Sensory-Motor Systems Lab, Department of Mechanical Engineering and Process Engineering, ETH Zurich, Zurich, Switzerland.
J Neuroeng Rehabil. 2012 Feb 3;9:6. doi: 10.1186/1743-0003-9-6.
The motivation of patients during robot-assisted rehabilitation after neurological disorders that lead to impairments of motor functions is of great importance. Due to the increasing number of patients, increasing medical costs and limited therapeutic resources, clinicians in the future may want patients to practice their movements at home or with reduced supervision during their stay in the clinic. Since people only engage in an activity and are motivated to practice if the outcome matches the effort at which they perform, an augmented feedback application for rehabilitation should take the cognitive and physical deficits of patients into account and incorporate a mechanism that is capable of balancing i.e. adjusting the difficulty of an exercise in an augmented feedback application to the patient's capabilities.
We propose a computational mechanism based on Fitts' Law that balances i.e. adjusts the difficulty of an exercise for upper-extremity rehabilitation. The proposed mechanism was implemented into an augmented feedback application consisting of three difficulty conditions (easy, balanced, hard). The task of the exercise was to reach random targets on the screen from a starting point within a specified time window. The available time was decreased with increasing condition difficulty. Ten subacute stroke patients were recruited to validate the mechanism through a study. Cognitive and motor functions of patients were assessed using the upper extremity section of the Fugl-Meyer Assessment, the modified Ashworth scale as well as the Addenbrookes cognitive examination-revised. Handedness of patients was obtained using the Edinburgh handedness inventory. Patients' performance during the execution of the exercises was measured twice, once for the paretic and once for the non-paretic arm. Results were compared using a two-way ANOVA. Post hoc analysis was performed using a Tukey HSD with a significance level of p < 0.05.
Results show that the mechanism was capable of balancing the difficulty of an exercise to the capabilities of the patients. Medians for both arms show a gradual decrease and significant difference of the number of successful trials with increasing condition difficulty (F(2;60) = 44.623; p < 0.01; η(2) = 0.623) but no significant difference between paretic and non-paretic arm (F(1;60) = 3.768; p = 0.057; η(2) = 0.065). Post hoc analysis revealed that, for both arms, the hard condition significantly differed from the easy condition (p < 0.01). In the non-paretic arm there was an additional significant difference between the balanced and the hard condition (p < 0.01). Reducing the time to reach the target, i.e., increasing the difficulty level, additionally revealed significant differences between conditions for movement speeds (F(2;59) = 6.013; p < 0.01; η(2) = 0.185), without significant differences for hand-closing time (F(2;59) = 2.620; p = 0.082; η(2) = 0.09), reaction time (F(2;59) = 0.978; p = 0.383; η(2) = 0.036) and hand-path ratio (F(2;59) = 0.054; p = 0.947; η(2) = 0.002). The evaluation of a questionnaire further supported the assumption that perceived performance declined with increased effort and increased exercise difficulty leads to frustration.
Our results support that Fitts' Law indeed constitutes a powerful mechanism for task difficulty adaptation and can be incorporated into exercises for upper-extremity rehabilitation.
在神经功能障碍导致运动功能障碍后，机器人辅助康复期间患者的动机非常重要。由于患者人数不断增加、医疗费用增加和治疗资源有限，未来的临床医生可能希望患者在诊所停留期间在家中或在减少监督的情况下进行运动练习。由于只有当结果与患者执行的努力相匹配时，人们才会从事一项活动并有动力进行练习，因此康复的增强反馈应用程序应该考虑到患者的认知和身体缺陷，并纳入一种能够平衡的机制，即根据患者的能力调整增强反馈应用程序中运动的难度。
我们提出了一种基于 Fitts 定律的计算机制，用于平衡即调整上肢康复运动的难度。所提出的机制被实现到一个增强反馈应用程序中，该应用程序由三个难度条件（简单、平衡、困难）组成。练习的任务是在规定的时间窗口内从起点到达屏幕上的随机目标。随着条件难度的增加，可用时间减少。招募了 10 名亚急性中风患者通过一项研究来验证该机制。使用 Fugl-Meyer 评估上肢部分、改良 Ashworth 量表以及 Addenbrookes 认知考试修订版评估患者的认知和运动功能。使用爱丁堡手性量表获得患者的手性。患者在执行练习时的表现测量了两次，一次是患侧，一次是非患侧。使用双向方差分析比较结果。使用 Tukey HSD 进行事后分析，显著性水平为 p < 0.05。
结果表明，该机制能够根据患者的能力平衡练习的难度。中位数对于两个手臂，随着条件难度的增加，成功试验的数量逐渐减少且存在显著差异（F(2;60) = 44.623;p < 0.01; η(2) = 0.623），但患侧和非患侧手臂之间没有显著差异（F(1;60) = 3.768;p = 0.057; η(2) = 0.065）。事后分析表明，对于两个手臂，困难条件与简单条件有显著差异（p < 0.01）。在非患侧手臂中，平衡和困难条件之间还有一个额外的显著差异（p < 0.01）。缩短到达目标的时间，即增加难度级别，还显示出运动速度的条件之间存在显著差异（F(2;59) = 6.013;p < 0.01; η(2) = 0.185），而手闭合时间没有显著差异（F(2;59) = 2.620;p = 0.082; η(2) = 0.09）、反应时间（F(2;59) = 0.978;p = 0.383; η(2) = 0.036）和手路径比（F(2;59) = 0.054;p = 0.947; η(2) = 0.002）。问卷调查的评估进一步支持了这样的假设，即感知表现随着努力的增加而下降，增加的练习难度会导致挫败感。
我们的结果支持 Fitts 定律确实构成了任务难度适应的强大机制，可以将其纳入上肢康复的练习中。
