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性：可行性研究。

背景技术在过去的几年中，机器人系统在上肢康复中变得越来越流行。然而，迄今为止，临床研究尚不能证实机器人疗法优于传统方法。已经建议根据患者的个体运动障碍来进行机器人辅助治疗的个性化，这是改善此类方法临床效果的关键步骤。方法在这里，我们提出了一种基于模型的方法，可以在培训课程中个性化机器人辅助的康复治疗。所提出的方法结合了机器人记录的来自不同运动性能指标的信息，以连续估算患者在不同方向上一系列点对点运动的运动水平。另外，它包含个性化例程，可自动适应康复训练。我们使用上肢外骨骼设计了方法。对17位健康受试者进行了运动适应性测试，并对两名表现出不同程度的运动障碍的亚急性中风患者进行了测试，这些患者参加了接受康复运动训练的试验。结果对健康受试者进行的探索性研究结果表明，参与者分为快速适配器和慢速适配器。该模型能够在建议个体训练方案的同时正确估计两组参与者之间明显的运动改善进展。对于两名先导患者，对所选运动表现指标的分析表明，在以后的阶段重新引入运动时，两名患者都能保留训练期间获得的改善。这些结果表明，对自动训练的适应性进行了适当的计时，并专门针对每个人的能力进行了调整。结论我们的探索性研究结果表明，基于模型的方法可用于机器人辅助康复治疗的个性化。对两名亚急性中风患者的先导性试验进一步支持了我们的方法，同时为临床应用提供了令人鼓舞的结果。试验注册本研究已在ClinicalTrials.gov中注册（NCT02770300，2016年3月30日注册，https：//clinicaltrials.gov/ct2/show/NCT02770300）。