Innovations in computer game interfaces continue to enhance the experience of players. Affective games - those that adapt or incorporate a player’s emotional state - have shown promise in creating exciting and engaging user experiences. However, a dearth of systematic exploration into what types of game elements should adapt to affective state leaves game designers with little guidance on how to incorporate affect into their games. We created an affective game engine, using it to deploy a design probe into how adapting the player’s abilities, the enemy’s abilities, or variables in the environment affects player performance and experience. Our results suggest that affectively adapting games can increase player arousal. Furthermore, we suggest that reducing challenge by adapting non-player characters is a worse design choice than giving players the tools that they need (through enhancing player abilities or a supportive environment) to master greater challenges.

This study aims to explore the question, how the use of applied gaming aspects through affective gaming, specifically as affective avatars, can promote an increment in play-motivation compared to today’s serious gaming. Similar studies in different fields have already shown, that the use of serious gaming can have a very positive impact on the motivation. This study should provide an initial step towards applying serious gaming and gamification through affective computing, for a more efficient motivational educating. By combining the fun of gaming, serious part and the affective avatar gaming, a much higher informational and motivational result could be achieved. A quantitative method was used to collect the data from 18 participants, participating in the research study. The participants were separated into three groups, equally distributed between experienced video game players and non-experienced ones, playing three different game versions, including and excluding affective elements. The collected and analysed data indicates, that participants do seemingly show a slightly higher play-motivation by having an affective avatar interaction. The gained result from this study could potentially show a new path of using affective avatars in a serious gaming setting and strengthen its possible potential.

Cette thèse étudie la reconnaissance des émotions à partir de signaux physiologiques dans le contexte des jeux vidéo et la faisabilité de sa mise en œuvre sur un système embarqué. Les défis suivants sont abordés : la relation entre les états émotionnels et les réponses physiologiques dans le contexte du jeu, les variabilités individuelles des réponses psycho-physiologiques et les problèmes de mise en œuvre sur un système embarqué. Les contributions majeures de cette thèse sont les suivantes. Premièrement, nous construisons une base de données multimodale dans le cadre de l'Affective Gaming (DAG). Cette base de données contient plusieurs mesures concernant les modalités objectives telles que les signaux physiologiques de joueurs et des évaluations subjectives sur des phases de jeu. A l'aide de cette base, nous présentons une série d'analyses effectuées pour la détection des moments marquant émotionnellement et la classification des émotions à l'aide de diverses méthodes d'apprentissage automatique. Deuxièmement, nous étudions la variabilité individuelle de la réponse émotionnelle et proposons un modèle basé sur un groupe de joueurs déterminé par un clustering selon un ensemble de traits physiologiques pertinents. Nos travaux mettent en avant le fait que le modèle proposé, basé sur un tel groupe personnalisé, est plus performant qu'un modèle général ou qu'un modèle spécifique à un utilisateur. Troisièmement, nous appliquons la méthode proposée sur un système ARM A9 et montrons que la méthode proposée peut répondre à l'exigence de temps de calcul. / This thesis investigates physiological-based emotion recognition in a digital game context and the feasibility of implementing the model on an embedded system. The following chanllenges are addressed: the relationship between emotional states and physiological responses in the game context, individual variabilities of the pschophysiological responses and issues of implementation on an embedded system. The major contributions of this thesis are : Firstly, we construct a multi-modal Database for Affective Gaming (DAG). This database contains multiple measurements concerning objective modalities: physiological signals (ECG, EDA, EMG, Respiration), screen recording, and player's face recording, as well as subjective assessments on both game event and match level. We presented statistics of the database and run a series of analysis on issues such as emotional moment detection and emotion classification, influencing factors of the overall game experience using various machine learning methods. Secondly, we investigate the individual variability in the collected data by creating an user-specific model and analyzing the optimal feature set for each individual. We proposed a personalized group-based model created the similar user groups by using the clustering techniques based on physiological traits deduced from optimal feature set. We showed that the proposed personalized group-based model performs better than the general model and user-specific model. Thirdly, we implemente the proposed method on an ARM A9 system and showed that the proposed method can meet the requirement of computation time.