## 5 Discussion  
The system presented in this work benefits from the use of IoT technologies. Importantly, the establishment of correlations between previously existing diseases, whether because of occupational exposure or not, symptoms presented by the employee, and harmful agents to which they are exposed in the work environment provides opportunities for carrying out personalized analyses that can contribute to planning preventive actions in OSH. This monitoring solution aims to help companies identify conditions that could damage workers' health in the longer term. This type of functionality was not found in the studies described in Section 2. In addition, addressing privacy in OSH monitoring systems is still not common. To achieve these goals, this system's proposal includes both the handling of environmental data and health history information. In this context, recent research has explored workers' concerns about incorporating IoT/Industry 4.0 and Industry 5.0 technologies in  
Figure 11: Health information update. Figure 12: New alert considering the health status update. OSH. The fact that workers do not know exactly how the data can be used has often been reported as a justification for resistance to the continued use of monitoring equipment. Workers are afraid of being harmed by the analysis of sensitive data, such as health data. On the companies' side, concerns are generally related to the costs or complexity of managing new technological solutions. A very interesting aspect of the research presented in Section 2 is the suggestion to incorporate functions into monitoring systems that provide personal feedback on sensitive information, predominantly physiological metrics, to the workers and non-sensitive information (such as environmental metrics) to safety managers. This approach may help to reduce workers' resistance to the incorporation of new technologies. Regarding the handling of sensitive data, workers' concerns are legitimate and should always be considered by companies. Employers must be aware of and up-to-date with data privacy legislation. In addition, it is essential to carry out appropriate training to make workers aware of the benefits arising from the adoption of new OSH solutions. The gradual use of equipment, for example, with small groups of workers, primarily for short periods, can be a viable alternative to help familiarize managers and workers with new technologies and, at the same time, demonstrate to workers the safety of the devices. Countries might differ slightly on legislation for processing personal data. However, workers must always know exactly what data will be collected, how each piece of information will be processed, and for what purposes it will be used, such as what improvements can be implemented in the company by using data. In addition, procedures to ensure the confidentiality of data sent over the network and adequate storage by the employer or third-party technology companies are also essential items. It is worth noting that although there are advanced regulations regarding data privacy in several countries, the limits of the use of artificial intelligence have not yet been well established, given the rapid evolution of algorithms and the large computational processing capabilities currently available. It is important to highlight that depending on the type of data collected and the analysis carried out, even if it is with the expressed authorization of the worker, solutions using machine learning may have a harmful bias toward certain groups of people. For the reasons stated above, the creation of regulations for the use of such technologies in OSH is necessary and urgent. The lack of regulations is one of the reasons why companies resist adopting IoT-based solutions. Without the protection provided by adequate regulations and given the still high cost of implementing technological solutions, small and medium-sized companies tend to postpone the adoption of modernizations to monitor risks in work environments.