Unobtrusive Sleep Monitoring using Smartphones
Abstract
How we feel is greatly influenced by how well we sleep. Emerging quantified-self apps and wearable devices allow people to measure and keep track of sleep duration, patterns and quality. However, these approaches are intrusive, placing a burden on the users to modify their daily sleep related habits
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
The NeuroSence application detects sleep and speech/conversation data using pre-trained classifiers on accelerometer and audio data.
“Furthermore, our application detects sleep [17] and speech/conversation data [54, 76] by incorporating pre-trained classifiers using accelerometer and audio data from a microphone, respectively.”
From Predicting Brain Functional Connectivity Using Mobile Sensing (2020) · cites this paper as [17]
The sleep classifier used has an error of +/− 30 minutes, but the trends remain accurate.
“While there is an error of +/− 30 minutes in the sleep classifier used in [17], the trends remain accurate.”
From Predicting Brain Functional Connectivity Using Mobile Sensing (2020) · cites this paper as [17]
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References 18 · Cited by 300
- Authors
- Chen, Z., Lin, M., Chen, F., Lane, N., Cardone, G., Wang, R., Li, T., Chen, Y., Choudhury, T., & Cambell, A.
- Affiliations
- Dartmouth College (US)
- Microsoft Research Asia (China) (CN)
- University of Bologna (IT)
- Chinese Academy of Sciences (CN)
- Cornell University (US)
- Corresponding author
- Chen, Zhenyu
- Venue
- Proceedings of the ICTs for improving Patients Rehabilitation Research Techniques
- Publisher
- IEEE
- Published
- 2013
- OpenAlex
- W1966445952
- Cited by
- 300
- References
- 18
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 8 · 2023: 22 · 2022: 21 · 2021: 23 · 2020: 41 · 2019: 31 · 2018: 39 · 2017: 39 · 2016: 24 · 2015: 29 · 2014: 17
- FWCI
- 14.89
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings of the ICTs for improving Patients Rehabilitation Research Techniques (publishedVersion)
- Topics
- 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
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- sleep monitoring, smartphones, unobtrusive sensing, sleep quality, sleep duration, sleep patterns, wearable devices, quantified self, user burden
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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