IoT-Enabled Wearable Safety Monitoring for Industry 5.0: A Human-Centric Architecture, Data Pipeline, and Deployment Framework

Occupational safety practices such as inspections, personal protective equipment, training, and supervision remain essential, but they offer limited visibility into rapidly changing worker and environmental conditions. Industry 5.0 places human well-being, sustainability, and resilience alongside productivity, creating a need for safety technology that supports workers rather than merely observing them. This paper presents a human-centric framework for Internet of Things (IoT)-enabled wearable safety monitoring. Following a structured technical synthesis of the literature, eight design requirements are derived and realised in a four-layer architecture comprising sensing, communication, edge processing, and application and analytics layers. The framework defines a data-processing pipeline with quality scoring, a context-aware sensor-fusion model that reports risk and confidence as separate quantities, a five-level alert escalation scheme with worker confirmation, an edge-resident fall-detection procedure, a threat-aware security and privacy design, and a four-phase deployment and validation roadmap with explicit evaluation measures. The framework treats analytical models as decision-support components, places time-critical decisions at the edge, and avoids unsupported claims of injury reduction by specifying the indicators that field studies must measure. It provides a practical basis for designing wearable safety systems that augment human supervision while preserving worker autonomy and organisational responsibility for safety.