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Neglected physical human-robot interaction may explain variable outcomes in gait neurorehabilitation research

  • M. Plooij
  • , S. Apte
  • , U. Keller
  • , P. Baines
  • , B. Sterke
  • , L. Asboth
  • , G. Courtine
  • , J. von Zitzewitz
  • , H. Vallery*
  • *Corresponding author for this work
  • Delft University of Technology
  • Demcon Advanced Mechatronics
  • a DIH brand
  • École Polytechnique Fédéral Lausanne (EPFL)
  • University of Zurich
  • University Hospital Lausanne

Research output: Contribution to journalArticleAcademicpeer-review

20 Citations (Scopus)

Abstract

During gait neurorehabilitation, many factors influence the quality of gait patterns, particularly the chosen body-weight support (BWS) device. Consequently, robotic BWS devices play a key role in gait rehabilitation of people with neurological disorders. The device transparency, support force vector direction, and attachment to the harness vary widely across existing robotic BWS devices, but the influence of these factors on the production of gait remains unknown. Because this information is key to designing an optimal BWS, we systematically studied these determinants in this work. We report that with a highly transparent device and a conventional harness, healthy participants select a small backward force when asked for optimal BWS conditions. This unexpected finding challenges the view that during human-robot interactions, humans predominantly optimize energy efficiency. Instead, they might seek to increase their feeling of stability and safety. We also demonstrate that the location of the attachment points on the harness strongly affects gait patterns, yet harness attachment is hardly reported in literature. Our results establish principles for the design of BWS devices and personalization of BWS settings for gait neurorehabilitation.

Original languageEnglish
Article numbereabf1888
JournalScience Robotics
Volume6
Issue number58
DOIs
Publication statusPublished - 22 Sept 2021

Bibliographical note

Funding Information:
The work in this paper was partially funded by Eurostars project 10152, by a Marie-Curie career integration grant PCIG13-GA-2013-618899, by Wings for Life accelerated translation program, and by the National Center of Competence in Research (NCCR) Robotics of the Swiss National Foundation.

Publisher Copyright:
Copyright © 2021 The Authors, some rights reserved;

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being
  2. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

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