Accurate Real Time Localization Tracking in A Clinical Environment using Bluetooth Low Energy and Deep Learning

TL;DR

This paper demonstrates that combining deep learning models, specifically CNNs and ANNs, enables highly accurate real-time localization of BLE tags in clinical environments, surpassing traditional methods.

Contribution

It introduces a novel deep learning approach that significantly improves BLE-based localization accuracy in healthcare settings.

Findings

CNN+ANN achieved 99.9% accuracy

Deep learning outperformed thresholding and triangulation methods

Real-time localization is feasible with affordable systems

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.