Evidence map›Paper›PMID 32884857›Full record

ArticleTranslational vision science & technology2020

Structured Laser Light Improves Tripping Hazard Recognition for People with Visual Impairments.

Michael Stahl, Eli Peli

Abstract read
In one paragraph

Article in Translational vision science & technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Michael StahlSchepens Eye Research Institute of Mass. Eye and Ear, Harvard Medical School, Boston, MA, USA.
Eli PeliSchepens Eye Research Institute of Mass. Eye and Ear, Harvard Medical School, Boston, MA, USA.

Funding

VISION RESEARCHP30EY003790 · NEI · SCHEPENS EYE RESEARCH INSTITUTE · PI Patricia Ann D'Amore · 1985 to 2026
$25.5M
Structured Light Detection and Delineation of Tripping HazardsF31EY023929 · NEI · SCHEPENS EYE RESEARCH INSTITUTE · PI STAHL, MICHAEL WILLIAM · 2013 to 2017
$208k
NEI NIH HHS F31 EY023929NEI NIH HHS P30 EY003790
6 · The paper itself

Abstract

Purpose: Using a geometrically derived model and a virtual curb simulator, we quantify the degree to which a wearable device that projects a laser line onto tripping hazards in a pedestrian's path improves visual recognition for people with visual impairments (VI). We confirm this with subjects' performance on computer simulations of low contrast curbs. Methods: We derive geometric expressions quantifying the visual cue users perceive when a single laser line is projected from their hip onto a curb. We show how the efficacy of this cue changes with the angle of the laser line relative to the subject's walking trajectory. We confirm this result with data from three subjects with VI in a simulated curb recognition task in which subjects classified computer images as an "Ascending," "Flat," or "Descending" curb. Results: The derived model predicts that human recognition performance depends strongly on the laser line angle and the subject data confirms this ( Conclusions: Recognition of curbs in low light can be improved by augmenting the scene with a single laser line projected from a user's hip, if the angle of laser line is appropriately selected. Translational Relevance: A majority of people with VI rely on their impaired residual vision for mobility, rather than a mobility aid, resulting in increased injury for this population. Enhancing residual vision could promote safety, increase independence, and reduce medical costs.

Indexed as

WalkingWearable Electronic DevicesComputer SimulationHumansLightVision Disorderselectronic travel aidstructured light

Identifiers

PMID32884857
PMCPMC7445360

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.