This study is aimed at exploring the prediction of the various hand gestures based on Force Myography (FMG) signals generated through piezoelectric sensors banded around the forearm for the implementation of a control system in a prosthetic hand. Matlab, Simulink software has been utilized for the analysis and classification. Several classification and recognition models have been considered, and the Tree Decision Learning (TDL) and Support Vector Machine (SVM) have shown high accuracy results. Both of these estimated models generate above ninety five percentage of accuracy in terms of classification. As the classification showed a distinct feature in the signal, a realtime control system based on the threshold value has been implemented in the prosthetic hand. The hand motion has been recorded through Virtual Motion Glove (VMD) to establish dynamic relationship between the FMG data and the different gestures through system identification. The classification of the hand gestures based on FMG signal will provide a useful foundation for future research in the interfacing and utilization of medical devices.
Force Myography Signal-Based Hand Gesture Classification for the Implementation of Real-Time Control System to a Prosthetic Hand
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Ha, N, Withanachchi, GP, & Yihun, Y. "Force Myography Signal-Based Hand Gesture Classification for the Implementation of Real-Time Control System to a Prosthetic Hand." Proceedings of the 2018 Design of Medical Devices Conference. 2018 Design of Medical Devices Conference. Minneapolis, Minnesota, USA. April 9–12, 2018. V001T10A013. ASME. https://doi.org/10.1115/DMD2018-6937
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