Continuous Stress Monitoring under Varied Demands Using Unobtrusive Devices
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 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 of duration spent between two consecutive questions. The feedforward back-propagation neural network achieves the best performance in the classification.
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
Citation : Lim, Y.M., Ayesh, A. and Stacey, M. (2019) Continuous Stress Monitoring under Varied Demands Using Unobtrusive Devices. International Journal of Human–Computer Interaction, 36 (4), pp. 326-340
ISSN : 1532-7590
Research Institute : Cyber Technology Institute (CTI)
Peer Reviewed : Yes