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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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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
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
Cited by
Sorted by citations
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
Contact-Based Methods for Measuring Respiratory Rate
Massaroni, C., Nicolò, A., Presti, D. L., Sacchetti, M., Silvestri, S., & Schena, E. (2019) · Sensors · doi:10.3390/s19040908 · Cited by 465
Kakria, P., Tripathi, N. K., & Kitipawang, P. (2015) · International Journal of Telemedicine and Applications · doi:10.1155/2015/373474 · Cited by 410
The Role of Nonlinear Dynamics in Affective Valence and Arousal Recognition
Valenza, G., Lanatà, A., & Scilingo, E. P. (2011) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.30 · Cited by 256
Self-citationWeft-Knitted Strain Sensor for Monitoring Respiratory Rate and Its Electro-Mechanical Modeling
Atalay, Ö., Kennon, R., & Demirok, E. (2014) · IEEE Sensors Journal · doi:10.1109/jsen.2014.2339739 · Cited by 198
SoC CMOS UWB Pulse Radar Sensor for Contactless Respiratory Rate Monitoring
Zito, D., Pepe, D., Mincica, M., Zito, F., Tognetti, A., Lanatà, A., & Rossi, D. D. (2011) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2011.2176937 · Cited by 174
Self-citationValenza, G., Nardelli, M., Lanatà, A., Gentili, C., Bertschy, G., Paradiso, R., & Scilingo, E. P. (2013) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2013.2290382 · Cited by 166
Self-citationLanatà, A., Valenza, G., Nardelli, M., Gentili, C., & Scilingo, E. P. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2360711 · Cited by 168
Self-citationLanatà, A., Valenza, G., Greco, A., Gentili, C., Bartolozzi, R., Bucchi, F., Frendo, F., & Scilingo, E. P. (2014) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2365681 · Cited by 147
Self-citationAcousticcardiogram: Monitoring Heartbeats using Acoustic Signals on Smart Devices
Qian, K., Wu, C., Xiao, F., Zheng, Y., Zhang, Y., Yang, Z., & Liu, Y. (2018) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2018.8485978 · Cited by 122
Development of Nanosensors Based Intelligent Packaging Systems: Food Quality and Medicine
Chelliah, R., Wei, S., Daliri, E. B., Rubab, M., Elahi, F., Yeon, S., Jo, K. H., Yan, P., Liu, S., & Oh, D. H. (2021) · Nanomaterials · doi:10.3390/nano11061515 · Cited by 122
Smart Textile Based on Piezoresistive Sensing Elements for Respiratory Monitoring
Massaroni, C., Tocco, J. D., Presti, D. L., Longo, U. G., Miccinilli, S., Sterzi, S., Formica, D., Saccomandi, P., & Schena, E. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2917617 · Cited by 91
Massaroni, C., Tocco, J. D., Bravi, M., Carnevale, A., Presti, D. L., Sabbadini, R., Miccinilli, S., Sterzi, S., Formica, D., & Schena, E. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2949608 · Cited by 80
Wearable Intrinsically Soft, Stretchable, Flexible Devices for Memories and Computing
Rajan, K., Garofalo, E., & Chiolerio, A. (2018) · Sensors · doi:10.3390/s18020367 · Cited by 72
Oscillations of Heart Rate and Respiration Synchronize During Affective Visual Stimulation
Valenza, G., Lanatà, A., & Scilingo, E. P. (2012) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2012.2197632 · Cited by 66
Self-citationRespiration Monitoring via Forcecardiography Sensors
Andreozzi, E., Centracchio, J., Punzo, V., Esposito, D., Polley, C., Gargiulo, G. D., & Bifulco, P. (2021) · Sensors · doi:10.3390/s21123996 · Cited by 54
