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Building occupancy estimation using single channel CW radar and deep learning.

Sourav Kumar Pramanik1, Md Shafkat Hossain1, Shekh Md Mahmudul Islam2

  • 1Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, 1000, Bangladesh.

Scientific Reports
|April 1, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel radar-based method for accurately estimating room occupancy without compromising privacy. Continuous Wave (CW) radar combined with deep learning achieves high accuracy, offering a robust solution for smart buildings.

Keywords:
CW radarDeep learningOccupancy estimationSmart buildings

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Area of Science:

  • Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Occupancy estimation is vital for smart building optimization, energy efficiency, and security.
  • Existing methods like WiFi or PIR sensors raise privacy concerns.
  • A privacy-preserving, accurate occupancy estimation method is needed.

Purpose of the Study:

  • To develop and evaluate a novel radar-based occupancy estimation method.
  • To leverage Continuous Wave (CW) radar and deep learning for non-intrusive sensing.
  • To ensure privacy preservation in occupancy monitoring.

Main Methods:

  • Utilized a 24-GHz CW radar system for data acquisition.
  • Applied time-frequency mapping techniques: Continuous Wavelet Transform (CWT) and power spectrum analysis.
  • Trained deep learning models (DarkNet19, MobileNetV2, ResNet18) on radar echo-derived time-frequency scalograms.

Main Results:

  • The radar-based approach achieved high accuracy in occupancy estimation.
  • DarkNet19 model demonstrated superior performance, reaching 92.7% accuracy with CWT images.
  • Effective performance was also shown in dynamic (walking) environments (86.5% accuracy).

Conclusions:

  • CW radar integrated with deep learning offers a non-intrusive and privacy-preserving solution for occupancy estimation.
  • This method is suitable for various smart building applications.
  • The study validates the effectiveness of radar-based sensing for real-time occupancy monitoring.