Citation

Augenstein TE, Remy CD, Claflin ES, Ranganathan R, Krishnan C. IEEE Trans. Haptics 2023; ePub(ePub): ePub.

Copyright

(Copyright © 2023, IEEE Computer Society)

DOI

10.1109/TOH.2023.3330368

PMID

37938965

Abstract

Semi-passive rehabilitation robots resist and steer a patient's motion using only controllable passive force elements (e.g., controllable brakes). Contrarily, passive robots use uncontrollable passive force elements (e.g., springs), while active robots use controllable active force elements (e.g., motors). Semi-passive robots can address cost and safety limitations of active robots, but it is unclear if they have utility in rehabilitation. Here, we assessed if a semi-passive robot could provide haptic guidance to facilitate motor learning. We first performed a theoretical analysis of the robot's ability to provide haptic guidance, and then used a prototype to perform a motor learning experiment that tested if the guidance helped participants learn to trace a shape. Unlike prior studies, we minimized the confounding effects of visual feedback during motor learning. Our theoretical analysis showed that our robot produced guidance forces that were, on average, 54° from the current velocity (active devices achieve 90). Our motor learning experiment showed, for the first time, that participants who received haptic guidance during training learned to trace the shape more accurately (97.57% error to 52.69%) than those who did not receive guidance (81.83% to 78.18%). These results support the utility of semi-passive robots in rehabilitation.

Language: en