IIARD International Institute of Academic Research and Development

WORLD JOURNAL OF INNOVATION AND MODERN TECHNOLOGY (WJIMT )

E-ISSN 2504-4766
P-ISSN 2682-5910
VOL. 9 NO. 1 2025
DOI: 10.56201/wjimt.v9.no1.2025.pg186.213

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Advancing Occupational Safety with AI-Powered Monitoring Systems: A Conceptual Framework for Hazard Detection and Exposure Control

Cynthia Obianuju Ozobu, Friday Emmanuel Adikwu, Cynthia, Oladipo Odujobi, Fidelis Othuke Onyeke, Emmanuella Onyinye Nwulu

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Abstract

Occupational safety remains a critical concern across industries, necessitating innovative solutions to mitigate hazards and control worker exposure to harmful environments. This paper presents a conceptual framework for leveraging Artificial Intelligence (AI)-powered monitoring systems to enhance occupational safety. The framework integrates advanced data analytics, machine learning algorithms, and Internet of Things (IoT) devices to enable real- time hazard detection and exposure control. By utilizing AI's predictive capabilities, the system identifies potential risks and triggers preemptive measures to prevent accidents and long-term health impacts. Key components of the framework include wearable IoT sensors for monitoring workers' vital signs and environmental parameters, AI-driven analysis to process large datasets, and adaptive feedback mechanisms to inform decision-making. The system also incorporates computer vision for identifying physical hazards and proximity detection for alerting workers in high-risk zones. These technologies collectively facilitate a proactive approach to workplace safety, shifting the paradigm from reactive to preventive measures. The framework emphasizes scalability and flexibility, making it adaptable to various industrial sectors, including construction, manufacturing, and healthcare. The integration of AI-powered systems aligns with Occupational Safety and Health Administration (OSHA) regulations and supports compliance with safety standards. Furthermore, the proposed framework addresses challenges such as false positives, data privacy concerns, and user adoption through robust algorithm design, secure data management practices, and user-centric interfaces. Preliminary simulations demonstrate the system's potential to significantly reduce workplace injuries and improve hazard response times. Case studies from the construction and oil and gas industries underscore the value of real-time haz

keywords:

Occupational Safety, Artificial Intelligence, Hazard Detection, Exposure Control, Iot, Wearable Sensors, Workplace Safety, Real-Time Monitoring, Computer Vision, Predictive Analytics.

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