Development of a real-time indoor location system using bluetooth low energy technology and deep learning to facilitate clinical applications.

Journal: Medical physics
Published Date: May 11, 2020

Abstract

PURPOSE: An indoor, real-time location system (RTLS) can benefit both hospitals and patients by improving clinical efficiency through data-driven optimization of procedures. Bluetooth-based RTLS systems are cost-effective but lack accuracy because Bluetooth signal is subject to significant fluctuation. We aim to improve the accuracy of RTLS using the deep learning technique.

Authors

  • Guanglin Tang
    Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75235, USA.
  • Yulong Yan
    Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, United States of America.
  • Chenyang Shen
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75002, USA.
  • Xun Jia
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas 75235.
  • Meyer Zinn
    Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75235, USA.
  • Zipalkumar Trivedi
    Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75235, USA.
  • Alicia Yingling
    Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75235, USA.
  • Kenneth Westover
    Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75235, USA.
  • Steve Jiang

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