In this paper, a real-time experimental of Hand Gesture sEMG signal using artificial neural networks for Wheel Vehicle Control is proposed. The raw SEMG signals been captured from SEMG amplifier, up to 8 channels of NI-DAQ card responses data will be combined and a fine tuning step by using pattern classification. The database then been build and use for real-time experimental control classification. Captured data will send through serial port and Wheel Machine will receive and move accordingly. The detail of the experiment and simulation conducted described here to verify the differentiation and effectiveness of combined channels sEMG pattern classification of hand gesture for real-time control.
['Md. Shafivulla', 'V. Rajesh', 'Habibulla Khan']
sEMG based human computer interface for robotic wheel