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

• Published in: PloS one Vol. 13; no. 10; p. e0205392
• Main Authors: Iqbal, Zohaib, Luo, Da, Henry, Peter, Kazemifar, Samaneh, Rozario, Timothy, Yan, Yulong, Westover, Kenneth, Lu, Weiguo, Nguyen, Dan, Long, Troy, Wang, Jing, Choy, Hak, Jiang, Steve
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 11.10.2018; Public Library of Science (PLoS)
• Subjects: Artificial intelligence; Artificial neural networks; Biology and Life Sciences; Bluetooth; Computer and Information Sciences; Datasets; Deep learning; Engineering and Technology; Feasibility studies; Information processing; International conferences; Laboratories; Learning theory; Localization; Medicine and Health Sciences; Model accuracy; Neural networks; Occupational safety; Oncology; Patient safety; Radiation; Radio communications; Real time; Research and Analysis Methods; Sensors; Signal strength; Teaching methods; Triangulation; Wireless networks; Workflow; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0205392

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.