兰州大学机构库 >信息科学与工程学院
Automatic Assessment of Depression and Anxiety through Encoding Pupil-wave from HCI in VR Scenes
Li, Mi1; Zhang, Wei2; Hu, B(胡斌)3,4; Kang, Jiaming2; Wang, Yuqi2; Lu, Shengfu1
2024-02
Source PublicationACM Transactions on Multimedia Computing Communications and Applications   Impact Factor & Quartile Of Published Year  The Latest Impact Factor & Quartile
ISSN1551-6857
EISSN1551-6865
Volume20Issue:2
page numbers22
Abstract

At present, there have beenmany studies on themethods of using the deep learning regression model to assess depression level based on behavioral signals (facial expression, speech, and language); however, the research on the assessment method of anxiety level using deep learning is absent. In this article, pupil-wave, a physiological signal collected by Human Computer Interaction (HCI) that can directly represent the emotional state, is developed to assess the level of depression and anxiety for the first time. In order to distinguish between different depression and anxiety levels, we use the HCI method to induce the participants' emotional experience through three virtual reality (VR) emotional scenes of joyful, sad, and calm, and construct two differential pupil-waves of joyful and sad with the calm pupil-wave as the baseline. Correspondingly, a dual-channel fusion depression and anxiety level assessment model is constructed using the improved multi-scale convolution module and our proposed width-channel attention module for one-dimensional signal processing. The test results show that the MAE/RMSE of the depression and anxiety level assessment method proposed in this article is 3.05/4.11 and 2.49/1.85, respectively, which has better assessment performance than other related research methods. This study provides an automatic assessment technique based on human computer interaction and virtual reality for mental health physical examination.

KeywordDeep Learning Virtual Reality (Vr) Human Computer Interaction (Hci) Pupil-wave Width-channel Attention Module
PublisherASSOC COMPUTING MACHINERY
DOI10.1145/3513263
Indexed BySCIE
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS IDWOS:001092595800012
Original Document TypeArticle
ESI Hot Paper2024-05
ESI Hicited Paper2024-05
Citation statistics
Document Type期刊论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/568794
Collection信息科学与工程学院
Corresponding AuthorHu, Bin
Affiliation
1.Beijing Univ Technol, Engn Res Ctr Intelligent Percept & Autonomous Con, Beijing Int Collaborat Base Brain Informat & Wisd, Fac Informat Technol,Minist Educ,Engn Res Ctr Dig, Beijing, Peoples R China;
2.Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China;
3.Beijing Inst Technol, Inst Engn Med, Beijing, Peoples R China;
4.Lanzhou Univ, Sch Informat Sci & Engn, Gansu Prov Key Lab Wearable Comp, Lanzhou, Peoples R China
Corresponding Author AffilicationSchool of Information Science and Engineering
Recommended Citation
GB/T 7714
Li, Mi,Zhang, Wei,Hu, Bin,et al. Automatic Assessment of Depression and Anxiety through Encoding Pupil-wave from HCI in VR Scenes[J]. ACM Transactions on Multimedia Computing Communications and Applications,2024,20(2).
APA Li, Mi,Zhang, Wei,Hu, Bin,Kang, Jiaming,Wang, Yuqi,&Lu, Shengfu.(2024).Automatic Assessment of Depression and Anxiety through Encoding Pupil-wave from HCI in VR Scenes.ACM Transactions on Multimedia Computing Communications and Applications,20(2).
MLA Li, Mi,et al."Automatic Assessment of Depression and Anxiety through Encoding Pupil-wave from HCI in VR Scenes".ACM Transactions on Multimedia Computing Communications and Applications 20.2(2024).
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