Development and evaluation of an optimization-based model for power-grip posture prediction

An optimization-based model for power-grip posture prediction was proposed. The model was based on the premise that the hand prehensile configuration in a power grip best conforms to the object shape. This premise was embodied by an optimization procedure that minimized the sum of distances from the finger joints to the object surface. The model was evaluated against data from an experiment that measured the grasp postures of 28 subjects having diverse anthropometry. The intra- and inter-person variabilities in grip postures were empirically assessed and used as benchmark values for model evaluation. The evaluation showed that the root-mean-square (RMS) values of angle differences between the predicted and measured postures had a 13.7° grand mean (across all joints, subjects, and two cylindrical handles grasped), whereas the RMS values of the inter- and intra-person variabilities in measured postures had grand means of 13.0° and 4.4°, respectively. The model can be readily generalized to the prediction of postures in power-grasping objects of different shapes, and adapted for testing alternative prehensile strategies or performance criteria.