More Than Automation: User Insights into the Functionality and Interface of Wheelchair-Mounted Robotic Arms

While voice-controlled automated Wheelchair-mounted robotic arms (WMRAs) could potentially offer more natural and simplified interactions than manual control, they introduce new challenges related to human-robot collaboration. To explore user expectations in terms of functionality and interface, we conducted semi-structured interviews with 13 powered wheelchair users who have upper limb impairments. A prototype with a robotic arm, a camera, and a Unity-based simulated interface was developed to help users understand the concept and functionality of the voice-controlled automated WMRA system during interviews. With safety as a priority, we found that users prioritized the WMRA's ability to grasp objects in challenging positions, such as on the ground or at high locations, and emphasized the need for a versatile gripper. Users also expected the WMRA to assist in performing complex daily tasks, with varying expectations for its performance based on task difficulty. Regarding the interface, users sought more information about the system's awareness and task execution, while emphasizing the importance of avoiding information overload. The demand for detailed safety information, such as temperature and gripping force, pointed to the need for enhanced sensor capabilities in the WMRA system. Additionally, concerns about privacy underscored the need for clear communication on privacy policies. Our results provide user-centered insights for automated WMRA, offering design implications and future research directions in areas such as user modeling, hardware, algorithms, and interface development.

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Institutions

Publication Details

Published
2025-03-04
DOI
https://doi.org/10.1109/hri61500.2025.10974057
Citations
2
Primary Topic
Gaze Tracking and Assistive Technology
Type
article
Field-Weighted Citation Impact
3.87

Funders

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article

More Than Automation: User Insights into the Functionality and Interface of Wheelchair-Mounted Robotic Arms

Na Du, Lesong Jia, Breelyn Kane Styler
2 citations
Gaze Tracking and Assistive Technology
3.87
article

More Than Automation: User Insights into the Functionality and Interface of Wheelchair-Mounted Robotic Arms

Na Du, Lesong Jia, Breelyn Kane Styler
article en
2 citations

Abstract

While voice-controlled automated Wheelchair-mounted robotic arms (WMRAs) could potentially offer more natural and simplified interactions than manual control, they introduce new challenges related to human-robot collaboration. To explore user expectations in terms of functionality and interface, we conducted semi-structured interviews with 13 powered wheelchair users who have upper limb impairments. A prototype with a robotic arm, a camera, and a Unity-based simulated interface was developed to help users understand the concept and functionality of the voice-controlled automated WMRA system during interviews. With safety as a priority, we found that users prioritized the WMRA's ability to grasp objects in challenging positions, such as on the ground or at high locations, and emphasized the need for a versatile gripper. Users also expected the WMRA to assist in performing complex daily tasks, with varying expectations for its performance based on task difficulty. Regarding the interface, users sought more information about the system's awareness and task execution, while emphasizing the importance of avoiding information overload. The demand for detailed safety information, such as temperature and gripping force, pointed to the need for enhanced sensor capabilities in the WMRA system. Additionally, concerns about privacy underscored the need for clear communication on privacy policies. Our results provide user-centered insights for automated WMRA, offering design implications and future research directions in areas such as user modeling, hardware, algorithms, and interface development.

University of Pittsburgh (US), VA Pittsburgh Healthcare System (US)
Center for Integrated Healthcare, U.S. Department of Veterans Affairs
Openalex Percentile: Top 8%
Gaze Tracking and Assistive Technology
3.87
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