A multi-modal deep learning approach for stress detection using physiological signals: integrating time and frequency domain features DOI Creative Commons
Jiawei Xiang, Qinyong Wang, Zaojun Fang

et al.

Frontiers in Physiology, Journal Year: 2025, Volume and Issue: 16

Published: April 1, 2025

This study aims to develop a multimodal deep learning-based stress detection method (MMFD-SD) using intermittently collected physiological signals from wearable devices, including accelerometer data, electrodermal activity (EDA), heart rate (HR), and skin temperature. Given the unique demands high-intensity work environment of nursing profession, measurement in nurses serves as representative case, reflecting levels other high-pressure occupations. We propose learning framework that integrates time-domain frequency-domain features for detection. To enhance model robustness generalization, data augmentation techniques such sliding window jittering are applied. Feature extraction includes statistical derived raw obtained via Fast Fourier Transform (FFT). A customized architecture employs convolutional neural networks (CNNs) process separately, followed by fully connected layers final classification. address class imbalance, Synthetic Minority Over-sampling Technique (SMOTE) is utilized. The trained evaluated on signal dataset with level labels. Experimental results demonstrate MMFD-SD achieves outstanding performance detection, an accuracy 91.00% F1-score 0.91. Compared traditional machine classifiers logistic regression, random forest, XGBoost, proposed significantly improves both robustness. Ablation studies reveal integration plays crucial role enhancing performance. Additionally, sensitivity analysis confirms model's stability adaptability across different hyperparameter settings. provides accurate robust approach integrating features. Designed occupational environments intermittent collection, it effectively addresses real-world monitoring challenges. Future research can explore fusion additional modalities, real-time improvements generalization its practical applicability.

Language: Английский

Detection of Stress from PPG and GSR Signals using AI Framework DOI Creative Commons

S. Barik,

Vinay Thakur,

Mohasin Ali Miah

et al.

Journal of The Institution of Engineers (India) Series B, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 3, 2025

Language: Английский

Citations

0
A multi-modal deep learning approach for stress detection using physiological signals: integrating time and frequency domain features DOI Creative Commons
Jiawei Xiang, Qinyong Wang, Zaojun Fang

et al.

Frontiers in Physiology, Journal Year: 2025, Volume and Issue: 16

Published: April 1, 2025

This study aims to develop a multimodal deep learning-based stress detection method (MMFD-SD) using intermittently collected physiological signals from wearable devices, including accelerometer data, electrodermal activity (EDA), heart rate (HR), and skin temperature. Given the unique demands high-intensity work environment of nursing profession, measurement in nurses serves as representative case, reflecting levels other high-pressure occupations. We propose learning framework that integrates time-domain frequency-domain features for detection. To enhance model robustness generalization, data augmentation techniques such sliding window jittering are applied. Feature extraction includes statistical derived raw obtained via Fast Fourier Transform (FFT). A customized architecture employs convolutional neural networks (CNNs) process separately, followed by fully connected layers final classification. address class imbalance, Synthetic Minority Over-sampling Technique (SMOTE) is utilized. The trained evaluated on signal dataset with level labels. Experimental results demonstrate MMFD-SD achieves outstanding performance detection, an accuracy 91.00% F1-score 0.91. Compared traditional machine classifiers logistic regression, random forest, XGBoost, proposed significantly improves both robustness. Ablation studies reveal integration plays crucial role enhancing performance. Additionally, sensitivity analysis confirms model's stability adaptability across different hyperparameter settings. provides accurate robust approach integrating features. Designed occupational environments intermittent collection, it effectively addresses real-world monitoring challenges. Future research can explore fusion additional modalities, real-time improvements generalization its practical applicability.

Language: Английский

Citations

0