Author: Jimenez-Molina Angel Retamal Cristian Lira Hernan
Publisher: MDPI
E-ISSN: 1424-8220|18|2|458-458
ISSN: 1424-8220
Source: Sensors, Vol.18, Iss.2, 2018-02, pp. : 458-458
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Abstract
Knowledge of the mental workload induced by a Web page is essential for improving users’ browsing experience. However, continuously assessing the mental workload during a browsing task is challenging. To address 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 mental workload by appropriately combining different signals (electrodermal activity (EDA), electrocardiogram, photoplethysmo-graphy (PPG), electroencephalogram (EEG), temperature and pupil dilation) obtained with non-invasive psychophysiological sensors. The results show that the Web browsing task involves four levels of mental workload. Also, by combining all the sensors, the efficiency of the classification reaches 93.7%.
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