This article focuses on personalised games, which we define as games that utilise player models for the purpose of tailoring the game experience to the individual player. The main contribution of the article is a motivation for personalised gaming, supported by an extensive overview of scientific literature. The motivatin concerns (a) the psychological foundation, (b) the effect on player satisfaction, (c) the contribution to game development, and (d) the requirement for achieving ambitions. The provided overview of scientific literature goes into the subject of player modelling, as well as eight adaptive components: (1) space adaptation, (2) mission / task adaptation, (3) character adaptation, (4) game mechanics adaptation, (5) narrative adaptation, (6) music / sound adaptation, (7) player matching (multiplayer), and (8) difficulty scaling. In the concluding sections, the relationship to procedural content generation is discussed, as well as the generalisation to other domains.

Current quantitative methods of measuring player experience in games are mostly intrusive to play and less suited to natural, non-laboratory play environments. This paper presents an initial study to validate the feasibility of using facial expressions analysis for evaluating player experiences. It builds on a prior position that video-based computer vision techniques can provide a less intrusive and more versatile solution for automatic evaluation of game user experiences. A user study was performed on an initial group of participants in a first-person puzzle shooter game (Portal 2) and a social drawing trivia game (Draw My Thing), and the results are shown to support our position.

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Understanding player experiences is central to game design. Video captures of players is a common practice for obtaining rich reviewable data for analysing these experiences. However, not enough has been done in investigating ways of preprocessing the video for a more efficient analysis process. This paper consolidates and extends prior work on validating the feasibility of using automated facial expressions analysis as a natural quantitative method for evaluating player experiences. A study was performed on participants playing a first-person puzzle shooter game (Portal 2) and a social drawing trivia game (Draw My Thing), and results were shown to exhibit rich details for inferring player experiences from facial expressions. Significant correlations were also observed between facial expression intensities and self reports from the Game Experience Questionnaire. In particular, the challenge dimension consistently showed positive correlations with anger and joy. This paper eventually presents a case for increasing the application of computer vision in video analyses of gameplay.

The Choice Stepping Reaction Time (CSRT) task is time-based clinical test that has shown to reliably predict falls in older adults. Its current mode of delivery involves the use of a custom-made dance mat device. This mat is a measurement tool that can reliably obtain step data to discriminate between fallers and non-fallers. One of the pitfalls of this test is that the technology in use still imposes an obstacle on the degree of freedom to be able to perform adaptive exercises suitable for the elderly. In this paper, we describe a Kinectbased system that measures stepping performance through the use of a hybrid version of the CSRT task. This study focuses on assessing this system's capabilities to reliably measure a time-based clinical test of fall risk. Results showed a favorable correspondence and agreement between the two systems, suggesting that this platform could be potentially useful in the clinical practice.