Preprint Article Version 1 This version not peer reviewed

Using Psychophysiological Sensors to Assess Mental Workload in Web Browsing

Version 1 : Received: 4 December 2017 / Approved: 4 December 2017 / Online: 4 December 2017 (08:35:51 CET)

A peer-reviewed article of this Preprint also exists.

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. Sensors 2018, 18, 458.

Journal reference: Sensors 2018, 18, 458
DOI: 10.3390/s18020458

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 %.

Subject Areas

psychophysiological sensors; mental workload; Web browsing tasks; signal processing.

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