Challenges of measuring physiological parameters as indicators of cognitive load in the context of human-machine interfaces


The rapid development of modern technologies enables an improvement of efficiency and speed in the production of high quality products. The high level of automation in manufacturing has increased productivity, however, operating modern machines often requires many complex activities in order to interact effectively with the interface. This results in a high cognitive demand, especially in relation to elderly, unexperienced or disabled machine operators. This may affect not only the work efficiency of employees but also their health and well-being. The aim of the INCLUSIVE project is to address this issue by developing a new concept of user-machine interactions, in which the behaviour of the automated systems adapts to human capabilities.

The present study has focused on the possibility of cognitive load assessment using various subjective (NASA-TLX scale), behavioural (error rate) and physiological measurements (heart rate, electrodermal activity, temperature, electroencephalography). The task carried out in this study required a resolution of mathematical problems at 5 difficulty levels. Although the results demonstrated a significant increase of subjective workload, coupled with an increasing error rate, significant differences were identified in only a single physiological parameter,  i.e. the electrodermal activity was significantly higher in the most difficult condition compared to the baseline.