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  • Predicting stress levels using physiological data: Real-time . . .
    ii) Wearable devices in stress monitoring: Reference the current utilization of wearable devices for collecting physiological data in stress prediction Wearable sensors provide individuals with real-time feedback on indicators or biomarkers of their health, allowing them to recognize patterns that may indicate the need for behavioral
  • Artificial intelligence driven mental health diagnosis based . . .
    The diversity of physiological signals the comprehensive annotation in the dataset enable detailed analysis of the complex interplay between physiological response stress By leveraging these multi-model data, the research aims to develop a robust accurate model for stress detection with the potential to enhance our understanding of
  • State-of-the-Art of Stress Prediction from Heart Rate . . .
    Recent advancements in the manufacturing and commercialisation of miniaturised sensors and low-cost wearables have enabled an effortless monitoring of lifestyle by detecting and analysing physiological signals Heart rate variability (HRV) denotes the time interval between consecutive heartbeats The HRV signal, as detected by the sensors and devices, has been popularly used as an indicative
  • Predicting stress levels using physiological data: Real-time . . .
    3 provides details on the physiological indicators of stress, details on the wearable devices in stress monitoring, commercial products and services that utilize stress-detection software, discussion on existing stress prediction models, and reference to open repositories and databases of stress-related data
  • Applications of artificial intelligence−machine learning for . . .
    Stress has been identified as “a condition of mental strain or pressure due to upsetting conditions” [] Chronic or prolonged exposure to stressful environments is a major contributor to
  • Stress Detection with Machine Learning and Deep Learning . . .
    Stress is a common part of everyday life that most people have to deal with on various occasions However, having long-term stress, or a high degree of stress, will hinder our safety and disrupt our normal lives Detecting mental stress earlier can prevent many health problems associated with stress When a person gets stressed, there are notable shifts in various bio-signals like thermal
  • A multi-modal deep learning approach for stress detection . . .
    The model is trained and evaluated on a multimodal physiological signal dataset with stress level labels Results: Experimental results demonstrate that the MMFD-SD method achieves outstanding performance in stress detection, with an accuracy of 91 00% and an F1-score of 0 91 Compared to traditional machine learning classifiers such as





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