Frailty Analysis of SEMG Signals for Different Hand Movements Based on Temporal and Spectral Approach

Surface electromyography (sEMG) signal is the electrical manifestation of neuromuscular activities. It is an intricate signal that depends on the anatomical and physiological properties of the contracting muscles beneath the skin. In this work, a single channel surface EMG amplifier is designed to acquire the signals non-invasively from the skin by using bio-potential electrodes for three different hand movements. Subjects of age group 1 (18-30 years) and group 2 (60-78 years) are considered for this analysis. The recorded signals are pre-processed using Empirical Mode Decomposition (EMD) method to remove unwanted noises. Various error measures and performance index are calculated from the pre-processed sEMG signals to compare the performance of EMD with conventional digital filters. Relevant time and frequency domain features are extracted from the pre-processed signals. It is observed that the statistical analysis performed over the extracted features show distinct variation between the age groups. Thus the methodology proposed in this work could be useful for the analysis of frailty especially for the subjects above 60 years.