research-article

DoppleSleep: a contactless unobtrusive sleep sensing system using short-range Doppler radar

Authors:

Tauhidur Rahman,

Alexander T. Adams,

Ruth Vinisha Ravichandran,

Shwetak N. Patel,

Julie A. Kientz,

Tanzeem ChoudhuryAuthors Info & Claims

UbiComp '15: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Pages 39 - 50

https://doi.org/10.1145/2750858.2804280

Published: 07 September 2015 Publication History

Abstract

In this paper, we present DoppleSleep -- a contactless sleep sensing system that continuously and unobtrusively tracks sleep quality using commercial off-the-shelf radar modules. DoppleSleep provides a single sensor solution to track sleep-related physical and physiological variables including coarse body movements and subtle and fine-grained chest, heart movements due to breathing and heartbeat. By integrating vital signals and body movement sensing, DoppleSleep achieves 89.6% recall with Sleep vs. Wake classification and 80.2% recall with REM vs. Non-REM classification compared to EEG-based sleep sensing. Lastly, it provides several objective sleep quality measurements including sleep onset latency, number of awakenings, and sleep efficiency. The contactless nature of DoppleSleep obviates the need to instrument the user's body with sensors. Lastly, DoppleSleep is implemented on an ARM microcontroller and a smartphone application that are benchmarked in terms of power and resource usage.

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Cited By

Lee KLiu WLin YChen ZHo YHuang YTsai ZHsu CYeh SLin HMajumdar AChen YKuan YLee KFeng PChen KKang JLee HHo STsai C(2025)Utilizing a Wireless Radar Framework in Combination With Deep Learning Approaches to Evaluate Obstructive Sleep Apnea Severity in Home-Setting EnvironmentsJournal of Multidisciplinary Healthcare10.2147/JMDH.S486261Volume 18(381-393)Online publication date: Jan-2025
https://doi.org/10.2147/JMDH.S486261
Li ZChen KXie Y(2024)A Deep Learning Method for Human Sleeping Pose Estimation with Millimeter Wave RadarSensors10.3390/s2418590024:18(5900)Online publication date: 11-Sep-2024
https://doi.org/10.3390/s24185900
Soares BGouveia CAlbuquerque DPinho P(2024)Impact and Classification of Body Stature and Physiological Variability in the Acquisition of Vital Signs Using Continuous Wave RadarApplied Sciences10.3390/app1402092114:2(921)Online publication date: 22-Jan-2024
https://doi.org/10.3390/app14020921
Show More Cited By

Index Terms

DoppleSleep: a contactless unobtrusive sleep sensing system using short-range Doppler radar
1. Hardware
  1. Communication hardware, interfaces and storage
    1. Signal processing systems

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Published In

cover image ACM Conferences

UbiComp '15: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 2015

1302 pages

ISBN:9781450335744

DOI:10.1145/2750858

General Chairs:
Kenji Mase
Nagoya University, JP
,
Marc Langheinrich
Università della Svizzera italiana (USI), CH
,
Daniel Gatica-Perez
IDIAP-EPFL, CH
,
Program Chairs:
Hans Gellersen
Lancaster University, UK
,
Tanzeem Choudhury
Cornell University
,
Koji Yatani
Univerisity of Tokyo, JP

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Microsoft: Microsoft
Bell Labs: Bell Laboratories
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
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Panasonic: Panasonic Corporation
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Telefónica: Telefónica
ISTC-PC: Intel Science and Technology Center for Pervasive Computing

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Association for Computing Machinery

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Published: 07 September 2015

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ISTC (Intel Science & Technology Center on Pervasive Computing)
National Science Foundation

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UbiComp '15

Sponsor:

Yahoo! Japan
SIGMOBILE
FX Palo Alto Laboratory, Inc.
ACM
Rakuten Institute of Technology
Microsoft
Bell Labs
SIGCHI
Panasonic
Telefónica
ISTC-PC

UbiComp '15: The 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 7 - 11, 2015

Osaka, Japan

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UbiComp '15 Paper Acceptance Rate 101 of 394 submissions, 26%;

Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

Lee KLiu WLin YChen ZHo YHuang YTsai ZHsu CYeh SLin HMajumdar AChen YKuan YLee KFeng PChen KKang JLee HHo STsai C(2025)Utilizing a Wireless Radar Framework in Combination With Deep Learning Approaches to Evaluate Obstructive Sleep Apnea Severity in Home-Setting EnvironmentsJournal of Multidisciplinary Healthcare10.2147/JMDH.S486261Volume 18(381-393)Online publication date: Jan-2025
https://doi.org/10.2147/JMDH.S486261
Li ZChen KXie Y(2024)A Deep Learning Method for Human Sleeping Pose Estimation with Millimeter Wave RadarSensors10.3390/s2418590024:18(5900)Online publication date: 11-Sep-2024
https://doi.org/10.3390/s24185900
Soares BGouveia CAlbuquerque DPinho P(2024)Impact and Classification of Body Stature and Physiological Variability in the Acquisition of Vital Signs Using Continuous Wave RadarApplied Sciences10.3390/app1402092114:2(921)Online publication date: 22-Jan-2024
https://doi.org/10.3390/app14020921
Mary Mammen PZakaria CMolom-Ochir TTrivedi AShenoy PBalan R(2024)WiSleep: Smartphone-driven Sleep Population Monitoring with Unsupervised LearningACM Journal on Computing and Sustainable Societies10.1145/37057223:1(1-25)Online publication date: 10-Dec-2024
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