AI-Based Stress And Mood Detection Model: A Deep Learning Perspective
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Abstract
An AI-based stress and mood detection model is designed to automatically identify an individual’s emotional state using data such as facial expressions, voice signals, text inputs, or physiological signals. The system applies machine learning and deep learning algorithms to analyze patterns and classify stress levels or moods accurately. It can be integrated into wearable devices, mobile applications, or healthcare systems for real-time monitoring. Such models help in early detection of mental health issues and enable timely intervention. They are widely used in healthcare, workplace wellness, and personalized user experience systems. Despite their advantages, challenges like data privacy, accuracy, and ethical concerns remain important considerations. Overall, AI-based mood detection systems have significant potential to improve mental well-being and quality of life.
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References
[1] A. S. Sano and R. W. Picard, “Stress recognition using wearable sensors and mobile phones,” Proc. IEEE Int. Conf. Affective Comput. Intelligent Interaction (ACII), IEEE Press, Sept. 2013, pp. 671–676, doi:10.1109/ACII.2013.117.
[2] G. Giannakakis, D. Grigoriadis, K. Giannakaki, O. Simantiraki, A. Roniotis, and M. Tsiknakis, “Review on state-of-the-art methods and applications of computing in stress detection,” Multimedia Tools Appl., vol. 78, pp. 27383–27409, Oct. 2019.
[3] H. J. Kim and M. J. Smith, “Deep learning architectures for emotion and mood recognition,” in Advances in Affective Computing, vol. II, J. Doe and R. Roe, Eds. London: Academic, 2022, pp. 112–145.
[4] [4] S. K. P. Subhashini, “Detection of physiological stress using convolutional neural networks,” unpublished.
[5] Ameta, Upasana and Patel, Mayank and Rathore, Narendra Singh, Fusing Artificial Intelligence with Scrum Framework (April 25, 2023). Available at SSRN: https://ssrn.com/abstract=4428286 or http://dx.doi.org/10.2139/ssrn.4428286
[6] B. R. Schmidt, “Signal processing of galvanic skin response for emotional state estimation,” IEEE Transl. J. Biomed. Eng. Germany, vol. 5, pp. 120–128, March 2021 [Digests 12th Annual Conf. Bio-Signals, p. 45, 2019].
[7] K. M. Lee, Artificial Intelligence in Mental Health Diagnostics. San Francisco, CA: University Science Press, 2024.
[8] Ameta, Upasana, Mayank Patel, and Ajay Kumar Sharma. "Scaled Agile Framework Implementation in Organizations', its Shortcomings and an AI Based Solution to Track Team's Performance." 2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT). IEEE, 2022.
[9] V. T. Radu and L. M. Garcia, “Longitudinal study of mood swings using smartphone-based passive sensing,” Nature Digital Medicine, vol. 14, June 2023, pp. 884–892, doi:10.1038/s41746-023-00789-w.
[10] Y. Tanaka, S. Hasegawa, and K. Nakamura, “Stress level classification using heart rate variability and ensemble learning,” Proc. IEEE Symp. Machine Learning in Healthcare, IEEE Press, Dec. 2025, pp. 201–209, doi:10.1109/MLH.2025.456789.