Virtual training for assembly tasks: a framework for the analysis of the cognitive impact on operators

The importance of training for operators in industrial contexts is widely highlighted in literature. Virtual Reality (VR) technology is considered an efficient solution for training, since it provides immersive, realistic, and interactive simulations environments where the operator can learn-by-doing, far from the risks of the real field. Its efficacy has been demonstrated by several studies, but a proper assessment of the operator’s cognitive response in terms of stress and cognitive load, during the use of such technology, is still lacking. This paper proposes a comprehensive methodology for the analysis of user’s cognitive states, suitable for each kind of training in the industrial sector and beyond. Preliminary feasibility analysis refers to virtual training for assembly of agricultural vehicles. The proposed protocol analysis allowed understanding the operators’ loads to optimize the VR training application, considering the mental demand during the training, and thus avoiding stress, mental overload, improving the user performance

Multi sensors platform for stress monitoring of workers in smart manufacturing context

In factories of the future, advanced automation systems (e.g., cobots, exoskeletons, cyber physical systems) will reduce the physical effort of workers and compensate their limitations as well as ensure more flexibility, agility, and competitiveness. However, the activities of the operator 4.0 will entail an increased share of complex cognitive tasks. Therefore, monitoring the mental load will be increasingly important to ensure work environments that promote healthy life and wellbeing for all at all ages. For this aim, this paper proposes a framework to analyze heart rate, galvanic skin response and electrooculogram signals in order to extract features able to detect an excessive stress or cognitive load. Two wearable devices are used: Empatica E4 wristband and J!NS MEME electrooculography glasses. The proposed framework has been experimented through a laboratory test focused on LEGO brick-based simulations of manufacturing activities