Name
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%. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/s18020458 | SENSORS |
Keywords | Field | DocType |
psychophysiological sensors,mental workload,Web browsing tasks,machine learning | Signal processing,Pupillary response,Web page,Workload,Electronic engineering,Human–computer interaction,Web navigation,Engineering,Electroencephalography | Journal |
Volume | Issue | Citations |
18 | 2.0 | 3 |
PageRank | References | Authors |
0.39 | 21 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Angel Jimenez-Molina | 1 | 46 | 4.75 |
| Cristian Retamal | 2 | 3 | 0.39 |
| Hernan Lira | 3 | 3 | 0.39 |