ArticleScientific reports2025
A deep learning approach to stress recognition through multimodal physiological signal image transformation.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advancements in Machine Learning-Assisted Flexible Electronics: Technologies, Applications, and Future Prospects.Biosensors · 2026Pooled it
- Personalized Classification of Scenario-Derived Operational Driver-State Classes from Non-Intrusive Wearable Signals in Real-World SAE Level 2 Automated Driving.Sensors (Basel, Switzerland) · 2026Article
- Machine-Learning-Enabled Hydrogel Biosensors for Wearable Health Monitoring.Gels (Basel, Switzerland) · 2026Review
- Supervised information gain-based feature selection for multimodal physiological signals in stress prediction.Scientific reports · 2026Article
- Bridging Space Perception, Emotions, and Artificial Intelligence in Neuroarchitecture.Brain sciences · 2026Review
- Evaluation of interactive digital art healing effects based on multi modal physiological signals and generative adversarial networks (GANs): an empirical study of the neural mechanisms of emotion regulation.Frontiers in psychology · 2026Article
- Single- and Multimodal Deep Learning of EEG and EDA Responses to Construction Noise: Performance and Ablation Analyses.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Stress is widely acknowledged as a significant contributor to health issues. Recognizing stress involves assessing an individual's physiological and psychological responses to stressors, which is crucial for human well-being. Physiological signal-based stress assessment offers greater accuracy and objectivity compared to traditional methods. To enhance stress level detection, we propose a novel approach using deep learning models that classify mental stress states (stress, baseline, amusement) based on multimodal physiological signals converted into RGB images through Gramian Summation Angular Field (GASF), Gramian Difference Angular Field (GADF), and Markov Transition Field (MTF) transformations. Experimental findings showcase the effectiveness of the proposed model, achieving an accuracy of 90.96% and an F1-score of 91.67%. The consistently high F1 scores across all categories demonstrate the model's exceptional performance. Experimental results underscore the method's effectiveness in capturing the relationship between multimodal physiological signals and stress, offering a promising tool for mental stress recognition.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.