A dynamic model of stress, and sustained attention
Human Factors
Authentication via keystroke dynamics
Proceedings of the 4th ACM conference on Computer and communications security
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Keystroke dynamics as a biometric for authentication
Future Generation Computer Systems - Special issue on security on the Web
Keystroke analysis of free text
ACM Transactions on Information and System Security (TISSEC)
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A Comparison of Classification Methods for Predicting Deception in Computer-Mediated Communication
Journal of Management Information Systems
Decision support for determining veracity via linguistic-based cues
Decision Support Systems
Automated stress detection using keystroke and linguistic features: An exploratory study
International Journal of Human-Computer Studies
Multimodal behavioral analysis for non-invasive stress detection
Expert Systems with Applications: An International Journal
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Stress is a part of everyday life, but chronic high stress can have psychological and physiological side effects. Systems that can detect harmful levels of stress could assist users in managing their stress and health. However, current assessments are often obtrusive or require specialized equipment. This research leverages attributes of everyday keyboard interactions to proactively and continuously monitor cognitive function. A laboratory study was conducted where typing samples were collected under stress and no-stress conditions. Keystroke and linguistic features were extracted from the samples and models were constructed for each participant. Correct classification rates ranged from 62% to 88% with a mean of 72%.