Mapping Grip Force to Muscular Activity Towards Understanding Upper Limb Musculoskeletal Intent using a Novel Grip Strength Model

Abstract

This work aims to evaluate a grip strength model, developed using a piecewise linear function based on the Woods and Bigland-Ritchie EMG-force model, which correlates the relationship between measured grip force and muscular activity. The grip strength model is compared against the results derived from an upper limb musculoskeletal model. Experimental results demonstrate the models efficacy in estimating surface electromyography (sEMG) readings from force measurements, with a mean root mean square error (RMSE) of 0.2035 and a standard deviation of 0.1207 for muscle activation (dimension-less). Moreover, incorporating sEMG readings associated with grip force does not significantly affect the optimization of muscle activation in the upper arm, as evidenced by kinematic data analysis from dynamic tasks. This validation underscores the models potential to enhance musculoskeletal model-based motion analysis pipelines without distorting results. Consequently, this research emphasizes the prospect of integrating external models into existing human motion analysis frameworks, presenting promising implications for physical Human-Robot Interactions (pHRI).