Journal of Medical Systems

, 43:24 | Cite as

Continuous Blood Pressure Monitoring as a Basis for Ambient Assisted Living (AAL) – Review of Methodologies and Devices

  • Aleksandra StojanovaEmail author
  • Saso Koceski
  • Natasa Koceska
Patient Facing Systems
Part of the following topical collections:
  1. Emerging Technologies for Connected Health


Blood pressure (BP) is a bio-physiological signal that can provide very useful information regarding human’s general health. High or low blood pressure or its rapid fluctuations can be associated to various diseases or conditions. Nowadays, high blood pressure is considered to be an important health risk factor and major cause of various health problems worldwide. High blood pressure may precede serious heart diseases, stroke and kidney failure. Accurate blood pressure measurement and monitoring plays fundamental role in diagnosis, prevention and treatment of these diseases. Blood pressure is usually measured in the hospitals, as a part of a standard medical routine. However, there is an increasing demand for methodologies, systems as well as accurate and unobtrusive devices that will permit continuous blood pressure measurement and monitoring for a wide variety of patients, allowing them to perform their daily activities without any disturbance. Technological advancements in the last decade have created opportunities for using various devices as a part of ambient assisted living for improving quality of life for people in their natural environment. The main goal of this paper is to provide a comprehensive review of various methodologies for continuous cuff-less blood pressure measurement, as well as to evidence recently developed devices and systems for continuous blood pressure measurement that can be used in ambient assisted living applications.


Blood pressure Electrocardiogram Photoplethysmogram Ambient assisted living Signal processing 


  1. 1.
    Klabunde, R. (2011). Cardiovascular physiology concepts. Lippincott Williams & Wilkins.Google Scholar
  2. 2.
    Centers for Disease Control and Prevention (CDC), Vital signs: prevalence, treatment, and control of hypertension--United States, 1999-2002 and 2005-2008. MMWR. Morb. Mortal. Wkly Rep. 60(4):103, 2011.Google Scholar
  3. 3.
    Mitchell, G. F., Arterial stiffness and hypertension. Hypertension 64(1):13–18, 2014.PubMedPubMedCentralGoogle Scholar
  4. 4.
    Rosendorff, C., Lackland, D. T., Allison, M., Aronow, W. S., Black, H. R., Blumenthal, R. S. et al., Treatment of hypertension in patients with coronary artery disease: a scientific statement from the American Heart Association, American College of Cardiology, and American Society of Hypertension. J. Am. Coll. Cardiol. 65(18):1998–2038, 2015.PubMedGoogle Scholar
  5. 5.
    World Health Organization (2013), A global brief on Hypertension, WHO/DCO/WHD/2013.2 Retrieved: August 2018.
  6. 6.
    Sawicka, K., Szczyrek, M., Jastrzebska, I., Prasal, M., Zwolak, A., and Daniluk, J., Hypertension–The silent killer. Journal of Pre-Clinical and Clinical Research, 5(2), 2011.Google Scholar
  7. 7.
    Marino, P. L., and Sutin, K. M., The ICU book (Vol. 2). Baltimore: Williams & Wilkins, 1998.Google Scholar
  8. 8.
    Chung, E., Chen, G., Alexander, B., and Cannesson, M., Non-invasive continuous blood pressure monitoring: a review of current applications. Frontiers of Medicine 7(1):91–101, 2013.PubMedGoogle Scholar
  9. 9.
    Mauck, G. W., Smith, C. R., Geddes, L. A., and Bourland, J. D., The meaning of the point of maximum oscillations in cuff pressure in the indirect measurement of blood pressure—part ii. J. Biomech. Eng. 102(1):28–33, 1980.PubMedGoogle Scholar
  10. 10.
    Ma, H. T., A blood pressure monitoring method for stroke management. BioMed Research International, 2014.Google Scholar
  11. 11.
    Drzewiecki, G. M., Melbin, J., and Noordergraaf, A., Arterial tonometry: review and analysis. J. Biomech. 16(2):141–152, 1983.PubMedGoogle Scholar
  12. 12.
