Abstract
Due to its limited capacity, working memory can become overloaded with extra activities that do not directly contribute to learning. According to cognitive load theory, working memory overload reduces task performance. Thus, monitoring the individual's current mental workload is essential to avoid dealing with the effects of cognitive overload. Heart rate and pupil dilation are two important metrics that can appropriately be measured at a low cost. These two signals have been generated to classify the participants' cognitive load levels in this study. Ninety-eight (98) participants volunteered in the studies, and we assessed their cognitive workloads using psychophysiological measurements generated during the experiment and performance characteristics obtained from the virtual driving system. The driving system continuously monitored the subjects' driving performance parameters, including heart rate and pupil dilation. The experiment involved driving tasks in a virtual environment, and some popular machine learning algorithms have been applied for user classification. Data analysis of the signals reveals that the heart rate and pupil dilation could appropriately be used to determine the cognitive workload of the individuals. Also, using multimodal data fusion, the accuracy of the cognitive load classification can be improved.
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