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Proceedings ArticleDOI

"Feed the Fish": an affect-aware game

TL;DR: An affective gaming interface and a user study which evaluates user response to affectiveGaming are reported on and the implementation of the game system, which takes a player's facial expressions as input and dynamically responds to the player by changing the game elements, is described.
Abstract: In this paper we report on an affective gaming interface and a user study which evaluates user response to affective gaming. "Feed the Fish" is an affect-aware game system which takes a player's facial expressions as input and dynamically responds to the player by changing the game elements. The goal of this system is to use human expressions to build a communication channel between the game and players so playing the game can be more enjoyable. We describe the implementation of the game system and discuss the result of the user study we have conducted with 22 participants. Participants enjoyed the game with the affect-aware system more than a non affective version of the game, and they felt it was more exciting since the game was more challenging and dynamic.

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BookDOI
05 Sep 2012
TL;DR: The book introduces ideas that can help in the quest to interpret intentional brain control and develop the ultimate input device, and challenges researchers to further explore passive brain sensing to evaluate interfaces and feed into adaptive computing systems.
Abstract: For generations, humans have fantasized about the ability to create devices that can see into a persons mind and thoughts, or to communicate and interact with machines through thought alone. Such ideas have long captured the imagination of humankind in the form of ancient myths and modern science fiction stories. Recent advances in cognitive neuroscience and brain imaging technologies have started to turn these myths into a reality, and are providing us with the ability to interface directly with the human brain. This ability is made possible through the use of sensors that monitor physical processes within the brain which correspond with certain forms of thought. Brain-Computer Interfaces: Applying our Minds to Human-Computer Interaction broadly surveys research in the Brain-Computer Interface domain. More specifically, each chapter articulates some of the challenges and opportunities for using brain sensing in Human-Computer Interaction work, as well as applying Human-Computer Interaction solutions to brain sensing work. For researchers with little or no expertise in neuroscience or brain sensing, the book provides background information to equip them to not only appreciate the state-of-the-art, but also ideally to engage in novel research. For expert Brain-Computer Interface researchers, the book introduces ideas that can help in the quest to interpret intentional brain control and develop the ultimate input device. It challenges researchers to further explore passive brain sensing to evaluate interfaces and feed into adaptive computing systems. Most importantly, the book will connect multiple communities allowing research to leverage their work and expertise and blaze into the future.

377 citations

Proceedings ArticleDOI
26 Apr 2014
TL;DR: A systematic review of 87 quantitative studies suggests that game enjoyment describes the positive cognitive and affective appraisal of the game experience, and may in part be associated with the support of player needs and values.
Abstract: Enjoyment has been identified as a central component of the player experience (PX), but various, overlapping concepts within PX make it difficult to develop valid measures and a common understanding of game enjoyment. We conducted a systematic review of 87 quantitative studies, analyzing different operationalizations and measures of game enjoyment, its determinants, and how these were related to other components of PX, such as flow, presence and immersion. Results suggest that game enjoyment describes the positive cognitive and affective appraisal of the game experience, and may in part be associated with the support of player needs and values. Further, we outline that enjoyment is distinct from flow in that it may occur independently of challenge and cognitive involvement, and argue that enjoyment may be understood as the valence of the player experience. We conclude with a discussion of methodological challenges and point out opportunities for future research on game enjoyment.

153 citations

Book ChapterDOI
01 Jul 2010
TL;DR: This chapter gives an overview of the state of the art ofBCI in games and discusses the consequences of applying knowledge from Human-Computer Interaction (HCI) to the design of BCI for games.
Abstract: Recently research into Brain-Computer Interfacing (BCI) applications for healthy users, such as games, has been initiated. But why would a healthy person use a still-unproven technology such as BCI for game interaction? BCI provides a combination of information and features that no other input modality can offer. But for general acceptance of this technology, usability and user experience will need to be taken into account when designing such systems. Therefore, this chapter gives an overview of the state of the art of BCI in games and discusses the consequences of applying knowledge from Human-Computer Interaction (HCI) to the design of BCI for games. The integration of HCI with BCI is illustrated by research examples and showcases, intended to take this promising technology out of the lab. Future research needs to move beyond feasibility tests, to prove that BCI is also applicable in realistic, real-world settings.

147 citations


Cites result from ""Feed the Fish": an affect-aware ga..."

  • ...A similar result was found in a study monitoring facial expressions to discriminate between positive and negative affective states (Obaid et al. 2008)....

