BACKGROUND In the past years, robotic systems have become increasingly popular in upper limb rehabilitation. Nevertheless, clinical studies have so far not been able to confirm superior efficacy of robotic therapy over conventional methods. The personalization of robot-aided therapy according to the patients' individual motor deficits has been suggested as a pivotal step to improve the clinical outcome of such approaches. METHODS Here, we present a model-based approach to personalize robot-aided rehabilitation therapy within training sessions. The proposed method combines the information from different motor performance measures recorded from the robot to continuously estimate patients' motor improvement for a series of point-to-point reaching movements in different directions. Additionally, it comprises a personalization routine to automatically adapt the rehabilitation training. We engineered our approach using an upper-limb exoskeleton. The implementation was tested with 17 healthy subjects, who underwent a motor-adaptation paradigm, and two subacute stroke patients, exhibiting different degrees of motor impairment, who participated in a pilot test undergoing rehabilitative motor training. RESULTS The results of the exploratory study with healthy subjects showed that the participants divided into fast and slow adapters. The model was able to correctly estimate distinct motor improvement progressions between the two groups of participants while proposing individual training protocols. For the two pilot patients, an analysis of the selected motor performance measures showed that both patients were able to retain the improvements gained during training when reaching movements were reintroduced at a later stage. These results suggest that the automated training adaptation was appropriately timed and specifically tailored to the abilities of each individual. CONCLUSIONS The results of our exploratory study demonstrated the feasibility of the proposed model-based approach for the personalization of robot-aided rehabilitation therapy. The pilot test with two subacute stroke patients further supported our approach, while providing encouraging results for the applicability in clinical settings. Trial registration This study is registered in ClinicalTrials.gov (NCT02770300, registered 30 March 2016, https://clinicaltrials.gov/ct2/show/NCT02770300).

中文翻译：

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个性化机器人辅助治疗的运动改善估计和任务适应：可行性研究。


背景技术在过去几年中，机器人系统在上肢康复中变得越来越流行。然而，临床研究迄今为止尚未能够证实机器人治疗优于传统方法的疗效。根据患者的个体运动缺陷进行机器人辅助治疗的个性化已被认为是改善此类方法临床结果的关键步骤。方法在这里，我们提出了一种基于模型的方法，在培训课程中个性化机器人辅助康复治疗。该方法结合了机器人记录的不同运动性能测量信息，连续估计患者在不同方向进行一系列点对点到达运动的运动改善情况。此外，它还包括一个个性化程序来自动适应康复训练。我们使用上肢外骨骼设计了我们的方法。该实施方案在 17 名接受运动适应模式的健康受试者和两名表现出不同程度运动障碍的亚急性中风患者中进行了测试，他们参加了康复运动训练的试点测试。结果对健康受试者的探索性研究结果表明，参与者分为快速适应者和慢速适应者。该模型能够正确估计两组参与者之间不同的运动改善进展，同时提出个人训练方案。对于两名飞行员患者，对所选运动表现测量的分析表明，当在后期重新引入伸展运动时，两名患者都能够保留训练期间获得的进步。 这些结果表明，自动化训练适应的时机适当，并且根据每个人的能力进行了专门定制。结论 我们的探索性研究结果证明了所提出的基于模型的机器人辅助康复治疗个性化方法的可行性。对两名亚急性中风患者进行的试点测试进一步支持了我们的方法，同时为临床环境的适用性提供了令人鼓舞的结果。试验注册 本研究已在 ClinicalTrials.gov 上注册（NCT02770300，注册日期为 2016 年 3 月 30 日，https://clinicaltrials.gov/ct2/show/NCT02770300）。