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Related Experiment Video

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Stress Monitoring Using Machine Learning, IoT and Wearable Sensors.

Abdullah A Al-Atawi1, Saleh Alyahyan2, Mohammed Naif Alatawi3

  • 1Department of Computer Science, Applied College, University of Tabuk, Tabuk 47512, Saudi Arabia.

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Summary

This study introduces Stress-Track, a novel machine learning system using wearable sensors to monitor stress levels via body temperature, sweat, and motion. It achieves 99.5% accuracy, offering a proactive approach to stress management in smart healthcare.

Keywords:
IoThealthcaremachine learningsensorstress

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Area of Science:

  • Health Informatics
  • Wearable Technology
  • Machine Learning in Healthcare

Background:

  • The Internet of Things (IoT) is transforming healthcare with interconnected devices for monitoring.
  • Wearable sensors and IoT enable cost-effective, continuous health tracking and stress monitoring.
  • Psychological stress significantly impacts physiological health, necessitating proactive management.

Purpose of the Study:

  • To introduce a novel machine learning-based system, Stress-Track, for proactive stress level monitoring.
  • To leverage wearable technology and IoT for enhanced wellness and preventative health management.
  • To address the critical need for effective stress management solutions in smart healthcare.

Main Methods:

  • Development of a machine learning system named Stress-Track.
  • Utilizing wearable sensors to collect data on body temperature, sweat, and motion rate.
  • Implementing wireless communication channels for real-time data transmission and analysis.

Main Results:

  • The Stress-Track system demonstrates a high accuracy rate of 99.5% in monitoring stress levels.
  • The system effectively analyzes physiological parameters to anticipate and track stress.
  • Successful integration of machine learning with wearable IoT devices for health applications.

Conclusions:

  • Stress-Track offers a promising, accurate, and proactive solution for stress management.
  • The system has significant potential to enhance preventative healthcare and patient wellness.
  • This research highlights the growing role of IoT and machine learning in personalized health monitoring.