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Updated: May 28, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Simulation-to-Real Trip-Fall Detection with Continuous-Wave Doppler Radar via Physics-Informed Kinematic Modeling and

Kosuke Okusa1

  • 1Department of Data Science for Business Innovation, Chuo University, Tokyo 112-8551, Japan.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
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Impact of Computer-Aided Detection on Endoscopist's Gaze-Shift Distance During Colonoscopy: a Randomized Controlled Trial (With Video).

Journal of gastroenterology and hepatology·2026
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This study demonstrates that physics-informed simulation can effectively train radar systems for detecting falls in older adults, reducing the need for extensive real-world fall data. This approach enhances fall detection capabilities for public health.

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Gerontology

Background:

  • Falls in older adults represent a significant public health challenge.
  • Acquiring large-scale real-world fall data for radar-based detection is ethically and practically challenging.
  • Existing fall detection methods often require extensive real-world data for training.

Purpose of the Study:

  • To evaluate the feasibility of using simulated data for training radar-based trip-fall detection systems.
  • To develop a method for generating realistic synthetic radar signals for fall events.
  • To assess the performance of a classifier trained on simulated data for distinguishing between falls, walking, and breathing.

Main Methods:

  • Coupled a physics-informed kinematic trip-fall model with a continuous-wave (CW) Doppler radar observation model to synthesize I/Q signals and Doppler spectrograms.
Keywords:
continuous-wave (CW) doppler radardomain randomizationphysics-informed simulationprivacy-preserving sensingsimulation-only trainingsimulation-to-real transfertime–frequency analysistrip-fall detection

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Last Updated: May 28, 2026

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  • Employed domain randomization to vary parameters such as body size, fall direction, and sensor placement.
  • Generated synthetic data for three classes: trip fall, walking, and seated quiet breathing.

    • Main Results:

    • Simulated spectrograms accurately reproduced key time-frequency characteristics of measured enacted trip-fall signals (mean SSIM of 0.782).
    • A ResNet-18 classifier trained solely on simulated data achieved a macro-F1 score of 0.912 on measured data, outperforming a real-data-trained baseline (0.748).
    • The simulation-based approach significantly improved classification performance compared to the baseline (p=0.006).

    Conclusions:

    • Physics-informed simulation combined with domain randomization can effectively reduce the reliance on real trip-fall data, especially under limited-data conditions.
    • This approach shows promise for developing robust radar-based fall detection systems.
    • Further research is needed to establish robustness across diverse fall types, activities, environments, and radar systems.