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Fall Detection Using Multiple Bioradars and Convolutional Neural Networks.

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Updated: Aug 30, 2025

Design and Analysis for Fall Detection System Simplification
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Contactless Fall Detection by Means of Multiple Bioradars and Transfer Learning.

Vera Lobanova1, Valeriy Slizov1, Lesya Anishchenko1

  • 1Remote Sensing Laboratory, Bauman Moscow State Technical University, 105005 Moscow, Russia.

Sensors (Basel, Switzerland)
|August 26, 2022
PubMed
Summary

This study introduces a multi-bioradar system for contactless fall detection in the elderly. Using wavelet transform and transfer learning, the four-bioradar system achieved 99% accuracy, significantly improving fall recognition performance.

Keywords:
bioradiolocationdeep learningfall detectionremote sensingtransfer learningwavelet analysis

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Area of Science:

  • Geriatric care
  • Biomedical engineering
  • Signal processing

Background:

  • Automatic fall detection is crucial for elderly individuals living alone to prevent serious health consequences.
  • Contactless fall detection using bioradiolocation shows promise but requires improved precision and view-independent recognition.
  • Existing systems can be enhanced for better accuracy in recognizing falls.

Purpose of the Study:

  • To develop and evaluate a more precise, contactless fall detection system for the elderly.
  • To investigate the effectiveness of multi-channel bioradar systems combined with advanced signal processing and machine learning.
  • To improve upon existing two-bioradar systems for fall recognition.

Main Methods:

  • A multi-channel bioradar system (three or four bioradars) was employed for data acquisition.
  • Wavelet transform was utilized for signal processing.
  • A pre-trained convolutional neural network (AlexNet) was fine-tuned using scalograms for binary classification of falls.
  • Experiments involved various radar configurations to record different movement types.

Main Results:

  • The four-bioradar system achieved an accuracy of 0.99 and a Cohen's kappa of 0.99.
  • The three-bioradar system demonstrated high performance with an accuracy of 0.98 and Cohen's kappa of 0.97.
  • Both multi-bioradar systems significantly outperformed the traditional two-bioradar system (accuracy 0.92, Cohen's kappa 0.86).

Conclusions:

  • A multi-channel bioradar system, utilizing wavelet transform and transfer learning, offers a significant improvement in contactless fall detection accuracy.
  • The proposed system holds potential for the development of advanced, reliable fall detection solutions for the elderly.
  • Further research can explore optimizing radar configurations and deep learning models for enhanced geriatric safety.