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Smart Helmet 5.0 for Industrial Internet of Things Using Artificial Intelligence.

Israel Campero-Jurado1, Sergio Márquez-Sánchez2, Juan Quintanar-Gómez3

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Summary

This study introduces a smart helmet using Artificial Intelligence (AI) and the Industrial Internet of Things (IIoT) to enhance worker safety. The AI-powered helmet effectively detects workplace risks, improving occupational health and safety outcomes.

Keywords:
OHSPPEconvolutional neural networkdeep learningmicrocontrollernaive Bayesrisk detectionsupport vector machine

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Area of Science:

  • Occupational Health and Safety
  • Artificial Intelligence
  • Wearable Technology

Background:

  • Information and communication technologies (ICTs) enhance worker security in occupational health and safety.
  • ICT-enabled Personal Protective Equipment (PPE) reduces workplace accidents by making real-time environmental decisions.
  • Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) enable advanced PPE with monitoring and risk detection.

Purpose of the Study:

  • To present a smart helmet prototype for continuous environmental monitoring and near real-time risk evaluation.
  • To investigate the application of AI and deep learning for detecting occupational risks.
  • To compare the performance of a Deep Convolutional Neural Network (CNN) against other machine learning models for risk detection.

Main Methods:

  • Development of a smart helmet prototype equipped with sensors to monitor the work environment.
  • Utilizing an AI-driven platform for analyzing sensor data.
  • Training a Deep Convolutional Neural Network (CNN) on 11,755 samples across 12 scenarios for risk detection.
  • Comparative analysis of CNN against Static Neural Network (NN), Naive Bayes (NB), and Support Vector Machine (SVM).

Main Results:

  • The smart helmet prototype effectively monitors environmental conditions and evaluates risks in near real-time.
  • The Deep Convolutional Neural Network (CNN) achieved a 92.05% accuracy in cross-validation for occupational risk detection.
  • CNN outperformed Static NN, Naive Bayes, and SVM in identifying potential workplace hazards.

Conclusions:

  • Smart helmets integrating AI and IIoT offer a significant advancement in occupational health and safety.
  • AI-driven risk detection systems, particularly CNNs, are highly effective in improving workplace safety.
  • Continuous monitoring and real-time risk assessment by smart PPE can substantially reduce accidents and enhance worker security.