An interactive VR platform with emotion recognition for self-attachment intervention
File(s)eai-2021.pdf (3.62 MB)
Published version
Author(s)
Polydorou, Neophytos
Edalat, Abbas
Type
Journal Article
Abstract
INTRODUCTION: Self-attachment is a new self-administrable psychotherapeutic intervention based on creating an affectional bond between the user and their childhood-self using their childhood photos to develop the capacity for affect self-regulation. Technological advances, such as virtual reality (VR), can enhance the procedure of this intervention and make it scalable.
METHODS: We have developed a user-friendly, interactive VR platform for self-attachment featuring a virtual assistant and a customised child avatar that resembles the user in their childhood. The virtual agent interacts with the user and using an emotion recognition algorithm can provide suggestions for the user to undertake an appropriate self-attachment sub-protocol. Furthermore, the platform allows user interaction with the child avatar, such as embracing the avatar.
RESULTS: We show by a small preliminary trial that such a VR experience can be realistic, leading to a positive emotion change in the user.
METHODS: We have developed a user-friendly, interactive VR platform for self-attachment featuring a virtual assistant and a customised child avatar that resembles the user in their childhood. The virtual agent interacts with the user and using an emotion recognition algorithm can provide suggestions for the user to undertake an appropriate self-attachment sub-protocol. Furthermore, the platform allows user interaction with the child avatar, such as embracing the avatar.
RESULTS: We show by a small preliminary trial that such a VR experience can be realistic, leading to a positive emotion change in the user.
Date Issued
2021-09-14
Date Acceptance
2021-08-25
Citation
EAI Endorsed Transactions on Pervasive Health and Technology, 2021, 7 (29), pp.1-14
Publisher
European Alliance for Innovation n.o.
Start Page
1
End Page
14
Journal / Book Title
EAI Endorsed Transactions on Pervasive Health and Technology
Volume
7
Issue
29
Copyright Statement
© 2021 Neophytos Polydorou et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
License URL
Identifier
https://eudl.eu/doi/10.4108/eai.14-9-2021.170951
Publication Status
Published
Date Publish Online
2021-09-14