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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.

Modeling normal human reach behavior is dependent on many factors. Anthropometry, age, gender, joint mobility and muscle strength are a few such factors related to the individual being modeled. Reach locations, seat configurations, and tool weights are a few other task factors that can affect dynamic reach postures. This paper describes how two different modeling approaches are being used in the University of Michigan Human Motion Simulation Laboratory to predict normal seated reaching motions. One type of model uses an inverse kinematic structure with an optimization procedure that minimizes the weighted sum of the instantaneous velocity of each body segment. The second model employs a new functional regression technique to fit polynomial equations to the angular displacements of each body segment. To develop and validate these models, 38 subjects of widely varying age and anthropometry were asked to perform reaching motions while seated in simulated vehicle or industrial workplace.