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First published in Sensors v.23 i.14 by MDPI. DOI: https://doi.org/10.3390/s23146631

Document Type

Article

Keywords

MAC layer, machine learning, sleep detection, sniffer, WiFi

Journal/Book/Conference Title

Sensors

Volume

23

Issue

14

Abstract

A good night’s sleep is of the utmost importance for the seamless execution of our cognitive capabilities. Unfortunately, the research shows that one-third of the US adult population is severely sleep deprived. With college students as our focused group, we devised a contactless, unobtrusive mechanism to detect sleep patterns, which, contrary to existing sensor-based solutions, does not require the subject to put on any sensors on the body or buy expensive sleep sensing equipment. We named this mechanism Packets-to-Predictions(P2P) because we leverage the WiFi MAC layer traffic collected in the home and university environments to predict “sleep” and “awake” periods. We first manually established that extracting such patterns is feasible, and then, we trained various machine learning models to identify these patterns automatically. We trained six machine learning models—K nearest neighbors, logistic regression, random forest classifier, support vector classifier, gradient boosting classifier, and multilayer perceptron. K nearest neighbors gave the best performance with 87% train accuracy and 83% test accuracy.

Department

Department of Computer Science

Original Publication Date

7-1-2023

Object Description

1 PDF File

DOI of published version

10.3390/s23146631

Repository

UNI ScholarWorks, Rod Library, University of Northern Iowa

Copyright

©2023 The Authors. CC BY 4.0 License

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Language

en

File Format

application/pdf

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