Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed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. https://doi.org/10.20944/preprints202009.0508.v2 Song, K. Using Machine Learning to Perform Proximity Detection - Classifying Bluetooth Beacon RSSI Values. Preprints 2020, 2020090508. https://doi.org/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.

Keywords

Bluetooth; RSSI; Classification; Machine Learning

Subject

Computer Science and Mathematics, Algebra and Number Theory

Comments (1)

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

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 1
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.