The DOI matches its Crossref record; 180 of the 180 references it cites were found in a registry or an index.
Contact-Based Methods for Measuring Respiratory Rate
Open accessAbstract
There is an ever-growing demand for measuring respiratory variables during a variety of applications, including monitoring in clinical and occupational settings, and during sporting activities and exercise. Special attention is devoted to the monitoring of respiratory rate because it is a vital sign, which responds to a variety of stressors. There are different methods for measuring respiratory rate, which can be classed as contact-based or contactless. The present paper provides an overview of the currently available contact-based methods for measuring respiratory rate. For these methods, the sensing element (or part of the instrument containing it) is attached to the subject’s body. Methods based upon the recording of respiratory airflow, sounds, air temperature, air humidity, air components, chest wall movements, and modulation of the cardiac activity are presented. Working principles, metrological characteristics, and applications in the respiratory monitoring field are presented to explore potential development and applicability for each method.
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Cites
STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT
Martin Bland, J., & Altman, D. (1986) · The Lancet · doi:10.1016/s0140-6736(86)90837-8 · Cited by 47,965
Photoplethysmography and its application in clinical physiological measurement
Allen, J. (2007) · Physiological Measurement · doi:10.1088/0967-3334/28/3/r01 · Cited by 3,967
BioelectromagnetismPrinciples and Applications of Bioelectric and Biomagnetic Fields
Malmivuo, J., & Plonsey, R. (1995) · doi:10.1093/acprof:oso/9780195058239.001.0001 · Cited by 1,834
Micromachined inertial sensors
Yazdi, N., Ayazi, F., & Najafi, K. (1998) · Proceedings of the IEEE · doi:10.1109/5.704269 · Cited by 1,823
Humidity Sensors Principle, Mechanism, and Fabrication Technologies: A Comprehensive Review
Farahani, H., Wagiran, R., & Hamidon, M. (2014) · Sensors · doi:10.3390/s140507881 · Cited by 1,286
Dry-Contact and Noncontact Biopotential Electrodes: Methodological Review
Chi, Y. M., Jung, T.-P., & Cauwenberghs, G. (2010) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2010.2084078 · Cited by 1,061
The Measurement, Instrumentation and Sensors Handbook
Webster, J. G. (1998) · CRC Press eBooks · doi:10.1201/9781003040019 · Cited by 1,029
BERNTSON, G. G., CACIOPPO, J. T., & QUIGLEY, K. S. (1993) · Psychophysiology · doi:10.1111/j.1469-8986.1993.tb01731.x · Cited by 998
Wearable photoplethysmographic sensors—past and present
Tamura, T., Maeda, Y., Sekine, M., & Yoshida, M. (2014) · Electronics · doi:10.3390/electronics3020282 · Cited by 912 · cited 3×
“, the green-yellow region of the visible spectrum) at the tissue [233], and (ii) one photodetector, PD, to record the intensity of the non-absorbed light reflected from the tissue [246].”
BackgroundMethodRespiratory rate: the neglected vital sign
Cretikos, M. A., Bellomo, R., Hillman, K., Chen, J., Finfer, S., & Flabouris, A. (2008) · Medical Journal of Australia · doi:10.5694/j.1326-5377.2008.tb01825.x · Cited by 889
Cited by
The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise
Nicolò, A., Massaroni, C., Schena, E., & Sacchetti, M. (2020) · Sensors · doi:10.3390/s20216396 · Cited by 440
Self-citationWearable System Based on Flexible FBG for Respiratory and Cardiac Monitoring
Lo Presti, D., Massaroni, C., D'Abbraccio, J., Massari, L., Caponero, M., Longo, U. G., Formica, D., Oddo, C. M., & Schena, E. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2916320 · Cited by 219
Self-citationFiber Bragg Grating Sensors for Cardiorespiratory Monitoring: A Review
Massaroni, C., Zaltieri, M., Lo Presti, D., Nicolo, A., Tosi, D., & Schena, E. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2020.2988692 · Cited by 128
Self-citationLo Presti, D., Romano, C., Massaroni, C., D’Abbraccio, J., Massari, L., Caponero, M. A., Oddo, C. M., Formica, D., & Schena, E. (2019) · Sensors · doi:10.3390/s19163581 · Cited by 126
Self-citationKlum, M., Urban, M., Tigges, T., Pielmus, A.-G., Feldheiser, A., Schmitt, T., & Orglmeister, R. (2020) · Sensors · doi:10.3390/s20072033 · Cited by 75
Metadata
- Authors
- Massaroni, C., Nicolò, A., Lo Presti, D., Sacchetti, M., Silvestri, S., & Schena, E.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2019-02-21
- Volume
- 19
- Issue
- 4
- Pages
- 908
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W2917388343
- Cited by
- 426
- References
- 180
- FWCI
- 18.33
- Type
- journal article
- Language
- en
- Open access
- gold
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