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

Phys Med Rehabil Int. 2015;2(9):1066. Epub 2015 Nov 12.


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.

Keywords: Force field; Gait rehabilitation; Rehabilitation robotics; Stroke; Walking.