I. Ghaznavi, Duncan Gillies, D. Nicholls, A. Edalat
{"title":"Photorealistic avatars to enhance the efficacy of Selfattachment psychotherapy","authors":"I. Ghaznavi, Duncan Gillies, D. Nicholls, A. Edalat","doi":"10.1109/AIVR50618.2020.00022","DOIUrl":null,"url":null,"abstract":"We have designed, developed, and tested an Immersive virtual reality (VR) platform to practice the protocols of Self-attachment psychotherapy. We made use of customized photorealistic avatars for the implementation of both the high-end version (based on Facebook’s Oculus) and the low-end version (based on Google’s cardboard) of our platform. Under the Selfattachment therapeutic framework, the causes of mental disorders such as chronic anxiety and depression are traced back to the individual’s insecure attachment with their primary caregiver during childhood and their subsequent problems in affect regulation. The conventional approach (without VR) to Selfattachment requires that the individual uses their childhood photographs to recall their childhood memories and then imagine that the child that they were is present with them. They thus establish a compassionate relationship with their childhood self and then, using love songs and dancing, create an affectional bond with them. Their adult self subsequently role plays a good parent and interacts with their imagined childhood self to perform various developmental and re-parenting activities. The goal is to enhance their capacities for self-regulation of emotion, which can lead them into earning secure attachment. It is hypothesized that our immersive virtual reality platform – which enables the users to interact with their customized 3D photorealistic childhood avatar - offers either a better alternative or at least a complementary visual tool to the conventional imaginal approach to Self-attachment. The platform was developed in Unity 3D, a cross-platform game engine, and takes advantage of the itSeez3D Avatar SDK for generating a customized photorealistic 3D avatar head from a 2D childhood image of the user. The platform also offers facial and body animations for some of the basic emotional states such as Happy, Sad, Scared and Joyful and it allows modifications to the avatar body (height/ width) and clothing color. A study to compare the use of the avatar-based approach (VR) to Self-attachment with the conventional photo-based approach showed promising results. Almost 85% of the participants reported that their photorealistic childhood avatar in VR was more relatable than their childhood photos. Both low-end and high-end VR based approaches were unanimously reported to be more effective than the conventional imaginal approach. Participants reported that the high-end version of the VR platform was more realistic and immersive than the low-end mobile VR version.","PeriodicalId":348199,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AIVR50618.2020.00022","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
We have designed, developed, and tested an Immersive virtual reality (VR) platform to practice the protocols of Self-attachment psychotherapy. We made use of customized photorealistic avatars for the implementation of both the high-end version (based on Facebook’s Oculus) and the low-end version (based on Google’s cardboard) of our platform. Under the Selfattachment therapeutic framework, the causes of mental disorders such as chronic anxiety and depression are traced back to the individual’s insecure attachment with their primary caregiver during childhood and their subsequent problems in affect regulation. The conventional approach (without VR) to Selfattachment requires that the individual uses their childhood photographs to recall their childhood memories and then imagine that the child that they were is present with them. They thus establish a compassionate relationship with their childhood self and then, using love songs and dancing, create an affectional bond with them. Their adult self subsequently role plays a good parent and interacts with their imagined childhood self to perform various developmental and re-parenting activities. The goal is to enhance their capacities for self-regulation of emotion, which can lead them into earning secure attachment. It is hypothesized that our immersive virtual reality platform – which enables the users to interact with their customized 3D photorealistic childhood avatar - offers either a better alternative or at least a complementary visual tool to the conventional imaginal approach to Self-attachment. The platform was developed in Unity 3D, a cross-platform game engine, and takes advantage of the itSeez3D Avatar SDK for generating a customized photorealistic 3D avatar head from a 2D childhood image of the user. The platform also offers facial and body animations for some of the basic emotional states such as Happy, Sad, Scared and Joyful and it allows modifications to the avatar body (height/ width) and clothing color. A study to compare the use of the avatar-based approach (VR) to Self-attachment with the conventional photo-based approach showed promising results. Almost 85% of the participants reported that their photorealistic childhood avatar in VR was more relatable than their childhood photos. Both low-end and high-end VR based approaches were unanimously reported to be more effective than the conventional imaginal approach. Participants reported that the high-end version of the VR platform was more realistic and immersive than the low-end mobile VR version.