An Integrated Safety Management Framework for Working at Height Using Smart PPE, Artificial Intelligence and Risk Assessment

Abstract

Working at height is one of the leading causes of occupational fatalities and serious injuries across construction and industrial sectors. Despite the implementation of engineering controls, administrative procedures, work permit systems, safety training, and personal protective equipment (PPE), fall-related accidents continue to occur, primarily due to unsafe acts, human error, and non-compliance with safety harness anchoring requirements. Conventional safety monitoring methods rely heavily on manual supervision, making it difficult to ensure continuous compliance during high-risk operations. Therefore, there is a growing need for intelligent and technology-driven safety solutions that can provide real-time monitoring and early warning to prevent fall accidents. This research presents the development of an AI and Internet of Things (IoT) enabled Smart Safety Harness System for enhancing working at height safety in industrial applications. The proposed system integrates sensor-based monitoring, AI communication, and real-time alert mechanisms to detect improper anchoring or unsafe usage of the safety harness. Whenever a deviation is identified, the system immediately generates an audio-visual warning to the worker while simultaneously transmitting the information to the supervisor for corrective action. In addition, all safety violations are automatically recorded in a centralized database for trend analysis, safety performance evaluation, and continuous improvement. The study adopts a systematic methodology involving hazard identification, working at height survey, risk assessment, evaluation of existing engineering and administrative controls, validation of technological solutions, and implementation of an intelligent fall protection system. High-risk activities such as roof sheet installation, scaffold erection, maintenance work, pipeline installation, and structural construction are analyzed to identify critical areas where smart wearable technology can significantly improve safety performance. The proposed approach follows the hierarchy of risk control by strengthening engineering controls while minimizing dependence on manual observation and human intervention. The outcome of this research is the development of a reliable, economical, and intelligent fall prevention system capable of reducing workplace injuries, improving PPE compliance, minimizing human error, and supporting proactive safety management. The integration of Artificial Intelligence, IoT, and smart wearable technology establishes a modern framework for industrial safety that enables real-time monitoring, predictive safety analytics, and data-driven decision-making. The proposed system has significant potential for application in construction, manufacturing, petrochemical, power, mining, and infrastructure industries, thereby contributing to the advancement of Industrial Safety Engineering and promoting a sustainable "Zero Harm" safety culture.