Publication Type
Journal Article
Version
publishedVersion
Publication Date
1-2023
Abstract
The availability of commercial wearable trackers equipped with features to monitor sleep duration and quality has enabled more useful sleep health monitoring applications and analyses. However, much research has reported the challenge of long-term user retention in sleep monitoring through these modalities. Since modern Internet users own multiple mobile devices, our work explores the possibility of employing ubiquitous mobile devices and passive WiFi sensing techniques to predict sleep duration as the fundamental measure for complementing long-term sleep monitoring initiatives. In this paper, we propose SleepMore, an accurate and easy-to-deploy sleep-tracking approach based on machine learning over the user's WiFi network activity. It first employs a semi-personalized random forest model with an infinitesimal jackknife variance estimation method to classify a user's network activity behavior into sleep and awake states per minute granularity. Through a moving average technique, the system uses these state sequences to estimate the user's nocturnal sleep period and its uncertainty rate. Uncertainty quantification enables SleepMore to overcome the impact of noisy WiFi data that can yield large prediction errors. We validate SleepMore using data from a month-long user study involving 46 college students and draw comparisons with the Oura Ring wearable. Beyond the college campus, we evaluate SleepMore on non-student users of different housing profiles. Our results demonstrate that SleepMore produces statistically indistinguishable sleep statistics from the Oura ring baseline for predictions made within a 5% uncertainty rate. These errors range between 15-28 minutes for determining sleep time and 7-29 minutes for determining wake time, proving statistically significant improvements over prior work. Our in-depth analysis explains the sources of errors.
Keywords
Electrical Engineering and Systems Science, Signal Processing, Computer Science - Machine Learning
Discipline
Graphics and Human Computer Interfaces | Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Volume
6
Issue
4
First Page
1
Last Page
32
ISSN
2474-9567
Identifier
10.1145/3569489
Publisher
Association for Computing Machinery (ACM)
Citation
ZAKARIA, Camellia; YILMAZ, Gizem; MAMMEN, Priyanka; CHEE, Michael; SHENOY, Prashant; and BALAN, Rajesh Krishna.
SleepMore: Inferring sleep duration at scale via multi-device WiFi sensing. (2023). Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 6, (4), 1-32.
Available at: https://ink.library.smu.edu.sg/sis_research/7746
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional URL
https://doi.org/10.1145/3569489