Abstract

Abstract. Robot-assisted rehabilitation has proven to improve a subject's upper-extremity motor function. However, it is still challenging to control the robot to provide minimal assistance based on the subject's performance. This paper proposes a motion-trend-based assistance control strategy to solve this problem. The control strategy provides the corresponding normal and tangential forces by constructing an adaptive virtual assistance force field around a predetermined training trajectory. In the normal direction, a performance function based on the position-tracking error and normal motion trend is established to adjust the normal assistance force field strength in real time; in the tangential direction, a performance function based on the tangential interaction force and tangential motion trend is established to adjust the tangential assistance force field strength in real time. Additionally, good motion trends can quickly reduce the assistance force field. The normal motion trend represents the state of the subject moving toward the target trajectory, and the tangential motion trend represents the state of increasing tangential interaction force. Finally, the performance of this control strategy was evaluated by training experiments with eight healthy subjects. Preliminary experiments showed that the normal assist force in the active movement phase was 92.48 % smaller than that in the poor phase, and the tangential assist force was 90.73 % smaller than that in the slack phase. And the normal assist force and tangential assist force will become zero within 0.2 s when the subject has a good tendency to move. This shows that the control strategy proposed in this paper can quickly adjust the assistance according to the subject's motor performance. In addition, the assistance can be quickly reduced when the subject has a good movement trend. Future work will incorporate OpenSim (muscle and bone simulation software) to develop a pathway suitable for the subject's arm rehabilitation.

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