Internet of Things Technologies for Occupational Health and Safety: A Review

Abstract

The Internet of Things (IoT) has significantly enhanced Occupational Health and Safety (OHS) by enabling real-time monitoring of workplace environments and employee well-being through smart sensors and connected devices. IoT systems help identify hazards, improve response times, and ensure compliance with safety regulations. Despite these advancements, the literature highlights a gap in investigating the technologies and algorithms specifically tailored for OHS applications. This paper presents a comprehensive review of IoT OHS technologies, structured around a four-tier IoT OHS system architecture, which serves as a framework for analyzing existing studies in the field. Our findings indicate that sensors from the perception tier are the most widely utilized component across IoT OHS implementations, underscoring their critical role in hazard detection and safety monitoring. In contrast, IoT cloud technologies remain underutilized, suggesting potential barriers to widespread adoption. From a machine learning (ML) perspective, supervised learning and classification models emerge as the dominant approaches, with classification models being employed in studies that incorporate all four sensor categories. In contrast, unsupervised learning models are infrequently applied in IoT OHS systems, indicating a preference for structured, labeled data analysis. Notably, accident prevention applications, particularly those addressing collisions and falls from height, leverage the full spectrum of ML approaches and sensor technologies, making them the most comprehensive category within the domain. By categorizing sensors, controllers, gateways, communication technologies, and cloud platforms, alongside analyzing ML methodologies, this paper provides valuable insights into the current landscape of IoT OHS systems and highlights key trends, challenges, and opportunities for future research.