Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Group emotion modelling and the use of middleware for virtual crowds in video-games

In this paper we discuss the use of crowd simulation in video-games to augment their realism. Using previous works on emotion modelling and virtual crowds we define a game world in an urban context. To achieve that, we explore a biologically inspired human emotion model, investigate the formation of groups in crowds, and examine the use of physics middleware for crowds. Furthermore, we assess the realism and computational performance of the proposed approach. Our system runs at interactive frame-rate and can generate large crowds which demonstrate complex behaviour

Using Frustration in the Design of Adaptive Videogames

In efforts to attract a wider audience, videogames are beginning to incorporate adaptive gameplay mechanics. Unlike the more traditional videogame, adaptive games can cater the gaming experience to the individual user and not just a particular group of users as with the former. Affective videogames, games that respond to the user's emotional state, may hold the key to creating such gameplay mechanics. In this paper we discus how the emotion frustration may be used in the design of adaptive videogames and the ongoing research into its detection and measurement

Do Emotions Benefit Investment Decisions? Anticipatory Emotion and Investment Decisions in Non-professional Investors

Increasing financial trading performance is big business. A lingering question within academia and industry concerns whether emotions improve or degrade trading performance. In this study, 30 participants distributed hypothetical wealth between a share (a risk) and the bank (paying a small, sure, gain) within four trading games. Skin Conductance Response was measured while playing the games to measure anticipatory emotion, a covert emotion signal that impacts decision-making. Anticipatory emotion was significantly associated with trading performance but the direction of the correlation was dependent upon the share’s movement. Thus, anticipatory emotion is neither wholly “good” nor “bad” for trading; instead, the relationship is context-dependent. This is one of the first studies exploring the association between anticipatory emotion and trading behaviour using trading games within an experimentally rigorous environment. Our findings elucidate the relationship between anticipatory emotion and financial decision-making and have applications for improving trading performance in novice and expert traders

Fuzzy Approach for Audio-Video Emotion Recognition in Computer Games for Children

Computer games are widespread nowadays and enjoyed by people of all ages. But when it comes to kids, playing these games can be more than just fun, it is a way for them to develop important skills and build emotional intelligence. Facial expressions and sounds that kids produce during gameplay reflect their feelings, thoughts, and moods. In this paper, we propose a novel framework that integrates a fuzzy approach for the recognition of emotions through the analysis of audio and video data. Our focus lies within the specific context of computer games tailored for children, aiming to enhance their overall user experience. We use the FER dataset to detect facial emotions in video frames recorded from the screen during the game. For the audio emotion recognition of sounds a kid produces during the game, we use CREMA-D, TESS, RAVDESS, and Savee datasets. Next, a fuzzy inference system is used for the fusion of results. Besides this, our system can detect emotion stability and emotion diversity during gameplay, which, together with prevailing emotion report, can serve as valuable information for parents worrying about the effect of certain games on their kids. The proposed approach has shown promising results in the preliminary experiments we conducted, involving 3 different video games, namely fighting, racing, and logic games, and providing emotion-tracking results for kids in each game. Our study can contribute to the advancement of child-oriented game development, which is not only engaging but also accounts for children's cognitive and emotional states.Comment: 8 pages. Prepared for the Elsevier conferenc

Learning and Games

Part of the Volume on the Ecology of Games: Connecting Youth, Games, and Learning In this chapter, I argue that good video games recruit good learning and that a game's design is inherently connected to designing good learning for players. I start with a perspective on learning now common in the Learning Sciences that argues that people primarily think and learn through experiences they have had, not through abstract calculations and generalizations. People store these experiences in memory -- and human long-term memory is now viewed as nearly limitless -- and use them to run simulations in their minds to prepare for problem solving in new situations. These simulations help them to form hypotheses about how to proceed in the new situation based on past experiences. The chapter also discusses the conditions experience must meet if it is to be optimal for learning and shows how good video games can deliver such optimal learning experiences. Some of the issues covered include: identity and learning; models and model-based thinking; the control of avatars and "empathy for a complex system"; distributed intelligence and cross-functional teams for learning; motivation, and ownership; emotion in learning; and situated meaning, that is, the ways in which games represent verbal meaning through images, actions, and dialogue, not just other words and definitions

Conflicted Minds: Recalibrational Emotions Following Trust-based Interaction.

Consistent with a modular view of the mind, both short-sighted and long-sighted programs may be simultaneously active in the mind and in conflict with one another when individuals face choice dilemmas in trust-based economic interactions. Recalibrational theory helps us identify the adaptive design features shared among subsets of superordinate emotion programs. According to this design logic and the computation of adaptive problem features produced by Trust games, we predict the activation of emotions after Trust games. While this study successfully predicts reports of twenty distinct emotional states, further studies are needed to demonstrate ultimate recalibrational functions of emotions.emotions, recalibrational theory, modularity, Trust game, experiments