Affective computing
Pupil size variation as an indication of affective processing
International Journal of Human-Computer Studies - Application of affective computing in humanComputer interaction
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Detecting stress during real-world driving tasks using physiological sensors
IEEE Transactions on Intelligent Transportation Systems
Bio-sensing for Emotional Characterization without Word Labels
Proceedings of the 13th International Conference on Human-Computer Interaction. Part III: Ubiquitous and Intelligent Interaction
Human-computer intelligent interaction: a survey
HCI'07 Proceedings of the 2007 IEEE international conference on Human-computer interaction
Intelligent emotion-oriented eCommerce systems
AIKED'10 Proceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
A prototype for a conversational companion for reminiscing about images
Computer Speech and Language
Call center stress recognition with person-specific models
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Computer Methods and Programs in Biomedicine
Collecting heart rate using a high precision, non-contact, single-point infrared temperature sensor
ICSR'12 Proceedings of the 4th international conference on Social Robotics
Subject-dependent biosignal features for increased accuracy in psychological stress detection
International Journal of Human-Computer Studies
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Affective Computing, one of the frontiers of Human-Computer Interaction studies, seeks to provide computers with the capability to react appropriately to a user's affective states. In order to achieve the required on-line assessment of those affective states, we propose to extract features from physiological signals from the user (Blood Volume Pulse, Galvanic Skin Response, Skin Temperature and Pupil Diameter), which can be processed by learning pattern recognition systems to classify the user's affective state. An initial implementation of our proposed system was set up to address the detection of "stress" states in a computer user. A computer-based "Paced Stroop Test" was designed to act as a stimulus to elicit emotional stress in the subject. Signal processing techniques were applied to the physiological signals monitored to extract features used by three learning algorithms: Naïve Bayes, Decision Tree and Support Vector Machine to classify relaxed vs. stressed states.