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

Zhipeng Li, Yu Jiang, Yihao Zhu, Ruijia Chen, Ruolin Wang, Yuntao Wang, Yukang Yan, and Yuanchun Shi. 2022. Modeling the Noticeability of User-Avatar Movement Inconsistency for Sense of Body Ownership Intervention. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 6, 2, Article 64 (July 2022), 26 pages. https://doi.org/10.1145/3534590

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 [45] or input augmentation [37, 59], 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.