Biomechanical Realism Versus Algorithmic Efficiency: A Trade-off in Human Motion Simulation Modeling

The purpose of this paper is to delineate why there exists a trade-off between biomechanical realism and algorithmic efficiency for human motion simulation models, and to illustrate how empirical human movement data and findings can be integrated with novel modeling techniques to overcome such a realism-efficiency tradeoff. We first review three major classes of biomechanical models for human motion simulation. The review of these models is woven together by a common fundamental problem of redundancy—kinematic and/or muscle redundancy. We describe how this problem is resolved in each class of models, and unveil how the trade-off arises, that is, how the computational demand associated with solving the problem is amplified as a model evolves from small scale to large scale, or from less realism to more realism. Several examples are then presented to illustrate how empirical motion data can be utilized for variable reduction and control simplification that help overcome the realism-efficiency trade-off. Examples are given for various levels of movement modeling, including joint motion activation information for minimizing the necessary degrees of freedom, joint motion coordination information for reducing kinematic model variables, and parameterization of inverse dynamics solutions for simplifying forward dynamics simulations. We conclude by remarking on future directions for the development of realistic, computationally efficient human motion simulation models.