions increases depending on the number of independent finger movements possible. From this perspective, in this study, six different finger movementswere given to a prosthesis handvia bioelectrical signals, and the functionality of the prosthesis handwas increased. Bioelectrical signalswere recordedby surface electromyography for four muscles with the help of surface electrodes. The recorded bioelectrical signals were subjected to a series of preprocessing and feature extraction processes. In order to create meaningful patterns of motion and an effective cognitive interaction network between the human and the prosthetic hand, fuzzy logic classification algorithms were developed. A five-fingered and 15-jointed prosthetic hand was designed via SolidWorks, and a prosthetic prototype was produced by a 3D printer. In addition, prosthetic hand simulatorwas designed inMatlab/SimMechanics. Pattern control of both the simulator and the prototype hand in real time was achieved. Position control of motors connected to each joint of the prosthetic hand was provided by a PID controller. Thus, an effective cognitive communication network established between the user, and the real-time pattern control of the prosthesiswas provided by bioelectrical signals.

EMG-Controlled Prosthetic Hand with Fuzzy Logic Classification Algorithm