Investigation on Smart Cushion Based on SFS Structure and its Application in Physiological and Activity Monitoring
Open accessAbstract
A smart cushion based on SFS (single-mode-few-mode-single-mode) structure is proposed and experimentally demonstrated, which can be applied in physiological and activity states monitoring simultaneously. The sensing unit embedded in a common cushion is designed as a sandwich structure, consisting of a piece of fiberglass mesh, a SFS structure layer and a polyvinyl chloride (PVC) layer thus the sensitivity can be significantly enhanced. With off-line processing and analyzing of the raw signals collected from the cushion system, the heartbeat rate (HR) and respiratory rate (RR) can be obtained with the maximum error of 1 bpm and the activity states on the cushion can be clearly distinguished within the accuracy of one second. Such a smart cushion has the advantages of low cost, compact structure and high comfort, especially suitable for office workers and the elderly in nursing homes.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
An SFS-based smart cushion with a fiberglass mesh/SFS/PVC sandwich structure can achieve a maximum error of 1 bpm.
“Furthermore, it is a smart cushion based on the SFS (single-mode-few-mode-single-mode) structure [221], where the sensing unit is a sandwich structure consisting of a piece of fiberglass mesh, an SFS structure layer, and a PVC layer and can achieve a maximum error of 1 bpm.”
From The Current State of Optical Sensors in Medical Wearables (2022) · cites this paper as [221]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Non-invasive human vital signs monitoring based on twin-core optical fiber sensors
Tan, F., Chen, S., Lyu, W., Liu, Z., Yu, C., Lu, C., & Tam, H.-Y. (2019) · Biomedical Optics Express · doi:10.1364/boe.10.005940 · Cited by 62 · see citing works
Self-citationMach–Zehnder Interferometer Based on Interference of Selective High-Order Core Modes
Wang, B., Zhang, W., Bai, Z., Zhang, L., Yan, T., Chen, L., & Zhou, Q. (2015) · IEEE Photonics Technology Letters · doi:10.1109/lpt.2015.2483518 · Cited by 29 · see citing works
Non-invasive smart health monitoring system based on optical fiber interferometers
Yu, C., Xu, W. L., Zhang, N., & Yu, C. (2017) · International Conference on Optical Communications and Networks (ICOCN) · doi:10.1109/icocn.2017.8121526 · Cited by 25 · see citing works
Self-citationNon-wearable respiration monitoring based on Mach-Zehnder interferometer
Li, K., Xu, W. L., Zhan, N., Wan, K., Yu, C., & Yu, C. (2017) · Conference on Lasers and Electro-Optics Pacific Rim (CLEO-PR) · doi:10.1109/cleopr.2017.8118820 · Cited by 9 · see citing works
Self-citationQu, J., Shen, Y., Xu, W. L., Tan, F., Yu, C., & Yu, C. (2019) · OptoElectronics and Communications Conference and International Conference on Photonics in Switching and Computing (OECC/PSC) · doi:10.23919/ps.2019.8817846 · Cited by 6 · see citing works
Self-citationFiber-optic Activity Monitoring with Machine Learning
Zeng, Q., Xu, W. L., Yu, C., Zhang, N., & Yu, C. (2018) · doi:10.1364/cleopr.2018.w4k.5 · Cited by 4 · see citing works
Self-citationFiber-optic MZI activity monitoring based on RLS algorithm
Wang, J., Xu, W. L., Dong, B., Yu, C., & Han, S. (2019) · International Conference on Optical Communications and Networks (ICOCN) · doi:10.1109/icocn.2019.8933918 · Cited by 2 · see citing works
Self-citationPrinciple component analysis and random forest based all-fiber activity monitoring
Han, S., Xu, W. L., You, S., Dong, B., Yu, C., & Zhao, W. (2019) · PolyU Institutional Research Archive (Hong Kong Polytechnic University) · Cited by 2 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
The Current State of Optical Sensors in Medical Wearables
Vavrinsky, E., Esfahani, N. E., Hausner, M., Kuzma, A., Rezo, V., Donoval, M., & Kosnacova, H. (2022) · Biosensors · doi:10.3390/bios12040217 · Cited by 103 · see citing works
Zhao, H., Zhu, G., Liu, F., Liu, X., Yuan, J., & Zhou, X. W. (2022) · IEEE Optoelectronics Global Conference (OGC) · doi:10.1109/ogc55558.2022.10050922 · Cited by 1 · see citing works
- Authors
- Han, S., Xu, W., You, S., Dong, B., Tan, F., Yu, C., Zhao, W., & Wang, Y.
- Affiliations
- Soochow University (CN)
- Chinese Academy of Sciences (CN)
- Shanxi University (CN)
- Hong Kong Polytechnic University (HK)
- Venue
- 2020 Opto-Electronics and Communications Conference (OECC)
- Publisher
- IEEE
- Published
- 2020-10-04
- Pages
- 1-3
- OpenAlex
- W3110852526
- Cited by
- 2
- References
- 8
- Citations by year
- 2022: 2
- FWCI
- 0.91
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Opto-Electronics and Communications Conference (OECC) (publishedVersion)
- PolyU Institutional Research Archive (Hong Kong Polytechnic University) (submittedVersion)
- Institutional Repository of Xi'an Institute of Optics and Fine Mechanics, Chinese Academy of Sciences (Xian Institute of Optics and Precision Mechanics) (submittedVersion, cc-by-nc-sa)
- Funders
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Sensor and Energy Harvesting Materials
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- piezoresistive sensing, physiological monitoring, activity monitoring, respiratory rate measurement, office workers, elderly care
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
