Spatiotemporal Self-Attentive Graph-TCN Framework for Enhanced Teen Stress Detection
By: P. Indumathy and R. Praveen Kumar
References
- Zhang, J., Yin, H., Zhang, J., Yang, G., Qin, J., & He, L. (2022). Real-time mental stress detection using multimodality expressions with a deep learning framework. Frontiers in Neuroscience, 16, 947168.
- Kogilathota, V. (2021, June). iponder: A novel approach to multimodal mental health diagnosis and therapy for teens via deep transfer learning and computer vision. In 2021 IEEE International Conference on Artificial Intelligence, Robotics, and Communication (ICAIRC) (pp. 20–24). IEEE.
- Ekundayo, O. S., & Viriri, S. (2021). Facial expression recognition: A review of trends and techniques. Ieee Access, 9, 136944–136973.
- Ge, H., Zhu, Z., Dai, Y., Wang, B., & Wu, X. (2022). Facial expression recognition based on deep learning. Computer Methods and Programs in Biomedicine, 215, 106621.
- Sajjad, M., Ullah, F. U. M., Ullah, M., Christodoulou, G., Cheikh, F. A., Hijji, M., … & Rodrigues, J. J. (2023). A comprehensive survey on deep facial expression recognition: challenges, applications, and future guidelines. Alexandria Engineering Journal, 68, 817–840.
- Nan, Y., Ju, J., Hua, Q., Zhang, H., & Wang, B. (2022). A-MobileNet: An approach of facial expression recognition. Alexandria Engineering Journal, 61(6), 4435–4444.
- Mao, J., Xu, R., Yin, X., Chang, Y., Nie, B., Huang, A., & Wang, Y. (2024). Poster++: A simpler and stronger facial expression recognition network. Pattern Recognition, 110951.
- Wen, Z., Lin, W., Wang, T., & Xu, G. (2023). Distract your attention: Multi-head cross attention network for facial expression recognition. Biomimetics, 8(2), 199.
- Seo, W., Kim, N., Park, C., & Park, S. M. (2022). Deep learning approach for detecting work-related stress using multimodal signals. IEEE Sensors Journal, 22(12), 11892–11902.
- Campanella, S., Altaleb, A., Belli, A., Pierleoni, P., & Palma, L. (2023). A method for stress detection using empatica E4 bracelet and machine-learning techniques. Sensors, 23(7), 3565.
- Li, B., & Lima, D. (2021). Facial expression recognition via ResNet-50. International Journal of Cognitive Computing in Engineering, 2, 57–64.
- Gupta, S., Kumar, P., & Tekchandani, R. K. (2023). Facial emotion recognition based real-time learner engagement detection system in online learning context using deep learning models. Multimedia Tools and Applications, 82(8), 11365–11394.
- Mohan, K., Seal, A., Krejcar, O., & Yazidi, A. (2021). FER-net: facial expression recognition using deep neural net. Neural Computing and Applications, 33(15), 9125–9136.
- Khattak, A., Asghar, M. Z., Ali, M., & Batool, U. (2022). An efficient deep learning technique for facial emotion recognition. Multimedia Tools and Applications, 81(2), 1649–1683.
- Savchenko, A. V., Savchenko, L. V., & Makarov, I. (2022). Classifying emotions and engagement in online learning based on a single facial expression recognition neural network. IEEE Transactions on Affective Computing, 13(4), 2132–2143.
- Minaee, S., Minaei, M., & Abdolrashidi, A. (2021). Deep-emotion: Facial expression recognition using attentional convolutional network. Sensors, 21(9), 3046.
- Zhang, Y., Wang, C., Ling, X., & Deng, W. (2022, October). Learn from all: Erasing attention consistency for noisy label facial expression recognition. In European Conference on Computer Vision (pp. 418–434). Cham: Springer Nature Switzerland.
- Li, S., & Deng, W. (2020). Deep facial expression recognition: A survey. IEEE transactions on affective computing, 13(3), 1195–1215.
