BodyCompass: Monitoring Sleep Posture with Wireless Signals
Open accessAbstract
Monitoring sleep posture is important for avoiding bedsores after surgery, reducing apnea events, tracking the progression of Parkinson's disease, and even alerting epilepsy patients to potentially fatal sleep postures. Today, there is no easy way to track sleep postures. Past work has proposed installing cameras in the bedroom, mounting accelerometers on the subject's chest, or embedding pressure sensors in their bedsheets. Unfortunately, such solutions jeopardize either the privacy of the user or their sleep comfort. In this paper, we introduce BodyCompass, the first RF-based system that provides accurate sleep posture monitoring overnight in the user's own home. BodyCompass works by studying the RF reflections in the environment. It disentangles RF signals that bounced off the subject's body from other multipath signals. It then analyzes those signals via a custom machine learning algorithm to infer the subject's sleep posture. BodyCompass is easily transferable and can apply to new homes and users with minimal effort. We empirically evaluate BodyCompass using over 200 nights of sleep data from 26 subjects in their own homes. Our results show that, given one week, one night, or 16 minutes of labeled data from the subject, BodyCompass's corresponding accuracy is 94%, 87%, and 84%, respectively.
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RF signal provides non-contact sleep monitoring solutions.
“It provides non-contact sleep monitoring solutions.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [183]
RF signal has been extended for sleep posture monitoring, which transmits radio signals and observes their reflections from the surroundings.
“Recently, RF signal has also been extended for sleep posture monitoring [183], which transmits the radio signals and observes their reflections from the surroundings.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [183]
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Yang, Y., Yuan, Y., Zhang, G., Wang, H., Chen, Y.-C., Liu, Y., Tarolli, C. G., Crepeau, D., Bukartyk, J., Junna, M. R., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01932-x · Cited by 296 · see citing works
Self-citationHuman-centred artificial intelligence for mobile health sensing: challenges and opportunities
Dang, T., Spathis, D., Ghosh, A., & Mascolo, C. (2023) · Royal Society Open Science · doi:10.1098/rsos.230806 · Cited by 30 · see citing works
- Authors
- Yue, S., Yang, Y., Wang, H., Rahul, H., & Katabi, D.
- Affiliations
- Massachusetts Institute of Technology (US)
- Venue
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
- Published
- 2020-06-15
- Volume
- 4
- Issue
- 2
- Pages
- 1-25
- ISSN
- 2474-9567 (ISSN-L)
- OpenAlex
- W3035511470
- Cited by
- 112
- References
- 57
- Citations by year
- 2026: 9 · 2025: 15 · 2024: 29 · 2023: 30 · 2022: 16 · 2021: 12 · 2020: 1
- FWCI
- 4.64
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- 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
- Physical Sciences › Engineering › Biomedical Engineering › Wireless Body Area Networks
- Keywords
- home health monitoring, privacy-preserving sensing, epilepsy monitoring, transfer learning
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