Myoelectric Potential Visualization Using Butterworth Band pass Filter

Abstract

Electrical revelation of neuromuscular information transmission generated in muscle contraction and relaxation is known as EMG signals. Electromyography (EMG) is a real-time-based method to assess and observe a series of electrical signals that are expressed from body muscular cells. In recent research, many systems have been provided 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 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 are transmitted using a wireless method and processed using the MATLAB tool. This technique makes our system more unique from previous methodologies. Finally, accurate EMG signals are displayed graphically. Thus, the expected configuration result will be with an accuracy of 98.74%, a mean specificity of 99%, and with a mean sensitivity of 96.58%. So the error occurrence will be approximately 0.5%, and the system is low cost, electrically safe, has low power consumption, and can identify muscle disorders through observed abnormal EMG range.