Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

Using Machine Learning to Perform Proximity Detection - Classifying Bluetooth Beacon RSSI Values

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)

How to cite: Song, K. Using Machine Learning to Perform Proximity Detection - Classifying Bluetooth Beacon RSSI Values. Preprints 2020, 2020090508 (doi: 10.20944/preprints202009.0508.v2). Song, K. Using Machine Learning to Perform Proximity Detection - Classifying Bluetooth Beacon RSSI Values. Preprints 2020, 2020090508 (doi: 10.20944/preprints202009.0508.v2).

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.

Subject Areas

Bluetooth; RSSI; Classification; Machine Learning

Comments (1)

Comment 1
Received: 12 November 2020
Commenter: Karen Song
Commenter's Conflict of Interests: Author
Comment: title change (fixed extra space)
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