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Accurate real time localization tracking in a clinical environment using Bluetooth Low Energy and deep learning

Author

Listed:
  • Zohaib Iqbal
  • Da Luo
  • Peter Henry
  • Samaneh Kazemifar
  • Timothy Rozario
  • Yulong Yan
  • Kenneth Westover
  • Weiguo Lu
  • Dan Nguyen
  • Troy Long
  • Jing Wang
  • Hak Choy
  • Steve Jiang

Abstract

Deep learning has started to revolutionize several different industries, and the applications of these methods in medicine are now becoming more commonplace. This study focuses on investigating the feasibility of tracking patients and clinical staff wearing Bluetooth Low Energy (BLE) tags in a radiation oncology clinic using artificial neural networks (ANNs) and convolutional neural networks (CNNs). The performance of these networks was compared to relative received signal strength indicator (RSSI) thresholding and triangulation. By utilizing temporal information, a combined CNN+ANN network was capable of correctly identifying the location of the BLE tag with an accuracy of 99.9%. It outperformed a CNN model (accuracy = 94%), a thresholding model employing majority voting (accuracy = 95%), and a triangulation classifier utilizing majority voting (accuracy = 95%). Future studies will seek to deploy this affordable real time location system in hospitals to improve clinical workflow, efficiency, and patient safety.

Suggested Citation

  • Zohaib Iqbal & Da Luo & Peter Henry & Samaneh Kazemifar & Timothy Rozario & Yulong Yan & Kenneth Westover & Weiguo Lu & Dan Nguyen & Troy Long & Jing Wang & Hak Choy & Steve Jiang, 2018. "Accurate real time localization tracking in a clinical environment using Bluetooth Low Energy and deep learning," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-13, October.
    • Handle: RePEc:plo:pone00:0205392
    DOI: 10.1371/journal.pone.0205392
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    Cited by:

    1. Fernando J. Aranda & Felipe Parralejo & Fernando J. Álvarez & Joaquín Torres-Sospedra, 2020. "Multi-Slot BLE Raw Database for Accurate Positioning in Mixed Indoor/Outdoor Environments," Data, MDPI, vol. 5(3), pages 1-20, July.
    2. Germán Martín Mendoza-Silva & Miguel Matey-Sanz & Joaquín Torres-Sospedra & Joaquín Huerta, 2019. "BLE RSS Measurements Dataset for Research on Accurate Indoor Positioning," Data, MDPI, vol. 4(1), pages 1-17, January.

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