An integrated methodology for the assessment of stress and mental workload applied on virtual training

The importance of training for operators in industrial contexts is widely highlighted in literature. Virtual Reality (VR) is considered an efficient solution for training, since it provides immersive, realistic, and interactive simulations environments promoting a learn-by-doing approach, 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 an integrated methodology for the analysis of user's cognitive states, suitable for each kind of training in the industrial sector and beyond, fostering the human-centred design and manufacturing perspective. The methodology has been assessed using an industrial case study where virtual training is used for assembly of agricultural vehicles. Experimental results highlighted that, with VR additional supportive information, while operators' errors drastically decrease, the stress increases for complex tasks, due to the greater amount of information to manage. The proposed protocol allows understanding the operators' cognitive conditions in order to optimize the VR training application, avoiding operators' stress, mental overload, and improving performance.