Usability studies of an egocentric vision-based robotic wheelchair

Mohammed Kutbi, Xiaoxue Du, Yizhe Chang, Bo Sun, Nikolaos Agadakos, Haoxiang Li, Gang Hua, Philippos Mordohai

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

Motivated by the need to improve the quality of life for the elderly and disabled individuals who rely on wheelchairs for mobility, and who may have limited or no hand functionality at all, we propose an egocentric computer vision based co-robot wheelchair to enhance their mobility without hand usage. The robot is built using a commercially available powered wheelchair modified to be controlled by head motion. Head motion is measured by tracking an egocentric camera mounted on the user's head and faces outward. Compared with previous approaches to hands-free mobility, our system provides a more natural human robot interface because it enables the user to control the speed and direction of motion in a continuous fashion, as opposed to providing a small number of discrete commands. This article presents three usability studies, which were conducted on 37 subjects. The first two usability studies focus on comparing the proposed control method with existing solutions while the third study was conducted to assess the effectiveness of training subjects to operate the wheelchair over several sessions. A limitation of our studies is that they have been conducted with healthy participants. Our findings, however, pave the way for further studies with subjects with disabilities.

Original languageEnglish
Article numberA4
JournalACM Transactions on Human-Robot Interaction
Volume10
Issue number1
DOIs
StatePublished - 2020

Keywords

  • Assistive technologies
  • Computer vision
  • Computer vision tasks
  • Computing methodologies
  • Human-centered computing
  • Interactive systems and tools
  • Social and professional topics

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