    Peňáz, J., Photoelectric measurement of blood pressure, volume and flow in the finger'In: Digest of the 10th International Conference on Medical and Biological Engineering. Dresden, 104, 1973.Google Scholar
  13. 13.
    Koceska, N., Koceski, S., Sazdovski, V., and Ciambrone, D., Robotic Assistant for Elderly Care: Development and Evaluation. Int. J. Autom. Technol. 11(3):425–432, 2017.Google Scholar
  14. 14.
    Chandrasekaran, V., Dantu, R., Jonnada, S., Thiyagaraja, S., and Subbu, K. P., Cuffless Differential Blood Pressure Estimation Using Smart Phones. IEEE Trans. Biomed. Eng. 60(4):1080–1089, 2013.PubMedGoogle Scholar
  15. 15.
    McAdams, E., Krupaviciute, A., Gehin, C., Dittmar, A., Delhomme, G., Rubel, P., … & McLaughlin, J., Wearable electronic systems: Applications to medical diagnostics/monitoring. In Wearable monitoring systems (pp. 179–203). Boston: Springer, 2011.Google Scholar
  16. 16.
    Fayn, J., and Rubel, P., Toward a personal health society in cardiology. IEEE Trans. Inf. Technol. Biomed. 14(2):401–409, 2010.PubMedGoogle Scholar
  17. 17.
    McAdams, E., Nugent, C. D., McLaughlin, J. et al., Biomedical sensors for ambient assisted living. In: Chandra Mukhopadhyay, S., Lay-Ekuakille, A. (Eds), Advances in Biomedical Sensing Measurements, Instrumentation and Systems (pp 240–262). Berlin: Springer, Heidelberg, 2010.Google Scholar
  18. 18.
    Ahmad, S., Chen, S., Soueidan, K., Batkin, I., Bolic, M., Dajani, H., and Groza, V., Electrocardiogram-assisted blood pressure estimation. IEEE Trans. Biomed. Eng. 59(3):608–618, 2012.PubMedGoogle Scholar
  19. 19.
    McAdams, E. T., Gehin, C., Noury, N., Ramon, C., Nocua, R., Massot, B., … and McLaughlin, J., Biomedical sensors for ambient assisted living. In Advances in Biomedical Sensing, Measurements, Instrumentation and Systems (pp. 240–262). Berlin: Springer, 2010.Google Scholar
  20. 20.
    Thomas, S. S., Nathan, V., Zong, C., Akinbola, E., Aroul, A. L. P., Philipose, L., … and Jafari, R. , BioWatch—A wrist watch based signal acquisition system for physiological signals including blood pressure. In Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE (pp. 2286–2289). IEEE, 2014.Google Scholar
  21. 21.
    Baek, H. J., Lee, H. B., Kim, J. S., Choi, J. M., Kim, K. K., and Park, K. S., Nonintrusive biological signal monitoring in a car to evaluate a driver’s stress and health state. Telemedicine and e-Health 15(2):182–189, 2009.PubMedGoogle Scholar
  22. 22.
    Gu, W. B., Poon, C. C. Y., Leung, H. K., Sy, M. Y., Wong, M. Y. M., and Zhang, Y. T., A novel method for the contactless and continuous measurement of arterial blood pressure on a sleeping bed. In Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE (pp. 6084–6086). IEEE, 2009.Google Scholar
  23. 23.
    Kim, J., Park, J., Kim, K., Chee, Y., Lim, Y., and Park, K., Development of a nonintrusive blood pressure estimation system for computer users. Telemedicine and e-Health 13(1):57–64, 2007.PubMedGoogle Scholar
  24. 24.
    Wu, C. M., Chuang, C. Y., Chen, Y. J., and Chen, S. C., A new estimate technology of non-invasive continuous blood pressure measurement based on electrocardiograph. Advances in Mechanical Engineering 8(6):1687814016653689, 2016.Google Scholar
  25. 25.
    Parák, J., and Havlík, J., ECG signal processing and heart rate frequency detection methods. Proceedings of Technical Computing Prague. 8, 2011.Google Scholar
  26. 26.
    Ubeyli, E. D., Feature extraction for analysis of ECG signals. In Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE (pp. 1080–1083). IEEE, 2008.Google Scholar
  27. 27.
