Using social science methods to induce a state of frustration in users, we collected physiological, video and behavioral data, and developed a strategy for coupling these data with real-world events. The effectiveness of the proposed strategy was tested in a study with thirty-six subjects, where the system was shown to reliably synchronize and gather data for affect analysis. Hidden Markov Models were applied to each subject 's physiological signals of skin conductivity and blood volume pressure in an effort to see if regimes of likely frustration could be automatically discriminated from regimes when all was proceeding smoothly. This pattern recognition approach correctly classified these two regimes 67.4% of the time. Mouse-clicking behavior was also synchronized to frustration-eliciting events, and analyzed, revealing NN distinct patterns of clicking responses Keywords: Affect, affective computing, user interface, pattern recognition, human-computer interaction, b...

Researchers are using emerging technologies to develop novel play environments, while established computer and console game markets continue to grow rapidly. Even so, evaluating the success of interactive play environments is still an open research challenge. Both subjective and objective techniques fall short due to limited evaluative bandwidth; there remains no corollary in play environments to task performance with productivity systems. This paper presents a method of modeling user emotional state, based on a user’s physiology, for users interacting with play technologies. Modeled emotions are powerful because they capture usability and playability through metrics relevant to ludic experience; account for user emotion; are quantitative and objective; and are represented continuously over a session. Furthermore, our modeled emotions show the same trends as reported emotions for fun, boredom, and excitement; however, the modeled emotions revealed differences between three play conditions, while the differences between the subjective reports failed to reach significance.

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This paper proposes an approach based on emotion recognition to maintain engagement of players in a game by modulating the game difficulty. Physiological and questionnaire data were gathered from 20 players during and after playing a Tetris game at different difficulty levels. Both physiological and self-report analyses lead to the conclusion that playing at different levels gave rise to different emotional states and that playing at the same level of difficulty several times elicits boredom. Emotion assessment from physiological signals was performed using a SVM (Support Vector Machine). An accuracy of 53.33 % was obtained on the discrimination of three emotional classes, namely boredom, anxiety, engagement.

This paper reviews the psychophysiological method in game research. The use of psychophysiological measurements provides an objective, continuous, real-time, non-invasive, precise, and sensitive way to assess the game experience, but for best results it requires carefully controlled experiments, large participant samples and specialized equipment. We briefly explain the theory behind the method and present the most useful measures. We review previous studies that have used psychophysiological measures in game research, and provide future directions.

The study of users ’ emotional behavior in HCI has been receiving increasing attention for the last few years. This paper focuses on emotions as an important part of a user’s overall experience when interacting with a system. Based on the multi-component approach to emotions proposed by Scherer [15], different aspects of emotions in an interactive context were investigated: subjective feelings, physiological activation, motor expression, cognitive appraisals, and behavioral tendencies. To induce different emotional states, two versions of an interactive system were employed which differed with respect to quality of use. The results suggest that systems of high usability lead to more positive emotions than systems with usability flaws. Differences were detected for a number of emotional components by using a variety of methods: rating scales for subjective feelings, EMG for facial muscles, heart rate, EDA, performance data and questionnaires on cognitive appraisals. We suggest that this combination provides a comprehensive basis for analyzing emotions in HCI.

Abstract. Educational games can induce a wide range of emotions, and so recognizing specific emotions may be valuable for an intelligent system that aims to adapt to varying student needs so as to improve learning. The long-term goal of this work is to understand how user affect impacts overall learning in an educational game. The main contribution of this paper is an investigation into the use of an unsupervised machine learning technique to help recognize meaningful patterns in biometric affective data. Results show that this method can identify interesting and sensible student reactions to different game events.

We examined the relationship of self-reported Spatial Presence with emotion-related psychophysiological responses to success (reaching the goal) in a video game among 36 young adults. Event-related changes in facial electromyographic (EMG) activity (an index of emotional valence), electrodermal activity (EDA; an index of arousal), and cardiac interbeat intervals (IBIs) were recorded. The results showed that Spatial Presence was associated with an increase in zygomatic and orbicularis oculi, and a decrease in corrugator supercilii, EMG activity in response to reaching the goal. Spatial Presence was also positively related to EDA, but unrelated to IBI. High Spatial Presence may result in a greater increase in positive affect, and a greater reduction in negative affect, in response to a positive event. Spatial Presence exerts an influence on both the valence and arousal dimensions of emotions. Phasic corrugator supercilii EMG activity may be a particularly useful measure in Presence research. Keywords--- Spatial Presence, emotions, facial electromyography, electrodermal activity, interbeat interval, video games. 1.

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this memoryenhancing effect of emotion and their temporal aspects, in particular, are not well understood. Taking advantage of the temporal resolution of event-related potentials (ERPs), we investigated the time-course of the electrophysiological correlates of encoding pleasant, unpleasant, and neutral pictures