The Impact of Normalization Procedures on Surface Electromyography (sEMG) Data Integrity: A Study of Bicep and Tricep Muscle Signal Analysis

Surface electromyography (sEMG) is a critical tool for quantifying muscle activity and inferring biomechanical function, enabling the detection of neuromuscular deficits through the analysis of electrical potential propagation. However, the inherent variability in sEMG signal amplitude, influenced by factors such as electrode placement, equipment characteristics, and individual physiology, necessitates robust normalization techniques for accurate comparative analysis. This study investigates the reliability and effectiveness of several normalization methods in the context of bicep and tricep muscle activation during dynamic and isometric exercises: maximum voluntary contraction (MVC), submaximal voluntary contraction (SMVC), remote voluntary contraction (RVC), mean, and peak normalization. We conducted a comprehensive experimental protocol involving healthy volunteers, capturing sEMG signals during controlled bicep curls, tricep extensions, and isometric contractions. The efficacy of each normalization method was evaluated based on its ability to minimize inter-subject variability and enhance signal consistency. Specifically, while SMVC, MVC, and RVC methods exhibited generally superior performance in normalizing bicep and tricep signals, the optimal method varied depending on the task and muscle, providing consistent and reliable data for biomechanical analysis. These results underscore the importance of selecting appropriate normalization techniques to improve the accuracy of sEMG-based assessments in clinical and sports biomechanics, contributing to the development of more effective rehabilitation protocols and performance enhancement strategies.