research-article

Toss 'n' turn: smartphone as sleep and sleep quality detector

Authors:

Afsaneh Doryab,

Shahriyar Amini,

John Zimmerman,

Jason I. HongAuthors Info & Claims

CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Pages 477 - 486

https://doi.org/10.1145/2556288.2557220

Published: 26 April 2014 Publication History

Abstract

The rapid adoption of smartphones along with a growing habit for using these devices as alarm clocks presents an opportunity to use this device as a sleep detector. This adds value to UbiComp and personal informatics in terms of user context and new performance data to collect and visualize, and it benefits healthcare as sleep is correlated with many health issues. To assess this opportunity, we collected one month of phone sensor and sleep diary entries from 27 people who have a variety of sleep contexts. We used this data to construct models that detect sleep and wake states, daily sleep quality, and global sleep quality. Our system classifies sleep state with 93.06% accuracy, daily sleep quality with 83.97% accuracy, and overall sleep quality with 81.48% accuracy. Individual models performed better than generally trained models, where the individual models require 3 days of ground truth data and 3 weeks of ground truth data to perform well on detecting sleep and sleep quality, respectively. Finally, the features of noise and movement were useful to infer sleep quality.

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Index Terms

Toss 'n' turn: smartphone as sleep and sleep quality detector
1. Human-centered computing

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cover image ACM Conferences

CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

April 2014

4206 pages

ISBN:9781450324731

DOI:10.1145/2556288

General Chairs:
Matt Jones
Swansea University, Wales, UK
,
Philippe Palanque
Université Paul Sabatier, France
,
Program Chairs:
Albrecht Schmidt
University of Stuttgart, Germany
,
Tovi Grossman
Autodesk Research, Canada

Copyright © 2014 ACM.

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Published: 26 April 2014

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April 26 - May 1, 2014

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CHI '14 Paper Acceptance Rate 465 of 2,043 submissions, 23%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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https://dl.acm.org/doi/10.1145/3705722
Dritsa DHouben S(2024)How Design Researchers Make Sense of Data Visualizations in Data-Driven Design: An Uncertainty-Aware Sensemaking ModelACM Transactions on Computer-Human Interaction10.1145/368526831:6(1-53)Online publication date: 31-Jul-2024
https://dl.acm.org/doi/10.1145/3685268
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