The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
DoppleSleep: a contactless unobtrusive sleep sensing system using short-range Doppler radar
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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In the citing paper
“In the past decade, high frequency and sub-millimetre wavelength radio technologies have demonstrated the ability to capture physiological signals without body contact.”
From The future of sleep health: a data-driven revolution in sleep science and medicine (2020) · cites this paper as [71]
In the citing paper
“Through signal processing, it is possible to extract biological information, such as breathing patterns, heart rate and full-body motion from these findings71,73–75.”
From The future of sleep health: a data-driven revolution in sleep science and medicine (2020) · cites this paper as [71]
In the citing paper
“The method has been shown to detect physical movements with an 86% recall rate and error rates of <10%71.”
From The future of sleep health: a data-driven revolution in sleep science and medicine (2020) · cites this paper as [71]
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Cited by
Sorted by citations
Soli: ubiquitous gesture sensing with millimeter wave radar
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The future of sleep health: a data-driven revolution in sleep science and medicine
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Extracting Multi-Person Respiration from Entangled RF Signals
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Metadata
- Authors
- Rahman, T., Adams, A. T., Ravichandran, R. V., Zhang, M., Patel, S. N., Kientz, J. A., & Choudhury, T.
- Affiliations
- Cornell University (US)
- University of Washington (US)
- Michigan State University (US)
- ORCID
- Venue
- Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing
- Publisher
- ACM
- Published
- 2015-09-07
- Pages
- 39-50
- OpenAlex
- W2055483942
- Cited by
- 175
- References
- 25
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 17 · 2023: 13 · 2022: 23 · 2021: 21 · 2020: 19 · 2019: 23 · 2018: 19 · 2017: 17 · 2016: 11 · 2015: 1
- FWCI
- 30.76
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) (publishedVersion)
- Funders
- ISTC (Intel Science & Technology Center on Pervasive Computing)
- National Science Foundation (IIS #1344587, IIS #1202141, CBET #1343058, IIS #1344613)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Keywords
- contactless sleep monitoring, Doppler radar, sleep quality, sleep stage classification, REM sleep detection, human motion detection, sleep latency, awakening, sleep efficiency, ARM microcontroller, mobile application, power consumption
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