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Fine-grained sleep monitoring: Hearing your breathing with smartphones
Abstract
Sleep monitoring has drawn increasingly attention as the quality and quantity of the sleep are important to maintain a person's health and well-being. For example, inadequate and irregular sleep are usually associated with serious health problems such as fatigue, depression and cardiovascular disease. Traditional sleep monitoring systems, such as PSG, involve wearable sensors with professional installations, and thus are limited to clinical usage. Recent work in using smartphone sensors for sleep monitoring can detect several events related to sleep, such as body movement, cough and snore. Such coarse-grained sleep monitoring however is unable to detect the breathing rate which is an important vital sign and health indicator. This work presents a fine-grained sleep monitoring system which is capable of detecting the breathing rate by leveraging smartphones. Our system exploits the readily available smartphone earphone placed close to the user to reliably capture the human breathing sound. Given the captured acoustic sound, our system performs noise reduction to remove environmental noise and then identifies the breathing rate based on the signal envelope detection. Our system can further detect detailed sleep events including snore, cough, turn over and get up based on the acoustic features extracted from the acoustic sound. Our experimental evaluation of six subjects over six months time period demonstrates that the breathing rate monitoring and sleep events detection are highly accurate and robust under various environments. By combining breathing rate and sleep events, our system can provide continuous and noninvasive fine-grained sleep monitoring for healthcare related applications, such as sleep apnea monitoring as evidenced by our experimental study.
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“another study explored the potential of breathing rate by leveraging smartphones for sleep monitoring, which detects sleep events including snore, cough, turn over and get up [30].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [30]
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Metadata
- Authors
- Ren, Y., Wang, C., Yang, J., & Chen, Y.
- Affiliations
- Stevens Institute of Technology (US)
- Florida State University (US)
- Venue
- 2015 IEEE Conference on Computer Communications (INFOCOM)
- Publisher
- IEEE
- Published
- 2015-04
- Pages
- 1194-1202
- OpenAlex
- W1535687102
- Cited by
- 119
- References
- 16
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 11 · 2023: 9 · 2022: 17 · 2021: 14 · 2020: 13 · 2019: 14 · 2018: 13 · 2017: 14 · 2016: 6
- FWCI
- 21.16
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Conference on Computer Communications (INFOCOM) (publishedVersion)
- Rutgers University Community Repository (Rutgers University) (submittedVersion)
- 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
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Phonocardiography and Auscultation Techniques
- Keywords
- sleep monitoring, breathing rate detection, smartphone sensors, acoustic signal processing, noise reduction, snoring, cough, sleep apnea monitoring, non-invasive vital sign monitoring
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