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Related Concept Videos

Introduction to Stress and Lifestyle01:27

Introduction to Stress and Lifestyle

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Stress is a multifaceted response to events perceived as challenging or threatening, highlighting physical, emotional, cognitive, and behavioral reactions. Physically, stress can lead to fatigue, sleep disruptions, and various health issues such as frequent colds, chest pains, and nausea. Emotionally, it can manifest as anxiety, depression, irritability, and anger triggered by both minor and major life events. Cognitively, it may result in difficulty in concentration, memory, and...
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Consider a structure made of a boom and a rod designed to support a load. These two components are connected by a pin and stabilized by brackets and pins. The boom and the rod are detached from their supports to assess the different stresses imposed on this structure, and a free-body diagram is drawn. Then, all the forces applied, including the load acting on the structure, are identified. The reaction forces exerted on both the boom and the rod are computed using the equilibrium equations.
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Physiological Foundation of Stress01:24

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Stress triggers a coordinated physiological response involving the sympathetic nervous system (SNS) and the hypothalamic-pituitary-adrenal (HPA) axis. This dual activation ensures that the body is prepared for both immediate and prolonged stress management. The process begins with the perception of a stressor. This initial phase activates the SNS, leading to the rapid release of adrenaline (epinephrine) from the adrenal glands.
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Stress Response System01:21

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The stress response system, also known as the fight-or-flight response, is the body's automatic physiological reaction to perceived threats. Hans Selye introduced the concept of General Adaptation Syndrome (GAS) to describe the predictable pattern of changes that occur in response to stress. GAS consists of three sequential stages: alarm, resistance, and exhaustion. This model helps explain how chronic stress can contribute to health problems.
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Human activity recognition for analyzing stress behavior based on Bi-LSTM.

Phataratah Sa-Nguannarm1, Ermal Elbasani1, Jeong-Dong Kim2

  • 1Division of Computer Science and Engineering, Sun Moon University, Asan, South Korea.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine
|March 6, 2023
PubMed
Summary

This study introduces a deep learning model for human activity recognition (HAR) to detect stress levels using wearable sensors. The model achieved high accuracy in recognizing physical activity and stress, aiding self-care and well-being.

Keywords:
Human activity recognitionbidirectional long short-term memorydeep learningrecurrent neural networkstress behavior recognition

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Area of Science:

  • Health Informatics
  • Wearable Technology
  • Artificial Intelligence

Background:

  • Human Activity Recognition (HAR) is crucial for monitoring physical and mental health, with wearable sensors and deep learning showing promise.
  • Early detection of stress via HAR can promote self-care and prevent critical health situations.

Purpose of the Study:

  • To develop a deep learning-based human lifelog monitoring model for recognizing stress behavior and levels during activity.
  • To integrate both activity and physiological data for enhanced stress and physical activity recognition.

Main Methods:

  • A Bidirectional Long Short-Term Memory (Bi-LSTM) model was employed, enhanced with hand-crafted feature generation techniques.
  • The WESAD dataset, collected via wearable sensors, was utilized for model evaluation, encompassing baseline, amusement, stress, and meditation states.

Main Results:

  • The proposed model, utilizing hand-crafted features with Bi-LSTM, demonstrated strong performance.
  • Achieved an accuracy of 95.6% and an F1-score of 96.6% in stress level recognition.

Conclusions:

  • The developed HAR model effectively identifies stress levels, contributing to the maintenance of physical and mental well-being.
  • This approach highlights the potential of deep learning and wearable sensors in proactive health monitoring.