Understanding Interactions for Smart Wheelchair Navigation in Crowds

Honorable Mention
Human Pose & Activity RecognitionSocial Robot InteractionHuman-Robot Collaboration (HRC)Social WorkersDisability Service Providers

Title of the Paper

Understanding Interactions for Smart Wheelchair Navigation in Crowds

Paper Information

  • Subject Area: Smart Wheelchairs, Shared Control, Crowd Navigation
  • Keywords: Smart Wheelchair, Shared Control, Wheelchair-Pedestrian Interaction, Wheelchair-User Interaction, Crowd Navigation

Research Background and Issues

  • Problems and Challenges:
    • Smart wheelchairs can assist users in navigating crowded environments, but existing studies largely overlook the perspective of wheelchair users.
    • In complex real-world environments, navigation challenges include balancing user autonomy with system safety control and effectively interacting with crowds.
    • Current research lacks a comprehensive exploration of internal interactions between the user and the wheelchair, as well as external interactions between the wheelchair and the crowd.
  • Significance:
    • Smart wheelchairs have the potential to significantly enhance the safety and social participation of wheelchair users in crowded environments.
    • Addressing these issues could improve the daily travel experience of wheelchair users and enhance their quality of life.
  • Research Motivation and Related Work:
    • The navigation needs of wheelchair users have not been fully studied. Shared control can maintain the user's driving intentions while reducing collision risks.
    • Existing technologies primarily focus on static or simple dynamic environments, with limited research on complex crowd scenarios.

Solutions

  • Methods and Solutions:
    • First Study: Conduct semi-structured interviews to explore wheelchair users' driving experiences and needs in crowded environments.
    • Second Study: Organize design workshops involving wheelchair users and designers to co-design adaptive interaction interface systems.
  • Innovations:
    • Design a bidirectional interaction system based on users' actual needs: internal (wheelchair-user) and external (wheelchair-crowd).
    • Propose adjustable interaction designs tailored to different scenarios and user states, balancing user autonomy and social acceptability.
  • Implementation Steps:
    • Collect user interview data and conduct semantic thematic analysis to extract key needs.
    • Use virtual collaboration tools in design workshops to simulate different scenarios (e.g., crowded streets and hospitals) for interface design.

Research Outcomes

  • Specific Outcomes:
    • Proposed design themes, including types of information, interaction methods, interface placement, and adaptive designs.
    • Creatively integrated visual, haptic, and auditory feedback to design wheelchair interaction interfaces.
  • Advantages:
    • Enhanced user trust in shared control systems.
    • Improved safety interactions and social acceptance between wheelchair users and pedestrians.
    • Targeted solutions to meet user needs in complex scenarios (e.g., hospitals and streets).
  • Experimental or Evaluation Results:
    • Found that wheelchair users have a strong demand for refined feedback mechanisms, such as system intentions, environmental information, and obstacle detection.
    • User-proposed designs indicated that visual feedback is the preferred choice in most cases, while haptic and auditory feedback play supplementary roles in specific scenarios.
    • The design of external interaction systems should be wheelchair-centered to avoid intrusive experiences for pedestrians.
  • Limitations and Future Directions:
    • The sample focused on current power wheelchair users, excluding potential user groups such as individuals unable to use traditional wheelchairs.
    • There was limited gender and regional representation, particularly with fewer female participants in the design workshops.
    • Future research could explore optimizing information load to prevent diminished user or pedestrian experience due to information overload.

The above summary comprehensively outlines the core content of the paper, providing a clear theoretical framework and key information for further in-depth reading or application.

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https://hci.top/en/papers/chi/68996/2022

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DOI: https://dl.acm.org/doi/abs/10.1145/3491102.3502085
At a Glance

Paper Snapshot

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Source
CHI
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Year
2022
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Award
Honorable Mention
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Authors
5 authors
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Subtopics
Human Pose & Activity Recognition, Social Robot Interaction, Human-Robot Collaboration (HRC)
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Professions
Social Workers, Disability Service Providers
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Content Status
Full text indexed
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Related Papers
1 related papers