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Abstract
Anxiety disorders are among the most widespread mental health challenges, yet conventional treatments face barriers of accessibility, cost, and reliance on subjective measures. Digital therapeutics offer scalable solutions, but current systems lack real-time emotion monitoring and adaptive personalization. To address this gap, this study proposes a multimodal emotion recognition-driven framework for personalized anxiety management. The framework fuses electroencephalography, heart rate variability, facial expression, and speech features via cross-modal attention, and employs a reinforcement learning–based decision engine to dynamically select interventions such as breathing exercises, mindfulness, or cognitive reframing. Adaptive feedback further tailors interventions to user responses. Experiments on DEAP and WESAD datasets showed superior performance over unimodal and traditional fusion baselines, with accuracies of 86.2% and 84.7% and AUROCs of 0.91 and 0.89. Anxiety reduction analysis demonstrated up to 24% improvement in State-Trait Anxiety Inventory scores. The study advances affective computing by linking multimodal sensing with adaptive therapeutic design, and offers a foundation for scalable, interpretable, and clinically relevant digital mental health interventions.
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References
- Javaid, S. F., Hashim, I. J., Hashim, M. J., Stip, E., Samad, M. A., & Ahbabi, A. A. (2023). Epidemiology of anxiety disorders: global burden and sociodemographic associations. Middle East Current Psychiatry, 30(1), 44.
- Mursaleen, M., Shaikh, S. I., & Imtiaz, S. (2025). The Role of Cognitive Behavioral Therapy (CBT) in Treating Anxiety Disorders: A Meta-Analytical Review. Journal of Applied Linguistics and TESOL (JALT), 8(1), 1074-1083.
- Fürstenau, D., Gersch, M., & Schreiter, S. (2023). Digital therapeutics (DTx). Business & Information Systems Engineering, 65(3), 349-360.
- Fatima, E., Dhanda, N., & Zaidi, T. (2025, March). AI-Driven Detection of Stress, Anxiety, and Depression: Techniques, Challenges, and Future Perspectives. In 2025 3rd International Conference on Disruptive Technologies (ICDT) (pp. 118-123). IEEE. https://doi.org/10.1109/icdt63985.2025.10986672
- Pillalamarri, R., & Shanmugam, U. (2025). A review on EEG-based multimodal learning for emotion recognition. Artificial Intelligence Review, 58(5), 131.
- Wang, C., He, T., Zhou, H., Zhang, Z., & Lee, C. (2023). Artificial intelligence enhanced sensors-enabling technologies to next-generation healthcare and biomedical platform. Bioelectronic Medicine, 9(1), 17.
- Udahemuka, G., Djouani, K., & Kurien, A. M. (2024). Multimodal Emotion Recognition using visual, vocal and Physiological Signals: a review. Applied Sciences, 14(17), 8071.
- Vaz, M., Summavielle, T., Sebastião, R., & Ribeiro, R. P. (2023). Multimodal classification of anxiety based on physiological signals. Applied Sciences, 13(11), 6368.
- Lee, A. G. (2024). AI-and XR-powered digital therapeutics (DTx) innovations. In Digital Frontiers-Healthcare, Education, and Society in the Metaverse Era. IntechOpen. https://doi.org/10.5772/intechopen.1006619
- Cho, C. H., Lee, H. J., & Kim, Y. K. (2024). The new emerging treatment choice for major depressive disorders: digital therapeutics. Recent Advances and Challenges in the Treatment of Major Depressive Disorder, 307-331.
