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Multi-Modal Bio signal Transformer Based Deep Learning Approach for Mental Stress Detection

Authors

  • Maria Saleem

    Mehran University of Engineering and Technology, Jamshoro
    Author

  • Bushra Naz Soomro

    Mehran University of Engineering and Technology, Jamshoro
    Author

  • Almas Ali

    Mehran University of Engineering and Technology, Jamshoro
    Author

  • Zartasha Baloch

    Mehran University of Engineering and Technology, Jamshoro
    Author

DOI:

https://doi.org/10.22581/0148

Keywords:

Deep Learning , Stress, Multimodal

Abstract

Mental stress detection is a critical challenge in healthcare. The detrimental effects of stress on health and the societal expenses that accompany it are becoming more well-known because of initiatives like the World Health Organization and the Occupational Safety and Health Administration. There are over 20 million people living in Pakistan. Estimates indicate that up to 34% of Pakistanis may experience stress at some point in their lives, making it a common mental health issue in the country.

 This research developed a novel multi-modal bio-signal transformer based deep learning approach, leveraging transformer architecture, for accurate mental stress detection, a previously unaddressed challenge. The novel multi-modal deep learning approach, leveraging transformer architecture, for accurate mental stress detection. This approach utilizes a combination of bio signals, including electrocardiogram ECG, electroencephalogram EEG, electromyogram EMG, electrodermal activity EDA, TEMP and galvanic skin response GSR data, collected from participants under varying stress conditions. By employing deep learning techniques in conjunction with an encoder transformer and CNN, we can effectively classify these bio signals into binary stress states, indicating whether a participant is experiencing stress or not.

The model has achieved a remarkable 95% accuracy, surpassing previous state-of-the-art methods. The proposed method has the potential to improve the early detection and management of mental stress-related disorders and set a new benchmark in mental stress detection.

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References

[1] A. F. A. Lee and A. Roussos, “A privacy-preserved IoMT-based mental stress detection framework,” 2024.

[2] M. Abd Al-Alim, R. Mubarak, N. M. Salem, and I. Sadek, “A machine-learning approach for stress detection using wearable sensors in free-living environments,” Computers in Biology and Medicine, vol. 179, p. 108918, 2024.

[3] A. Abd-Alrazaq et al., “The performance of wearable AI in detecting stress among students: A systematic review and meta-analysis,” Journal of Medical Internet Research, vol. 26, p. e52622, 2024.

[4] V. Adarsh and G. R. Gangadharan, “Mental stress detection from ultra-short heart rate variability using explainable graph convolutional network with network pruning and quantization,” Machine Learning, vol. 113, no. 8, pp. 5467–5494, 2024.

[5] Y. Badr et al., “A review on evaluating mental stress by deep learning using EEG signals,” Neural Computing and Applications, vol. 36, no. 21, pp. 12629–12654, 2024.

[6] N. Bashir, S. Narejo, and B. Naz, “A machine learning framework for major depressive disorder (MDD) detection using non-invasive EEG signals,” Wireless Personal Communications, 2023, doi: 10.1007/s11277-023-10445-w.

[7] D. S. Benita, A. S. Ebenezer, L. Susmitha, M. S. P. Subathra, and S. J. Priya, “Stress detection using CNN on the WESAD dataset,” in Proc. 2024 Int. Conf. Emerging Systems and Intelligent Computing (ESIC), Feb. 2024, pp. 308–313.

[8] M. Bolpagni, S. Pardini, M. Dianti, and S. Gabrielli, “Personalized stress detection using biosignals from wearables: A scoping review,” Sensors, vol. 24, no. 10, p. 3221, 2024.

[9] K. M. S. Boopathybalan, “Transformer-based deep learning model for mental health assessment,” 2025.

[10] A. Dasgupta and A. Basu, “Measurement and instrumentation for mental stress detection in humans,” IEEE Instrumentation & Measurement Magazine, vol. 28, no. 2, pp. 10–17, 2025.

[11] S. de Vries et al., “Real-time stress detection based on artificial intelligence for people with an intellectual disability,” 2024.

[12] M. A. Hafeez and S. Shakil, “EEG-based stress identification and classification using deep learning,” Multimedia Tools and Applications, vol. 83, no. 14, pp. 42703–42719, 2024.

[13] M. Khandelwal and A. Sharma, “Machine learning and deep learning techniques to detect mental stress using various physiological signals: A critical insight,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 15, no. 3, p. e70035, 2025.

[14] H. Lee, J. Kim, B. Han, S. M. Park, and J. Chang, “Developing an explainable deep neural network for stress detection using bio signals and human-engineered features,” Biomedical Signal Processing and Control, vol. 109, p. 107960, 2025.

[15] M. Naeem, S. A. Fawzi, H. Anwar, and A. S. Malek, “Wearable ECG systems for accurate mental stress detection: A scoping review,” Journal of Public Health, vol. 33, no. 6, pp. 1181–1197, 2025.

[16] N. Bashir, S. Narejo, and B. Naz, “EEG-based major depressive disorder (MDD) detection using machine learning,” in Proc. 5th Int. Conf. Pattern Recognition and Artificial Intelligence (MedPRAI 2021), Dec. 17–18, 2021.

[17] A. Pandey and S. Kumar, “Mental health and stress prediction using NLP and transformer-based techniques,” in Proc. 2024 IEEE Symp. Wireless Technology & Applications (ISWTA), Jul. 2024, pp. 61–66.

[18] M. Qorich and R. El Ouazzani, “Lightweight advanced deep-learning models for stress detection on social media,” Engineering Applications of Artificial Intelligence, vol. 140, p. 109720, 2025.

[19] R. K. Rai and D. K. Singh, “Stress detection through wearable EEG technology: A signal-based approach,” Computers and Electrical Engineering, vol. 126, p. 110478, 2025.

S. Hajano, B. Naz, S. Talpur, and S. Hajano, “Classification of amputee EMG signals using machine learning techniques,” International Journal of Innovations in Science & Technology, Y category, 2023.

[20] G. R. Reddy et al., “Enhancing stress detection employing physiological signals from the WESAD dataset: A machine learning approach with SMOTE,” Milestone Transactions on Medical Technometrics, vol. 2, no. 2, pp. 79–87, 2024.

[21] R. Tanwar, O. C. Phukan, G. Singh, P. Pal, and S. Tiwari, “Attention-based hybrid deep learning model for wearable-based stress recognition,” Engineering Applications of Artificial Intelligence, vol. 127, p. 107391, 2024.

[22] G. Taskasaplidis, D. A. Fotiadis, and P. D. Bamidis, “Review of stress detection methods using wearable sensors,” IEEE Access, vol. 12, pp. 38219–38246, 2024.

[23] G. V. Tsakalidis et al., “Advances, benefits, and challenges of wearable sensors for healthcare and stress management: A focus on hemodynamic parameters and cortisol measurement,” Engineering Proceedings, vol. 82, no. 1, p. 42, 2024.

[24] X. Yang and L. Yu, “Intelligent detection of cognitive stress in subway train operators using multimodal electrophysiological and behavioral signals,” Symmetry, vol. 17, no. 8, p. 1298, 2025.

Published

2026-01-01

Issue

Vol. 45 No. 1 (2026): January Issue

Section

Articles

License

Copyright (c) 2026 Mehran University Research Journal of Engineering and Technology

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

Multi-Modal Bio signal Transformer Based Deep Learning Approach for Mental Stress Detection. (2026). Mehran University Research Journal of Engineering and Technology, 45(1), 01-13. https://doi.org/10.22581/0148

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