Heart rate variability: indicator of user state as an aid to human-computer interaction
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
Physiological indicators for the evaluation of co-located collaborative play
CSCW '04 Proceedings of the 2004 ACM conference on Computer supported cooperative work
Entertainment Modeling in Physical Play Through Physiology Beyond Heart-Rate
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
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
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
IEEE Transactions on Affective Computing
Affective preference from physiology in videogames: a lesson learned from the TORCS experiment
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
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Affective Computing has always aimed to answer the question: which measurement is most suitable to predict the subject's affective state? Many experiments have been devised to evaluate the relationships among three types of variables (the affective triad): stimuli, self-reports, and measurements. Being the real affective state hidden, researchers have faced this question by looking for the measure most related either to the stimulus, or to self-reports. The first approach assumes that people receiving the same stimulus are feeling the same emotion; a condition difficult to match in practice. The second approach assumes that emotion is what people are saying to feel, and seems more likely. We propose a novel method, which extends the mentioned ones by looking for the physiological measurement mostly correlated to the selfreport due to emotion, not the stimulus. This guarantees to find a measure best related to subject's affective state.