Abstract
In order to be accepted by users, one of the most important requirement for any assisted living technology is unobtrusiveness. This is particularly true when this technology is intended for continuous monitoring of vital signs, since traditional approaches require the subject to be tethered to measurement devices. The radar sensing is a very promising technology, enabling the measurement of vital signs at a distance, and thus meeting both requirements of unobtrusiveness and accuracy. In particular, impulse-radio ultra-wideband radar has attracted considerable attention in recent years thanks to many properties that make it useful for assisted living purposes. The aim of this paper is to investigate such radar technology for vital signs monitoring in assisted living scenarios. An algorithmic framework for the detection of respiration and heart rates during various activities of daily living is presented, including a method for compensation of movements of both the monitored subject and a second person present in the same environment (e.g., family member, caregiver, etc.). Experiments are carried out in various conditions that can be frequently encountered in assisted living scenarios. The reported results show that vital signs can be detected also while carrying out ADLs, with accuracy varying according to the level of movements and kind of involved body’s parts and postures.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Siciliano, P., Leone, A., Diraco, G., Distante, C., Malfatti, M., Gonzo, L., et al. (2009). A networked multisensor system for ambient assisted living application. In 3rd International Workshop on Advances in Sensors and Interfaces, IWASI 2009 (pp. 139–143).
Rantz, M. J., Skubic, M., & Miller, S. J. (2009). Using sensor technology to augment traditional healthcare. In: Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 (pp. 6159–6162).
Wild, K., Boise, L., Lundell, J., & Foucek, A. (2008). Unobtrusive in-home monitoring of cognitive and physical health: Reactions and perceptions of older adults. Journal of Applied Gerontology, 27(2), 181–200.
Janssens, J.-P., Pautex, S., Hilleret, H., & Michel, J.-P. (2000). Sleep disordered breathing in the elderly. Aging Clinical and Experimental Research, 12(6), 417–429.
Casolo, G., Balli, E., Taddei, T., Amuhasi, J., & Gori, C. (1989). Decreased spontaneous heart rate variability in congestive heart failure. The American Journal of Cardiology, 64(18), 1162–1167.
Sajadieh, A., Nielsen, O. W., Rasmussen, V., Hein, H. O., Abedini, S., & Hansen, J. F. (2004). Increased heart rate and reduced heart-rate variability are associated with subclinical inflammation in middle-aged and elderly subjects with no apparent heart disease. European Heart Journal, 25(5), 363–370.
van der Kooy, K. G., van Hout, H. P. J., van Marwijk, H. W. J., de Haan, M., Stehouwer, C. D. A., & Beekman, A. T. F. (2006). Differences in heart rate variability between depressed and non-depressed elderly. International Journal of Geriatric Psychiatry, 21(2), 147–150.
Alonso, A., Huang, X., Mosley, T. H., Heiss, G., & Chen, H. (2015). Heart rate variability and the risk of parkinson disease: The atherosclerosis risk in communities study. Annals of Neurology, 77(5), 877–883.
Greneker, E. F., III. (1997). Radar sensing of heartbeat and respiration at a distance with security applications. In Proceedings of SPIE—The International Society for Optical Engineering (Vol. 3066, pp. 22–27).
Malik, M. (1996). Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. Circulation, 93(5), 1043–1065.
Hafner, N., & Lubecke, V. (2009). Performance assessment techniques for Doppler radar physiological sensors. In Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 (pp. 4848–4851).
Kumar, M., Veeraraghavan, A., & Sabharwal, A. (2015). Distanceppg: Robust noncontact vital signs monitoring using a camera. Biomedical Optics Express, 6(5), 1565–1588.
Lubecke, O. B., Ong, P.-W., & Lubecke, V. M. (2002, January). 10 GHz Doppler radar sensing of respiration and heart movement. In Proceedings of the IEEE Annual Northeast Bioengineering Conference, NEBEC, 2002 (pp. 55–56).
Immoreev, I., & Tao, T.-H. (2008). UWB radar for patient monitoring. IEEE Aerospace and Electronic Systems Magazine, 23(11), 11–18.
Chen, V. C., Li, F., Ho, S. S., & Wechsler, H. (2006). Micro-Doppler effect in radar: phenomenon, model, and simulation study. IEEE Transactions on Aerospace and Electronic Systems, 42(1), 2–21.
Caro, C. G., & Bloice, J. A. (1971). Contactless apnoea detector based on radar. The Lancet, 298(7731), 959–961.
Franks, C. I., Brown, B. H., & Johnston, D. M. (1976). Contactless respiration monitoring of infants. Medical & Biological Engineering, 14(3), 306–312.
Lazaro, A., Girbau, D., & Villarino, R. (2014). Techniques for clutter suppression in the presence of body movements during the detection of respiratory activity through UWB radars. Sensors (Switzerland), 14(2), 2595–2618.
Lin, J. C., Kiernicki, J., Kiernicki, M., & Wollschlaeger, P. B. (1979). Microwave apexcardiography. IEEE Transactions on Microwave Theory and Techniques, 27(6), 618–620.
Lin, J. C. (1992). Microwave sensing of physiological movement and volume change: A review. Bioelectromagnetics, 13(6), 557–565.
Ferini-Strambi, L., Franceschi, M., Pinto, P., Zucconi, M., & Smirne, S. (1992). Respiration and heart rate variability during sleep in untreated parkinson patients. Gerontology, 38(1–2), 92–98.
Folke, M., Cernerud, L., Ekström, M., & Hök, B. (2003). Critical review of non-invasive respiratory monitoring in medical care. Medical and Biological Engineering and Computing, 41(4), 377–383.
