Industrial Safety Sentinel: A Wearable-Based Real-Time System for Automated Hazard Detection and Machinery Shutdown

Abstract

Industrial environments remain inherently hazardous, particularly where human operators must work in close proximity to heavy or automated machinery. This paper presents the Industrial Safety Sentinel, a novel wearable-based safety framework that proactively mitigates workplace accidents by integrating real-time hazard detection with automated machinery shutdown. The system embeds a microcontroller-driven chip into worker attire, enabling continuous wireless communication with surrounding equipment. Upon detecting encroachment into predefined danger zones, the Sentinel autonomously initiates machine shutdown, thereby reducing reliance on human reflexes and minimizing latency in emergency response. Development followed the Spiral Model methodology, incorporating iterative prototyping, structured risk assessment, user feedback integration, and staged performance evaluation. Experimental validation in simulated industrial scenarios demonstrated a mean hazard response latency of 2.3 seconds, proximity detection accuracy of 97.8%, and operational availability of 99.7%, confirming the system’s reliability and robustness. By bridging wearable sensing with cyber-physical automation, the Sentinel advances a scalable, worker-centric safety paradigm adaptable across diverse industrial contexts, contributing to the next generation of proactive occupational safety systems.