Non-invasive player experience estimation from body motion and game context

In this paper, we investigate on the relationship between player experience and body movements in a non-physical 3D computer game. During an experiment, the participants played a series of short game sessions and rated their experience while their body movements were tracked using a depth camera. The data collected was analysed and a neural network was trained to find the mapping between player body movements, player ingame behaviour and player experience. The results reveal that some aspects of player experience, such as anxiety or challenge, can be detected with high accuracy (up to 81%). Moreover, taking into account the playing context, the accuracy can be raised up to 86%. Following such a multi-modal approach, it is possible to estimate the player experience in a non-invasive fashion during the game and, based on this information, the game content could be adapted accordingly.