In recent decades, concern with workers’ health has become a priority in several countries, but statistics still show that it is urgent to perform more actions to prevent accidents and illnesses related to work. Industry 4.0 is a new production paradigm that has brought significant advances in the relationship between man and machine, driving a series of advances in the production process and new challenges in occupational safety and health (OSH). This paper addresses occupational risks, diseases, opportunities, and challenges in Industry 4.0. It also covers Internet-of-Things-related technologies that, by the real-time measurement and analysis of occupational conditions, can be used to create smart solutions to contribute to reducing the number of workplace accidents and for the promotion of healthier and safer workplaces. Proposals involving smart personal protective equipment (smart PPE) and monitoring systems are analyzed, and aspects regarding the use of artificial intelligence and the data privacy concerns are also discussed.

This paper addresses the design of a system to facilitate individual environmental risk assessment and long-term risk management in the Industry 4.0 work context. The solution is based on IoT to provide data related to workers’ exposure to hazardous agents (dust, noise, ultraviolet radiation, poor lighting, and inappropriate temperature and humidity) through a simple interface for employees and employers. The system includes a monitoring device and a server and performs employee registration, receives secure messages from the monitoring devices, shows information about the workers’ exposure, and triggers alarms when the measures reach or exceed the limits established by applicable legislation and/or standards. The system was tested in a controlled environment, and the results are presented in this work. Our solution is still under development, and in future stages, it will include a smartphone app for employees to check their exposures and the use of artificial intelligence on the server, which is expected to enable long-term planning by companies based on the analysis of employees’ health history and their exposures to hazardous agents.

This paper describes an integrated monitoring system designed for individualized environmental risk assessment and management in the workplace. The system incorporates monitoring devices that measure dust, noise, ultraviolet radiation, illuminance, temperature, humidity, and flammable gases. Comprising monitoring devices, a server-based web application for employers, and a mobile application for workers, the system integrates the registration of workers’ health histories, such as common diseases and symptoms related to the monitored agents, and a web-based recommendation system. The recommendation system application uses classifiers to decide the risk/no risk per sensor and crosses this information with fixed rules to define recommendations. The system generates actionable alerts for companies to improve decision-making regarding professional activities and long-term safety planning by analyzing health information through fixed rules and exposure data through machine learning algorithms. As the system must handle sensitive data, data privacy is addressed in communication and data storage. The study provides test results that evaluate the performance of different machine learning models in building an effective recommendation system. Since it was not possible to find public datasets with all the sensor data needed to train artificial intelligence models, it was necessary to build a data generator for this work. By proposing an approach that focuses on individualized environmental risk assessment and management, considering workers’ health histories, this work is expected to contribute to enhancing occupational safety through computational technologies in the Industry 5.0 approach.

This paper presents a monitoring system to measure dust, noise, ultraviolet radiation, illuminance, temperature, and humidity and to check the presence of flammable gases—liquefied petroleum gas (LPG), propane, hydrogen, butane, methane, and carbon monoxide. The system is composed of monitoring devices, a server to process data received from the devices and that runs a web application for employers, and a mobile application for workers to check their exposure data. During employee registration, the health history of the worker concerning common diseases and symptoms related to the monitored agents must be informed. This information is analyzed through a set of predefined rules to generate alerts that the company can consider for planning professional activities to minimize exposure to risk agents. The tests presented in this work are focused on verifying the functionalities of the web interface for employee registration, especially regarding the provision of health information, the updating of this information, and the generation of corresponding alerts. At this stage, fictitious employee information was used. The results showed that the system adequately performs the proposed functionalities, and it was concluded that the solution is viable for testing in a work environment in the future stages of development. The innovative features of this study concern the correlation between workers’ health history and harmful agents to build suggestions and alerts to guide long-term OSH-related decisions.

Dear Author, Thank you for providing a revised version of your paper. All of the suggestions regarding the Methods, Engineering Controls, and Discussion sections have been incorporated into this new version. In my opinion, your paper is now ready fo...