A single-user feasibility study demonstrating real-time raycast-based stair and elevation-change detection on consumer smartphone LiDAR hardware
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.
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BibTeX
@article{herdt2026singleuser,
title = {{A single-user feasibility study demonstrating real-time raycast-based stair and elevation-change detection on consumer smartphone LiDAR hardware}},
author = {Nikhil Herdt and Terry Braun and Tyler Bell and Stephen Russell},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-73419-5},
url = {https://www.nature.com/articles/s41598-026-73419-5},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A single-user feasibility study demonstrating real-time raycast-based stair and elevation-change detection on consumer smartphone LiDAR hardware
AU - Nikhil Herdt
AU - Terry Braun
AU - Tyler Bell
AU - Stephen Russell
PY - 2026
DA - 2026/10/02
JO - Scientific Reports
DO - 10.1038/s41598-026-73419-5
UR - https://www.nature.com/articles/s41598-026-73419-5
AB - 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.
SN - 2045-2322
ER -