Spectral analysis of sinus arrhythmia: a measure of mental effort
Human Factors - Cognitive psychophysiology
Affective computing
Toward Machine Emotional Intelligence: Analysis of Affective Physiological State
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
The human-computer interaction handbook
The flow principle in interactivity
Proceedings of the second Australasian conference on Interactive entertainment
Measuring multiple components of emotions in interactive contexts
CHI '06 Extended Abstracts on Human Factors in Computing Systems
A Theory of Fun for Game Design
A Theory of Fun for Game Design
Measuring emotional valence to understand the user's experience of software
International Journal of Human-Computer Studies
International Journal of Human-Computer Studies
Entertainment capture through heart rate activity in physical interactive playgrounds
User Modeling and User-Adapted Interaction
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
TOWARDS OPTIMIZING ENTERTAINMENT IN COMPUTER GAMES
Applied Artificial Intelligence
A user-centered approach to affective interaction
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Computing emotion awareness through facial electromyography
ECCV'06 Proceedings of the 2006 international conference on Computer Vision in Human-Computer Interaction
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To optimize a player's experience, an emotionally adaptive game continuously adapts its mechanics to the player's emotional state, measured in terms of emotion-data. This paper presents the first of two studies that aim to realize an emotionally adaptive game. It investigates the relations between game mechanics, a player's emotional state and his/her emotion-data. In an experiment, one game mechanic (speed) was manipulated. Emotional state was self-reported in terms of valence, arousal and boredom-frustration-enjoyment. In addition, a number of (mainly physiology-based) emotion-data features were measured. Correlations were found between the valence/arousal reports and the emotion-data features. In addition, seven emotion-data features were found to distinguish between a boring, frustrating and enjoying game mode. Taken together, these features convey sufficient data to create a first version of an emotionally adaptive game.