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Detecting Depression, Anxiety and Mental Stress in One Sequential Model with Multi-task Learning

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HCI International 2022 – Late Breaking Papers: HCI for Health, Well-being, Universal Access and Healthy Aging (HCII 2022)

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Abstract

Recent studies employing mobile data to detect psychological disorders have made great progress. Mobile devices record user’s behavior data and physical signals continuously. Such data are able to detect symptoms of mental disorders like depression, anxiety and stress via machine learning models. However, existing researches mainly focus on independently detecting one of these disorders, ignoring the symptom relationship among them. Besides, current studies using statistical features accumulated in a period of time lose the time correlation within data, which makes it difficult to predict future symptoms. Instead, in this paper, we firstly propose a parameter sharing model with multi-task learning to transfer the common representation of three symptoms of mental disorders, and we use biRNN approach to predict these disorders by using mobile sequential features. We obtain 175 participants completing collection for at least 2 weeks with weekly questionnaire. In the experiments, our proposed sharing model achieves average overall accuracy of 0.78 and average AUC of 0.78, outperforming three single-task model and machine learning methods that use statistical features significantly. These results suggest that multi-task learning with the sequential feature enables detecting severity of depression, anxiety and stress symptoms.

S. Zhang—This work was done during intership in SRC-Beijing.

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Author information

Authors and Affiliations

  1. School of Software, Beihang University, Beijing, China

    Shen Zhang

  2. Samsung Research China-Beijing (SRC-B), Beijing, China

    Shen Zhang, Mei Tu, Yueru Yan & Yimeng Zhuang

  3. School of Statistics, Beijing Normal University, Beijing, China

    Yueru Yan

  4. CAS Key Lab of Behavioral Science, Institute of Psychology, Beijing, China

    Likun Ge & Gaoxia Wei

Authors
  1. Shen Zhang
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  2. Mei Tu
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  3. Yueru Yan
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  4. Yimeng Zhuang
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  5. Likun Ge
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  6. Gaoxia Wei
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Corresponding author

Correspondence to Mei Tu .

Editor information

Editors and Affiliations

  1. Purdue University West Lafayette, West Lafayette, IN, USA

    Vincent G. Duffy

  2. Tsinghua University, Beijing, China

    Qin Gao

  3. Chongqing University, Chongqing, China

    Jia Zhou

  4. Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Margherita Antona

  5. University of Crete and Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Constantine Stephanidis

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Zhang, S., Tu, M., Yan, Y., Zhuang, Y., Ge, L., Wei, G. (2022). Detecting Depression, Anxiety and Mental Stress in One Sequential Model with Multi-task Learning. In: Duffy, V.G., Gao, Q., Zhou, J., Antona, M., Stephanidis, C. (eds) HCI International 2022 – Late Breaking Papers: HCI for Health, Well-being, Universal Access and Healthy Aging. HCII 2022. Lecture Notes in Computer Science, vol 13521. Springer, Cham. https://doi.org/10.1007/978-3-031-17902-0_14

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  • DOI: https://doi.org/10.1007/978-3-031-17902-0_14

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  • Online ISBN: 978-3-031-17902-0

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