Affective computing
Toward Machine Emotional Intelligence: Analysis of Affective Physiological State
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Emotion recognition from physiological signals using wireless sensors for presence technologies
Cognition, Technology and Work
An empirical study of machine learning techniques for affect recognition in human–robot interaction
Pattern Analysis & Applications
International Journal of Human-Computer Studies
Entertainment capture through heart rate activity in physical interactive playgrounds
User Modeling and User-Adapted Interaction
Entertainment modeling through physiology in physical play
International Journal of Human-Computer Studies
Early Prediction of Student Frustration
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
Flow and immersion in first-person shooters: measuring the player's gameplay experience
Future Play '08 Proceedings of the 2008 Conference on Future Play: Research, Play, Share
Affective game engines: motivation and requirements
Proceedings of the 4th International Conference on Foundations of Digital Games
Towards affective camera control in games
User Modeling and User-Adapted Interaction
Enjoyment recognition from physiological data in a car racing game
Proceedings of the 3rd international workshop on Affective interaction in natural environments
Genetic search feature selection for affective modeling: a case study on reported preferences
Proceedings of the 3rd international workshop on Affective interaction in natural environments
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
IEEE Transactions on Affective Computing
Control vs. complexity in games: comparing arousal in 2D game prototypes
Proceedings of the 4th International Conference on Fun and Games
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This paper examines the generality of features extracted from heart rate (HR) and skin conductance (SC) signals as predictors of self-reported player affect expressed as pairwise preferences. Artificial neural networks are trained to accurately map physiological features to expressed affect in two dissimilar and independent game surveys. The performance of the obtained affective models which are trained on one game is tested on the unseen physiological and selfreported data of the other game. Results in this early study suggest that there exist features of HR and SC such as average HR and one and two-step SC variation that are able to predict affective states across games of different genre and dissimilar game mechanics.