@article {102187,
	title = {SMART FATIGUE DETECTION AND HEALTH MONITORING SYSTEM FOR ASSEMBLY LINE WORKERS USING IOT AND COMPUTER VISION TECHNOLOGIES},
	journal = {Academic Journal of Manufacturing Engineering},
	volume = {23},
	number = {2},
	year = {2025},
	abstract = {
	\&nbsp;Ensuring the safety and health of assembly line workers is critical to increasing productivity and preventing accidents. This research presents a real-time monitoring system that combines computer vision (AI), wearable Internet of Things (IoT) devices, and cloud-based technologies to detect worker fatigue and health risks. The system calculates eye aspect ratio (EAR) and mouth aspect ratio (MAR) to identify fatigue symptoms such as eye closure and yawning, while wearable IoT devices monitor physiological parameters such as heart rate (HR) and blood oxygen saturation (SpO$_{2}$) to detect potential health issues. Alerts are automatically triggered based on pre-defined thresholds, allowing for immediate intervention. All data is processed in real-time with input from wearables and computer vision, and transmitted to a cloud platform for analysis, reporting and storage. This integration of AI-powered computer vision, wearable IoT and cloud connectivity ensures continuous monitoring and provides actionable insights to supervisors, improving workplace safety and operational efficiency. The results of the study demonstrate the effectiveness of this innovative system in identifying fatigue and health issues, reducing accidents and promoting a safer working environment. By using the latest technology, the proposed solution addresses the urgent need for advanced safety measures in demanding work environments.

},
	url = {https://zenodo.org/records/15862610},
	author = {DJEGHAR, Dina and AKSA, Karima and Bounceur, Ahc{\`e}ne and Mounir Aouadj}
}
