CSE - 791 FPGA Circuits and Applications Fall 2009 Project Report on Signal Processing and Pattern Recognition using Continuous Wavelets Under guidance of Prof . Fred Schlereth By Ronak Gandhi

Goal  This work aims at designing and implementing FPGA based module to process and perform pattern recognition on EMG (Electromyography) signals that are received from human muscular movements that are otherwise complex to analyze on some standard methods. Purpose  On the completion of this work we want to gain proficiency in following areas  Studying the available algorithms for processing wavelets  Generation and composition of periodic and random signals similar to natural EMG signals using available MATLAB functions  Gaining experience and analyzing the stages involved for processing EMG data  Examining the EMG signals using Wavelet functions and Toolbox on MATLAB  Developing our own algorithm for EMG detection using MATLAB  Implementing our algorithm on real time FPGA based system and familiarize with Xilinx tools  Final aim is to implement the design on Spartan 3E kit and analyzing the results on real time input from hardware interface. Abstract In recent time biological signals like EMG which are used in medical diagnose and researchers have studied not only time field but also frequency field. Electromyography (EMG) is a technique for evaluating and recording the activation signal of muscles. EMG recording technique is performed using an instrument called an electromyograph, to produce a record called an electromyogram. Mathematical models to analyze electromyogram includes wavelet transform, time-frequency approaches, Fourier transform, Wigner-Ville Distribution (WVD), statistical measures, and higher-order statistics. All approaches towards signal recognition include Artificial Neural Networks (ANN), dynamic recurrent neural networks (DRNN), and fuzzy logic system. In most of the present approach Genetic Algorithm (GA) has also been applied in evolvable hardware chip for the mapping of EMG inputs to desired hand actions. Metrics method has also been used to quantatively analyzing EMG signals by accessing their timing and amplitude characteristics in the past where the method tries to correlate the portions of EMG signals with the previously available metrics. We designed our mother wavelet and performed Wavelet transformation method to detect the specific pattern of generated wavelet in the give EMG signal. These signals are then categorized for different hand movement and disablitites.