Evidence map›Paper›PMID 41277938›Full record

ArticleIranian journal of public health2025

Development of a Software to Drowsiness Detection for Drivers Using Image Processing and Neural Networks.

Ali Askari, Ali Salehi Sahlabadi, Maliheh Eshaghzadeh, Mohsen Poursadeghiyan, Gebraeil Nasl Saraji

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In one paragraph

Article in Iranian journal of public health, 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
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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

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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

5 authors.

Ali AskariDepartment of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Ali Salehi SahlabadiSafety Promotion and Injury Prevention Research Center, Research Institute for Health Sciences and Environment, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Maliheh EshaghzadehDepartment of Nursing, School of Nursing and Midwifery, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran.
Mohsen PoursadeghiyanSocial Determinants of Health Research Center, School of Health, Ardabil University of Medical Sciences, Ardabil, Iran.
Gebraeil Nasl SarajiDepartment of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: During driving, drowsiness may happen for a few moments, but its consequences can be terrible. Drowsiness in the driver can be detected in the early stages. Each method used for detecting drowsiness has its own strengths and weaknesses or benefits and flaws. The main contribution of our research was improving Driver Drowsiness Detection (D.D.D) systems. Methods: In accordance with the research objective, it is imperative to address the subsequent inquiries (Q) throughout the process of constructing, testing, and delivering the ultimate D.D.D software model: Q1. What is the methodology employed for constructing the initial model of drowsiness detection software? Q2. How is the initial model of drowsiness detection software tested and refined during the development phase? Q3. What is the operational mechanism of the final model of drowsiness detection software? Results: The results were able to detect different facial conditions (with hair and glasses) with a 92.3 percentage detection rate. Conclusion: This model could help improve D.D.D systems, and detect drowsiness in different environments and situations.

Indexed as

Driver monitoring systemImage processingNeural networkSoftware drowsiness detectionViola-Jones algorithm

Identifiers

PMID41277938
PMCPMC12630370

What OpenQuestion holds

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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.