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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High frequency and sub-millimetre wavelength radio technologies have demonstrated the ability to capture physiological signals without body contact.
“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]
Through signal processing, it is possible to extract biological information, such as breathing patterns, heart rate and full-body motion, from radio signals.
“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]
The ultrasound method has been shown to detect physical movements with an 86% recall rate and error rates of <10%.
“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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A Novel Signal Separation and De-Noising Technique for Doppler Radar Vital Signal Detection
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Engagement-Free and Contactless Bed Occupancy and Vital Signs Monitoring
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RF-Egg: An RF Solution for Fine-Grained Multi-Target and Multi-Task Egg Incubation Sensing
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Unobtrusive acquisition of cardiorespiratory signals
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SleepGuru: Personalized Sleep Planning System for Real-life Actionability and Negotiability
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The role of telemedicine in obstructive sleep apnea management
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Material Identification Using RF Sensors and Convolutional Neural Networks
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Self-citation
- 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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