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

Academic Article

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.
  • Authors

    Published In

  • PLoS ONE  Journal
  • Keywords

  • Algorithms, Deep Learning, Humans, Local Area Networks, Mobile Applications, Patient Identification Systems, Radiation Oncology, Wireless Technology
  • Digital Object Identifier (doi)

    Author List

  • Iqbal Z; Luo D; Henry P; Kazemifar S; Rozario T; Yan Y; Westover K; Lu W; Nguyen D; Long T
  • Start Page

  • e0205392
  • Volume

  • 13
  • Issue

  • 10