Evidence map›Paper›PMID 42637836›Full record

ArticleNature biomedical engineering2026

Improving outdoor navigation for people with blindness using an AI-driven smartphone application and personalized audio guidance.

Raymond Liu, Patrick Slade

Abstract read
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In one paragraph

Article in Nature biomedical engineering, 2026. 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.

Raymond LiuJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA.
Patrick SladeJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA. slade@seas.harvard.edu.ORCID http://orcid.org/0000-0001-9302-3911

Funding

National Science Foundation (NSF) DGE-2140743
6 · The paper itself

Abstract

Globally, 340 million people have blindness or moderate-to-severe visual impairment (BVI), which limits independent outdoor navigation and negatively affects their health and quality of life. We surveyed 112 people with BVI and found that an ideal outdoor navigation aid must be able to perform turn-by-turn directions, path guidance and obstacle detection and avoidance. Existing navigation tools such as white canes, guide dogs and electronic travel aids often lack one or more of these criteria and may be expensive or inaccessible. Here we introduce Mobilio, a smartphone application that incorporates machine learning, sensor fusion algorithms and personalized audio feedback to meet all of the outdoor navigation criteria. We assessed the reliability of the smartphone sensors and models used for navigation with engineering tests in representative navigation scenarios. We performed a series of experiments in which Mobilio personalized audio feedback for participants with BVI (n = 14), guided them along an outdoor community path and helped them to navigate an obstacle course. Participants walking with Mobilio and a white cane navigated a community path in 13 ± 3% less time and reduced environmental contacts by 41 ± 5% compared with using Google Maps and a white cane. Mobilio achieved similar outdoor navigation reliability to a human guide. Participant surveys reported that Mobilio was easy to use, had a low perceived workload and provided intuitive audio feedback. This work provides an accessible and personalized tool that may be an effective outdoor navigation aid to increase independence for people with BVI.

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Registered trials

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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.