Data augmentation for depression detection using skeleton-based gait information.
In recent years, the incidence of depression is rising rapidly worldwide, but large-scale depression screening is still challenging. Gait analysis provides a non-contact, low-cost, and efficient early screening method for depression. However, the early screening of depression based on gait analysis...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 60; no. 9; pp. 2665 - 2680 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158447317&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158447317 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2022 vid: 60 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 158447317 157951882 158447317 NLM35829811 10.1007/s11517-022-02595-z NLM35829811 158447317 ppf: 2665 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Data augmentation for depression detection using skeleton-based gait information. aug: au: Yang, Jingjing Lu, Haifeng Li, Chengming Hu, Xiping Hu, Bin affil: School of information Science and Engineering, Lanzhou University, Lanzhou, China sug: subj: Depression Diagnosis Skeleton Gait ab: In recent years, the incidence of depression is rising rapidly worldwide, but large-scale depression screening is still challenging. Gait analysis provides a non-contact, low-cost, and efficient early screening method for depression. However, the early screening of depression based on gait analysis lacks sufficient effective sample data. In this paper, we propose a skeleton data augmentation method for assessing the risk of depression. First, we propose five techniques to augment skeleton data and apply them to depression and emotion datasets. Then, we divide augmentation methods into two types (non-noise augmentation and noise augmentation) based on the mutual information and the classification accuracy. Finally, we explore which augmentation strategies can capture the characteristics of human skeleton data more effectively. Experimental results show that the augmented training dataset that retains more of the raw skeleton data properties determines the performance of the detection model. Specifically, rotation augmentation and channel mask augmentation make the depression detection accuracy reach 92.15% and 91.34%, respectively. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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