Abstract
Based on evidence from the previous research in rehabilitation robot control strategies, we found that the common feature of the effective control strategies to promote subjects’ engagement is creating a reward–punishment feedback mechanism. This article proposes a reward–punishment feedback control strategy based on energy information. Firstly, an engagement estimated approach based on energy information is developed to evaluate subjects’ performance. Secondly, the estimated result forms a reward–punishment term, which is introduced into a standard model-based adaptive controller. This modified adaptive controller is capable of giving the reward–punishment feedback to subjects according to their engagement. Finally, several experiments are implemented using a wrist rehabilitation robot to evaluate the proposed control strategy with 10 healthy subjects who have not cardiovascular and cerebrovascular diseases. The results of these experiments show that the mean coefficient of determination ( R 2) of the data obtained by the proposed approach and the classical approach is 0.7988, which illustrate the reliability of the engagement estimated approach based on energy information. And the results also demonstrate that the proposed controller has great potential to promote patients’ engagement for wrist rehabilitation.
Highlights
Stroke has become one of the major diseases that threaten people’s physical and mental health in the world.[1]
We proposed an engagement estimated approach based on energy information and incorporated the estimated results into the controller
Based on the previous adaptive controller including the constant reward–punishment factor, we designed the adaptive controller with the variable reward–punishment factor for wrist rehabilitation robot, which could create the reward–punishment feedback mechanism according to different engagement of subjects
Summary
Stroke has become one of the major diseases that threaten people’s physical and mental health in the world.[1]. Keywords Wrist rehabilitation, robot, adaptive control, engagement estimated, reward–punishment feedback We proposed a reward–punishment feedback control strategy to promote subjects’ engagement for wrist rehabilitation.
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