Dynamic alignment and fusion of multimodal physiological patterns for stress recognition
Abstract
Stress has been identified as one of major causes of health issues. To detect the stress levels with higher accuracy, fusion of multimodal physiological signals is a promising technique. However, there is an asynchrony between physiological signals observed from different perspectives. Exploring the temporal alignment relationship between modalities is helpful to improve the quality of multimodal fusion. This paper proposes an end-to-end multimodal stress detection model based on Bidirectional Cross- and Self-modal Attention (BCSA) mechanism. Specifically, we first construct different feature extractors based on the characteristics of Blood Volume Pulse (BVP) and Electrodermal Activity (EDA) to complete automated temporal feature extraction. Secondly, cross-modal attention is used to seek the alignment relationship between the two modalities and fully fuse cross-modal information. The self-modal attention is used to attenuate noise and redundant information, highlight important information and obtain salient stress representations. Finally, the stress representations of the two modalities are processed separately, and the mean square error (MSE) is used to narrow the gap between them. Experimental results on the UBFC-Phys dataset and WESAD dataset show that the proposed model can effectively improve the accuracy of stress recognition, and outperforms several state-of-the-art methods.
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Cross-modal alignment maps information from modalities into a shared space to understand relationships.
“Crossmodal alignment maps information from modalities, such as images and text, into a shared space to understand their relationships [177].”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [177]
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- Authors
- Zhang, X., Wei, X., Zhou, Z., Zhao, Q., Zhang, S., Yang, Y., Li, R., & Hu, B.
- Affiliations
- Lanzhou University (CN)
- Beijing Institute of Technology (CN)
- Venue
- IEEE Transactions on Affective Computing
- Published
- 2024-04
- Volume
- 15
- Issue
- 2
- Pages
- 685-696
- ISSN
- 1949-3045 (ISSN-L)
- 2371-9850
- OpenAlex
- W4382371644
- Cited by
- 38
- References
- 47
- Citations by year
- 2026: 19 · 2025: 8 · 2024: 9 · 2023: 2
- FWCI
- 9.82
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Affective Computing (publishedVersion)
- Funders
- National Key R&D Program of China (2019YFA0706200)
- National Natural Science Foundation of China (62072219, 61632014)
- Natural Science Foundation of Gansu Province (22JR5RA401)
- Fundamental Research Funds for the Central Universities (lzujbky-2022-ey13)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- stress detection, multimodal fusion, physiological signals, temporal alignment, cross-modal attention, blood volume pulse, electrodermal activity, noise attenuation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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