{
  "abstract": "For individuals who are blind or visually impaired (BVI), undetected stairways, curbs, and sudden elevation changes represent a leading cause of fall-related injury. Herein we present a preliminary, single-operator feasibility evaluation of a computationally efficient geometric algorithm for detecting stair and elevation changes in real time, implemented entirely on an LiDAR-equipped consumer smartphone (iPhone 17 Pro Max, iOS 26.2). The algorithm uses a set of eight downward- and forward-facing raycasts projected into an ARKit (Artificial Reality developer Kit)-reconstructed mesh and performs line-segment angular analysis at 60 Hz within a 16.7 ms per-frame budget, without requiring cloud processing, GPU inference, or custom hardware. A single trained operator conducted 120 trials spanning eight real-world hazard locations, five step heights (4.5 to 7.5 in; 11.4 to 19.1 cm), four stair grades (22 to 33 degrees), varied lighting conditions (indoor artificial, outdoor daytime, and outdoor nighttime), and both ascending and descending approaches, yielding an overall detection rate of 92.5% (95% CI 86.4–96.0%) and a mean alert distance of 2.31 ft (0.70 m; SD = 0.91 ft). No false-positive alerts were observed during the hazard-approach or traversal segments of these trials, although dedicated hazard-free control trials were not conducted, so this should not be read as a validated false-positive rate across general non-hazard environments. Detection rate and alert distance both tended to increase with step height, with the smallest steps and one specific indoor location accounting for most missed detections; however, pairwise differences across step-height, lighting, and approach-direction categories did not reach statistical significance at the sample sizes tested (Fisher’s exact p > 0.05 in all cases), and step geometry covaried with test location in this design, so these patterns should be treated as preliminary and hypothesis-generating rather than as an established reliability threshold. Descending approaches produced numerically lower detection rates and shorter warning distances than ascending approaches, consistent with a geometric limitation intrinsic to depth-based sensing of hazards that only become visible at close range. These results indicate that low-latency elevation-hazard detection is technically feasible on commodity mobile hardware under controlled, single-operator conditions; confirming this feasibility finding for real-world use by BVI individuals will require larger, multi-participant studies incorporating natural scanning behavior and dedicated negative-condition testing.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "Nikhil Herdt"
    },
    {
      "name": "Terry Braun"
    },
    {
      "name": "Tyler Bell"
    },
    {
      "name": "Stephen Russell"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7ehbam3yz3e9q.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-73419-5",
  "fulltext_url": null,
  "id": "7ehbam3yz3e9q",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-73419-5",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3609|Assistive Technology in Communication and Mobility"
    ]
  ],
  "openalex": "W7216527087",
  "pdf_url": "https://www.nature.com/articles/s41598-026-73419-5.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-02",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/7ehbam3yz3e9q.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:19Z",
      "native_id": "10.1038/s41598-026-73419-5",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-73419-5"
    }
  ],
  "subjects": [],
  "title": "A single-user feasibility study demonstrating real-time raycast-based stair and elevation-change detection on consumer smartphone LiDAR hardware",
  "title_text": "A single-user feasibility study demonstrating real-time raycast-based stair and elevation-change detection on consumer smartphone LiDAR hardware",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:19Z",
  "url": "https://research.draconitic.com/paper/7ehbam3yz3e9q",
  "venue": "Scientific Reports",
  "versions": []
}