Myoelectric Potential Visualization Using Butterworth Band pass Filter

Abstract

Electrical revelation of neuromuscular information transmission generated in muscles contraction and  relaxation is known as EMG signals. Electromyography (EMG) is real-time based method to assess and observe a series of electrical signals that expressed from body muscular cells. In recent researchers had provided many systems for monitoring myoelectric signals (EMG) to identify various abnormalities such as EMG, Microcontroller sensors technology, EMG software signal processor (SPU), PWM method, capacitive sensing method, UML method, EIM method, ADU integration, MC sensors method, Human computer interfacing (HCI) technology and Functional electrical stimulation (FES). This paper proposes a system to implement a wireless transformation technology for monitoring the electrical potential from muscles to identify internal injuries, blood clots, muscle cramps, muscle fatigue, muscle contraction, limb stiffness, and immobility. In this paper, electrical signals acquired from the muscles are detected using EMG sensors. These occurring signals transmitted by using wireless method and processed by using MAT Lab tool. This technique makes our system more unique from the previous methodologies. Finally accurate EMG signals are displayed graphically. Thus the expected configuration result will be with an accuracy of 98.74%, mean specificity of 99% and with a mean sensitivity of 96.58%.So the error occurrence will be Approximately 0.5% and also system is low cost, electrical safety, low power consumption, and can identify muscle disorders through observed abnormal EMGrange.