Evidence map›Paper›PMID 42197739›Full record

ArticleSensors (Basel, Switzerland)2026

AI-Assisted Vision Alarming System for Blind and Vision- Impaired People.

Le Chung Tran, Sinh Khai Ly, Rhys Blacklidge, Jonathan Shemmell, Nathan Difford, Daniel Edward Cox, Theresa Harada

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 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

7 authors.

Le Chung TranSchool of Engineering, University of Wollongong, Northfield Avenue, Wollongong, NSW 2522, Australia.ORCID 0000-0002-2677-8721
Sinh Khai LySchool of Engineering, University of Wollongong, Northfield Avenue, Wollongong, NSW 2522, Australia.ORCID 0009-0008-1209-5955
Rhys BlacklidgeSchool of Engineering, University of Wollongong, Northfield Avenue, Wollongong, NSW 2522, Australia.
Jonathan ShemmellSchool of Engineering, University of Wollongong, Northfield Avenue, Wollongong, NSW 2522, Australia.ORCID 0000-0002-4575-133X
Nathan DiffordSchool of Engineering, University of Wollongong, Northfield Avenue, Wollongong, NSW 2522, Australia.
Daniel Edward CoxTrans Traffic Survey, 3 Hepburn Way, Melbourne, VIC 3023, Australia.
Theresa HaradaSchool of Engineering, University of Wollongong, Northfield Avenue, Wollongong, NSW 2522, Australia.ORCID 0000-0002-9687-556X

Funding

University of Wollongong Global Challenges Number 6
6 · The paper itself

Abstract

Navigating through everyday environments, like walking down a sidewalk, which many people often take for granted, is a difficult task for millions of people with vision impairments since it involves sophisticated object detection, depth perception, and situational awareness, all working seamlessly to guide a person through complex surroundings. Many current assistive devices for vision-impaired people are either expensive, information-overabundant, or missing critical information. This paper details our Vision Alarming System (VAS), which can improve the safety for blind and vision-impaired people by providing awareness of both positions and nature of nearby obstacles; thus, assisting users to make decisions to avoid collisions, reduce accidents and casualties, while enhance their experience, independence, and confidence when participating in traffic. VAS is an Artificial Intelligence/Internet-of-Things (AI/IoT)-powered system developed utilizing the cutting-edge Raspberry Pi 5, a Light Detection and Ranging (LiDAR) sensor, and an AI depth camera, operating as different containers in a Docker architecture, and leveraging a Robotic Operating System 2 (ROS 2) backbone. VAS communicates the obstacle detections to users via Bluetooth interface, using the neural Text-To-Speech (TTS) system, namely, Piper, and the Sound eXchange (SoX) technologies. Our proof-of-concept system proves that VAS can be a standalone, open-source, extremely low cost, low power consumption assistive device which can synergistically utilize the cutting-edge AI/IoT technologies to provide blind and vision-impaired users with an appropriate amount of critical information about their surrounding environments.

Indexed as

Artificial IntelligenceBlindnessPersons with Visual DisabilitiesHumansInternet of ThingsRoboticsAIblindDepthAIIoTLiDAROak D-LiteRaspberry Pi 5ROS 2SoXTTSvision-impairedYOLO

Identifiers

PMID42197739
PMCPMC13211286

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.