International Journal of Human-Computer Studies
Data mining approaches for intelligent E-Social care decision support system
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
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Researchers are integrating emerging technologies into interactive play environments, and established game markets continue to expand, yet evaluating play environments is challenging. While task performance metrics are commonly used to objectively and quantitatively analyse productivity systems; with play systems, the quality of the experience, not the performance of the participant is important. This research presents three experiments that examine users' physiological signals to continuously model user emotion during interaction with play technologies. Modeled emotions are powerful because they capture usability and playability, account for user emotion, are quantitative and objective, and can be represented continuously. In Experiment One we explored how physiological signals respond to interaction with play technologies. We collected a variety of physiological measures while observing participants playing a computer game in four difficulty conditions, providing a basis for experimental exploration of this domain. In Experiment Two we investigated how physiological signals differ between play conditions, and how physiological signals co-vary with subjective reports. A different physiological response was observed when playing a computer game against a co-located friend versus a computer. When normalized, the physiological results mirrored subjective reports. In Experiment Three we developed a method for modeling emotion using physiological data. A fuzzy logic model transformed four physiological signals into arousal and valence. A second fuzzy logic model transformed arousal and valence into five emotions: boredom, challenge, excitement, frustration, and fun. The modeled emotions' means were evaluated with test data, and exhibited the same trends as the reported emotions for fun, boredom, and excitement, but modeled emotions revealed differences between three play conditions, while differences between reported emotions were not significant. Mean emotion modeled from physiological data fills a knowledge gap for objective and quantitative evaluation of entertainment technologies. Using our technique, user emotion can be analyzed over an entire experience, revealing variance within and between conditions. This continuous representation has a high evaluative bandwidth, and is important because the process , not the outcome of playing determines success. The continuous representation of modeled emotion is a powerful evaluative tool, that when combined with other approaches, forms a robust method for evaluating user interaction with play technologies.