Abstract
Cognitive load has been widely studied to help understand human performance. It is desirable to monitor user cognitive load in applications such as automation, robotics, and aerospace to achieve operational safety and to improve user experience. This can allow efficient workload management and can help to avoid or to reduce human error. However, tracking cognitive load in real time with high accuracy remains a challenge. Hence, we propose a framework to detect cognitive load by non-intrusively measuring physiological data from the eyes and heart. We exemplify and evaluate the framework where participants engage in a task that induces different levels of cognitive load. The framework uses a set of classifiers to accurately predict low, medium and high levels of cognitive load. The classifiers achieve high predictive accuracy. In particular, Random Forest and Naive Bayes performed best with accuracies of 91.66% and 85.83% respectively. Furthermore, we found that, while mean pupil diameter change for both right and left eye were the most prominent features, blinking rate also made a moderately important contribution to this highly accurate prediction of low, medium and high cognitive load. The existing results on accuracy considerably outperform prior approaches and demonstrate the applicability of our framework to detect cognitive load.
Highlights
In the past few decades, cognitive load (CL) has been shown to negatively impact human performance in various tasksPers Ubiquit Comput factories, where supervisors observe autonomous operations [2, 20]
We would expect there to be a negative correlation between third phase Performance, which should vary with CL, and third phase Subjective CL, from the TLX ratings
We found that there was a negative correlation between Subjective CL and Performance, r(41) = −.456, p
Summary
In the past few decades, cognitive load (CL) has been shown to negatively impact human performance in various tasksPers Ubiquit Comput factories, where supervisors observe autonomous operations [2, 20]. Intelligent user interfaces are needed to provide situation awareness to the supervisors. This will help them to observe, analyse, and supervise autonomous operations safely and efficiently by managing their CL [35]. Germane load refers to the ability of the user to fully understand the material. We believe that both extraneous load and germane load are relevant factors affecting the operators’ interaction. An interface presenting data in a particular manner can result in an increase in both extraneous and germane load, which could induce high CL. To the best of our knowledge, it remains a challenge to measure CL in a robust and non-intrusive manner
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