In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) has been redirecting to new domain since Jan. 1st, 2025.

Multi-task cross-modal learning approach for out-of-distribution detection of emotional stress

Wan Yichen1,2
Xing Kai1,2
Liu Yu4
Yang Hui3
Xu Junhan1,2
Yuan Yanxue1,2
1. School of Computer Science & Technology, University of Science & Technology of China, Hefei Anhui 230026, China
2. Suzhou Institute for Advanced Research, University of Science & Technology of China, Suzhou Jiangsu 215004, China
3. School of Life Sciences, Northwestern Polytechnical University, Xi'an Shanxi 710072, China
4. Nanjing Drum Tower Hospital, Nanjing Jiangsu 210008, China

Abstract

Recent research indicates that emotional stress detection systems based on photoplethysmography (PPG) signals could be a potential convenient solution. However, PPG-based methods usually induce severe out-of-distribution (OOD) issues when detecting stress in previously unseen subjects due to significant variations in PPG signals across individuals. To address this challenge, this paper proposes a cross-modal stress detection model based on multi-task learning (CSMT) . By introducing ECG signal reconstruction and multiple cardiovascular feature prediction as auxiliary tasks to enhance the feature extraction capability of PPG signals, the proposed method performs collaborative optimization of PPG-based stress detection in high-dimensional representation space, thereby learning robust stress detection representations across individuals. Experimental results on the WESAD dataset demonstrate that in Leave-One-Subject-Out validation tests, CSMT achieves best accuracy and F1 scores compared to existing methods in both three-class (neutral/stress/amusement) and binary (stress/non-stress) classification tasks, meanwhile effectively mitigating the OOD generalization problem in stress detection. The ablation experiments further validate the effectiveness of CSMT in enhancing model generalization capability.

Foundation Support

江苏省重点研发项目(BE2020665)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.10.0457
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 6

Publish History

[2025-03-10] Accepted Paper

Cite This Article

万奕晨, 邢凯, 刘宇, 等. 一种面向情绪压力分布外检测的多任务跨模态学习方法 [J]. 计算机应用研究, 2025, 42 (6). (2025-03-10). https://doi.org/10.19734/j.issn.1001-3695.2024.10.0457. (Wan Yichen, Xing Kai, Liu Yu, et al. Multi-task cross-modal learning approach for out-of-distribution detection of emotional stress [J]. Application Research of Computers, 2025, 42 (6). (2025-03-10). https://doi.org/10.19734/j.issn.1001-3695.2024.10.0457. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.


Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)