Evidence map›Paper›PMID 41094829›Full record

ArticleSensors (Basel, Switzerland)2025

Development of a Fully Autonomous Offline Assistive System for Visually Impaired Individuals: A Privacy-First Approach.

Fitsum Yebeka Mekonnen, Mohammad F Al Bataineh, Dana Abu Abdoun, Ahmed Serag, Kena Teshale Tamiru, Winner Abula, Simon Darota

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. 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.

Fitsum Yebeka MekonnenDepartment of Mechanical and Aerospace Engineering, United Arab Emirates University, Al Ain 15551, United Arab Emirates.
Mohammad F Al BatainehElectrical and Communication Engineering Department, United Arab Emirates University, Al Ain 15551, United Arab Emirates.ORCID 0000-0003-0148-7618
Dana Abu AbdounElectrical and Communication Engineering Department, United Arab Emirates University, Al Ain 15551, United Arab Emirates.ORCID 0000-0003-3699-8020
Ahmed SeragDepartment of Mechanical and Aerospace Engineering, United Arab Emirates University, Al Ain 15551, United Arab Emirates.
Kena Teshale TamiruDepartment of Computer Science and Software Engineering, United Arab Emirates University, Al Ain 15551, United Arab Emirates.
Winner AbulaDepartment of Computer Science and Software Engineering, United Arab Emirates University, Al Ain 15551, United Arab Emirates.
Simon DarotaDepartment of Computer Science and Software Engineering, United Arab Emirates University, Al Ain 15551, United Arab Emirates.ORCID 0009-0005-8921-4711

Funding

United Arab Emirates University G00004797
6 · The paper itself

Abstract

Visual impairment affects millions worldwide, creating significant barriers to environmental interaction and independence. Existing assistive technologies often rely on cloud-based processing, raising privacy concerns and limiting accessibility in resource-constrained environments. This paper explores the integration and potential of open-source AI models in developing a fully offline assistive system that can be locally set up and operated to support visually impaired individuals. Built on a Raspberry Pi 5, the system combines real-time object detection (YOLOv8), optical character recognition (Tesseract), face recognition with voice-guided registration, and offline voice command control (VOSK), delivering hands-free multimodal interaction without dependence on cloud infrastructure. Audio feedback is generated using Piper for real-time environmental awareness. Designed to prioritize user privacy, low latency, and affordability, the platform demonstrates that effective assistive functionality can be achieved using only open-source tools on low-power edge hardware. Evaluation results in controlled conditions show 75-90% detection and recognition accuracies, with sub-second response times, confirming the feasibility of deploying such systems in privacy-sensitive or resource-constrained environments.

Indexed as

Persons with Visual DisabilitiesSelf-Help DevicesArtificial IntelligenceHumansPrivacyassistive technologyface recognitionobject detectionOCRoffline AIRaspberry Pivoice recognition

Identifiers

PMID41094829
PMCPMC12526525

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.