지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.6
- 수록면
- 209 - 224 (16page)
- DOI
- 10.5143/JESK.2023.42.3.209
이용수
초록· 키워드
오류제보하기
Objective: The purpose of this study is to identify research trends in studies that apply machine learning (ML) techniques to user experience (UX) improvement for virtual reality (VR) systems through systematic literature analysis and to provide insights into future research directions.
Background: UX research for VR systems is important because VR systems are composed of various components, which can lead to different user interactions. Recently, there have been many studies to improve the UX of VR experiences from various perspectives by applying ML techniques. Therefore, it is necessary to identify the research status and to suggest discussion points for future research directions through a systematic literature review.
Method: A systematic review protocol was adopted in this study. Articles were collected from academic research databases. After removing redundant and irrelevant articles, the full text of remaining articles was carefully reviewed according to the eligibility criteria.
Results: A total of 117 unique articles were collected, and after going through the systematic review process, 21 articles were finally selected for review analysis. In the results, ML techniques have been frequently used for users" physiological state prediction (cybersickness, workload, stress, immersion, etc.), user identification, gesture recognition, and VR system quality evaluation. In addition, the ML models were developed with data collected from VR system (e.g., hardware characteristics, environment variables, and contents properties) and users (e.g., physiological and behavioral responses, personal background, and performance results). Also, many studies compared the performance of different ML techniques with the same data. They show that until now it is difficult to conclude that there is a certain algorithm superior or advantageous in a specific domain or purpose when applying the ML techniques in the UX research of VR systems.
Conclusion: This study analyzed the studies that applied ML techniques to improve the UX of VR systems according to a systematic literature analysis procedure. Accordingly, research trends on ML techniques used to improve the UX of VR systems, application purposes, and types of training data are presented.
Application: ML techniques are gradually becoming more sophisticated, and computing technology for processing large amounts of data is also rapidly developing. Just as ML techniques are applied to our daily lives everywhere in the real world to provide expanded services, it is judged that it will be important to apply ML techniques as core technologies in the virtual world in the future. It is expected that the suggestions of this study will help VR system researchers (e.g., hardware developers, software engineers, or service planners) gain insights for future research plans.
Background: UX research for VR systems is important because VR systems are composed of various components, which can lead to different user interactions. Recently, there have been many studies to improve the UX of VR experiences from various perspectives by applying ML techniques. Therefore, it is necessary to identify the research status and to suggest discussion points for future research directions through a systematic literature review.
Method: A systematic review protocol was adopted in this study. Articles were collected from academic research databases. After removing redundant and irrelevant articles, the full text of remaining articles was carefully reviewed according to the eligibility criteria.
Results: A total of 117 unique articles were collected, and after going through the systematic review process, 21 articles were finally selected for review analysis. In the results, ML techniques have been frequently used for users" physiological state prediction (cybersickness, workload, stress, immersion, etc.), user identification, gesture recognition, and VR system quality evaluation. In addition, the ML models were developed with data collected from VR system (e.g., hardware characteristics, environment variables, and contents properties) and users (e.g., physiological and behavioral responses, personal background, and performance results). Also, many studies compared the performance of different ML techniques with the same data. They show that until now it is difficult to conclude that there is a certain algorithm superior or advantageous in a specific domain or purpose when applying the ML techniques in the UX research of VR systems.
Conclusion: This study analyzed the studies that applied ML techniques to improve the UX of VR systems according to a systematic literature analysis procedure. Accordingly, research trends on ML techniques used to improve the UX of VR systems, application purposes, and types of training data are presented.
Application: ML techniques are gradually becoming more sophisticated, and computing technology for processing large amounts of data is also rapidly developing. Just as ML techniques are applied to our daily lives everywhere in the real world to provide expanded services, it is judged that it will be important to apply ML techniques as core technologies in the virtual world in the future. It is expected that the suggestions of this study will help VR system researchers (e.g., hardware developers, software engineers, or service planners) gain insights for future research plans.
목차
1. Introduction
2. Method
3. Results
4. Discussion
5. Conclusion
References
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