The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Comparative Evaluation of Susceptibility to Motion Artifact in Different Wearable Systems for Monitoring Respiratory Rate
Open accessAbstract
The purpose of this study is to comparatively evaluate the performance of different wearable systems based on indirect breathing monitoring in terms of susceptibility to motion artifacts. These performances are compared with direct respiratory measurements using a spirometer, which is accurate, reliable, and less sensitive to movement artifacts, but cannot be integrated into truly wearable form. Experiments were carried out on four indirect methods implemented into wearable systems, inductive plethysmography, impedance plethysmography, piezoresistive pneumography, and piezoelectric pneumography, to ascertain the performance of each of them in terms of noise due to movement artifacts, as well as to study the effects of different movements or gestures during each test. A group of volunteers was asked to wear all of the breath monitoring systems simultaneously along with the face mask of the spirometer while carrying out four physical exercises in a gym under controlled conditions. Data are analyzed in the time and frequency domain to estimate the frequency respiration from each wearable system and compare it with those of the spirometer. Results confirmed that all the wearable systems are somehow affected by movement artifacts, but statistical investigation showed that for most of the physical exercises, three out of four, piezoelectric pneumography provided best performance in terms of robustness and reduced susceptibility to movement artifacts.
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“The analysis showed that piezoelectric pneumography performs better than spirometry.”
From A review of wearable sensors and systems with application in rehabilitation (2012) · cites this paper as [29]
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“A comparative analysis of different wearable systems for monitoring respiratory function was presented by Lanata et al. [29].”
From A review of wearable sensors and systems with application in rehabilitation (2012) · cites this paper as [29]
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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
Goldstein, H. (2010) · Wiley series in probability and statistics · doi:10.1002/9780470973394 · Cited by 1,581
A Wearable Health Care System Based on Knitted Integrated Sensors
Paradiso, R., Loriga, G., & Taccini, N. (2005) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2005.854512 · Cited by 729
Self-citationSchuster, A. (1898) · Terrestrial Magnetism and Atmospheric Electricity · doi:10.1029/tm003i001p00013 · Cited by 641
Elite: A Digital Dedicated Hardware System for Movement Analysis Via Real-Time TV Signal Processing
Ferrigno, G., & Pedotti, A. (1985) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.1985.325627 · Cited by 580
Principles of Applied Biomedical Instrumentation.
(1975) · Annals of Internal Medicine · doi:10.7326/0003-4819-83-6-924_2 · Cited by 561
Ambulatory measurement of respiratory sinus arrhythmia and respiration rate
de Geus, E. J. C., Willemsen, G. A. H. M., Klaver, C. H. A. M., & van Doornen, L. J. P. (1995) · Biological Psychology · doi:10.1016/0301-0511(95)05137-6 · Cited by 328
Movements of the thoracic cage and diaphragm in respiration*
Wade, O. L. (1954) · The Journal of Physiology · doi:10.1113/jphysiol.1954.sp005099 · Cited by 278
Performance Evaluation of Sensing Fabrics for Monitoring Physiological and Biomechanical Variables
Scilingo, E. P., Gemignani, A., Paradiso, R., Taccini, N., Ghelarducci, B., & DeRossi, D. (2005) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2005.854506 · Cited by 247
Self-citationLmberis, A., & Dittmar, A. (2007) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/memb.2007.364926 · Cited by 194
Research works printed but not linked: 3
- milledge, 1977, inductive plethysmographya new respiratory transducer, J Physiol, 267, 4
- birkhoff, 1965, piecewise polynomial interpolation and approximation, Proc Gen Motors Symp, 164
- snedecor, 1989, Statistical Methods
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A review of wearable sensors and systems with application in rehabilitation
Patel, S., Park, H., Bonato, P., Chan, L., & Rodgers, M. (2012) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-9-21 · Cited by 2,396
Smart Homes that Monitor Breathing and Heart Rate
Adib, F., Mao, H., Kabelac, Z., Katabi, D., & Miller, R. C. (2015) · Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702200 · Cited by 916
Recent advances in wearable tactile sensors: Materials, sensing mechanisms, and device performance
Yang, T., Xie, D., Li, Z., & Zhu, H. (2017) · Materials Science and Engineering R Reports · doi:10.1016/j.mser.2017.02.001 · Cited by 767
Unobtrusive Sensing and Wearable Devices for Health Informatics
Zheng, Y.-L., Ding, X., Poon, C. C. Y., Lo, B. P. L., Zhang, H., Zhou, X.-L., Yang, G., Zhao, N., & Zhang, Y. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2309951 · Cited by 725
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
Metadata
- Authors
- Lanatà, A., Scilingo, E. P., Nardini, E., Loriga, G., Paradiso, R., & De-Rossi, D.
- Affiliations
- University of Pisa (IT)
- Smartex (Italy) (IT)
- Venue
- IEEE Transactions on Information Technology in Biomedicine
- Published
- 2010-03
- Volume
- 14
- Issue
- 2
- Pages
- 378-386
- ISSN
- 1089-7771 (ISSN-L)
- 1558-0032
- OpenAlex
- W2123350533
- Cited by
- 111
- References
- 24
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 3 · 2023: 7 · 2022: 5 · 2021: 9 · 2020: 6 · 2019: 10 · 2018: 8 · 2017: 4 · 2016: 4 · 2015: 9 · 2014: 14 · 2013: 11 · 2012: 8
- FWCI
- 3.20
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- IEEE Transactions on Information Technology in Biomedicine (publishedVersion)
- PubMed (publishedVersion)
- Florence Research (University of Florence) (submittedVersion, other-oa)
- CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Engineering › Biomedical Engineering › Wireless Body Area Networks
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Keywords
- respiratory rate estimation, wearable sensors, motion artifacts, respiratory inductance plethysmography, impedance plethysmography, spirometer, physical activity, artifact robustness, frequency domain analysis
- MeSH terms
- Adult, Clothing, Female, Humans, Male, Motor Activity, Plethysmography, Signal Processing, Computer-Assisted, Spirometry, Artifacts, Monitoring, Ambulatory, Statistics, Nonparametric, Respiratory Rate
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