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.

中文翻译：

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基于能量信息的腕部康复奖惩反馈控制策略

基于以往康复机器人控制策略研究的证据，我们发现促进受试者参与的有效控制策略的共同特征是建立奖惩反馈机制。本文提出了一种基于能量信息的奖惩反馈控制策略。首先，开发了一种基于能量信息的参与度估计方法来评估受试者的表现。其次，估计结果形成一个奖惩项，将其引入到基于标准模型的自适应控制器中。这种改进的自适应控制器能够根据受试者的参与度向他们提供奖励-惩罚反馈。最后，使用腕部康复机器人进行了多项实验，以评估所提出的控制策略，其中包括 10 名没有心脑血管疾病的健康受试者。这些实验的结果表明，所提出的方法和经典方法获得的数据的平均决定系数（R 2 ）为0.7988，这说明了基于能量信息的参与估计方法的可靠性。结果还表明，所提出的控制器在促进患者参与腕部康复方面具有巨大潜力。这说明了基于能源信息的参与度估计方法的可靠性。结果还表明，所提出的控制器在促进患者参与腕部康复方面具有巨大潜力。这说明了基于能源信息的参与度估计方法的可靠性。结果还表明，所提出的控制器在促进患者参与腕部康复方面具有巨大潜力。