Enhancing operator health and safety in manufacturing: an intelligent digital humanization approach

Abstract

The manufacturing industry relies heavily on human labour, making the health and safety of operators paramount. Emerging technologies such as Human-Cyber-Physical Systems (HCPS) and Machine Learning (ML) have the potential to transform the approach to these critical issues. In fact, such technologies enable the creation of virtual replicas of tangible systems, providing innovative solutions for assessing operator safety. In this context, this work presents a digital humanization methodology designed to comprehensively evaluate the health and safety risks associated with operator involvement in production processes. Using cutting-edge sensors, advanced machine learning algorithms, and scenario simulations, such methodology generates real-time virtual representations of the physical condition and behaviour of the operator. These representations allow the risk characterization by estimating magnitude, priority, and occurrence, thereby facilitating early detection and preventive measures against potential hazards. A case study is considered to demonstrate the practical application of the proposed framework.