IoT-Based Worker Safety System with Real-Time Health Monitoring and Emergency Rescue using RSSI

Abstract

In the construction industry, worker safety is a critical concern, particularly during emergencies such as building collapses, where delayed response and inadequate health monitoring can result in severe injuries or fatalities. Traditional systems often lack real-time tracking and health monitoring capabilities, making it difficult to locate and assist affected workers promptly. To address these challenges, we propose a Smart Employee Health Monitoring and Emergency Rescue System utilizing Received Signal Strength Indicator (RSSI) technology. The system integrates ESP32 and Zigbee-based Wireless Sensor Network (WSN) modules to enable continuous health monitoring and precise indoor positioning of workers on construction sites. By leveraging RSSI-based location tracking and real-time communication with a central control unit, the system ensures low-power, reliable data transmission for efficient emergency alerts and worker status updates. This integration significantly enhances the speed and accuracy of rescue operations, while also enabling proactive health management, ultimately improving occupational safety and emergency response in high-risk environments.

Introduction

Summary:

The project addresses safety challenges in high-risk workplaces like construction sites, where structural collapses pose serious threats to workers. Traditional emergency response methods often lack speed, precision, and real-time monitoring, delaying rescues and increasing casualties. To overcome these issues, the proposed Smart Employee Health Monitoring and Emergency Rescue System (SH-ERS) combines wearable health sensors with an indoor positioning system based on Received Signal Strength Indicator (RSSI) technology, using ESP32 microcontrollers and Zigbee wireless sensor networks.

The system continuously monitors vital signs (body temperature, heart rate, oxygen levels) and environmental conditions, transmitting data wirelessly to a central control unit. It precisely locates workers indoors, even in complex environments where GPS fails, enabling rapid identification and rescue of trapped or injured employees. Real-time alerts inform emergency teams, enhancing response times and worker safety.

The architecture consists of two main modules: a worker module (with biometric and environmental sensors, LCD display, buzzer, and wireless transceiver) and a control center module (which processes data, visualizes it, issues alerts, and uploads data to cloud platforms). The system was developed and tested through simulation and real hardware implementation, demonstrating effective health monitoring, location tracking, and emergency notification. This integrated IoT solution aims to improve occupational safety, emergency management, and operational oversight on construction sites and other hazardous workplaces.

Conclusion

The Smart Employee Health Monitoring and Emergency Rescue System has demonstrated its effectiveness in improving worker safety and enhancing emergency response capabilities on construction sites. By leveraging Received Signal Strength Indicator (RSSI) technology for precise indoor positioning and integrating real-time health monitoring through ESP32 and Zigbee-based Wireless Sensor Network (WSN) modules, the system provides reliable, low-power communication and efficient tracking of workers. The system’s ability to continuously monitor workers\' vital signs and instantly relay location data during emergencies contributes to faster and more accurate rescue operations, ultimately enhancing overall safety in high-risk construction environments. In this system represents a significant advancement in addressing the critical issue of worker safety on construction sites. It combines health monitoring with real-time tracking, ensuring that potential health risks are detected early and that workers can be located and assisted quickly in emergencies. The successful implementation and testing of the system suggest that it can provide substantial benefits in terms of reducing the likelihood of fatalities and severe injuries on construction sites.

References

[1] Chen, Haiyan, et al. \"The impact of wearable devices on the construction safety of building workers: a systematic review.\" Sustainability 15.14 (2023): 11165. [2] Ding, Yifei, Yuxin Zhang, and Xinyan Huang. \"Intelligent emergency digital twin system for monitoring building fire evacuation.\" Journal of Building Engineering 77 (2023): 107416. [3] Kang, Jian, et al. \"Improving the emergency management of energy infrastructure using scenario construction.\" International Journal of Hydrogen Energy 48.23 (2023): 8731-8742. [4] Lee, Poizot Li. \"Enhancing Construction Worker Safety: Real-time Health Monitoring through Wearable Technology.\" International Journal of Enterprise Modelling 17.3 (2023): 139-147. [5] Rane, Nitin, Saurabh Choudhary, and Jayesh Rane. \"Leading-edge wearable technologies in enhancing personalized safety on construction sites: a review.\" Available at SSRN 4641480 (2023). [6] Schroth, Christian A., et al. \"Emergency response person localization and vital sign estimation using a semi-autonomous robot mounted sfcw radar.\" IEEE Transactions on Biomedical Engineering 71.6 (2024): 1756-1769. [7] Wang, Mingzhu, Jiayu Chen, and Jun Ma. \"Monitoring and evaluating the status and behaviour of construction workers using wearable sensing technologies.\" Automation in Construction 165 (2024): 105555. [8] Xu, Wenpei, and Ting-Kwei Wang. \"Construction worker safety prediction and active warning based on computer vision and the gray absolute decision analysis method.\" Journal of Construction Engineering and Management 149.4 (2023): 04023014. [9] Yang, Hang, and Xunbo Li. \"Performance Evaluation of Three-Dimensional UWB Real-Time Locating Auto-Positioning System for Fire Rescue.\" Intelligent Automation & Soft Computing 37.3 (2023). [10] Zhu, Aiyu, et al. \"Deep reinforcement learning for real-time assembly planning in robot-based prefabricated construction.\" IEEE Transactions on Automation Science and Engineering 20.3 (2023): 1515-1526.

Copyright

Copyright © 2025 Dr. M. Newlin Rajkumar, Anush Kumar S, Subiya V M. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.