User Modeling and User-Adapted Interaction
Inferring user goals from personality and behavior in a causal model of user affect
Proceedings of the 8th international conference on Intelligent user interfaces
Psychophysiological indicators of the impact of media quality on users
CHI '01 Extended Abstracts on Human Factors in Computing Systems
MAUI: a multimodal affective user interface
Proceedings of the tenth ACM international conference on Multimedia
Multimodal affective driver interfaces for future cars
Proceedings of the tenth ACM international conference on Multimedia
Overfitting in making comparisons between variable selection methods
The Journal of Machine Learning Research
Affective Learning — A Manifesto
BT Technology Journal
Automatic recognition of affective cues in the speech of car drivers to allow appropriate responses
OZCHI '05 Proceedings of the 17th Australia conference on Computer-Human Interaction: Citizens Online: Considerations for Today and the Future
Toward a decision-theoretic framework for affect recognition and user assistance
International Journal of Human-Computer Studies - Human-computer interaction research in the managemant information systems discipline
Biometric valence and arousal recognition
OZCHI '07 Proceedings of the 19th Australasian conference on Computer-Human Interaction: Entertaining User Interfaces
Automated stress detection using keystroke and linguistic features: An exploratory study
International Journal of Human-Computer Studies
A decision theoretic model for stress recognition and user assistance
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Predicting Stress Level Variation from Learner Characteristics and Brainwaves
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Estimating cognitive load using remote eye tracking in a driving simulator
Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications
Performance analysis of acoustic emotion recognition for in-car conversational interfaces
UAHCI'07 Proceedings of the 4th international conference on Universal access in human-computer interaction: ambient interaction
Emotion on the road: necessity, acceptance, and feasibility of affective computing in the car
Advances in Human-Computer Interaction - Special issue on emotion-aware natural interaction
Data-Driven refinement of a probabilistic model of user affect
UM'05 Proceedings of the 10th international conference on User Modeling
USAB'11 Proceedings of the 7th conference on Workgroup Human-Computer Interaction and Usability Engineering of the Austrian Computer Society: information Quality in e-Health
Simple statistical inference algorithms for task-dependent wellness assessment
Computers in Biology and Medicine
Towards long term monitoring of electrodermal activity in daily life
Personal and Ubiquitous Computing
Proceedings of the Conference on Design, Automation and Test in Europe
Towards a mobile galvanic skin response measurement system for mentally disordered patients
BodyNets '13 Proceedings of the 8th International Conference on Body Area Networks
Applied Soft Computing
Modeling observer stress for typical real environments
Expert Systems with Applications: An International Journal
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Smart physiological sensors embedded in an automobile afford a novel opportunity to capture naturally occurring episodes of driver stress. In a series of ten ninety minute drives on public roads and highways, electrocardiogram, electromyogram, respiration and skin conductance sensors were used to measure autonomic nervous system activation. The signals were digitized in real time and stored on the SmartCar's Pentium class computer. Each drive followed a pre-specified route through fifteen different events, from which four stress level categories were created according to the results of the subject's self-report questionnaires. In total, 545 one-minute segments were classified. A linear discriminant function was used to rank each feature individually based on recognition performance and a sequential forward floating selection (SFFS) algorithm was used to find an optimal set of features for recognizing patterns of driver stress (88.6%). Using multiple features improved performance significantly over the best single feature performance (62.2%).