    AlMahamdy, M., and Riley, H. B., Performance study of different denoising methods for ECG signals. Procedia Computer Science 37:325–332, 2014.Google Scholar
  28. 28.
    Diab, M.K., Masimo Corp., Plethysmograph pulse recognition processor. U.S. Patent 7,044,918, 2006.Google Scholar
  29. 29.
    Pilt, K., Ferenets, R., Meigas, K., Lindberg, L. G., Temitski, K., and Viigimaa, M., New photoplethysmographic signal analysis algorithm for arterial stiffness estimation. The Scientific World Journal, 2013.Google Scholar
  30. 30.
    Elgendi, M., Norton, I., Brearley, M., Abbott, D., and Schuurmans, D., Detection of a and b waves in the acceleration photoplethysmogram. Biomed. Eng. Online 13(1):139, 2014.PubMedPubMedCentralGoogle Scholar
  31. 31.
    Bagha, S., and Shaw, L., A Real Time Analysis of PPG Signal for Measurement of SpO2 and Pulse Rate. Int. J. Comput. Appl. 36(11):45–50, 2011.Google Scholar
  32. 32.
    Joseph, G., Joseph, A., Titus, G., Thomas, R. M., and Jose, D., Photoplethysmogram (PPG) signal analysis and wavelet de-noising. In Emerging Research Areas: Magnetics, Machines and Drives (AICERA/iCMMD), 2014 Annual International Conference on (pp. 1–5). IEEE, 2014.Google Scholar
  33. 33.
    Sharma, M., Barbosa, K., Ho, V., Griggs, D., Ghirmai, T., Krishnan, S. K., Hsiai, T. K., Chiao, J. C., and Cao, H., Cuff-Less and Continuous Blood Pressure Monitoring. A Methodological Review Technologies 5(2):21, 2017.Google Scholar
  34. 34.
    Goli, S., and Jayanthi, T., Cuff less continuous non-invasive blood pressure measurement using pulse transit time measurement. Int J Recent Dev Eng Technol 2:86–91, 2014.Google Scholar
  35. 35.
    Poon, C. C. Y., and Zhang, Y. T., Cuff-less and noninvasive measurements of arterial blood pressure by pulse transit time. In Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the (pp. 5877–5880). IEEE, 2006.Google Scholar
  36. 36.
    He, X., Goubran, R. A., and Liu, X. P., Evaluation of the correlation between blood pressure and pulse transit time. In Medical Measurements and Applications Proceedings (MeMeA), 2013 IEEE International Symposium on (pp. 17–20). IEEE, 2013.Google Scholar
  37. 37.
    Gao, M., Olivier, N. B., and Mukkamala, R., Comparison of noninvasive pulse transit time estimates as markers of blood pressure using invasive pulse transit time measurements as a reference. Phys. Rep. 4(10):e12768, 2016.Google Scholar
  38. 38.
    Chen, Y., Wen, C., Tao, G., and Bi, M., Continuous and noninvasive measurement of systolic and diastolic blood pressure by one mathematical model with the same model parameters and two separate pulse wave velocities. Ann. Biomed. Eng. 40(4):871–882, 2012.PubMedGoogle Scholar
  39. 39.
    Li, P., Liu, M., Zhang, X., Hu, X., Pang, B., Yao, Z., and Chen, H., Novel wavelet neural network algorithm for continuous and noninvasive dynamic estimation of blood pressure from photoplethysmography. SCIENCE CHINA Inf. Sci. 59(4):042405, 2016.Google Scholar
  40. 40.
    Zhang, Q., Zhou, D., and Zeng, X., Highly wearable cuff-less blood pressure and heart rate monitoring with single-arm electrocardiogram and photoplethysmogram signals. Biomed. Eng. Online 16(1):23, 2017.PubMedPubMedCentralGoogle Scholar
  41. 41.
    Mukkamala, R., Hahn, J. O., Inan, O. T., Mestha, L. K., Kim, C. S., Toreyin, H., and Kyal, S., Toward ubiquitous blood pressure monitoring via pulse transit time: theory and practice. IEEE Trans Biomed Engineering 62(8):1879–1901, 2015.Google Scholar
  42. 42.
