Processing and Multi-scale Analysis for the Classification of Surface Electromyographic Signal and Finding out its Correlation with Different Motor Neuron Activities

Abstract

Surface Electromyographic (sEMG) signals have become prevalent in a variety of applications, such as human-computer interface, rehabilitation, and medical diagnostics. To improve the classification of sEMG signals and clarify their link with various motor neuron activities, this study explores new directions in signal processing and multi-scale analysis. The study aims to explore and understand the intricate dynamics of neuromuscular control through electromyographic (EMG) signal processing and multiscale analysis, elucidating fundamental mechanisms underlying movement execution and coordination. By analyzing EMG signals using multiscale analysis, researchers unveil intricate patterns of muscle activation, offering insights into single motor unit firings and coordinated movements involving multiple muscle groups. Through meticulous examination, the research unveils the correlation between surface electromyographic (sEMG) signals and motor neuron functions, highlighting potential applications in medical diagnostics and rehabilitation robotics. The study begins with a systematic literature review (SLR) that provides a comparative overview of recent research on sEMG-based hand gesture recognition systems, identifying gaps and evaluating data collection, processing, and classification algorithms. The research presents a simple approach to decomposing sEMG signals, crucial for understanding muscle activation patterns and improving prosthetics and ergonomic interfaces. Utilizing Maximal Overlapping Discrete Wavelet Transform (MODWT) for signal decomposition, the research achieves up to 94% accuracy in identifying neural activity. Correlation analysis reveals discriminative features for differentiating signals, enhancing classification accuracy for finger movements. The insights gleaned from the correlation analysis pave the way for future investigations into the complexities of neuromuscular function and motor control mechanisms. A proposed algorithm for classifying finger gestures demonstrates the effectiveness of processing raw sEMG signals and extracting dominant features using machine learning classifiers. With an ii average classification accuracy of 94.15% from the observed dominating channels, the study emphasizes the importance of an effective model for myoelectric pattern recognition systems in controlling prosthetic limbs. As the research advances, its implications hold promise for enhancing the precision and efficacy of various neurorehabilitation strategies and augmenting our understanding of human motor control mechanisms. The benefits and drawbacks of the algorithms and techniques used to identify, process, and quantify certain patterns and properties of myoelectric signals were covered. These techniques pave the way for more effective clinical diagnoses and rehabilitation interventions, advancing our understanding of human movement and enhancing motor function restoration.