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BeSafe B2.0 Smart Multisensory Platform for Safety in Workplaces.

Sergio Márquez-Sánchez1,2, Israel Campero-Jurado3, Daniel Robles-Camarillo4

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Summary

Wearable sensors and AIoT enhance worker safety by real-time health monitoring. This system detects anomalies, reducing workplace accidents and occupational diseases effectively.

Keywords:
AIoTGaussian mixture modelanomaly detectionartificial intelligencedeeptechhuman activity classificationmachine learningsmart PPEsmart bracelet

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Area of Science:

  • Engineering
  • Computer Science
  • Occupational Health

Background:

  • Wearable technologies, including smartwatches, helmets, and belts, are increasingly vital for personal health monitoring and workplace safety.
  • These devices offer real-time data on vital signs and environmental factors, functioning as advanced Personal Protective Equipment (PPE).
  • The integration of Artificial Intelligence and the Internet of Things (AIoT) presents opportunities to significantly improve safety protocols in hazardous work environments.

Purpose of the Study:

  • To conduct a comprehensive review of electronic systems for human activity behavior monitoring.
  • To develop and present a smart multisensory bracelet and control platform designed to enhance operator security in industrial settings.
  • To leverage AIoT for real-time health and safety monitoring, specifically targeting high-risk environments like construction sites and power stations.

Main Methods:

  • A review of existing literature on electronics for human activity behavior.
  • Development of a smart multisensory bracelet integrated with other devices into a control platform.
  • Implementation of a hybrid machine learning system combining Gaussian Mixture Model (GMM) for unsupervised learning and Long Short-Term Memory (LSTM) for supervised human activity classification.
  • Real-time data transmission to a central server for processing and alarm generation.

Main Results:

  • The Gaussian Mixture Model achieved performance rates of 80%, 85%, 70%, and 80% for four distinct real-time classified activities.
  • The Long Short-Term Memory model demonstrated high accuracy in activity recognition, with specific results of 0.769 for carrying-displacing, 0.892 for falls, and 0.921 for walking-standing activities.
  • The developed platform successfully processed data in real-time, enabling an effective alarm system for detected anomalies.

Conclusions:

  • The proposed AIoT-based system, utilizing a hybrid machine learning approach, significantly enhances operator safety through real-time health and activity monitoring.
  • The smart multisensory bracelet and integrated platform provide a robust solution for early anomaly detection, contributing to accident prevention and improved occupational health.
  • This technology holds substantial potential for application in various high-risk industries, offering a proactive approach to worker safety and well-being.