    Buxi, D., Redouté, J. M., and Yuce, M. R., A survey on signals and systems in ambulatory blood pressure monitoring using pulse transit time. Physiol. Meas. 36(3):R1, 2015.PubMedGoogle Scholar
  43. 43.
    Cattivelli, F. S., and Garudadri, H., Noninvasive cuffless estimation of blood pressure from pulse arrival time and heart rate with adaptive calibration. In Wearable and Implantable Body Sensor Networks, 2009. BSN 2009. Sixth International Workshop on (pp. 114–119). IEEE, 2009.Google Scholar
  44. 44.
    Mottaghi, S., Moradi, M. H., and Roohisefat, L., Cuffless blood pressure estimation during exercise stress test. International Journal of Bioscience, Biochemistry and Bioinformatics 2(6):394, 2012.Google Scholar
  45. 45.
    Payne, R. A., Symeonides, C. N., Webb, D. J., and Maxwell, S. R. J., Pulse transit time measured from the ECG: an unreliable marker of beat-to-beat blood pressure. J. Appl. Physiol. 100(1):136–141, 2006.PubMedGoogle Scholar
  46. 46.
    Mazaheri, S., and Zahedi, E., A comparative review of blood pressure measurement methods using pulse wave velocity. In Smart Instrumentation, Measurement and Applications (ICSIMA), 2014 IEEE International Conference on (pp. 1–5). IEEE, 2014.Google Scholar
  47. 47.
    Pereira T, Sanches R, Reis P, Pego J, and Simoes R., Correlation study between blood pressure and pulse transit time. In: IEEE 4th Portuguese Meeting on bioengineering (ENBENG). p. 1–5, 2015.Google Scholar
  48. 48.
    Ye, S. Y., Kim, G. R., Jung, D. K., Baik, S. W., and Jeon, G. R., Estimation of systolic and diastolic pressure using the pulse transit time. World Academy of Science. Eng. Technol. 67:726–731, 2010.Google Scholar
  49. 49.
    Ghosh, S., Banerjee, A., Ray, N., Wood, P. W., Boulanger, P., and Padwal, R., Continuous blood pressure prediction from pulse transit time using ECG and PPG signals. In Healthcare Innovation Point-Of-Care Technologies Conference (HI-POCT), 2016 IEEE (pp. 188–191). IEEE, 2016.Google Scholar
  50. 50.
    Wibmer, T., Doering, K., Kropf-Sanchen, C., Rüdiger, S., Blanta, I., Stoiber, K. M., … & Schumann, C., Pulse transit time and blood pressure during cardiopulmonary exercise tests. Physiological Research, 63(3), 2014.Google Scholar
  51. 51.
    Esmaili, A., Kachuee, M., and Shabany, M., Nonlinear Cuffless Blood Pressure Estimation of Healthy Subjects Using Pulse Transit Time and Arrival Time. IEEE Trans. Instrum. Meas. 66(12):3299–3308, 2017.Google Scholar
  52. 52.
    Lin, H., Xu, W., Guan, N., Ji, D., Wei, Y., and Yi, W., Noninvasive and continuous blood pressure monitoring using wearable body sensor networks. IEEE Intell. Syst. 6:38–48, 2015.Google Scholar
  53. 53.
    Puke, S., Suzuki, T., Nakayama, K., Tanaka, H., & Minami, S., Blood pressure estimation from pulse wave velocity measured on the chest. In Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE (pp. 6107–6110). IEEE, 2013.Google Scholar
  54. 54.
    Jain, M., Kumar, N., & Deb, S., An affordable cuff-less blood pressure estimation solution. In Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the (pp. 5294–5297). IEEE, 2016.Google Scholar
  55. 55.
    Kachuee, M., Mahdi Kiani, M., Mohammadzade, H., and Shabany, M., Cuffless Blood Pressure Estimation Algorithms for Continuous Health-Care Monitoring. IEEE Trans. Biomed. Eng. 64(4):859–869, 2017.PubMedGoogle Scholar
  56. 56.
