A human-cyber-physical system for Operator 5.0 smart risk assessment

In the context of Industry 5.0, characterized by the human-centred transformation of manufacturing processes, assessing operator risk is crucial for ensuring workplace safety and well-being. In this respect, this paper presents the development of a human-cyber-physical system (HCPS) capable of estimating operator risk by leveraging diverse sensing data. By comprehensively analysing complex patterns and interactions among physiological, environmental, and manufacturing variables, the HCPS offers an advanced approach to operator risk assessment. Through the integration of cutting-edge sensing technologies, real-time data collection, and sophisticated analytics paradigms, the HCPS accurately identifies meaningful patterns and anomalies. It dynamically adapts to changing manufacturing conditions, generating risk profiles for operators and work processes. Timely alerts and notifications enable proactive interventions, enhancing safety measures and optimizing work processes. The HCPS empowers decision-making and supporting the well-being and productivity of operators in the Industry 5.0 paradigm, while maintaining a safe working environment. A simulated case study is reported to validate the proposed framework on a variety of industrial scenarios.</p

Cloud-based platform for intelligent healthcare monitoring and risk prevention in hazardous manufacturing contexts

This paper presents an intelligent cloud-based platform for workers healthcare monitoring and risk prevention in potentially hazardous manufacturing contexts. The platform is structured according to sequential modules dedicated to data acquisition, processing and decision-making support. Several sensors and data sources, including smart wearables, machine tool embedded sensors and environmental sensors, are employed for data collection, comprising information on offline clinical background, operational and environmental data. The cloud data processing module is responsible for extracting relevant features from the acquired data in order to feed a machine learning-based decision-making support system. The latter provides a classification of workers’ health status so that a prompt intervention can be performed in particularly challenging scenarios

Manufacturing process impacts on occupational health: a machine learning framework

The Operator 4.0 generation denotes a smart and skilled operator accomplishing ‘cooperative work’ with robots, machines and cyber-physical systems. In this taxonomy, a healthy operator is an operator equipped with wearable technology to monitor biometrics in a workplace to monitor and ideally prevent urgent threats to safety, stress in manufacturing and production quality. In a digitalized context, a cloud manufacturing platform for occupational health assessment, capable of collecting physiological, environmental and manufacturing process data can potentially enable prompt action to prevent fatalities. This paper proposes a novel machine learning-based framework and associated methods to classify physiological data acquired using wearable sensors during manufacturing work, to be utilized in a fuzzy-based expert system to determine the level and type of health risk for Operator 4.0. Classification algorithms are presented and a manufacturing case study is illustrated to exemplify the proposed methodology and to evaluate the industrial suitability.</p