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An Analytical and Effective Solution to Social Problems Through Artificial Intelligence and Technologies

Year: 2025

Type: conference-paper

Abstract: This article analyzes technological and scientific approaches developed to reduce road traffic accidents caused by fatigue and drowsiness among transportation operators. It covers devices based on EEG and ECG signals used to monitor drivers' attention levels, microsleep detection algorithms powered by artificial intelligence, and risk prediction methods through statistical and mathematical modeling. Based on the research results, the use of the Eye Aspect Ratio (EAR) — an index that determines the state of eye openness — is proposed as an effective solution to enhance driver alertness and reduce accident rates.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15284717

Author: Jo'raqulova Munisa O'ktam qizi1, Tojidinov Azizbek Ilkhomjon o'g'li2, Bektamishov Quvonchbek G'ayrat o'g'li3

Language: English

Topic: Sleep and Work-Related Fatigue

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

SDGs: Industry, innovation and infrastructure, Good health and well-being

Open Access status: green

OpenAlex ID: W6930636467

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