Non-Contact Vital Signs Monitoring Through Visible Light Sensing
Abuella, H., & Ekin, S. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2960194 · Cited by 52
Yang, T., Jiang, X., Huang, Y., Tian, Q., Zhang, L., Dai, Z., & Zhu, H. (2022) · iScience · doi:10.1016/j.isci.2021.103728 · Cited by 51
Lu, K., Yang, L., Seoane, F., Abtahi, F., Forsman, M., & Lindecrantz, K. (2018) · Sensors · doi:10.3390/s18093092 · Cited by 49
Improving emotion recognition systems by embedding cardiorespiratory coupling
Valenza, G., Lanatà, A., & Scilingo, E. P. (2013) · Physiological Measurement · doi:10.1088/0967-3334/34/4/449 · Cited by 48
Self-citationLanata, A., Guidi, A., Baragli, P., Valenza, G., & Scilingo, E. P. (2015) · PLOS ONE · doi:10.1371/journal.pone.0140783 · Cited by 42
Self-citationA Wearable System for the Evaluation of the Human-Horse Interaction: A Preliminary Study
Guidi, A., Lanatà, A., Baragli, P., Valenza, G., & Scilingo, E. P. (2016) · Electronics · doi:10.3390/electronics5040063 · Cited by 42
Self-citationBetella, A., Zucca, R., Cetnarski, R., Greco, A., Lanatà, A., Mazzei, D., Tognetti, A., Arsiwalla, X. D., Omedas, P., Rossi, D. D., et al. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00286 · Cited by 41
Self-citationA Proximity Coupling RF Sensor for Wrist Pulse Detection Based on Injection-Locked PLL
Kim, B., Hong, Y., An, Y., Kim, S., Lee, H., Kim, S.-W., Hong, S.-B., Yun, G., & Yook, J. (2016) · IEEE Transactions on Microwave Theory and Techniques · doi:10.1109/tmtt.2016.2549531 · Cited by 37
Cochrane, C., Hertleer, C., & Schwarz, A. (2016) · Elsevier eBooks · doi:10.1016/b978-0-08-100574-3.00002-3 · Cited by 36
Zhang, Z., & Yang, G. (2015) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2015.7299409 · Cited by 34
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Taffoni, F., Rivera, D., Camera, A. L., Nicolò, A., Velasco, J. R., & Massaroni, C. (2018) · Journal of Healthcare Engineering · doi:10.1155/2018/1878354 · Cited by 33
Zhang, Z., Silva, I., Wu, D., Zheng, J., Wu, H., & Wang, W. (2014) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-014-1201-7 · Cited by 31
Gu, C., & Li, C. (2014) · IEEE Microwave Magazine · doi:10.1109/mmm.2014.2308763 · Cited by 30
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Kim, H., Kim, J.-Y., & Im, C. (2016) · Sensors · doi:10.3390/s16091494 · Cited by 26
Келемен, М., Antoško, M., Szabó, S., Socha, Ľ., Jevcak, J., Choma, L., & Tobisová, A. (2019) · Transport Problems · doi:10.20858/tp.2019.14.3.13 · Cited by 25
Vega-Barbas, M., Diaz-Olivares, J. A., Lu, K., Forsman, M., Seoane, F., & Abtahi, F. (2019) · Sensors · doi:10.3390/s19051225 · Cited by 22
Raiano, L., Tocco, J. D., Massaroni, C., Pino, G. D., Schena, E., & Formica, D. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3043140 · Cited by 22
Fazio, R. D., Vittorio, M. D., & Visconti, P. (2022) · Future Internet · doi:10.3390/fi14060183 · Cited by 20
Curone, D., Secco, E. L., Caldani, L., Lanatà, A., Paradiso, R., Tognetti, A., & Magenes, G. (2012) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2011.2182615 · Cited by 18
Self-citationRobust Head Mounted Wearable Eye Tracking System for Dynamical Calibration
Lanatà, A., Valenza, G., Greco, A., & Scilingo, E. P. (2015) · Journal of Eye Movement Research · doi:10.16910/jemr.8.5.2 · Cited by 18
Self-citationValidation of a body sensor network for cardiorespiratory monitoring during dynamic activities
Angelucci, A., Camuncoli, F., Dotti, F., Bertozzi, F., Galli, M., Tarabini, M., & Aliverti, A. (2024) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2024.09.002 · Cited by 17
