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An investigation into keystroke dynamics and heart rate variability as indicators of stress

Unni, Srijith, Suryanarayana Gowda, Sushma and Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389 (2022) An investigation into keystroke dynamics and heart rate variability as indicators of stress. In: MMM 2022 28th International Conference on Multimedia Modeling, 6-10 June 2022, Phu Quoc, Vietnam. ISBN 978-3-030-98357-4

Lifelogging has become a prominent research topic in recent years. Wearable sensors like Fitbits and smart watches are now increasingly popular for recording ones activities. Some researchers are also exploring keystroke dynamics for lifelogging. Keystroke dynamics refers to the process of measuring and assessing a persons typing rhythm on digital devices. A digital footprint is created when a user interacts with devices like keyboards, mobile phones or touch screen panels and the timing of the keystrokes is unique to each individual though likely to be affected by factors such as fatigue, distraction or emotional stress. In this work we explore the relationship between keystroke dynamics as measured by the timing for the top-10 most frequently occurring bi-grams in English, and the emotional state and stress of an individual as measured by heart rate variability (HRV). We collected keystroke data using the Loggerman application while HRV was simultaneously gathered. With this data we performed an analysis to determine the relationship between variations in keystroke dynamics and variations in HRV. Our conclusion is that we need to use a more detailed representation of keystroke timing than the top-10 bigrams, probably personalised to each user.
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Additional Information:alan.smeaton@dcu.ie
Uncontrolled Keywords:Keystroke dynamics; Heart rate variability
Subjects:Computer Science > Lifelog
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > INSIGHT Centre for Data Analytics
Published in: Jónsson, Björn Þór, Gurrin, Cathal, Tran, Minh-Triet and Dang-Nguyen, Duc-Tien, (eds.) 28th International Conference, MMM 2022, Proceedings, Part II. LNCS 13141. Springer. ISBN 978-3-030-98357-4
Official URL:https://doi.org/10.1007/978-3-030-98358-1_30
Copyright Information:© 2022 Springer
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 P2, co-funded by the European Regional Development Fund
ID Code:26471
Deposited On:05 Apr 2022 13:17 by Alan Smeaton . Last Modified 09 May 2022 11:32

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