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Introduction to AEDAn Automated External Defibrillator (AED) is a portable medical device that analyzes the heart's rhythm and, if necessary, delivers an electrical shock to help the heart re-establish an effective rhythm during sudden cardiac arrest (SCA). SCA occurs when the heart suddenly and unexpectedly stops beating, leading to a loss of blood flow to the brain and other vital organs. In such emergencies, time is of the essence, and using an AED, combined with Cardiopulmonary...
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Related Experiment Video

Updated: Sep 7, 2025

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AEDNav: indoor navigation for locating automated external defibrillator.

Gaurav Rao1, Vijay Mago2, Pawan Lingras3

  • 1Department of Mathematics and Computing Science, Saint Mary's University, Halifax, NS, Canada. gaurav.rao@smu.ca.

BMC Medical Informatics and Decision Making
|June 20, 2022
PubMed
Summary

This study introduces AEDNav, a new system for indoor navigation to Automated External Defibrillators (AEDs). AEDNav uses existing smartphone technology to improve survival rates during sudden cardiac arrest.

Keywords:
Automated external defibrillator (AED)Dynamic time warping (DTW)Indoor navigationMagnetic dataSudden cardiac arrest (SCA)Wi-FiWi-Fi fingerprinting

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Area of Science:

  • Computer Science
  • Biomedical Engineering
  • Emergency Medicine

Background:

  • Sudden cardiac arrest necessitates immediate CPR and AED use.
  • Existing AED locator apps lack indoor navigation, hindering timely access.
  • Current indoor navigation systems are often impractical due to hardware or data requirements.

Purpose of the Study:

  • To develop a practical indoor navigation system for locating Automated External Defibrillators (AEDs).
  • To enhance patient survival rates during sudden cardiac arrest by reducing AED retrieval time.

Main Methods:

  • The AEDNav system utilizes smartphone-collected Wi-Fi and magnetic field data.
  • Four techniques (turn detection, magnetic pattern matching, Wi-Fi fingerprinting, closest Wi-Fi) estimate user location.
  • Fusion of these techniques refines location accuracy for indoor navigation.

Main Results:

  • Individual methods achieved up to 80% accuracy with varying error distances.
  • The combined system demonstrated robust performance with 80% accuracy and a ±2.74m error distance.
  • The proposed system outperforms existing Wi-Fi and magnetic data-based navigation solutions.

Conclusions:

  • AEDNav offers a novel, low-infrastructure approach to indoor navigation.
  • The system achieves accuracy comparable to existing solutions without special hardware or extensive data.
  • Successful implementation of AEDNav can significantly decrease AED location time, improving cardiac arrest survival outcomes.