Measuring affective, physiological and behavioural differences in solo, competitive and collaborative games

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017. In this paper, we aim to measure affect and behaviour indicators of players to understand how they feel in different play modes and how games could be improved to enhance user experience, immersion and engagement. We analyse the affective states in sets of two users playing a Wii video game in three play modes: solo, competitive and collaborative. We measured their physiological signals and observed the nonverbal behaviours to infer their affective states. Although other studies have looked at these signals in gaming, this work focuses on the differences between the three play modes aforementioned. Our results show that: (1) Players experience similar levels of arousal during both solo and collaborative play modes; (2) players’ heart rates are significantly correlated during the competitive mode but not during the collaborative one; and (3) heart rate variability is a good indicator of engagement when playing video games

Exploring Mixed-methods Instruments for Performance Evaluation of Immersive Collaborative Environments

Presently, there is a clear trend for both businesses and public institutions to move towards open or collaborative innovation. Nevertheless, engaging all stakeholders, especially users, for cocreating innovative solutions and usage scenarios is, as revealed in previous studies, not so obvious. We do believe that Immersive and Collaborative Environments (ICEs) based on the use of Virtual, Augmented and Mixed Reality technologies would be the right place for co-creating, exploring, experimenting and evaluating innovative ideas and concepts in order to quickly reach a common understanding. However, there is a need to design a proper method and instruments that would allow evaluating and comparing ICEs. Our previous paper presented the outcomes of an investigation to identifying and disentangling factors characterising a group immersion and collaboration in the context of co-creation. As a step further, this paper reports about our exploratory study towards the design of mixed methods quantitative and qualitative instruments for the evaluation of Immersive and Collaborative Environments (ICE) based on previously identified factors

Environnements virtuels émotionnellement intelligents

Les émotions ont été étudiées sous différents angles dans le domaine de l'interaction homme-machine y compris les systèmes tutoriel intelligents, les réseaux sociaux, les plateformes d’apprentissage en ligne et le e-commerce. Beaucoup d’efforts en informatique affective sont investis pour intégrer la dimension émotionnelle dans les environnements virtuels (tel que les jeux vidéo, les jeux sérieux et les environnements de réalité virtuelle ou de réalité augmenté). Toutefois, les stratégies utilisées dans les jeux sont encore empiriques et se basent sur des modèles psychologiques et sociologiques du joueur : Courbe d’apprentissage, gestion de la difficulté, degré d’efficience dans l’évaluation des performances et de la motivation du joueur. Or cette analyse peut malmener le système dans la mesure où les critères sont parfois trop vagues ou ne représentent pas les réelles compétences du joueur, ni ses vraies difficultés. Étant donné que la stratégie d’intervention est très influencée par la précision de l’analyse et l’évaluation du joueur, de nouveaux moyens sont nécessaires afin d’améliorer les processus décisionnels dans les jeux et d’organiser les stratégies d’adaptation de façon optimale. Ce travail de recherche vise à construire une nouvelle approche pour l’évaluation et le suivi du joueur. L’approche permet une modélisation du joueur plus efficace et moins intrusive par l’intégration des états mentaux et affectifs obtenus à partir de senseurs physiologiques (signaux cérébraux, Activité électrodermale, …) ou/et instruments optiques (Webcam, traceur de regard, …). Les états affectifs et mentaux tels que les émotions de base (basées sur les expressions faciales), l’état d’engagement, de motivation et d’attention sont les plus visés dans cette recherche. Afin de soutenir l’adaptation dans les jeux, des modèles des émotions et de la motivation du joueur basé sur ces indicateurs mentaux et affectifs, ont été développés. Nous avons implémenté cette approche en développant un système sous forme d’une architecture modulaire qui permet l’adaptation dans les environnements virtuels selon les paramètres affectifs du joueur détectés en temps-réel par des techniques d’intelligence artificielle.Emotions were studied from different angles in the field of human-machine interaction including intelligent tutorial systems, social networks, online learning platforms and e-commerce. Much effort in affective computing are invested to integrate the emotional dimension in virtual environments (such as video games, serious games and virtual reality environments or augmented reality). However, the strategies used in games are still empirical and are based on psychological and sociological models of the player: Learning Curve, trouble management, degree of efficiency in the evaluation of performance and motivation of the player. But this analysis can mislead the system to the extent that the criteria are sometimes too vague and do not represent the actual skills of the player, nor his real difficulties. Since the intervention strategy is influenced by the accuracy of the analysis and evaluation of the player, new ways are needed to improve decision-making in games and organizing adaptation strategies in optimal way. This research aims to build a new approach to the evaluation and monitoring of the player. The approach enables more effective and less intrusive player modeling through the integration of mental and emotional states obtained from physiological sensors (brain signals, electro-dermal activity, ...) or/and optical instruments (Webcam, eye-tracker, ...). The emotional and mental states such as basic emotions (based on facial expressions), the states of engagement, motivation and attention are the most targeted in this research. In order to support adaptation in games, models of emotions and motivation of the player based on these mental and emotional indicators, have been developed. We have implemented this approach by developing a system in the form of a modular architecture that allows adaptation in virtual environments according to the player's emotional parameters detected in real time by artificial intelligence methods