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Using Psychophysiological Sensors to Assess Mental Workload During Web Browsing.

Angel Jimenez-Molina1, Cristian Retamal2, Hernan Lira3

  • 1Department of Industrial Engineering, Faculty of Physical and Mathematical Sciences, University of Chile, Santiago 8370456, Chile. ajimenez@dii.uchile.cl.

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|February 7, 2018
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Summary

This study measures mental workload during web browsing using physiological signals. Combining multiple sensors achieved 93.7% accuracy in classifying four mental workload levels, enhancing user experience.

Keywords:
Web browsing tasksmachine learningmental workloadpsychophysiological sensors

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Area of Science:

  • Human-Computer Interaction
  • Cognitive Science
  • Biomedical Engineering

Background:

  • Assessing user mental workload during web browsing is crucial for optimizing user experience.
  • Continuous mental workload assessment presents significant challenges in real-time browsing scenarios.

Purpose of the Study:

  • To develop and validate a method for classifying mental workload levels during web browsing.
  • To investigate the correlation between physiological responses and mental workload.
  • To enhance the accuracy of mental workload assessment by integrating multiple psychophysiological signals.

Main Methods:

  • Utilized high-frequency, non-invasive psychophysiological sensors including eye-tracking (pupil dilation), electrodermal activity (EDA), electrocardiogram (ECG), photoplethysmography (PPG), electroencephalogram (EEG), and temperature.
  • Developed a classification method combining data from multiple sensors to identify distinct mental workload levels.
  • Conducted an experiment to measure physiological responses during a web browsing task.

Main Results:

  • Identified four distinct levels of mental workload associated with web browsing tasks.
  • Pupil dilation analysis effectively indicated different mental workload levels.
  • The combined sensor approach achieved a classification accuracy of 93.7% for mental workload.

Conclusions:

  • Integrating multiple psychophysiological signals significantly improves the accuracy of mental workload classification.
  • The developed method offers a robust approach for real-time mental workload assessment during web browsing.
  • Findings provide valuable insights for designing more user-centric and less demanding web interfaces.