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<p>Stress has emerged as a major issue in today’s world, impacting people in numerous areas of their lives. It originates from multiple sources and can be classified into different types and categories. Physiological stress, exerts high pressure on the human body, disrupting daily activities and overall well-being. Prolonged exposure to elevated stress levels may lead to severe health complications, including cardiovascular diseases and other stress-induced disorders. To mitigate these risks, it is crucial to continuously monitor stress levels, enabling early detection and timely intervention. This systematic review explores various stress detection methodologies, including data-driven approaches that leverage online social networks (OSNs) and physiological signals, such as Electroencephalography (EEG) and Electrocardiography (ECG). Additionally, it explores stress indicators derived from wearable devices, including Galvanic Skin Response (GSR), and Skin Temperature (ST). The study further investigates the application of Machine Learning (ML) techniques and Deep Learning (DL) techniques in examining these signals to improve the accuracy of stress detection. Furthermore, this paper examines the utilization of stress detection models across multiple sectors, including the workplace, education, and the automotive industry. It also highlights key research aspects such as objectives, data sources, data analysis, ML or DL methodologies, and model performance evaluation. The review follows the PICO framework (Population, Intervention, Comparison, Outcome) to systematically identify relevant studies, define inclusion criteria, and evaluate the impact of computational models on stress detection outcomes. To provide a quantitative summary of performance across studies, this systematic review incorporates meta-analysis using forest plots and funnel plots. By reviewing existing methodologies, this review aims to identify research gaps and outline potential future directions in the field of stress monitoring, thereby supporting the development of more effective and dependable stress management solutions.</p>

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Stress Detection Using Machine Learning and Deep Learning Techniques: A Systematic Review and Meta-Analysis

  • 期刊论文

  • 发表日期：

    2025年10月24日

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  • Logesh Kumar Kulanthaivel Lakshmanan,
  • Kavisankar Leelasankar,
  • Balasubramani Subbiyan

摘要

Stress has emerged as a major issue in today’s world, impacting people in numerous areas of their lives. It originates from multiple sources and can be classified into different types and categories. Physiological stress, exerts high pressure on the human body, disrupting daily activities and overall well-being. Prolonged exposure to elevated stress levels may lead to severe health complications, including cardiovascular diseases and other stress-induced disorders. To mitigate these risks, it is crucial to continuously monitor stress levels, enabling early detection and timely intervention. This systematic review explores various stress detection methodologies, including data-driven approaches that leverage online social networks (OSNs) and physiological signals, such as Electroencephalography (EEG) and Electrocardiography (ECG). Additionally, it explores stress indicators derived from wearable devices, including Galvanic Skin Response (GSR), and Skin Temperature (ST). The study further investigates the application of Machine Learning (ML) techniques and Deep Learning (DL) techniques in examining these signals to improve the accuracy of stress detection. Furthermore, this paper examines the utilization of stress detection models across multiple sectors, including the workplace, education, and the automotive industry. It also highlights key research aspects such as objectives, data sources, data analysis, ML or DL methodologies, and model performance evaluation. The review follows the PICO framework (Population, Intervention, Comparison, Outcome) to systematically identify relevant studies, define inclusion criteria, and evaluate the impact of computational models on stress detection outcomes. To provide a quantitative summary of performance across studies, this systematic review incorporates meta-analysis using forest plots and funnel plots. By reviewing existing methodologies, this review aims to identify research gaps and outline potential future directions in the field of stress monitoring, thereby supporting the development of more effective and dependable stress management solutions.