A-spiro: Towards continuous respiration monitoring
Whitlock, J., Sill, J. M., & Jain, S. (2020) · Smart Health · doi:10.1016/j.smhl.2019.100105 · Cited by 17
A Respiration-Derived Posture Method Based on Dual-Channel Respiration Impedance Signals
Liu, G., Li, K., Zheng, L., Chen, W., Zhou, G., & Jiang, Q. Y. (2017) · IEEE Access · doi:10.1109/access.2017.2737461 · Cited by 14
Guest Editorial Special Section on Personal Health Systems
Maglaveras, N., Bonato, P., & TAMURA, T. (2010) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2010.2044110 · Cited by 14
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Liu, G., Zhou, G., Chen, W., & Jiang, Q. Y. (2015) · IEEE Sensors Journal · doi:10.1109/jsen.2015.2411288 · Cited by 13
Towards Relating Physiological Signals to Usability Metrics: A Case Study with a Web Avatar
Foglia, P., & Zanda, M. (2014) · CINECA IRIS Institutial research information system (University of Pisa) · Cited by 13
A Robust Electrode Configuration for Bioimpedance Measurement of Respiration
Wang, H., Yen, C., Liang, J., Wang, Q., Liu, G., & Song, R. (2014) · Journal of Healthcare Engineering · doi:10.1260/2040-2295.5.3.313 · Cited by 12
A mobile biosensor to detect cardiorespiratory activity for stress tracking
Carbonaro, N., Cipresso, P., Tognetti, A., Anania, G., Rossi, D. D., Gaggioli, A., & Riva, G. (2013) · Proceedings of the ICTs for improving Patients Rehabilitation Research Techniques · doi:10.4108/icst.pervasivehealth.2013.252357 · Cited by 11
Removing movement artifacts from equine ECG recordings acquired with textile electrodes
Lanatà, A., Guidi, A., Baragli, P., Paradiso, R., Valenza, G., & Scilingo, E. P. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7318767 · Cited by 11
Self-citationKonstantinou, G. Ν., Anderson, W. C., Bagkis, E., Brown, Z., Karatzas, K. D., Kassandros, T., Sardeli, C., Singla, R., & Nyenhuis, S. M. (2025) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2025.09.013 · Cited by 10
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Chelliah, R., Khan, I., Wei, S., Madar, I. H., Sultan, G., Daliri, E. B., Swamidoss, C. M. A., & Oh, D. H. (2021) · Nanotechnology in the life sciences · doi:10.1007/978-3-030-84262-8_19 · Cited by 10
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Lymberis, A. (2010) · Lecture notes in electrical engineering · doi:10.1007/978-3-642-15687-8_13 · Cited by 10
Fontecave-Jallon, J., Videlier, B., Baconnier, P., Tanguy, S., Calabrèse, P., & Guméry, P.-Y. (2013) · Physiological Measurement · doi:10.1088/0967-3334/34/9/1085 · Cited by 10
Structural Parameters of Functional Membranes for Integration in Smart Wearable Materials
Eldessouki, M., Eldessouki, M., Aysha, T. S., Ratičáková, M., Šašková, J., Padil, V. V. T., Ibrahim, M., & Černík, M. (2017) · Fibres and Textiles in Eastern Europe · doi:10.5604/01.3001.0010.4631 · Cited by 10
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Lee, S. J., Motai, Y., Weiss, E., & Sun, S. S. (2012) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2012.2214395 · Cited by 9
Raiano, L., Tocco, J. D., Massaroni, C., Pino, G. D., Schena, E., & Formica, D. (2020) · IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT) · doi:10.1109/metroind4.0iot48571.2020.9138265 · Cited by 9
Sensors and Signal Processing Methods for a Wearable Physiological Parameters Monitoring System
Dosinas, A., Lukočius, R., Vaitkūnas, M., Nedzinskaite, G., Vaskys, P., Gudžius, S., & Jonaitis, A. (2017) · Elektronika ir Elektrotechnika · doi:10.5755/j01.eie.23.5.19245 · Cited by 8
