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September 10, 2025Open Access

Real-Time Safety Alerting System for Dynamic, Safety-Critical Environments

NANima AbdollahpourSimon Fraser University MMMehrdad MoallemSimon Fraser University MNMohammad NarimaniSimon Fraser University

Key Points

The system achieves a localization accuracy of 97%, ensuring effective real-time safety monitoring.
It employs machine learning algorithms such as k-NN, Random Forest, and SVM for analyzing data effectively.
Experimental validation indicates a quick response time of only 1.2 seconds in safety-critical environments.
The architecture supports effective safety zoning, enhancing overall safety in real-world applications.

Abstract

This paper presents a proof-of-concept real-time safety alerting system for safety-critical environments such as construction sites. Key components of the system include Bluetooth Low Energy (BLE) devices for indoor localization, integrated with a customized Android application using the Framework for Internal Navigation and Discovery (FIND). Administrative control and data management are handled by a server-side component, supported by an interactive website for real-time safety monitoring. The architecture supports safety zoning and employs machine learning algorithms, including k-NN, Random Forest, and SVM, for analyzing localization data. Experimental validation in a laboratory setup demonstrates a localization accuracy of 97%, a response time of 1.2 s, and a maximum spatial error of 1.2 m. These results highlight the system’s reliability and potential for enhancing safety compliance in real-world deployment scenarios.

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