A multi-modal deep learning approach for stress detection using physiological signals: integrating time and frequency domain features
The proposed MMFD-SD model provides an accurate and robust stress detection approach by integrating time-domain and frequency-domain features. Designed for occupational environments with intermittent data collection, it effectively addresses real-world stress monitoring challenges. Future research can explore the fusion of additional modalities, real-time stress detection, and improvements in model generalization to enhance its practical applicability.
Authors
- Jiawei Xiang (ORCID: https://orcid.org/0000-0003-4028-985X)
- Zaojun Fang (ORCID: https://orcid.org/0000-0003-4025-039X)
- Qinyong Wang (ORCID: https://orcid.org/0000-0002-8913-2696)
- Zhixian Su
- James A. Esquivel (ORCID: https://orcid.org/0009-0004-8862-9299)
Institutions
- Ningbo University (CN)
- Angeles University Foundation (PH)
- Wenzhou Medical University (CN)
- First Affiliated Hospital of Wenzhou Medical University (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- Frontiers in Physiology
- Published
- 2025-04-01
- DOI
- https://doi.org/10.3389/fphys.2025.1584299
- Citations
- 32
- Primary Topic
- Non-Invasive Vital Sign Monitoring
- Type
- article
- Field-Weighted Citation Impact
- 21.57
Funders
- Wenzhou Municipal Science and Technology Bureau