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Short-term Performance-based Error-augmentation versus Error-reduction Robotic Gait Training for Individuals with Chronic Stroke: A Pilot Study

机译:短期性能为基础的错误增强与减少错误的机器人步态训练对慢性卒中患者的一项初步研究

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摘要

The success of locomotion training with robotic exoskeletons requires identifying control algorithms that effectively retrain gait patterns in neurologically impaired individuals. Here we report how the two training paradigms, performance-based error-augmentation versus error-reduction, modified walking patterns in four chronic post-stroke individuals as a proof-of-concept for future locomotion training following stroke. Stroke subjects were instructed to match a prescribed walking pattern template derived from neurologically intact individuals. Target templates based on the spatial paths of lateral ankle malleolus positions during walking were created for each subject. Robotic forces were applied that either decreased (error-reduction) or increased (error-augmentation) the deviation between subjects' instantaneous malleolus positions and their target template. Subjects' performance was quantified by the amount of deviation between their actual and target malleolus paths. After the error-reduction training, S1 showed a malleolus path with reduced deviation from the target template by 16%. In contrast, S4 had a malleolus path further away from the template with increased deviation by 12%. After the error-augmentation training, S2 had a malleolus path greatly approximating the template with reduced deviation by 58% whereas S3 walked with higher steps than his baseline with increased deviation by 37%. These findings suggest that an error-reduction force field has minimal effects on modifying subject's gait patterns whereas an error-augmentation force field may promote a malleolus path either approximating or exceeding the target walking template. Future investigation will need to evaluate the long-term training effects on over-ground walking and functional capacity.
机译:用机器人外骨骼进行运动训练的成功需要确定控制算法,以有效地重新训练神经系统受损个体的步态模式。在这里,我们报告了两种训练范例,即基于性能的错误增强与减少错误,如何在四个慢性卒中后个体中改变步行方式,作为未来卒中后运动训练的概念证明。指示中风受试者匹配从神经学上完整的个体得到的处方步行模式模板。为每个对象创建基于步行过程中踝踝外踝位置空间路径的目标模板。施加的机械力会减小(减少错误)或增加(增加错误)受试者的瞬时踝关节位置与其目标模板之间的偏差。受试者的表现通过其实际和目标踝关节路径之间的偏差量来量化。减少错误的训练后,S1显示出一条踝骨路径,与目标模板的偏差减少了16%。相反,S4的踝关节路径距离模板较远,偏差增加了12%。在进行错误增强训练之后,S2的踝关节路径大大地接近了模板,偏差减少了58%,而S3的步伐比基线走得更高,偏差增加了37%。这些发现表明,减少错误的力场对改变受试者的步态模式影响最小,而增加错误的力场可能会促进踝关节路径接近或超过目标步行模板。未来的研究将需要评估长期训练对地面行走和功能能力的影响。

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