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Usability Study Data of a Shared Control-based Wheelchair for the Built Environment

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IEEE2026-04-17 收录
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https://ieee-dataport.org/documents/conav-chair-development-and-evaluation-shared-control-based-wheelchair-built-environment
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As the global population of people with disabilities (PWD) continues to grow, so will the need for mobility solutions that promote independent living and social integration. Wheelchairs are vital for the mobility of PWD in both indoor and outdoor environments. The current state-of-the-art in powered wheelchairs are based on either manually controlled or fully autonomous modes of operation, offering limited flexibility and often proving difficult to navigate in spatially constrained environments. Moreover, research on robotic wheelchairs has focused predominantly on complete autonomy or improved manual control; approaches that can compromise efficiency and user trust. To overcome these challenges, this paper introduces the CoNav Chair\u2014a smart wheelchair based on the Robot Operating System (ROS) and features shared control navigation and obstacle avoidance capabilities that are intended to enhance navigational efficiency, safety, and ease fo use for the user. The paper outlines the CoNav Chair\u2019s design and presents a preliminary usability evaluation comparing three distinct navigation modes, namely, manual, shared, and fully autonomous, conducted with 21 healthy, unimpaired participants traversing an indoor building environment. Study findings indicated that the shared control navigation framework had significantly fewer collisions and performed comparably if not superior to the autonomous and manual modes on task completion time, trajectory length and smoothness; and was perceived as being safer and more efficient based on user-reported subjective assessments of usability. Overall, the CoNav system demonstrated acceptable safety and performance laying the foundation for subsequent usability testing with end users, namely, PWDs who rely on a powered wheelchair for mobility. 
提供机构:
Qianwei Wang; Vineet Kamat; Carol Menassa; Yifan Xu; Jordan Lillie; Clive Rahul D'Souza
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