Abstract— Significance of rehabilitation engineering is gaining popularity with the advancement in technology as more amputees desire to perform day to day tasks. Researchers are proposing designs and devices related to prosthesis which can achieve principle functions. Ideal upper limb prosthesis is one which can mimic actual hand. Control of Electromyography (EMG) based prosthesis is still in primitive stage as large number of channels is required even for the recognition of only few hand gestures. This study presents classification of essential hand movements for dexterous control of upper limb active prosthesis using surface Electromyography (EMG). Forearm muscles were used to detect these signals. Four pairs of surface electrodes were used with one reference electrode. Thus lesser number of channels used as compared to previous studies. Off- line analysis was used to figure out classification accuracy. Time domain feature extraction was done in the initial stage with support vector machine (SVM) analysis used for classification in the later stage. Results showed that hand movements were decoded accurately under latencies of 300ms. Five different movements were classified with the average accuracy between 84-90%. Keywords—Electromyography (EMG), Support Vector Machine (SVM), Prosthesis, Gesture Recognition I. INTRODUCTION Amputation is one of the most visible and psychological mortifying event that can happen to any person. According to extrapolated statistics there are more than 1.1 million amputees in Pakistan. Due to ongoing conflict this number is still increasing. Many of these disarticulations are upper limb which are below elbow. Most of these people are not provided with devices which can help them in their daily chores. Those provided with these prosthetics are having very low functionality and can perform very limited tasks. Trauma is the main cause of amputation [1]. Many high tech artificial limbs are available in the market with variable set of gestures such as The Hand with multiple grip patterns by RSL Steeper [2], trainable Michelangelo by Ottoback [3], i-Limb ultra- revolution with powered rotating thumb by Touch Bionics [4]. But the device which can mimic actual hand is still a long way to go. For EMG based control prosthesis it is basically a tradeoff between degrees of freedom attained by the limb and the number of channels used. Electromyography (EMG) is field that deals with detection (from needle, surface and cup electrodes), signal processing (Power Lab or Lab view) and use of electrical signals generated by the voluntary contractions of muscles (for active prosthesis) [5]. This potential generated varies from 0-5 mV when they are flexed or neurologically activated. EMG surface activity stimulated by voluntary contraction of the targeted muscle is considered as the intention of myo-limb user. Comparisons of the predetermined threshold value of EMG signal with Mean Absolute Value (MAV) give us the intension of the user. Human body consists of muscles, composed of fibers having motor points in it. These points when activated generate motor point active potential. Motor unit is the composition of anterior horn cell, its axon and muscle fibers innervated by the motor neuron. Motor unit action potential (MUAP) is a train of pulses or summation of a group of muscle fiber action potential (MFAP) where superimposed information of muscles and generated pulses is determined by each MFAP. As long as force is maintained or even increased motor unit generates pulses continuously and consequently muscle contracts [6]. Number of activated motor points will increase as human muscle apply more force, so we can drive that throwing a heavy stone activates more motor points than throwing a lighter stone. Greater number of activation of MUAP’s makes things difficult for the neurophysiologist to distinguish between individual signals of muscles. Decomposition and careful grouping of these potentials can provide useful information which can lead neurophysiologist to the diagnoses of many neuromuscular disorders. MUAP’s is the symptom of muscle control of human body that incorporates the data of user’s intent to flex his muscle. Recent studies have shown that human muscle generates repeated patterns of EMG signals before the intension to perform a certain movement [7].So the importance of these signals increase many fold because the control of active prosthesis is based on the intension of user. Recent development in electronics and computer technology made automated EMG signal analysis possible. Many studies have been done on able as well as disable bodies to authenticate the expediency and performance of different classification algorithms. These techniques used EMG signal taken from forearm. Variable number of electrode pairs was used ranging from 4-12. Shenoy et al presented a technique to classify eight different motions (griping, opening of hand, rightward, leftward, upward, downward movements of hand, Classification of Functional Motions of Hand for Upper Limb Prosthesis with Surface Electromyography Muhammad Asim Waris, Mohsin Jamil, Yasar Ayaz and Syed Omer Gilani INTERNATIONAL JOURNAL OF BIOLOGY AND BIOMEDICAL ENGINEERING Volume 8, 2014 ISSN: 1998-4510 15