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Several approaches are currently employed to address the predictive simulation of human motion, having in common their high computational demand. Muscle modeling seems to be an essential ingredient to provide human likeness to the obtained movements, at least for some activities, but it increases even more the computational load. This paper studies the efficiency and accuracy yielded by several alternatives of muscle modeling in the forward-dynamics analysis of captured motions, as a method that encompasses the computationally intensive character of predictive simulation algorithms with a known resulting motion which simplifies the comparisons. Four muscle models, the number of muscles, muscle torque generators, muscular synergies, and look-up tables for musculotendon lengths and moment arms are considered and analyzed, seeking to provide criteria on how to include the muscular component in human multibody models so that its effect on the resulting motion is captured while keeping a reasonable computational cost. Gait and vertical jump are considered as examples of slow- and fast-dynamics motions. Results suggest that: (i) the rigid-tendon model with activation dynamics offers a good balance between accuracy and efficiency, especially for short-tendon muscles; (ii) including muscles in the model leads to a decrease in efficiency which is highly dependent on the muscle model employed and the number of muscles considered; (iii) muscle torque generators keep the efficiency of skeletal models; (iv) muscular synergies offer almost no advantage for this problem; and (v) look-up tables for configuration-dependent kinematic magnitudes have a non-negligible impact on the efficiency, especially for simplified muscle models.

Comparison of several muscle modeling alternatives for computationally intensive algorithms in human motion dynamics