Computational modeling study demonstrates accurate estimation of fingertip forces and muscle activation in human grasping, indicating physiological validity for biomechanical analysis.
We developed a method to estimate fingertip forces, torques, and muscle activations during object grasping with a digital hand model. Grasping an object requires several equilibrium conditions: force and moment balance on the object, consistency between fingertip forces and joint torques, alignment between joint torques and muscle forces, and a no-slip condition at the contact surface. Our method estimates fingertip forces, torques, and muscle activations by minimizing the sum of squared muscle activations while satisfying these conditions. Moment arms for grasping postures were calculated with a digital hand model that included a muscle path model to simulate muscle trajectories. To assess accuracy, we measured the forces exerted by the thumb and index finger while grasping objects with different surface materials and postures, and compared the results with the estimates. The estimated vertical and horizontal forces, as well as fingertip torques, were generally consistent with the measurements. The predicted active muscles also corresponded to those reported in previous studies, supporting the validity and physiological relevance of the method.
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Nohara et al. (2026) studied this question.
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