Metzger Jean-Claude, Lambercy Olivier, Califfi Antonella, Dinacci Daria, Petrillo Claudio, Rossi Paolo, Conti Fabio M, Gassert Roger
Rehabilitation Engineering Laboratory, ETH Zurich, Leonhardstrasse 27, 8092 Zurich, Switzerland.
J Neuroeng Rehabil. 2014 Nov 15;11:154. doi: 10.1186/1743-0003-11-154.
Selecting and maintaining an engaging and challenging training difficulty level in robot-assisted stroke rehabilitation remains an open challenge. Despite the ability of robotic systems to provide objective and accurate measures of function and performance, the selection and adaptation of exercise difficulty levels is typically left to the experience of the supervising therapist.
We introduce a patient-tailored and adaptive robot-assisted therapy concept to optimally challenge patients from the very first session and throughout therapy progress. The concept is evaluated within a four-week pilot study in six subacute stroke patients performing robot-assisted rehabilitation of hand function. Robotic assessments of both motor and sensory impairments of hand function conducted prior to the therapy are used to adjust exercise parameters and customize difficulty levels. During therapy progression, an automated routine adapts difficulty levels from session to session to maintain patients' performance around a target level of 70%, to optimally balance motivation and challenge.
Robotic assessments suggested large differences in patients' sensorimotor abilities that are not captured by clinical assessments. Exercise customization based on these assessments resulted in an average initial exercise performance around 70% (62% ± 20%, mean ± std), which was maintained throughout the course of the therapy (64% ± 21%). Patients showed reduction in both motor and sensory impairments compared to baseline as measured by clinical and robotic assessments. The progress in difficulty levels correlated with improvements in a clinical impairment scale (Fugl-Meyer Assessment) (r s = 0.70), suggesting that the proposed therapy was effective at reducing sensorimotor impairment.
Initial robotic assessments combined with progressive difficulty adaptation have the potential to automatically tailor robot-assisted rehabilitation to the individual patient. This results in optimal challenge and engagement of the patient, may facilitate sensorimotor recovery after neurological injury, and has implications for unsupervised robot-assisted therapy in the clinic and home environment.
ClinicalTrials.gov, NCT02096445.
在机器人辅助的中风康复中，选择并维持一个引人入胜且具有挑战性的训练难度水平仍是一个未解决的难题。尽管机器人系统有能力提供客观且准确的功能和表现测量，但运动难度水平的选择和调整通常由主管治疗师的经验决定。
我们引入了一种针对患者定制且自适应的机器人辅助治疗概念，以便从首次治疗 session 开始并在整个治疗过程中为患者提供最佳挑战。该概念在一项为期四周的试点研究中进行评估，该研究涉及六名进行手部功能机器人辅助康复的亚急性中风患者。治疗前对手部功能的运动和感觉障碍进行的机器人评估用于调整运动参数并定制难度水平。在治疗过程中，一个自动化程序会逐 session 调整难度水平，以将患者的表现维持在 70% 的目标水平左右，从而最佳地平衡动机和挑战。
机器人评估表明患者的感觉运动能力存在很大差异，而临床评估并未捕捉到这些差异。基于这些评估进行的运动定制导致平均初始运动表现约为 70%（62% ± 20%，均值 ± 标准差），并且在整个治疗过程中保持这一水平（64% ± 21%）。通过临床和机器人评估测量，与基线相比，患者的运动和感觉障碍均有所减轻。难度水平的进展与临床损伤量表（Fugl-Meyer 评估）的改善相关（r s = 0.70），表明所提出的治疗在减少感觉运动障碍方面是有效的。
初始机器人评估与逐步难度适应相结合，有可能自动为个体患者量身定制机器人辅助康复。这会使患者获得最佳挑战和参与度，可能促进神经损伤后的感觉运动恢复，并对临床和家庭环境中的无监督机器人辅助治疗具有启示意义。
ClinicalTrials.gov，NCT02096445。
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