A virtual sEMG prosthetic hand development system based on LabVIEW and PCI-1710HG A/D card

This paper introduces a virtual sEMG prosthetic hand development system based on LabVIEW. The system is aimed at finding proper method to correctly and promptly discriminate hand motions via sEMG. Waveform Length has been proved effective as extracted feature in this paper due to its high performance and short computing time. A back-propagation Neural Networks(BP Networks) is chosen as classifier in order to train the system automatically. The result shows that the performance of the whole system is successful when discriminating the following four hand motions: flexion and extension of the wrist, open and grasp of hand.