Authors: Sahil Arun Bodke, Devika Deepak More, Samruddhi Mahendra Pansare, Prof. P. A. Mande, Prof. Bangar A.P., Prof. Bhosale S.B.
Abstract: Industrial workplaces continue to pose significant hazards to workers, including toxic gas exposure, thermal stress, mechanical injuries, and fatigue-related accidents. Conventional safety systems have largely remained reactive, responding to incidents after they occur rather than preventing them proactively. The convergence of Artificial Intelligence (AI), the Internet of Things (IoT), and advanced wearable sensor technologies has opened transformative opportunities for proactive occupational safety. This paper presents a comprehensive literature review of existing research on AI-integrated industrial safety wearable devices, covering sensor technologies, machine learning algorithms, edge computing strategies, cloud-based analytics, and alert mechanisms. We synthesize findings from over 25 peer-reviewed studies published in IEEE, Springer, and Web of Science indexed journals between 2019 and 2025. Key research gaps identified include the lack of multi-modal sensor fusion with real-time edge AI, insufficient datasets for industrial fatigue prediction, limited ergonomic wearable designs for harsh environments, and the absence of Explainable AI (XAI) in safety-critical decision making. Based on the review, we propose an integrated four-layer system architecture combining physiological and environmental sensing, edge-level AI inference, MQTT-based cloud communication, and a multi-level alert mechanism.
DOI: https://doi.org/10.5281/zenodo.20176697
