AI-Enhanced Analysis to Investigate the Feasibility of EMG Signals for Prosthetic Hand Force Control Incorporating Anthropometric Measures

Background/Objectives: The potential application of electromyography (EMG) as a method for precise force control in prosthetic devices is investigated, expanding on its traditional use in gesture detection. Variability in EMG signals among individuals is influenced by physiological factors such as muscle mass, body fat percentage, and subcutaneous fat, as well as demographic variables like age, gender, height, and weight. This study aims to evaluate how these factors impact EMG signal quality and force output. Methods: EMG data was normalized using the maximum voluntary contraction (MVC) method, recorded at 100%, 50%, and 25% of MVC with simultaneous grip force measurement. Physiological parameters, including fat percentage, subcutaneous fat, and muscle mass, were analyzed. An extreme gradient boosting algorithm was applied to model the relationship between EMG amplitude and grip force. Results: The findings demonstrated significant linear correlations, with r2 coefficients reaching up to 0.93 and 0.83 in most cases. Muscle mass and fat levels were identified as key determinants of EMG variability, with significance coefficients ranging from 0.36592 to 0.0856 for muscle mass and 0.281918 to 0.06001 for fat levels. Conclusions: These results underscore the potential of EMG to enhance force control in prosthetic limbs, particularly in tasks such as grasping, holding, and releasing objects. Incorporating body composition factors into EMG-based prediction algorithms offers a refined approach to improving the precision and functionality of prosthetic control systems for complex motor tasks.