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

Physical medicine and rehabilitation international Pub Date : 2015-01-01 Epub Date: 2015-11-12
P C Kao, S Srivastava, J S Higginson, S K Agrawal, J P Scholz
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Abstract

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.

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基于短期表现的错误增强与减少错误的机器人步态训练对慢性中风患者:一项试点研究。
机器人外骨骼运动训练的成功需要识别控制算法,有效地重新训练神经损伤个体的步态模式。在这里,我们报告了两种训练模式,基于表现的错误增强和错误减少,如何改变四名慢性中风后个体的行走模式,作为未来中风后运动训练的概念验证。中风受试者被指示与来自神经完整个体的规定步行模式模板相匹配。基于行走过程中踝关节外踝位置的空间路径为每个受试者创建目标模板。施加机器人力,要么减少(误差减少),要么增加(误差增加)受试者的瞬时内踝位置和目标模板之间的偏差。受试者的表现是通过他们的实际和目标内踝路径之间的偏差量来量化的。经过减错训练后,S1显示出与目标模板偏差减少16%的内踝路径。相比之下,S4的踝径离模板更远,偏差增加了12%。经过误差增强训练后,S2的内踝路径与模板非常接近,偏差减少了58%,而S3的步数比基线高,偏差增加了37%。这些研究结果表明,误差减小力场对受试者步态模式的影响很小,而误差增强力场可能会促进内踝路径接近或超过目标步行模板。未来的调查将需要评估长期训练对地上行走和功能能力的影响。
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