[Poster Presentation]Multimodal fusion diagnosis of depression and anxiety based on face video

Multimodal fusion diagnosis of depression and anxiety based on face video

ID：54 Submission ID：65 View Protection：ATTENDEE Updated Time：2021-10-30 18:04:16 Hits：753 Poster Presentation

Start Time：2021-11-13 10:05 （Asia/Shanghai）

Duration：5min

Session：[Pos] Poster » [Pos] Poster Session

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Abstract

In order to diagnose depression and anxiety, clinicians will conduct interviews with subjects. If large-scale screening is carried out, this method is too costly and difficult to implement. Because facial expressions play an important role in the diagnosis of clinicians, this provides an opportunity to solve this problem. Therefore, we recorded 303 subjects who answered the self-rated anxiety scale (SAS) and the self-rated depression scale (SDS) Video. Based on Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM), by using either of these two types of videos alone as a binary classification experiment, the accuracy of the diagnosis of depression is 72.53%, and the diagnosis of anxiety is 72.08%. In addition, by fusing the two types of videos to diagnose anxiety, depression, and normal in three categories, the accuracy of the model is 80.22%. Through the comparison of the results, the multimodal fusion diagnosis can not only diagnose the three categories but also has the highest accuracy. This model can be deployed on smartphones, not only for large-scale screening but also to assist doctors in diagnosis.

Keywords

Depression detection, Anxiety detection, Face video, Long Short-Term Memory Networks,Classification

Speaker

Chen Wang

Harbin Engineering University

Submission Author

Chen Wang Harbin Engineering University

Lizhong Liang Sun Yat-Sen University；Marine Biomedical Research Institute of Guangdong

Xiaofeng Liu Harvard Medical School；Suzhou Fanhan Information Technology Co., Ltd

Yao Lu Sun Yat-Sen University；Marine Biomedical Research Institute of Guangdong

Jihong Shen Harbin Engineering University

Hui Luo Southern Marine Science and Engineering Guangdong Laboratory；Marine Biomedical Research Institute of Guangdong

Wanqing Xie Harbin Engineering University；Suzhou Fanhan Information Technology Co., Ltd

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Important Dates

Abstract submission date:

~~2021-08-31~~

2021-10-25

Full paper submission date:

~~2021-09-15~~

2021-10-25

Notification of acceptance date:

~~2021-09-30~~

2021-11-01

Conference date:

2021-11-12~2021-11-14

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