Continuous Stress Monitoring under Varied Demands Using Unobtrusive Devices

Date

2019-07-22

Advisors

Journal Title

Journal ISSN

ISSN

1532-7590
1044-7318

Volume Title

Publisher

Taylor & Francis

Type

Article

Peer reviewed

Yes

Abstract

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.

Description

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.

Keywords

Stress monitoring, Mouse dynamics, Keystroke dynamics, Job duration, Affective computing

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

Rights

Research Institute

Cyber Technology Institute (CTI)