Modeling the Noticeability of User-Avatar Movement Inconsistency for Sense of Body Ownership Intervention

TL;DR

This paper models how likely users are to notice avatar movement inconsistencies in VR, enabling subtle interventions that can enhance interaction without breaking immersion, with applications in rehabilitation and gaming.

Contribution

It introduces a statistical model predicting noticeability of movement inconsistencies and demonstrates a technique for unnoticeable movement amplification in VR.

Findings

Noticeability increases quadratically with offset size

Offsets at different joints negatively influence each other

Medium and balanced offsets optimize unnoticeable movement amplification

Abstract

An avatar mirroring the user's movement is commonly adopted in Virtual Reality(VR). Maintaining the user-avatar movement consistency provides the user a sense of body ownership and thus an immersive experience. However, breaking this consistency can enable new interaction functionalities, such as pseudo haptic feedback or input augmentation, at the expense of immersion. We propose to quantify the probability of users noticing the movement inconsistency while the inconsistency amplitude is being enlarged, which aims to guide the intervention of the users' sense of body ownership in VR. We applied angular offsets to the avatar's shoulder and elbow joints and recorded whether the user identified the inconsistency through a series of three user studies and built a statistical model based on the results. Results show that the noticeability of movement inconsistency increases roughly quadratically with the enlargement of offsets and the offsets at two joints negatively affect the probability distributions of each other. Leveraging the model, we implemented a technique that amplifies the user's arm movements with unnoticeable offsets and then evaluated implementations with different parameters(offset strength, offset distribution). Results show that the technique with medium-level and balanced-distributed offsets achieves the best overall performance. Finally, we demonstrated our model's extendability in interventions in the sense of body ownership with three VR applications including stroke rehabilitation, action game and widget arrangement.