Continuous Stress Monitoring under Varied Demands Using Unobtrusive Devices

Yee Mei Lim* (Corresponding Author), Aladdin Ayesh, Martin Stacey

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


This research aims to identify a feasible model to predict a learner’s stress in an online learning platform. It is desirable to produce a cost-effective, unobtrusive and objective method to measure a learner’s emotions. The few signals produced by mouse and keyboard could enable such solution to measure real-world individual’s affective states. It is also important to ensure that the measurement can be applied regardless of the type of task carried out by the user. This preliminary research proposes a stress classification method using mouse and keystroke dynamics to classify the stress levels of 190 university students when performing three different e-learning activities. The results show that the stress measurement based on mouse and keystroke dynamics is consistent with the stress measurement according to the changes in duration spent between two consecutive questions. The feedforward back-propagation neural network achieves the best performance in the classification.

Original languageEnglish
Pages (from-to)326-340
Number of pages15
JournalInternational Journal of Human-Computer Interaction
Issue number4
Early online date22 Jul 2019
Publication statusPublished - 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2019, © 2019 Taylor & Francis Group, LLC.


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