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A Robot-based Gait Training System for Post-Stroke Rehabilitation

Banh, Sharon and Zheng, Emily and Kubota, Alyssa and Riek, Laurel D. (2021) A Robot-based Gait Training System for Post-Stroke Rehabilitation. In: HRI '21 Companion: Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction. Association for Computing Machinery , New York, NY, pp. 452-456. ISBN 9781450382908.

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As the prevalence of stroke survivors increases, the demand for rehabilitative services will rise. While there has been considerable development in robotics to address this need, few systems consider individual differences in ability, interests, and learning. Robots need to provide personalized interactions and feedback to increase engagement, enhance human motor learning, and ultimately, improve treatment outcomes. In this paper, we present 1) our design process of an embodied, interactive robotic system for post-stroke rehabilitation, 2) design considerations for stroke rehabilitation technology and 3) a prototype to explore how feedback mechanisms and modalities affect human motor learning. The objective of our work is to improve motor rehabilitation outcomes and supplement healthcare providers by reducing the physical and cognitive demands of administering rehabilitation. We hope our work inspires development of human-centered robots to enhance recovery and improve quality of life for stroke survivors.

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Additional Information:© 2021 Copyright held by the owner/author(s). Research reported in this paper is supported by the National Science Foundation under CMMI-1935500 and the Computing Research Association DREU program.
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Record Number:CaltechAUTHORS:20210315-072608742
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Official Citation:Sharon Banh, Emily Zheng, Alyssa Kubota, Laurel D. Riek. 2021. A Robot-based Gait Training System for Post-Stroke Rehabilitation. In Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction (HRI’21 Companion), March 8-11, 2021, Boulder, CO, USA. ACM, NY, NY, USA, 5 pages.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108422
Deposited By: Tony Diaz
Deposited On:19 Mar 2021 20:11
Last Modified:19 Mar 2021 20:11

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