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The prevalence of stress in modern life demands innovative solutions to monitor and mitigate its effects on individual well-being. This study explores stress detection through a robust dataset derived from wearable devices, analysing physiological signals, perceived stress scores, and task performance metrics. Given the widespread impact of stress across diverse populations, this research seeks to unravel the complexities of stress responses, paving the way for personalized interventions and enhancing our understanding of the physiological mechanisms underlying stress. On the Stress Predict dataset, the accuracy by Random Forest was the highest at 83% as compared to Logistic regression - 54% and SVM - 82%.

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Stress Detection and Prediction Using Wearable Sensors and Machine Learning: A Data-Driven Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Asma Shahin,
  • Deepa Deshpande

摘要

The prevalence of stress in modern life demands innovative solutions to monitor and mitigate its effects on individual well-being. This study explores stress detection through a robust dataset derived from wearable devices, analysing physiological signals, perceived stress scores, and task performance metrics. Given the widespread impact of stress across diverse populations, this research seeks to unravel the complexities of stress responses, paving the way for personalized interventions and enhancing our understanding of the physiological mechanisms underlying stress. On the Stress Predict dataset, the accuracy by Random Forest was the highest at 83% as compared to Logistic regression - 54% and SVM - 82%.