Comparison of Different Physiological Models for Estimating Muscle Forces Based on Inverse-Dynamics

The quantification of individual muscle forces from motion data is a relevant tool to better evaluate the biomechanical constraints of occupational tasks and decreased the risk of musculoskeletal injury. The aim of this paper was to develop a new method for estimating muscle forces from experimental kinematic data. The original method calculates muscle forces using the conventional numerical optimization by including physiological muscle properties. Then, the muscle forces estimated by the physiological optimization method (PO) were compared with those found by a traditional static optimization method (SO). In both optimization problems, muscle forces of the upper limb were estimated by minimizing a specific performance criterion. Although the muscles were treated as ideal force generators in the traditional method, they were constrained by their force–length and force–velocity relationships in the physiological method. Experimental methods to validate mathematical approach are limited. In the present study, we have chosen the EMG-to-force approach as a reference method with which to compare both numerical optimization results during a pointing task. The findings suggest that the physiological optimization provides better estimates of muscle forces during pointing task than traditional static optimization. Muscle forces from PO method were well matched with those from the EMG-to-force validation method, for flexors elbow muscles (BIC, BRA and BRD), as well as for main flexors shoulder (Coracobrachialis and Deltoid Anterior). In addition, the PO model predicted extensors muscle function for Triceps long in the elbow joint, and for Deltoid Posterior and Latissimus dorsi in the shoulder joint. However, SO solutions showed little or no recruitment of extensors muscles which are the Triceps long and the Latissimus dorsi, and preferentially activated flexors muscles with large moment arms.