- Tayarani-N, M. H., & Shahid, S. I. (2025). Detecting Anxiety via Machine Learning Algorithms: A Literature Review. IEEE Transactions on Emerging Topics in Computational Intelligence. https://doi.org/10.1109/tetci.2025.3543307
- Alasmrai, M. A., Ismail, R. M., & Ali Al-Abyadh, M. H. (2025). Personalized Cognitive Behavioral Therapy for Adults Using Machine Learning: A Multi-Factor, Reinforcement-Based Approach. Fusion: Practice & Applications, 20(2). https://doi.org/10.54216/fpa.200205
- Wanniarachchi, V. U., Greenhalgh, C., Choi, A., & Warren, J. R. (2025). Personalization variables in digital mental health interventions for depression and anxiety in adolescents and youth: a scoping review. Frontiers in Digital Health, 7, 1500220. https://doi.org/10.3389/fdgth.2025.1500220
- Manole, A., Cârciumaru, R., Brînzaș, R., & Manole, F. (2024). An exploratory investigation of chatbot applications in anxiety management: a focus on personalized interventions. Information, 16(1), 11. https://doi.org/10.3390/info16010011
- Ramaswamy, M. P. A., & Palaniswamy, S. (2024). Multimodal emotion recognition: A comprehensive review, trends, and challenges. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 14(6), e1563. https://doi.org/10.1002/widm.1563
References
Javaid, S. F., Hashim, I. J., Hashim, M. J., Stip, E., Samad, M. A., & Ahbabi, A. A. (2023). Epidemiology of anxiety disorders: global burden and sociodemographic associations. Middle East Current Psychiatry, 30(1), 44.
Mursaleen, M., Shaikh, S. I., & Imtiaz, S. (2025). The Role of Cognitive Behavioral Therapy (CBT) in Treating Anxiety Disorders: A Meta-Analytical Review. Journal of Applied Linguistics and TESOL (JALT), 8(1), 1074-1083.
Fürstenau, D., Gersch, M., & Schreiter, S. (2023). Digital therapeutics (DTx). Business & Information Systems Engineering, 65(3), 349-360.
Fatima, E., Dhanda, N., & Zaidi, T. (2025, March). AI-Driven Detection of Stress, Anxiety, and Depression: Techniques, Challenges, and Future Perspectives. In 2025 3rd International Conference on Disruptive Technologies (ICDT) (pp. 118-123). IEEE. https://doi.org/10.1109/icdt63985.2025.10986672
Pillalamarri, R., & Shanmugam, U. (2025). A review on EEG-based multimodal learning for emotion recognition. Artificial Intelligence Review, 58(5), 131.
Wang, C., He, T., Zhou, H., Zhang, Z., & Lee, C. (2023). Artificial intelligence enhanced sensors-enabling technologies to next-generation healthcare and biomedical platform. Bioelectronic Medicine, 9(1), 17.
Udahemuka, G., Djouani, K., & Kurien, A. M. (2024). Multimodal Emotion Recognition using visual, vocal and Physiological Signals: a review. Applied Sciences, 14(17), 8071.
Vaz, M., Summavielle, T., Sebastião, R., & Ribeiro, R. P. (2023). Multimodal classification of anxiety based on physiological signals. Applied Sciences, 13(11), 6368.
Lee, A. G. (2024). AI-and XR-powered digital therapeutics (DTx) innovations. In Digital Frontiers-Healthcare, Education, and Society in the Metaverse Era. IntechOpen. https://doi.org/10.5772/intechopen.1006619
Cho, C. H., Lee, H. J., & Kim, Y. K. (2024). The new emerging treatment choice for major depressive disorders: digital therapeutics. Recent Advances and Challenges in the Treatment of Major Depressive Disorder, 307-331.
Tayarani-N, M. H., & Shahid, S. I. (2025). Detecting Anxiety via Machine Learning Algorithms: A Literature Review. IEEE Transactions on Emerging Topics in Computational Intelligence. https://doi.org/10.1109/tetci.2025.3543307
Alasmrai, M. A., Ismail, R. M., & Ali Al-Abyadh, M. H. (2025). Personalized Cognitive Behavioral Therapy for Adults Using Machine Learning: A Multi-Factor, Reinforcement-Based Approach. Fusion: Practice & Applications, 20(2). https://doi.org/10.54216/fpa.200205
Wanniarachchi, V. U., Greenhalgh, C., Choi, A., & Warren, J. R. (2025). Personalization variables in digital mental health interventions for depression and anxiety in adolescents and youth: a scoping review. Frontiers in Digital Health, 7, 1500220. https://doi.org/10.3389/fdgth.2025.1500220
Manole, A., Cârciumaru, R., Brînzaș, R., & Manole, F. (2024). An exploratory investigation of chatbot applications in anxiety management: a focus on personalized interventions. Information, 16(1), 11. https://doi.org/10.3390/info16010011
Ramaswamy, M. P. A., & Palaniswamy, S. (2024). Multimodal emotion recognition: A comprehensive review, trends, and challenges. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 14(6), e1563. https://doi.org/10.1002/widm.1563
