Predicting Occupational Heat Stress in Critical Sectors: A Sector-Based Systematic Review of Wearable Sensing, IoT Platforms, and Machine Learning Models

Bonifacio, Roger Fernando Asto
Milla, Blanca Yeraldine Buendia
Rojas, Jezzy James Huaman

Journal of Robotics and Control (JRC)
Vol. 6 No. 5 (2025)

Occupational heat stress is a growing threat to the health and productivity of workers exposed to extreme environmental conditions. This issue is particularly acute in sectors such as construction, mining, agriculture, and heavy industry, where high heat exposure and physical workload are constant. This systematic review analyzes 96 scientific articles published in recent years, aiming to identify emerging technological systems focused on the prediction, monitoring, and mitigation of occupational heat stress. The main contribution of this study lies in the cross-sectoral categorization of recent solutions, providing a comparative framework that highlights knowledge gaps, methodological limitations, and opportunities for innovation. Following PRISMA guidelines, data were extracted on sensor type, predictive models, validation environments, and the sector of application. Technologies were classified into five main categories: wearable sensors, IoT-based monitoring platforms, hybrid thermal indices, predictive models based on environmental and physiological inputs, and decision-support tools. The results reveal a strong presence of wearable systems. Adoption is further constrained by socio-technical barriers such as worker compliance, PPE burden, costs, data privacy, and interoperability gaps. However, only a small fraction of studies conducted in-field validation under real thermal stress conditions, and even fewer included longitudinal ergonomic trials, limiting generalizability, with additional concerns about heterogeneous outcome measures and inconsistent definitions of heat stress across studies. A sectoral imbalance is also observed, with construction and industrial environments receiving more research attention than mining, agriculture, and indoor workplaces. In conclusion, we propose a practical roadmap for the adoption of standardized data schemas and protocols, field trials across complete work cycles, privacy-preserving analytics (federated learning), and integration of ergonomic and organizational controls. In highly humid or high radiation settings, complementing or replacing WBGT with hybrid indices (UTCI) can improve risk estimation and enable more actionable work rest and hydration alerts.