Modelling Player Preferences in AR Mobile Games

Abstract

In this paper, we use preference learning techniques to model players' emotional preferences in an AR mobile game. This exploratory study uses player behaviour to make these preference predictions. The described techniques successfully predict players' frustration and challenge levels with high accuracy while all other preferences tested (boredom, excitement and fun) perform better than random chance. This paper describes the AR treasure hunt game we developed, the user study conducted to collect player preference data, analysis performed, and preference learning techniques applied to model this data. This work is motivated to personalize players' experiences by using these computational models to optimize content creation and game balancing systems in these environments. The generality of our technique, limitations, and usability as a tool for personalization of AR mobile games is discussed.

The potential of remote XR experimentation: Defining benefits and limitations through expert survey and case study

“…The types of data logging found in XR experiments are much the same as those listed in Weibel's exploration of physiological measures in non-immersive virtual reality (Weibel et al, 2018), with studies using skin conductance (Yuan and Steed, 2010), heart rate (Egan et al, 2016), blood pressure (Hoffman et al, 2003), as well as electroencephalogram (EEG) (Amores et al, 2018). Built-in inertial sensors that are integral to providing an XR experience, such as head and hand position for VR HMDs, have also been widely used for investigations, including posture assessment (Brookes et al, 2020), head interaction tracking (Zhang and Healey, 2018), gaze and loci of attention (Piumsomboon et al, 2017) and gesture recognition (Kehl and Van Gool, 2004), while velocity change (Warriar et al, 2019) has also been used in both VR and AR interventions.…”

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