Kim, K.-B., Suh, J.-S., Shin, D.-H., & Park, S.-O. (2014). High sensitivity doppler radar system for detecting respiration and heart rate using improved isolation technique. In 2014 31th URSI General Assembly and Scientific Symposium, URSI GASS 2014.
Vinci, G., Lindner, S., Barbon, F., Hofmann, M., Fischer, G., Kissinger, D., et al. (2012). 24 GHz six-port medical radar for contactless respiration detection and heartbeat monitoring. In European Microwave Week 2012: “Space for Microwaves”, EuMW 2012, Conference Proceedings—9th European Radar Conference, EuRAD 2012 (pp. 75–78).
Li, C., Lubecke, V. M., Boric-Lubecke, O., & Lin, J. (2013). A review on recent advances in doppler radar sensors for noncontact healthcare monitoring. IEEE Transactions on Microwave Theory and Techniques, 61(5), 2046–2060.
Schleicher, B., Nasr, I., Trasser, A., & Schumacher, H. (2013). IR-UWB radar demonstrator for ultra-fine movement detection and vital-sign monitoring. IEEE Transactions on Microwave Theory and Techniques, 61(5), 2076–2085.
Nguyen, C., & Han, J. (2014). Time-domain ultra-wideband radar, sensor and components: Theory, analysis and design (p. 133). SpringerBriefs in Electrical and Computer Engineering. New York: Springer.
Jia, Y., Kong, L., Yang, X., & Wang, K. (2013). Through-wall-radar localization for stationary human based on life-sign detection. In IEEE National Radar Conference—Proceedings, 2013.
Li, C., Cummings, J., Lam, J., Graves, E., & Wu, W. (2009). Radar remote monitoring of vital signs. IEEE Microwave Magazine, 10(1), 47–56.
Wang, H., Cheng, J.-H., Kao, J.-C., & Huang, T.-W. (2014). Review on microwave/millimeter-wave systems for vital sign detection. In WiSNet 2014—Proceedings: 2014 IEEE Topical Conference on Wireless Sensors and Sensor Networks (pp. 19–21).
Adib, F., Mao, H., Kabelac, Z., Katabi, D., & Miller, R. C. (2015, April). Smart homes that monitor breathing and heart rate. In Conference on Human Factors in Computing Systems—Proceedings (pp. 837–846).
Ren, L., Koo, Y. S., Wang, H., Wang, Y., Liu, Q., & Fathy, A. E. (2015). Noncontact multiple heartbeats detection and subject localization using UWB impulse Doppler radar. IEEE Microwave and Wireless Components Letters, 25(10), 690–692.
Rivera, N. V., Venkatesh, S., Anderson, C., & Buehrer, R. M. (2006). Multi-target estimation of heart and respiration rates using ultra wideband sensors. In European Signal Processing Conference.
Baboli, M., Boric-Lubecke, O., & Lubecke, V. (2012). A new algorithm for detection of heart and respiration rate with UWB signals. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (pp. 3947–3950).
Baldi, M., Appignani, F., Zanaj, B., & Chiaraluce, F. (2012). Body movement compensation in UWB radars for respiration monitoring. In: Proceedings—2012 IEEE 1st AESS European Conference on Satellite Telecommunications, ESTEL 2012.
Sharafi, A., Baboli, M., Eshghi, M., & Ahmadian, A. (2012). Respiration-rate estimation of a moving target using impulse-based ultra wideband radars. Australasian Physical and Engineering Sciences in Medicine, 35(1), 31–39.
Lazaro, A., Girbau, D., & Villarino, R. (2010). Analysis of vital signs monitoring using an IR-UWB radar. Progress in Electromagnetics Research, 100, 265–284.
Time Domain. (2015, May 27). Pulson®p410 radar kit (Online). Available http://www.timedomain.com/.
Abujarad, F., Nadim, G., & Omar, A. (2005). Clutter reduction and detection of landmine objects in ground penetrating radar data using singular value decomposition (SVD). In Proceedings of the 3rd International Workshop on Advanced Ground Penetrating Radar, IWAGPR 2005 (pp. 37–41).
Mostafanezhad, I., Yavari, E., Boric-Lubecke, O., Lubecke, V. M., & Mandic, D. P. (2013). Cancellation of unwanted doppler radar sensor motion using empirical mode decomposition. IEEE Sensors Journal, 13(5), 1897–1904.
Heptagon (Online). Available http://hptg.com/industrial.
Smartex. Wearable wellness system (Online). Available http://www.smartex.it/en/our-products/232-wearable-wellness-systemwws/.
Diraco, G., Leone, A., & Siciliano, P. (2015). People occupancy detection and profiling with 3D depth sensors for building energy management. Energy and Buildings, 92, 246–266.
Diraco, G., Leone, A., & Siciliano, P. (2014). In-home hierarchical posture classification with a time-of-flight 3D sensor. Gait Posture, 39(1), 182–187.
Diraco, G., Leone, A., & Siciliano, P. (2011, June 27–30). Geodesic-based human posture analysis by using a single 3D TOF camera. In IEEE International Symposium on Industrial Electronics (ISIE),.
Acknowledgements
This work was carried out within the project “ACTIVE AGEING AT HOME” funded by the Italian Ministry of Education, Universities and Research, within the National Operational Programme for “Research and Competitiveness” 2007–2013.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Diraco, G., Leone, A., Siciliano, P. (2019). Radar Sensing of Vital Signs in Assisted Living Applications. In: Casiddu, N., Porfirione, C., Monteriù, A., Cavallo, F. (eds) Ambient Assisted Living. ForItAAL 2017. Lecture Notes in Electrical Engineering, vol 540. Springer, Cham. https://doi.org/10.1007/978-3-030-04672-9_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-04672-9_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04671-2
Online ISBN: 978-3-030-04672-9
eBook Packages: EngineeringEngineering (R0)