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Large-scale, Longitudinal, Hybrid Participatory Design Program to Create Navigation Technology for the Blind

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arxiv 2410.00192 v2 pith:SUTARZIO submitted 2024-09-30 cs.HC

classification cs.HC
keywords designblindin-personlarge-scalelongitudinalonlineparticipantsparticipatory
verification ladder T0 review T1 audit T2 compute T3 formal
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Empowering people who are blind or visually impaired (BVI) to enhance their orientation and mobility skills is critical to equalizing their access to social and economic opportunities. To manage this crucial challenge, we employed a novel design process based on a large-scale, longitudinal, community-based structure. Across three annual programs we engaged with the BVI community in online and in-person modes. In total, our team included 67 total BVI participatory design participants online, 11 BVI co-designers in-person, and 4 BVI program coordinators. Through this design process we built a mobile application that enables users to generate, share, and navigate maps of indoor and outdoor environments without the need to instrument each environment with beacons or fiducial markers. We evaluated this app at a healthcare facility, and participants in the evaluation rated the app highly with respect to its design, features, and potential for positive impact on quality of life.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Static or Temporal? Semantic Scene Simplification to Aid Wayfinding in Immersive Simulations of Bionic Vision

    cs.HC 2025-07 conditional novelty 6.0 of 10

    In simulated prosthetic vision, semantic edge highlighting improved wayfinding success while time-staggered semantic rastering reduced collisions, both outperforming a plain edge-detection baseline.

  2. Safety-oriented sidewalk and road segmentation for smartphone-based assistive navigation

    cs.CV 2026-07 conditional novelty 5.0 of 10

    For smartphone sidewalk navigation, selecting models by mIoU alone is insufficient: DeepLabV3Plus-MobileNetV3 had lower accuracy but the lowest dangerous road-as-sidewalk error and fastest on-phone runtime.

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