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Deep learning has started to revolutionize several different industries, andthe applications of these methods in medicine are now becoming morecommonplace. This study focuses on investigating the feasibility of trackingpatients and clinical staff wearing Bluetooth Low Energy (BLE) tags in aradiation oncology clinic using artificial neural networks (ANNs) andconvolutional neural networks (CNNs). The performance of these networks wascompared to relative received signal strength indicator (RSSI) thresholding andtriangulation. By utilizing temporal information, a combined CNN+ANN networkwas capable of correctly identifying the location of the BLE tag with anaccuracy of 99.9%. It outperformed a CNN model (accuracy = 94%), a thresholdingmodel employing majority voting (accuracy = 95%), and a triangulationclassifier utilizing majority voting (accuracy = 95%). Future studies will seekto deploy this affordable real time location system in hospitals to improveclinical workflow, efficiency, and patient safety.

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