Designing an ESP-32 IoT-Based Fall Prevention Early Warning System for Workers’ Safety

Abstract

Abstract: This article presents a design and implementation of an ESP-32 IoT-based fall prevention system that combines sensor-based monitoring, computer vision, and real-time alerts to enhance worker safety in potential fall hazard situations. The system utilizes an altimeter to measure the worker's height relative to a reference point and a limit switch to detect the status of the hook for fall protection. Data acquisition and processing are performed by the ESP32 microcontroller, which acts as the central hub. The system also incorporates data filtering and noise reduction techniques to ensure accurate sensor readings. It integrates computer vision and sensor-based monitoring, providing a comprehensive solution for fall prevention. Real-time alerts are generated by the system, enhancing worker safety by prompting swift corrective actions in potential fall hazard situations. These outcomes emphasize the importance of educating workers on the proper use, maintenance, and limitations of the fall prevention system to ensure optimal utilization and effectiveness.

KEYWORDS: ESP-32, Fall prevention, IoT, Real-time, Sensor-based monitoring