The objectives of this study were three-fold: (i) to investigate new occupational health and safety risks that may arise with the integration of Industry 4.0 in production environments; (ii) to identify and group these risks into various categories; and (iii) to prioritize these groups. With this prioritization, a number of recommendations will be made to take into account new risk groups that may arise during the reorganizing of production environments according to Industry 4.0. This study used the Hesitant Fuzzy Analytic Hierarchy Process prioritization approach to determine the ranking of risks, which arose with the use of Industry 4.0. In this paper, possible occupational health and safety risks of the selected method consisted of eye-related disorders, mental fatigue, disorders stemming from the static working position, exposure to unknown dangerous particles as a result of cooperation with robots, and psychological pressure (stemming from the problems of adaptation to duties that require creativity). According to the results, the two most important risk factors were mental fatigue and psychological pressure. Mental fatigue as the principal risk factor was not unexpected owing to the many encounters between humans and machines. However, with respect to psychological pressure, we were unable interpret the results in the same way as the mental fatigue case. Psychological pressure is a complicated problem, and a more in-depth and detailed investigation is needed.

The main contribution of this study was the concurrent application of EEG and eye tracking techniques during n-back tasks as part of the methodology for addressing the problem of mental workload classification through machine learning algorithms. The experiments involved 15 university students, consisting of 7 women and 8 men. Throughout the experiments, the researchers utilized the n-back memory task and the NASA-Task Load Index (TLX) subjective rating scale to assess various levels of mental workload. The results indicating the relationship between EEG and eye tracking measures and mental workload are consistent with previous research. Regarding the four-class classification task, mental workload level could be predicted with 76.59% accuracy using 34 selected features. This study makes a significant contribution to the literature by presenting a four-class mental workload estimation model that utilizes different machine learning algorithms.