Methodology to Customize Maximal Isometric Forces for Hill-Type Muscle Models.
One approach to increasing the confidence of muscle force estimation via musculoskeletal models is to minimize the root mean square error (RMSE) between joint torques estimated from electromyographic-driven musculoskeletal models and those computed using inverse dynamics. We propose a method that reduces RMSE by selecting subsets of combinations of maximal voluntary isometric contraction (MVIC) trials that minimize RMSE. Twelve participants performed 3 elbow MVIC in flexion and in extension. An upper-limb electromyographic-driven musculoskeletal model was created to optimize maximum muscle stress and estimate the maximal isometric force of the biceps brachii, brachialis, brachioradialis, and triceps brachii. Maximal isometric forces were computed from all possible combinations of flexion-extension trials. The combinations producing the smallest RMSE significantly reduced the normalized RMSE to 7.4% compared with the combination containing all trials (9.0%). Maximal isometric forces ranged between 114-806 N, 64-409 N, 236-1511 N, and 556-3434 N for the brachii, brachialis, brachioradialis, and triceps brachii, respectively. These large variations suggest that customization is required to reduce the difference between models and actual participants' maximal isometric force. While the smallest previously reported RMSE was 10.3%, the proposed method reduced the RMSE to 7.4%, which may increase the confidence of muscle force estimation.
['Dal Maso|Fabien|F|', 'Begon|Mickaël|M|', 'Raison|Maxime|M|']
D000328:Adult D000465:Algorithms D003198:Computer Simulation D004551:Elbow Joint D004576:Electromyography D056966:Excitation Contraction Coupling D005260:Female D006801:Humans D007537:Isometric Contraction D008297:Male D008954:Models, Biological D018482:Muscle, Skeletal D005082:Physical Exertion D015203:Reproducibility of Results D012680:Sensitivity and Specificity D013314:Stress, Mechanical
musculoskeletal model maximum muscle stress antagonist muscles best combination electromyography numerical optimization