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Related Experiment Video

Updated: Jan 15, 2026

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
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Estimation of Compression Depth During CPR Using FMCW Radar with Deep Convolutional Neural Network.

Insoo Choi1, Stephen Gyung Won Lee2, Hyoun-Joong Kong2

  • 1Department of Electronic Engineering, Sogang University, Seoul 04107, Republic of Korea.

Sensors (Basel, Switzerland)
|October 16, 2025
PubMed
Summary

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Resuscitation plus·2025

This study introduces frequency-modulated continuous-wave (FMCW) radar for accurate, remote chest compression depth monitoring during cardiopulmonary resuscitation (CPR). A Wigner-Ville distribution-based deep learning model achieved the highest accuracy, improving emergency medical response.

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Effective cardiopulmonary resuscitation (CPR) demands precise chest compression depth.
  • Current out-of-hospital monitoring technologies for CPR lack accuracy and have limitations.
  • Accurate, real-time measurement of chest compression depth is crucial for improving CPR outcomes.

Purpose of the Study:

  • To develop and evaluate a non-contact method for accurately measuring chest compression depth during CPR using FMCW radar.
  • To compare the performance of different signal processing and deep learning techniques for analyzing radar data.
  • To demonstrate the potential of FMCW radar and AI in enhancing emergency medical response.

Main Methods:

  • Utilized frequency-modulated continuous-wave (FMCW) radar to capture range, Doppler, and angular data of chest movements.
Keywords:
Doppler frequencyWigner–Ville distribution (WVD)cardiopulmonary resuscitation (CPR)deep convolutional neural network (DCNN)frequency-modulated continuous-wave (FMCW) radarmicro-doppler signatureregression

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  • Employed micro-Doppler signatures and integrated Doppler shifts to estimate chest displacement.
  • Compared a regression model with deep convolutional neural networks (DCNNs) trained on spectrograms from STFT and WVD.

    • Main Results:

    • The regression model achieved a root mean square error (RMSE) of 0.535 cm.
    • The STFT-based DCNN improved accuracy with an RMSE of 0.505 cm.
    • The WVD-based DCNN achieved the best performance with an RMSE of 0.447 cm, an 11.5% improvement over the STFT-based DCNN.

    Conclusions:

    • FMCW radar combined with deep learning, particularly using WVD, offers a promising approach for accurate, non-contact chest compression depth measurement.
    • This technology has the potential to significantly improve CPR quality and patient outcomes in out-of-hospital settings.
    • The study supports the development of advanced, real-time monitoring systems for emergency medical services.