{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/accurate-real-time-localization-tracking-in-a","title":"Accurate Real Time Localization Tracking in A Clinical Environment using Bluetooth Low Energy and Deep Learning","arxiv_id":"1711.08149","date":"2017-11-22","proceeding":null,"authors":["Zohaib Iqbal","Da Luo","Peter Henry","Samaneh Kazemifar","Timothy Rozario","Yulong Yan","Kenneth Westover","Weiguo Lu","Dan Nguyen","Troy Long","Jing Wang","Hak Choy","Steve Jiang"],"abstract":"Deep learning has started to revolutionize several different industries, and\nthe applications of these methods in medicine are now becoming more\ncommonplace. This study focuses on investigating the feasibility of tracking\npatients and clinical staff wearing Bluetooth Low Energy (BLE) tags in a\nradiation oncology clinic using artificial neural networks (ANNs) and\nconvolutional neural networks (CNNs). The performance of these networks was\ncompared to relative received signal strength indicator (RSSI) thresholding and\ntriangulation. By utilizing temporal information, a combined CNN+ANN network\nwas capable of correctly identifying the location of the BLE tag with an\naccuracy of 99.9%. It outperformed a CNN model (accuracy = 94%), a thresholding\nmodel employing majority voting (accuracy = 95%), and a triangulation\nclassifier utilizing majority voting (accuracy = 95%). Future studies will seek\nto deploy this affordable real time location system in hospitals to improve\nclinical workflow, efficiency, and patient safety.","url_abs":"http://arxiv.org/abs/1711.08149v3","url_pdf":"http://arxiv.org/pdf/1711.08149v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"accurate-real-time-localization-tracking-in-a","repo_url":"https://github.com/zoball/BLE-Tracking-with-Deep-Learning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"tag","task_name":"TAG"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}