A generic and efficient emotion-driven approach toward personalized assessment and adaptation in serious games

Abstract

Emotions have a major role in the player-game interaction. In serious games playing contexts, real-time assessment of the player’s emotional state is crucially important to enable an emotion-driven adaptation during gameplay. In addition, a personalized assessment and adaptation based on the player's characteristics remains a challenge for serious games designers. This paper presents a generic and efficient emotion-driven approach for personalized assessment and adaptation in serious games, in which two main methods and their algorithms are proposed. The first one is a method for assessing, in real time, the player's emotion taking into account the personality type and the playing style of the player. The second one is an emotion-driven personalized adaptation method based on Markov modeling of dependency between the serious game events and the change in the player's emotional state. Therefore, the proposed approach has been evaluated by playing an affective vs. non-affective version of a serious game that we have developed to illustrate the applicability of the above-mentioned methods. The overall results showed that owing to our approach, a serious game become able to enhance its adaptivity toward playing outcomes and improve its overall playability.