International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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HUMAN MENTAL STRESS DETECTION USING MACHINE LEARNING: A COMPREHENSIVE REVIEW

Author(s) Mr. JITENDRA KUMAR, Dr. SWEETY MANIAR
Country India
Abstract Mental stress, a pervasive global health concern, significantly impacts individual well-being, economic productivity, and contributes to a spectrum of physical and psychological disorders. The concurrent proliferation of wearable sensors, ubiquitous smart devices, and online social platforms has generated unprecedented volumes of multimodal data, creating a fertile ground for the objective, continuous, and automated detection of human stress. This paper provides a comprehensive and in-depth review of the application of machine learning (ML) techniques for mental stress detection. We survey the diverse landscape of data modalities, from direct physiological signals such as Electroencephalography (EEG) and Heart Rate Variability (HRV), to indirect behavioral cues derived from video and speech analysis, and rich textual data mined from social media. A detailed analysis of the machine learning paradigms employed is presented, covering classical models like Support Vector Machines (SVM) and Ensemble Learning, as well as advanced Deep Learning architectures including Convolutional and Recurrent Neural Networks (CNNs, RNNs) and state-of-the-art transformer-based models for Natural Language Processing (NLP). Finally, we discuss promising future directions poised to overcome these hurdles, including the development of sophisticated multimodal fusion techniques, the creation of closed-loop systems for real-time stress mitigation, and the integration of more advanced, context-aware artificial intelligence.
Keywords Mental Stress, Stress Detection, Machine Learning, Deep Learning, Physiological Signals, Wearable Sensors, Affective Computing, Electroencephalography (EEG), Heart Rate Variability (HRV), Natural Language Processing (NLP), Multimodal Fusion, Interpretability, Mental Health Technology.
Field Computer
Published In Volume 7, Issue 3, May-June 2025
Published On 2025-06-28
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E-ISSN 2582-2160
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