EMG Signal Acquisition and Classification System

Abstract

Measuring muscle activation via electric potential, referred to as electromyography (EMG). EMG has traditionally been used for medical research and diagnosis of neuromuscular disorders. However, with the advent of ever shrinking yet more powerful microcontrollers and integrated circuits, EMG circuits and sensors have found their way into prosthetics, robotics and other control systems. Electromyography (EMG) is the non–invasive recording of electrical muscle activity that is used to diagnose neuromuscular disorders, among other applications. Muscle fibers are activated by motor neurons and the resulting electrical signals produced by the muscle fibers can be detected by electrodes placed on the surface of the skin. Electromyography (EMG) measures the electrical impulses of muscles at rest and during contraction. As with other electrophysiological signals, an EMG signal is small and needs to be amplified with an amplifier that is specifically designed to measure physiological signals. When EMG is measured from electrodes, the electrical signal is composed of all the action potentials occurring in the muscles underlying the electrode. This signal could either be of positive or negative voltage since it is generated before muscle force is produced and occurs at random intervals. A method of pattern recognition of EMG signals of hand gesture using spectral estimation and neural network. The work proposed in this project is motivated by the need for stronger classifiers that would help to implement the human-machine interface. The electrical activity of skeletal muscle finds useful applications in many fields, such as biomechanics, rehabilitation medicine, neurology, gait analysis, exercise physiology, pain management, orthotics, incontinence control, prosthetic device control, even unvoiced speech recognition and man–machine interfaces.