VHDL Modeling of EMG Signal Classification using Artificial Neural Network

Abstract

Electromyography (EMG) signal based research is ongoing for the development of simple, robust, user friendly, efficient interfacing devices/systems. An EMG signal based reliable and efficient hand gesture identification system has been developed for human computer interaction which in turn will increase the quality of life of the disabled or aged people. The acquired and processed EMG signal requires classification before utilizing it in the development of interfacing which is the most difficult part of the development process. A back-propagation neural network with Levenberg-Marquardt training algorithm has been used for the classification of EMG signals. This study presents the neural network based classifier modeling using Hardware Description Language (HDL) for hardware realization. VHDL (Very High Speed Integrated Circuit Hardware Description Language) has been used to model the algorithm implemented into the target device FPGA (Field Programmable Gate Array). The designed model has been synthesized and fitted into Altera’s Stratix III, chipset EP3SE50F780I4L using the Quartus II version 9.1 Web Edition.