Application of Big Data and VR Technology in the Treatment of Acrophobia
- 10.2991/978-94-6463-034-3_93How to use a DOI?
- Virtual reality technology; Big data; Artificial intelligence; Acrophobia
Under the background of the Internet, the application fields of big data processing technology, artificial intelligence technology and Internet of things technology are becoming more and more extensive. Creating a new form of VRET has become an inevitable trend in the treatment of acrophobia in the future. Through big data and VR online technology, therapists can adjust the exposure level and time in time according to the situation of patients, so as to effectively reduce the anxiety of patients. This paper discusses the application status of big data and virtual reality technology in the treatment of acrophobia, including scene layout, visual immersion, listening and tactile and other multi sensory common perception, and further expands in detail. Finally, it points out the limitations of virtual reality technology in the treatment and the prospect of future treatment. The relevant literature in recent years was searched through the literature method, the exposure experiment was carried out in combination with big data and VR technology, and the results were counted by mathematical statistics. The application of big data and virtual reality technology has great advantages over traditional technology in the treatment of acrophobia, which can greatly reduce patients’ pain and anxiety. At the same time, there are also deficiencies at this stage, such as how to break the technical restrictions and integrate different individual differences.
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Cite this article
TY - CONF AU - Shoucheng Wang AU - Huajie Sui AU - Changze Wu PY - 2022 DA - 2022/12/23 TI - Application of Big Data and VR Technology in the Treatment of Acrophobia BT - Proceedings of the 2022 3rd International Conference on Big Data and Informatization Education (ICBDIE 2022) PB - Atlantis Press SP - 903 EP - 912 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-034-3_93 DO - 10.2991/978-94-6463-034-3_93 ID - Wang2022 ER -