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As some functional diseases in the brain, such as cerebellum dysfunction and Parkinson's disease, cause disability related to human movement control, a compensation method was developed for improving the performance of hand movement. The compensation can be carried out by adding assistant force, which is generated from artificial equipment attached to a human arm. From the experiment of visual target tracking, the tracking trajectories recorded from both healthy persons and patients with movement disability were analyzed. It was found that the tracking trajectories were represented sufficiently by a dynamic model of a robot arm in which the differences between healthy persons and patients were characterized by the model parameters. Based on the model, it was demonstrated that the hand movement of patients could be improved by introducing an appropriate compensation. The effectiveness of the proposed compensation method was verified from a simulation study of a robot arm. The design of artificial equipment for compensating the hand movement was also presented and discussed.