Robotic rehabilitation system using human hand trajectory generation model in virtual curling task

The present paper integrates a computational model of human hand trajectory generation in the virtual curing into a robotic rehabilitation system for upper limbs to assist and teach smooth movements depending on task conditions. In training, a trainee manipulates the handle of an impedance-controlled robot to move a virtual stone to the center of a circular target on the ice while predicting transient behaviors of the released stone. A reference hand motion is clarified through a set of preliminary experiments with well-trained subjects for different task conditions and computationally expressed in the framework of a minimum jerk model with task-related constraints. Training experiments with novice healthy volunteers demonstrates the effectiveness of the proposed approach that presents the reference motion produced by the computational model to a trainee in assisting and training the smoothness of hand movements.