Jimenez-Molina, A.; Retamal, C.; Lira, H. Using Psychophysiological Sensors to Assess Mental Workload During Web Browsing. Sensors2018, 18, 458.
Jimenez-Molina, A.; Retamal, C.; Lira, H. Using Psychophysiological Sensors to Assess Mental Workload During Web Browsing. Sensors 2018, 18, 458.
Jimenez-Molina, A.; Retamal, C.; Lira, H. Using Psychophysiological Sensors to Assess Mental Workload During Web Browsing. Sensors2018, 18, 458.
Jimenez-Molina, A.; Retamal, C.; Lira, H. Using Psychophysiological Sensors to Assess Mental Workload During Web Browsing. Sensors 2018, 18, 458.
Abstract
The mental workload induced by a Web page is essential for improving the user’s browsing experience. However, continuously assessing the mental workload during a browsing task is challenging. In order to face this issue, this paper leverages the correlation between stimuli and physiological responses, which are measured with high-frequency, non-invasive psychophysiological sensors during very short span windows. An experiment was conducted to identify levels of mental workload through the analysis of pupil dilation measured by an eye-tracking sensor. In addition, a method was developed to classify real-time mental workload by appropriately combining different signals (electrodermal activity (EDA), electrocardiogram, photoplethysmography (PPG), electroencephalogram (EEG), temperature and eye gaze) obtained with non-invasive psychophysiological sensors. The results show that the Web browsing task involves on average four levels of mental workload. Also, by combining EEG with the PPG and EDA, the accuracy of the classification reaches 95.73 %.
Keywords
psychophysiological sensors; mental workload; Web browsing tasks; signal processing.
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
