Industry 4.0 or “smart factories” of the future will be characterized by wide-scale automatization, connectivity and AI-driven technology, resulting in a manufacturing process that will become more and more efficient. Amidst this evolution stands the human operator, who will still play a vital role for product quality and factory productivity (Peruzzini et al., 2017). Since it is not unlikely that the increasing complexity and the decreased sense of control over machines will make it harder for this human operator to do his job, it is highly important to accurately measure cognitive load and explore ways to avoid or reduce cognitive overload.

In this project, we will focus on a multi-dimensional approach of cognitive load measurement and remediation and the translation to the complexity of real-life contexts. We will do this by means of experimental lab research, measuring different psychophysiological signals like EEG and skin conductance and taking subjective experiences and performance measures into account.