- Abdullah, S. M. S., & Abdulazeez, A. M. (2021). Facial expression recognition based on deep learning convolution neural network: A review. Journal of Soft Computing and Data Mining, 2(1), 53–65.
- Mellouk, W., & Handouzi, W. (2020). Facial emotion recognition using deep learning: review and insights. Procedia Computer Science, 175, 689–694.
- Mollahosseini, A., Chan, D., & Mahoor, M. H. (2016, March). Going deeper in facial expression recognition using deep neural networks. In 2016 IEEE Winter conference on applications of computer vision (WACV) (pp. 1–10). IEEE.
- Bisogni, C., Castiglione, A., Hossain, S., Narducci, F., & Umer, S. (2022). Impact of deep learning approaches on facial expression recognition in healthcare industries. IEEE Transactions on Industrial Informatics, 18(8), 5619–5627.
- Kuo, C. M., Lai, S. H., & Sarkis, M. (2018). A compact deep learning model for robust facial expression recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (pp. 2121–2129).
- Chowdary, M. K., Nguyen, T. N., & Hemanth, D. J. (2023). Deep learning-based facial emotion recognition for human–computer interaction applications. Neural Computing and Applications, 35(32), 23311–23328.
- Umer, S., Rout, R. K., Pero, C., & Nappi, M. (2022). Facial expression recognition with trade-offs between data augmentation and deep learning features. Journal of Ambient Intelligence and Humanized Computing, 1–15.
- Akhand, M. A. H., Roy, S., Siddique, N., Kamal, M. A. S., & Shimamura, T. (2021). Facial emotion recognition using transfer learning in the deep CNN. Electronics, 10(9), 1036.
- Ng, H. W., Nguyen, V. D., Vonikakis, V., & Winkler, S. (2015, November). Deep learning for emotion recognition on small datasets using transfer learning. In Proceedings of the 2015 ACM on international conference on multimodal interaction (pp. 443–449).
- Helaly, R., Messaoud, S., Bouaafia, S., Hajjaji, M. A., & Mtibaa, A. (2023). DTL-I-ResNet18: facial emotion recognition based on deep transfer learning and improved ResNet18. Signal, Image and Video Processing, 17(6), 2731–2744.
- Rescigno, M., Spezialetti, M., & Rossi, S. (2020). Personalized models for facial emotion recognition through transfer learning. Multimedia Tools and Applications, 79(47), 35811–35828.
- Albraikan, A. A., Alzahrani, J. S., Alshahrani, R., Yafoz, A., Alsini, R., Hilal, A. M., … & Gupta, D. (2022). Intelligent facial expression recognition and classification using optimal deep transfer learning model. Image and Vision Computing, 128, 104583.
- Ha, N. Y. Y., Ong, L. Y., & Leow, M. C. (2024). Slowfast-tcn: a deep learning approach for visual speech recognition. Emerging Science Journal, 8(6), 2554–2569.
- Kuklin, V., Ivanov, N., Muranov, A., Alexandrov, I., & Linskaya, E. (2024). Improving The Reliability of Biometric Authentication Processes Using A Model For Reducing Data Drift. Emerging Science Journal Учредители: European High-tech and Emerging Research Association, 8(6), 2449–2464.
- Tuan, T. A., Dai An, T., Nghia, N. H., & Loan, D. T. T. (2024). An analysis of machine learning for detecting depression, anxiety, and stress of recovered COVID-19 patients. Journal of Human Earth and Future, 5(1), 1–18.
- Tuan, T. A., Nghia, N. H., An, T. D., & Loan, D. T. T. (2024). Exploring mental stress expressions in online communities: A subreddit analysis. Journal of Human Earth and Future, 5(2), 131–150.
DOI: https://doi.org/10.2478/ijssis-2026-0033 | Journal eISSN: 1178-5608
Language: English
Submitted on: Aug 19, 2025
Published on: Jun 27, 2026
Published by: International Journal on Smart Sensing and Intelligent Systems
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
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© 2026 P. Indumathy, R. Praveen Kumar, published by International Journal on Smart Sensing and Intelligent Systems
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.