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Journal ArticleDOI
TL;DR: This study introduces a method to design a curriculum for machine-learning to maximize the efficiency during the training process of deep neural networks (DNNs) for speech emotion recognition and proposes metrics that quantify the inter-evaluation agreement to define the curriculum for regression problems and binary and multi-class classification problems.
Abstract: This study introduces a method to design a curriculum for machine-learning to maximize the efficiency during the training process of deep neural networks (DNNs) for speech emotion recognition. Previous studies in other machine-learning problems have shown the benefits of training a classifier following a curriculum where samples are gradually presented in increasing level of difficulty. For speech emotion recognition, the challenge is to establish a natural order of difficulty in the training set to create the curriculum. We address this problem by assuming that, ambiguous samples for humans are also ambiguous for computers. Speech samples are often annotated by multiple evaluators to account for differences in emotion perception across individuals. While some sentences with clear emotional content are consistently annotated, sentences with more ambiguous emotional content present important disagreement between individual evaluations. We propose to use the disagreement between evaluators as a measure of difficulty for the classification task. We propose metrics that quantify the inter-evaluation agreement to define the curriculum for regression problems and binary and multi-class classification problems. The experimental results consistently show that relying on a curriculum based on agreement between human judgments leads to statistically significant improvements over baselines trained without a curriculum.

70 citations


Cites methods from ""Feed the Fish": an affect-aware ga..."

  • ...Emotion recognition systems have also been used in designing interactive games [2], [3] and tutoring sys-...

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References
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Journal Article

28,685 citations


""Feed the Fish": an affect-aware ga..." refers background in this paper

  • ...[8] J. Klein, Y. Moon and R.W. Picard (2002), "This Computer Responds to User Frustration," Interacting with Computers, Volume 14, No. 2, (2002), pp. 119-140....

    [...]

  • ...For example, Klein, Moon and Picard [8] looked at the problem of user frustration while using a computer system....

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  • ...[7] R.W. Picard (1999), "Affective Computing for HCI," Proceedings HCI, 1999, Munich, Germany....

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  • ...For example, Klein, Moon and Picard [8] looked at the problem of user frustration while using a computer system....

    [...]

Journal ArticleDOI
TL;DR: In this paper, the authors provide an up-to-date critical survey of still-and video-based face recognition research, and provide some insights into the studies of machine recognition of faces.
Abstract: As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years. At least two reasons account for this trend: the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after 30 years of research. Even though current machine recognition systems have reached a certain level of maturity, their success is limited by the conditions imposed by many real applications. For example, recognition of face images acquired in an outdoor environment with changes in illumination and/or pose remains a largely unsolved problem. In other words, current systems are still far away from the capability of the human perception system.This paper provides an up-to-date critical survey of still- and video-based face recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the studies of machine recognition of faces. To provide a comprehensive survey, we not only categorize existing recognition techniques but also present detailed descriptions of representative methods within each category. In addition, relevant topics such as psychophysical studies, system evaluation, and issues of illumination and pose variation are covered.

6,384 citations

Book
01 Jan 1975

1,938 citations


""Feed the Fish": an affect-aware ga..." refers background in this paper

  • ...In the past, emotion researchers have used methods such as questionnaires, observation, and physiological measurements to collect data for assessing emotional states [14, 15]....

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Book
01 Jan 1975

1,924 citations

Journal ArticleDOI
TL;DR: An Automatic Face Analysis (AFA) system to analyze facial expressions based on both permanent facial features and transient facial features in a nearly frontal-view face image sequence and Multistate face and facial component models are proposed for tracking and modeling the various facial features.
Abstract: Most automatic expression analysis systems attempt to recognize a small set of prototypic expressions, such as happiness, anger, surprise, and fear. Such prototypic expressions, however, occur rather infrequently. Human emotions and intentions are more often communicated by changes in one or a few discrete facial features. In this paper, we develop an automatic face analysis (AFA) system to analyze facial expressions based on both permanent facial features (brows, eyes, mouth) and transient facial features (deepening of facial furrows) in a nearly frontal-view face image sequence. The AFA system recognizes fine-grained changes in facial expression into action units (AU) of the Facial Action Coding System (FACS), instead of a few prototypic expressions. Multistate face and facial component models are proposed for tracking and modeling the various facial features, including lips, eyes, brows, cheeks, and furrows. During tracking, detailed parametric descriptions of the facial features are extracted. With these parameters as the inputs, a group of action units (neutral expression, six upper face AU and 10 lower face AU) are recognized whether they occur alone or in combinations. The system has achieved average recognition rates of 96.4 percent (95.4 percent if neutral expressions are excluded) for upper face AU and 96.7 percent (95.6 percent with neutral expressions excluded) for lower face AU. The generalizability of the system has been tested by using independent image databases collected and FACS-coded for ground-truth by different research teams.

1,773 citations


""Feed the Fish": an affect-aware ga..." refers methods in this paper

  • ...Computer vision techniques have been applied to recognize gaze information [11], face pose [12] and emotional state [13]....

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