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Design and Analysis for Fall Detection System Simplification
08:05

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Published on: April 6, 2020

Falls event detection using triaxial accelerometry and barometric pressure measurement.

Federico Bianchi1, Stephen J Redmond, Michael R Narayanan

  • 1Department of Biomedical Engineering, Politecnico di Milano, Milano, Italy.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|December 8, 2009
PubMed
Summary

This study introduces a wearable device using barometric pressure and accelerometry for improved fall detection. The new algorithm significantly enhances accuracy in identifying falls compared to existing methods.

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Area of Science:

  • Biomedical Engineering
  • Wearable Technology
  • Sensor Fusion

Background:

  • Falls are a major health risk, especially for the elderly.
  • Existing accelerometry-based fall detection systems have limitations in accuracy.
  • Barometric pressure sensors can provide altitude information, potentially improving fall detection.

Purpose of the Study:

  • To evaluate barometric pressure measurement as a surrogate for altitude to enhance accelerometry-based fall detection algorithms.
  • To develop and assess a novel signal processing and classification algorithm for fall detection.
  • To compare the performance of the new algorithm against existing accelerometry-only methods.

Main Methods:

  • A Bluetooth-based wearable device with a triaxial accelerometer and barometric pressure sensor was utilized.
  • A signal processing and classification algorithm analyzed accelerometry and barometric pressure data from a waist-mounted device.
  • Performance was evaluated using laboratory-based simulated falls and activities of daily living performed by 15 healthy volunteers.

Main Results:

  • The algorithm incorporating barometric pressure information achieved high sensitivity (97.8%) and specificity (96.7%) in detecting falls.
  • This novel algorithm outperformed two existing algorithms that relied solely on accelerometry signals.
  • The system effectively discriminated between falls and activities of daily living.

Conclusions:

  • Integrating barometric pressure data significantly improves the accuracy of wearable-based fall detection systems.
  • The developed algorithm offers a more reliable solution for fall detection compared to accelerometry-only approaches.
  • This technology holds promise for enhancing the safety and well-being of individuals prone to falls.