Signal Processing and Classification Techniques of Surface Electromyography to Understand Muscle Atrophy Due to Aging Presenter

Signal analysis is a key field in engineering with diverse applications, from wireless technology to medical diagnostics. Medical applications focus on analyzing physiological signals for diagnosis, treatment, and research. Electromyography (EMG) is one such technique, used to improve diagnostic and therapeutic care. This study investigates the acquisition, analysis, decomposition, and interpretation of bio-signals obtained from surface EMG (sEMG). By analyzing sEMG signals from sensors, the research explores the impact of aging on muscle information processing, coding, and transmission. Data is collected using Vernier EKG three- lead sensors, along with measurements of Grip Strength, Angle Flexion, Strength (force), and sEMG from two distinct EKG sensors per test. Techniques like Fast Fourier Transform (FFT), Averaging, Root Mean Squared (RMS), Peak Amplitude Values, and Filtering are applied using MATLAB to quantify the signals. Preliminary findings indicate that muscle atrophy becomes more prominent with age, especially in the 40-50 year group, showing a 15% decrease in RMS values of sEMG frequencies. This comparison helps distinguish muscle atrophy due to aging from other factors and relates the findings to motor neuron activity and muscle fiber contraction. In conclusion, signal analysis facilitates the interpretation of sEMG data, bridging the gap between signal processing and biological applications.