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Updated: Jun 4, 2026

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Generic neural network model for estimating exposure levels in industrial environments.

David Plets1, Christos Apostolidis2, Peter Gajšek3

  • 1Ghent University-imec-WAVES, Tech Lane Science Park 126, 9052 Ghent, Belgium.

Journal of Radiological Protection : Official Journal of the Society for Radiological Protection
|May 12, 2026
PubMed
Summary

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This study presents a user-friendly neural network model to estimate radio frequency exposure levels in industrial settings. The accessible method simplifies exposure assessment for workers, enhancing workplace safety.

Area of Science:

  • Electromagnetics and Computational Physics
  • Artificial Intelligence and Machine Learning
  • Occupational Health and Safety

Background:

  • Estimating radio frequency (RF) exposure in industrial environments is complex, often requiring specialized expertise and software.
  • Existing methods for RF exposure assessment can be inaccessible to non-experts and on-site workers.
  • There is a need for simplified, reliable tools to assess RF exposure in diverse industrial settings.

Purpose of the Study:

  • To develop and validate a neural network-based method for estimating RF exposure levels (E50 and E95) in industrial environments.
  • To create an accessible tool usable by laymen and industrial workers without requiring detailed technical inputs.
  • To demonstrate the feasibility of accurate RF exposure prediction using a simplified, broadly accessible approach.
Keywords:
EMF RF exposureindustrial environmentsindustry 4.0modellingneural networksvalidation

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Main Methods:

  • A simulation pipeline using Blender and MATLAB ray-tracing was employed to generate over 20,000 wireless configurations.
  • Eleven input parameters, including transmit power, clutter density, and transmitter location/height, were varied.
  • A multi-layer fully connected neural network regression model was developed and trained on simulated data.

Main Results:

  • Correlation analysis identified transmit power, clutter density, transmitter density, and transmitter location/height as key influencing factors.
  • The neural network model achieved high prediction accuracy on unseen data (RMSE < 0.173 V/m, R² > 95%).
  • Validation with real-world measurements in three industrial environments showed an average absolute deviation of 20.4%.

Conclusions:

  • The developed neural network model reliably estimates RF exposure levels in industrial environments with high accuracy.
  • This novel approach offers a broadly accessible solution for RF exposure assessment, reducing reliance on experts and complex software.
  • The method empowers industrial workers and layman personnel to understand and manage RF exposure risks effectively.