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A Study on Deep-Learning based In-place Locomotion Technique in Virtual Reality using Multimodal Data Pipeline
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 유종빈 | - |
| dc.contributor.author | 백승원 | - |
| dc.date.accessioned | 2022-11-29T03:01:06Z | - |
| dc.date.available | 2022-11-29T03:01:06Z | - |
| dc.date.issued | 2022-08 | - |
| dc.identifier.other | 32188 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/20747 | - |
| dc.description | 학위논문(석사)--아주대학교 일반대학원 :인공지능학과,2022. 8 | - |
| dc.description.tableofcontents | Chapter 1. Introduction 1 Chapter 2. Related Works 4 Chapter 2.1. Supporting immersive experience in VR 4 Chapter 2.2. Body movement data used in VR 4 Chapter 2.3. Locomotion research in VR 5 Chapter 2.4. Analysis and modeling of multimodal sensor data in VR 7 Chapter 2.5. Learning and predicting movement in VR 8 Chapter 3. Study Procedure 10 Chapter 4. STUDY #1: Backward Movement Detection 11 Chapter 4.1. Study Procedure 11 Chapter 4.2. Data pipeline development 12 Chapter 4.3. Model feature engineering 14 Chapter 4.4. Model development 15 Chapter 4.5. Results 16 Chapter 5. STUDY #2: Effectiveness of Backward Movement Detection Model 18 Chapter 5.1. VR scenarios 18 Chapter 5.2. Methods 20 Chapter 5.2.1. Independent variables 20 Chapter 5.2.2. Dependent variables 21 Chapter 5.3. Study procedure 22 Chapter 5.4. Results 24 Chapter 5.4.1. System log analysis 24 Chapter 5.4.2. User experience analysis 25 Chapter 5.4.3. Interview analysis 28 Chapter 6. DISCUSSION 30 Chapter 6.1. Movement data management 30 Chapter 6.2. Reflection on model development and application 31 Chapter 6.3. Limitations and future work 32 Chapter 7. CONCLUSION 34 Bibliography 35 | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | A Study on Deep-Learning based In-place Locomotion Technique in Virtual Reality using Multimodal Data Pipeline | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.department | 일반대학원 인공지능학과 | - |
| dc.date.awarded | 2022. 8 | - |
| dc.description.degree | Master | - |
| dc.identifier.localId | 1254205 | - |
| dc.identifier.uci | I804:41038-000000032188 | - |
| dc.identifier.url | https://dcoll.ajou.ac.kr/dcollection/common/orgView/000000032188 | - |
| dc.subject.keyword | Deep learning | - |
| dc.subject.keyword | Locomotion | - |
| dc.subject.keyword | User experience | - |
| dc.subject.keyword | Virtual Reality | - |
| dc.description.alternativeAbstract | Movement is one of the key elements in virtual reality (VR) and significantly influences user experience. In particular, walking-in place is a method of supporting movement in a limited space, and many studies are being conducted on its effective support. However, most studies have focused on forward movement despite many situations in which backward movement is needed. In this paper, we present the development of a prediction model for forward/backward movement while considering a user’s orientation and the verification of the model’s effectiveness. We built a deep learning-based model by collecting sensor data through virtual data pipeline which contains a user’s head, waist, and feet. The study was conducted through two technical elements: a data pipeline for collecting signals that could represent a user and a prediction model for a user movement. We developed three realistic VR scenarios that involve backward movement, set three conditions (controller-based, treadmill-based, and model-based) for movement, and evaluated user experience in each condition through a study of 36 participants. As a result, the model-based condition showed the highest sensory sensitivity, effectiveness, and satisfaction and similar cognitive burden compared with the other two conditions. The results of our study demonstrated that movement support through modeling is possible, suggesting its potential for use in many VR applications. | - |
- Appears in Collections:
- Graduate School of Ajou University > Department of Artificial Intelligence > 3. Theses(Master)
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