Abstract
To provide flexible support ways and intelligent support contents for users in VR contexts, compared with the existing support ways of either single or combination of sensing functions, e.g., support of gesture, head or body movement. In our proposal, to provide flexible support functions conditioned on VR contexts or user’s feedbacks, we propose to use a semi-automatic selection of interactive supports. In modeling of semi-selection by user’s feedbacks and VR contexts, we propose to evaluate the performance by consideration of both intelligent AI evaluation, based on data of users’ performance in VR, and user’s initiative feedbacks. Furthermore, to provide customizable or personalized estimation in the VR support, we propose to apply the machine learning of self-supervised learning. Therefore, we are able to train or retrain estimation models with efficiency of low-cost of data works, including reduction of data-labeling cost or reuse of existing models. We require to evaluate the timing of applying selection or modification of support ways, the balance of ratios of automatics or user-initiative due to user preference or experiences or smoothness of VR contexts, and even user awareness or understanding, etc. Further, we require to evaluate the scale, numbers, size, and limitation of data or training that are needed for stable, accurate, and useful estimations of VR support.
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Acknowledgement
This work was supported by Japan Science and Technology Agency (JST CREST: JPMJCR19F2, Research Representative: Prof. Yoichi Ochiai, University of Tsukuba, Japan), and by University of Tsukuba (Basic Research Support Program Type A).
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Appendices
Demonstration
Demonstration of prediction of user support using gazing in VR game: We showed a simple demo of AI support VR by using gazing, body language, and voice. The user was expected to support the shooting by AI using the analyzed gazing behaviors or head or hand movement during the VR game (Fig. 4).
User’s gaze (pink line) and hand position (the position of gun) were shown in a shooting game of VR. (Color figure online)
A modeling of self-supervised learning by using contrastive learning on images and related data of users.
Modeling of Self-supervised Learning
We also show a example of modeling self-supervised learning by using multi-factor data of users in VR contexts. In the model of Fig. 5, the given images from VR context can be trained to be suitable representation of features, thus the prediction of user supports can be updated or improved by a dataset of VR without additional data works on labeling or cleaning data.
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Mukherjee, R., Lu, JL., Ochiai, Y. (2022). Designing AI-Support VR by Self-supervised and Initiative Selective Supports. In: Antona, M., Stephanidis, C. (eds) Universal Access in Human-Computer Interaction. User and Context Diversity. HCII 2022. Lecture Notes in Computer Science, vol 13309. Springer, Cham. https://doi.org/10.1007/978-3-031-05039-8_17
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DOI: https://doi.org/10.1007/978-3-031-05039-8_17
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