Article
Version 1
Preserved in Portico This version is not peer-reviewed
Using Machine Learning to Perform Proximity Detection - Classifying Bluetooth Beacon RSSI V alues
Version 1
: Received: 20 September 2020 / Approved: 22 September 2020 / Online: 22 September 2020 (04:16:57 CEST)
Version 2 : Received: 10 November 2020 / Approved: 12 November 2020 / Online: 12 November 2020 (08:31:41 CET)
Version 2 : Received: 10 November 2020 / Approved: 12 November 2020 / Online: 12 November 2020 (08:31:41 CET)
How to cite: Song, K. Using Machine Learning to Perform Proximity Detection - Classifying Bluetooth Beacon RSSI V alues. Preprints 2020, 2020090508. https://doi.org/10.20944/preprints202009.0508.v1 Song, K. Using Machine Learning to Perform Proximity Detection - Classifying Bluetooth Beacon RSSI V alues. Preprints 2020, 2020090508. https://doi.org/10.20944/preprints202009.0508.v1
Abstract
This project focuses on using machine learning classification algorithms to determine whether two people are 6 feet apart or not. Two Raspberry Pis were used simulate smart phones. RSSI values of the Bluetooth beacons transmitted between the Raspberry Pis were collected and recorded to train the classifier. The Gaussian Support Vector Machine Classifer yielded the highest testing accuracy of 79.670 and the Decision Tree Classifier yielded the highest AUC of 0.80.
Keywords
Bluetooth, RSSI, Classification, Machine Learning
Subject
Computer Science and Mathematics, Algebra and Number Theory
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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