Suvodip Chakraborty

From the initial phases of human–computer interaction, where the computer was unaware of the users' mental states, we are now progressing toward cognition-aware user interfaces. One crucial cognitive state considered by research on cognition-aware user interfaces is the cognitive load. Eye-tracking has been suggested as one particularly unobtrusive method for estimating cognitive load. Although the accuracy of cognitive load detection has improved in recent work, it is still insufficient for cognition-aware user interfaces, which require high accuracy for getting accepted by the user. This article introduces two new eye-tracking metrics for estimating perceived cognitive load based on benign anisocoria (BA). Unlike previous pupil-based metrics, our metrics are based on pupil size asymmetry between the left and right eye. As a case study, we illustrate the effectiveness of the proposed metrics on a recently published eye-tracking dataset recorded under laboratory conditions. The results show that our proposed features based on BA can improve the performance of classifiers for detecting the perceived mental workload associated with an $N$ -back test. The best classification accuracy was 84.24% while the classification accuracy in the absence of the proposed features was 81.91% for the light gradient boosting classifier.

From the initial phases of human-computer interaction (HCI), where the computer was unaware of the users' mental states, we are now progressing towards cognition-aware user interfaces. One crucial cognitive state considered by research on cognition-aware user interfaces is the cognitive load. Eye-tracking has been suggested as one particularly unobtrusive method for estimating cognitive load. Although the accuracy of cognitive load detection has improved in recent work, it is still insufficient for cognition-aware user interfaces, which require high accuracy for getting accepted by the user. This paper introduces two new eyetracking metrics for estimating perceived cognitive load based on benign anisocoria (BA). Unlike previous pupil-based metrics, our metrics are based on pupil size asymmetry between the left and right eye. As a case study, we illustrate the effectiveness of the proposed metrics on a recently published eye-tracking dataset recorded under laboratory conditions. The results show that our proposed features based on BA can improve the performance of classifiers for detecting the perceived mental workload associated with an N-back test. The best classification accuracy was 84.24% while the classification accuracy in the absence of the proposed features was 81.91% for the light gradient boosting classifier.