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VOL. 13, ISSUE 1 (2026)
AI-Driven stress detection using deep learning and machine learning: A review
Authors
Dr. T Sumadhi
Abstract
Stress is a prevalent
psychological and physiological condition that significantly impacts human
health and productivity. With the increasing availability of wearable sensors,
physiological data, and behavioral information, artificial intelligence (AI) has
emerged as a promising tool for automatic stress detection. This paper presents
a comprehensive survey of AI-driven approaches for stress detection using
machine learning (ML) and deep learning (DL) techniques. It discusses the
various data modalities—such as physiological signals (EEG, ECG, GSR), facial
expressions, speech, and textual data—used for stress assessment. The paper
also compares traditional ML models (SVM, Random Forest, KNN) with modern DL
architectures (CNN, LSTM, Transformer-based models). Finally, it highlights
existing challenges, datasets, and research gaps, and proposes future
directions for integrating AI-based stress detection with personalized yoga
recommendation systems for holistic mental well-being.
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Pages:561-565
How to cite this article:
Dr. T Sumadhi "AI-Driven stress detection using deep learning and machine learning: A review". International Journal of Multidisciplinary Research and Development, Vol 13, Issue 1, 2026, Pages 561-565
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