From Tumor Targeting to Speech Monitoring
Gu, C., & Li, C. (2014) · Cited by 7
Raiano, L., Tocco, J. D., Massaroni, C., Pino, G. D., Schena, E., & Formica, D. (2021) · IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT) · doi:10.1109/metroind4.0iot51437.2021.9488448 · Cited by 7
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Sobhan, S., Islam, S., Valero, M., Shahriar, H., & Ahamed, S. I. (2021) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac51774.2021.00097 · Cited by 6
Bonfiglio, A., Curone, D., Secco, E. L., Magenes, G., & Tognetti, A. (2010) · Springer eBooks · doi:10.1007/978-1-4419-7384-9_10 · Cited by 6
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Fontecave-Jallon, J., Guméry, P.-Y., Calabrèse, P., Briot, R., & Baconnier, P. (2013) · International Journal of E-Health and Medical Communications · doi:10.4018/jehmc.2013010102 · Cited by 6
Suliman, A., Farahmand, M., Galeotti, L., & Scully, C. G. (2024) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2024.3386493 · Cited by 7
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Salvi, S., Dhar, R., & Karamchandani, S. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-4149-7_43 · Cited by 5
Shahbakhti, M., Hakimi, N., Horschig, J. M., Floor, M., Claassen, J. A. H. R., & Colier, W. N. J. M. (2023) · Sensors · doi:10.3390/s23073632 · Cited by 6
Ren, H., Li, Y., Zhang, H., Hu, Z., Wang, J., Song, Y., Su, K., Ding, G., Liu, H., & Yang, Z. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3322683 · Cited by 5
Enokibori, Y., Suzuki, A., Mizuno, H., Shimakami, Y., Kawabe, T., & Mase, K. (2013) · doi:10.1109/mhs.2013.6710482 · Cited by 5
Enokibori, Y., Ito, Y., Suzuki, A., Mizuno, H., Shimakami, Y., Kawabe, T., & Mase, K. (2013) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2494091.2494157 · Cited by 5
Design, preparation, and performance research of a knitted sensor for respiratory rate detection
Zhou, M., Cai, Q., Chen, W., & Wang, J. (2023) · Materials Today Communications · doi:10.1016/j.mtcomm.2023.107234 · Cited by 5
da Cunha-Martins, B. S. M., Ribeiro, G., & Jandre, F. C. (2021) · Research on Biomedical Engineering · doi:10.1007/s42600-020-00114-3 · Cited by 5
Controlling vital signs of patients in emergencies by wearable smart sensors
Rezayi, S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-85854-0.00011-3 · Cited by 4
Water Quality Sensor Noise Classification based on Drone Movement using Machine Learning
Mirda, I., Sarno, R., Sungkono, K. R., Septiyanto, A. F., Amri, T. C., & Taufany, F. (2025) · International Conference on Computer Sciences, Engineering, and Technology Innovation (ICoCSETI) · doi:10.1109/icocseti63724.2025.11019111 · Cited by 4
MEC-IoT-Healthcare: Analysis and Prospects
Wang, H., Dauwed, M., Khan, I., Sani, N. S., Omar, H. A., Amano, H., & Mostafa, S. M. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.030958 · Cited by 5
Body Fitness Monitoring Using IoT Device
Govinda, K. (2018) · Advances in healthcare information systems and administration book series · doi:10.4018/978-1-5225-5036-5.ch009 · Cited by 4
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Pavlovsky, A. V., Filipas, A. A., & Sorokozherdiev, V. O. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3646047 · Cited by 3
Electromyogram in Cigarette Smoking Activity Recognition
