A framework to estimate cognitive load using physiological data

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.

Review of Eye Tracking Metrics Involved in Emotional and Cognitive Processes

“…In [145], blink duration and frequency were also shown to maximize at the lowest speed in the video game, and as the workload increased, blink frequency decreased. In [17] blink rate decreased from low to medium cognitive load but no further change was observed in high cognitive load. Borys et al also showed that maximum blink duration correlated strongly with the number of errors in high cognitive load of an arithmetic task [146].…”

“…According to cognitive load theory, there are three types of cognitive load: 1) intrinsic load 2) extraneous load and 3) germane load [16]. The intrinsic load comes from the complexity of the task and its association with the user, extraneous load is caused by the presentation style of the material and germane load refers to the ability of the user to fully understand the material [17].…”

“…Microsaccade amplitude seems to increase with increasing task difficulty [160], [154], while the behaviour of microsaccade peak velocity and peak amplitude is not clear [154]. [166], [167], [168], [169], [17], [170], [154], [143], [ ↑ / ↓ : significant increase/decrease during cognitive workload at 0.05 level ↑↑ / ↓↓ : significant increase/decrease during cognitive workload at 0.01 level ns: non-significant difference…”

“…Pupil area is strongly associated with the user's ongoing task difficulty [166], [132], [172] and mean pupil diameter has been shown to have a positive correlation with the cognitive workload in several different tasks [17], [170], [154]. The standard deviation of the pupil size also increases with cognitive load [173].…”

“…However, in [194], the scientists aim to identify three levels of cognitive load (low/medium/high) but with low accuracy, that of 43.8%. In contrast and in [17], researchers while trying to identify between the same three categories, exploited heart rate related metrics combined with pupil diameter and blink rate and thus managed to achieve over double the accuracy percentage with 91.66%. The other study [193], uses drive measurement features along with pupil size and reaches up to 80.7% accuracy percentage for the binary classification of the level of cognitive load.…”