Correlation between Entropy and Prediction Error in VR Head Motion Trajectories

The general understanding of user behaviour has been often overlooked in the field of Virtual Reality (VR) and Extended Reality (XR) at large. In this work, we want to fill this gap by exploring the relationship between the way in which users navigate in immersive content and the predictability of their trajectories. Inspired by works from social science, our key assumption is that there are navigation trajectories that can be accurately predicted, while others exhibit eclectic patterns that are more challenging to anticipate. However, it is not yet clear how to effectively distinguish between these behaviours. In this context, we conduct an extensive data analysis across multiple datasets investigating users' movements in VR. The ultimate goal is to understand if a specific metric from information theory, such as the entropy of trajectory, can be adopted as a discriminating metric between predictable navigation trajectories and unpredictable ones. Our findings reveal that users with highly regular navigation styles tend to exhibit lower entropy, indicating higher predictability of their movements. Conversely, users with more diverse navigation patterns show higher entropy and lower predictability in their trajectories. Answering the question "how can we distinguish users more predictable than others?'' would be crucial for different purposes in future immersive applications such as enabling new modalities for live streaming services but also for the design of more personalised and engaging VR experiences.