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Karpiel, I. S., Wołoszyn, J., Olesz, K., Mysiński, M., Urzeniczok, M., Feige, D., Sobotnicki, A., & Czerw, M. (2021) · International Conference Mixed Design of Integrated Circuits and Systems · doi:10.23919/mixdes52406.2021.9497647 · Cited by 3
Design and Evaluation of Typical Antennas for Monitoring Vital Signs
Mpanda, R. S., Qi, L., Liang, Q., Xu, L., Shi, J., & Zhao, L. (2019) · The Applied Computational Electromagnetics Society Journal (ACES) · Cited by 3
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Bonfiglio, A. (2014) · Elsevier eBooks · doi:10.1016/b978-0-444-53632-7.00521-9 · Cited by 2
Irregular Breathing Classification from Multiple Patient Datasets
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Psychometric Assessment of Cardio-Respiratory Activity Using a Mobile Platform
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Wearable Systems for e-Health: Telemonitoring and Telerehabilitation
Rossi, D. D., Lorussi, F., & Tognetti, A. (2012) · IASTED African Conferences · doi:10.2316/p.2012.763-004 · Cited by 1
Psychometric Assessment of Cardio-Respiratory Activity Using a Mobile Platform
Carbonaro, N., Cipresso, P., Tognetti, A., Anania, G., Rossi, D. D., Pallavicini, F., Gaggioli, A., & Riva, G. (2014) · International Journal of Handheld Computing Research · doi:10.4018/ijhcr.2014010102 · Cited by 1
Respiratory Volume Estimation by a Stretchable Textile Sensor
Enokibori, Y., Ito, Y., Ikeda, K., Suzuki, A., Shimakami, Y., Kawabe, T., & Mase, K. (2012) · Advances in science and technology · doi:10.4028/www.scientific.net/ast.80.136 · Cited by 1
Human body interaction driven wearable technology for vital signal sensing
Jacob, T. P., Pravin, A., Manikandan, R., Kumar, A., & Gupta, D. K. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-85854-0.00001-0 · Cited by 1
Methods for measuring respiratory rate based on the analysis of chest wall movements
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Peng, X., Liu, X., Zhang, Q., Gu, X., Wang, Y., & Cai, H. (2024) · Journal of soil science and plant nutrition · doi:10.1007/s42729-024-01697-x · Cited by 1
Videlier, B., Fontecave-Jallon, J., Calabrèse, P., Baconnier, P., & Guméry, P.-Y. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6346498 · Cited by 1
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Bonnici, T. (2019) · doi:10.13140/rg.2.2.26843.92967 · Cited by 1
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Nasim, A., Marcantoni, I., Sbrollini, A., Morettini, M., & Burattini, L. (2019) · IEEE International Symposium on Consumer Electronics (ISCE) · doi:10.1109/isce.2019.8901002 · Cited by 1
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Katoh, M., Kanazawa, T., Abe, Y., Sun, G., & Matsui, T. (2023) · Electronics and Communications in Japan · doi:10.1002/ecj.12427 · Cited by 1
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Patel, S. (2012) · doi:10.17760/d20002546 · Cited by 2
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Lu, K. (2018) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 1
Data Acquisition: Experimental Procedures and Wearable Monitoring Systems
Valenza, G., & Scilingo, E. P. (2013) · Series in bioengineering · doi:10.1007/978-3-319-02639-8_3
Self-citationA low-complexity C-band radar for non-invasive respiration measurement
Gerrits, J. F. M., Vetter, R., Farserotu, J. R., Hennemann, C., Hübner, M., & Decotignie, J.-D. (2010) · International Symposium on Applied Sciences in Biomedical and Communication Technologies · doi:10.1109/isabel.2010.5702907
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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