Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The media equation: how people treat computers, television, and new media like real people and places
Modeling cognition-emotion of users for improved interaction with software systems
UM '99 Proceedings of the seventh international conference on User modeling
Does computer-generated speech manifest personality? an experimental test of similarity-attraction
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Toward Machine Emotional Intelligence: Analysis of Affective Physiological State
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Machine Learning
Modeling Multimodal Expression of User's Affective Subjective Experience
User Modeling and User-Adapted Interaction
User Modeling and User-Adapted Interaction
MAUI: a multimodal affective user interface
Proceedings of the tenth ACM international conference on Multimedia
Wearable and automotive systems for affect recognition from physiology
Wearable and automotive systems for affect recognition from physiology
Ambient Intelligence, Wireless Networking, And Ubiquitous Computing
Ambient Intelligence, Wireless Networking, And Ubiquitous Computing
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
Using noninvasive wearable computers to recognize human emotions from physiological signals
EURASIP Journal on Applied Signal Processing
Developing a generalizable detector of when students game the system
User Modeling and User-Adapted Interaction
LISTEN: a user-adaptive audio-augmented museum guide
User Modeling and User-Adapted Interaction
Affect and Emotion in Human-Computer Interaction: From Theory to Applications
Affect and Emotion in Human-Computer Interaction: From Theory to Applications
User identification for cross-system personalisation
Information Sciences: an International Journal
Learning and inferencing in user ontology for personalized Semantic Web search
Information Sciences: an International Journal
Affectively Intelligent User Interfaces for Enhanced E-Learning Applications
HCD 09 Proceedings of the 1st International Conference on Human Centered Design: Held as Part of HCI International 2009
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
Modeling of operators' emotion and task performance in a virtual driving environment
International Journal of Human-Computer Studies
Information Sciences: an International Journal
Understanding physiological responses to stressors during physical activity
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
The automotive ontology: managing knowledge inside the vehicle and sharing it between cars
Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications
Information Sciences: an International Journal
MoodWings: a wearable biofeedback device for real-time stress intervention
Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments
Leveraging biosignal and collaborative filtering for context-aware recommendation
Proceedings of the 1st ACM international workshop on Multimedia indexing and information retrieval for healthcare
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In this article, we describe a new approach to enhance driving safety via multi-media technologies by recognizing and adapting to drivers' emotions with multi-modal intelligent car interfaces. The primary objective of this research was to build an affectively intelligent and adaptive car interface that could facilitate a natural communication with its user (i.e., the driver). This objective was achieved by recognizing drivers' affective states (i.e., emotions experienced by the drivers) and by responding to those emotions by adapting to the current situation via an affective user model created for each individual driver. A controlled experiment was designed and conducted in a virtual reality environment to collect physiological data signals (galvanic skin response, heart rate, and temperature) from participants who experienced driving-related emotions and states (neutrality, panic/fear, frustration/anger, and boredom/sleepiness). k-Nearest Neighbor (KNN), Marquardt-Backpropagation (MBP), and Resilient Backpropagation (RBP) Algorithms were implemented to analyze the collected data signals and to find unique physiological patterns of emotions. RBP was the best classifier of these three emotions with 82.6% accuracy, followed by MBP with 73.26% and by KNN with 65.33%. Adaptation of the interface was designed to provide multi-modal feedback to the users about their current affective state and to respond to users' negative emotional states in order to decrease the possible negative impacts of those emotions. Bayesian Belief Networks formalization was employed to develop the user model to enable the intelligent system to appropriately adapt to the current context and situation by considering user-dependent factors, such as personality traits and preferences.