Affective computing
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
User authentication through keystroke dynamics
ACM Transactions on Information and System Security (TISSEC)
Physiological responses to different WEB page designs
International Journal of Human-Computer Studies - Application of affective computing in human—Computer interaction
Using mental load for managing interruptions in physiologically attentive user interfaces
CHI '04 Extended Abstracts on Human Factors in Computing Systems
Keystroke analysis of free text
ACM Transactions on Information and System Security (TISSEC)
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Real-time estimation of emotional experiences from facial expressions
Interacting with Computers
International Journal of Human-Computer Studies
Identity verification through dynamic keystroke analysis
Intelligent Data Analysis
Fundamentals of physiological computing
Interacting with Computers
Automated stress detection using keystroke and linguistic features: An exploratory study
International Journal of Human-Computer Studies
Keystroke analysis of different languages: a case study
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
A parallel decision tree-based method for user authentication based on keystroke patterns
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning how to feel again: towards affective workplace presence and communication technologies
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Feedback-based gameplay metrics: measuring player experience via automatic visual analysis
Proceedings of The 8th Australasian Conference on Interactive Entertainment: Playing the System
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Identifying emotions expressed by mobile users through 2D surface and 3D motion gestures
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Reactions: Twitter based mobile application for awareness of friends' emotions
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Detecting linguistic HCI markers in an online aphasia support group
Proceedings of the 14th international ACM SIGACCESS conference on Computers and accessibility
A personal touch: recognizing users based on touch screen behavior
Proceedings of the Third International Workshop on Sensing Applications on Mobile Phones
Proceedings of the 2013 international conference on Intelligent user interfaces
CAAT: a discrete approach to emotion assessment
CHI '13 Extended Abstracts on Human Factors in Computing Systems
Estimating user interruptibility by measuring table-top pressure
CHI '13 Extended Abstracts on Human Factors in Computing Systems
The physiological measurements as a critical indicator in users' experience evaluation
Proceedings of the 17th Panhellenic Conference on Informatics
Sensor-less sensing for affective computing and stress management technology
Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare
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The ability to recognize emotions is an important part of building intelligent computers. Emotionally-aware systems would have a rich context from which to make appropriate decisions about how to interact with the user or adapt their system response. There are two main problems with current system approaches for identifying emotions that limit their applicability: they can be invasive and can require costly equipment. Our solution is to determine user emotion by analyzing the rhythm of their typing patterns on a standard keyboard. We conducted a field study where we collected participants' keystrokes and their emotional states via self-reports. From this data, we extracted keystroke features, and created classifiers for 15 emotional states. Our top results include 2-level classifiers for confidence, hesitance, nervousness, relaxation, sadness, and tiredness with accuracies ranging from 77 to 88%. In addition, we show promise for anger and excitement, with accuracies of 84%.