IEEE Trans Neural Syst Rehabil Eng. 2018 Feb;26(2):307-323. doi: 10.1109/TNSRE.2017.2763458. Epub 2017 Oct 16.
The wide variation in upper extremity motor impairments among stroke survivors necessitates more intelligent methods of customized therapy. However, current strategies for characterizing individual motor impairments are limited by the use of traditional clinical assessments (e.g., Fugl-Meyer) and simple engineering metrics (e.g., goal-directed performance). Our overall approach is to statistically identify the range of volitional movement capabilities, and then apply a robot-applied force vector field intervention that encourages under-expressed movements. We investigated whether explorative training with such customized force fields would improve stroke survivors' (n = 11) movement patterns in comparison to a control group that trained without forces (n = 11). Force and control groups increased Fugl-Meyer UE scores (average of 1.0 and 1.1, respectively), which is not considered clinically meaningful. Interestingly, participants from both groups demonstrated dramatic increases in their range of velocity during exploration following only six days of training (average increase of 166.4% and 153.7% for the Force and Control group, respectively). While both groups showed evidence of improvement, we also found evidence that customized forces affected learning in a systematic way. When customized forces were active, we observed broader distributions of velocity that were not present in the controls. Second, we found that these changes led to specific changes in unassisted motion. In addition, while the shape of movement distributions changed significantly for both groups, detailed analysis of the velocity distributions revealed that customized forces promoted a greater proportion of favorable changes. Taken together, these results provide encouraging evidence that patient-specific force fields based on individuals' movement statistics can be used to create new movement patterns and shape them in a customized manner. To the best of our knowledge, this paper is the first to directly link engineering assessments of stroke survivors' exploration movement behaviors to the design of customized robot therapy.
脑卒中幸存者上肢运动障碍的广泛变化需要更智能的定制治疗方法。然而，目前用于描述个体运动障碍的策略受到传统临床评估（例如 Fugl-Meyer）和简单工程指标（例如目标导向性能）的限制。我们的总体方法是从统计学上确定自愿运动能力的范围，然后应用机器人施加的力矢量场干预，鼓励表达不足的运动。我们研究了与没有力的对照组相比，探索性训练是否会改善脑卒中幸存者（n=11）的运动模式，而对照组则没有进行力训练（n=11）。力组和对照组的 Fugl-Meyer UE 评分均有所提高（分别为 1.0 和 1.1，平均），这被认为没有临床意义。有趣的是，两组参与者在仅进行六天训练后，在探索过程中的速度范围都有了显著的增加（力组和对照组的平均增加分别为 166.4%和 153.7%）。虽然两组都有改善的证据，但我们也发现定制力以系统的方式影响学习。当定制力处于活动状态时，我们观察到速度分布的范围更广，而对照组则没有。其次，我们发现这些变化导致了非辅助运动的特定变化。此外，虽然两组的运动分布形状都发生了显著变化，但对速度分布的详细分析表明，定制力促进了更大比例的有利变化。总的来说，这些结果提供了令人鼓舞的证据，表明基于个体运动统计数据的患者特异性力场可以用于创建新的运动模式，并以定制的方式塑造它们。据我们所知，本文首次将脑卒中幸存者探索性运动行为的工程评估与定制机器人治疗的设计直接联系起来。
