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The purpose 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.

A prediction model for hand prehensile movements was developed and validated. The model is based on a new approach that blends forward dynamics and a simple parametric control scheme. In the development phase, model parameters were first estimated using a set of hand grasping movement data, and then statistically analyzed. In the validation phase, the model was applied to novel conditions created by varying the subject group and size of the object grasped. The model performance was evaluated by the prediction errors under various novel conditions as compared to the benchmark values with no extrapolation. Analyses of the model parameters led to insights into human movement production and control. The resulting model also offers computational simplicity and efficiency, a much desired attribute for digital applications.