    Kumar N, Agrawal A, and Deb, S., Cuffless BP measurement using a correlation study of pulse transient time and heart rate. In Int. Conf. Adv. Comp. Info. (ICACCI). IEEE, pp. 1538–1541, 2014.Google Scholar
  57. 57.
    Lameski, P., Zdravevski, E., Koceski, S., Kulakov, A., and Trajkovik, V., Suppression of Intensive Care Unit False Alarms Based on the Arterial Blood Pressure Signal. IEEE Access 5:5829–5836, 2017.Google Scholar
  58. 58.
    Ding, X., Yan, B. P., Zhang, Y. T., Liu, J., Zhao, N., and Tsang, H. K., Pulse transit time based continuous cuffless blood pressure estimation: A new extension and a comprehensive evaluation. Sci. Rep. 7(1):11554, 2017.PubMedPubMedCentralGoogle Scholar
  59. 59.
    Heravi, Y., Amin, M., Keivan, V., and Sima, J., A New Approach for Blood Pressure Monitoring based on ECG and PPG Signals by using Artificial Neural Networks. Int. J. Comput. Appl. 103(12):36–40, 2014.Google Scholar
  60. 60.
    He, R., Huang, Z. P., Ji, L. Y., Wu, J. K., Li, H., and Zhang, Z. Q., Beat-to-beat ambulatory blood pressure estimation based on random forest. In Wearable and Implantable Body Sensor Networks (BSN), 2016 IEEE 13th International Conference on (pp. 194–198). IEEE, 2016.Google Scholar
  61. 61.
    Peter, L., Noury, N., and Cernya, M., A review of methods for non-invasive and continuous blood pressure monitoring: Pulse transit time method is promising? IRBM 35(5):271–282, 2014.Google Scholar
  62. 62.
    Muehlsteff J, Aubert X and Schuett M., Cuffless estimation of systolic blood pressure for short effort bicycle tests: the prominent role of the pre-ejection period in 2006 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'06), New York, pp. 5088–5092, IEEE, 2010.Google Scholar
  63. 63.
    Gesche, H., Grosskurth, D., Küchler, G., and Patzak, A., Continuous blood pressure measurement by using the pulse transit time: comparison to a cuff-based method. Eur. J. Appl. Physiol. 112(1):309–315, 2012.PubMedGoogle Scholar
  64. 64.
    Tabatabai, D., Cuff-less and calibration free blood pressure estimation using the pulse transit time method, ECE 699–002, Learning from Data Project, 2015.Google Scholar
  65. 65.
    Kachuee, M., Kiani, M. M., Mohammadzade, H., and Shabany, M., Cuff-less high-accuracy calibration-free blood pressure estimation using pulse transit time. In Circuits and Systems (ISCAS), 2015 IEEE International Symposium on (pp. 1006–1009). IEEE, 2015.Google Scholar
  66. 66.
    Jain, M., Kumar, N., Deb, S., and Majumdar, A., A sparse regression based approach for cuff-less blood pressure measurement. In Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on (pp. 789–793). IEEE, 2016.Google Scholar
  67. 67.
    Wang, R., Jia, W., Mao, Z. H., Sclabassi, R. J., and Sun, M., Cuff-free blood pressure estimation using pulse transit time and heart rate, Int Conf Signal Process Proc. pp:115–118, 2014.Google Scholar
  68. 68.
    Chen, Y., Wen, C., Tao, G., Bi, M., and Li, G., Continuous and noninvasive blood pressure measurement: a novel modeling methodology of the relationship between blood pressure and pulse wave velocity. Ann. Biomed. Eng. 37(11):2222–2233, 2009.PubMedGoogle Scholar
  69. 69.
    Zheng, D., and Murray, A., Non-invasive quantification of peripheral arterial volume distensibility and its non-linear relationship with arterial pressure. J. Biomech. 42:1032–1037, 2009.PubMedGoogle Scholar
  70. 70.
    Arza, A., Lázaro, J., Gil, E., Laguna, P., Aguiló, J., and Bailon, R., Pulse transit time and pulse width as potential measure for estimating beat-to-beat systolic and diastolic blood pressure. In Computing in Cardiology Conference (CinC), 2013 (pp. 887–890). IEEE, 2013.Google Scholar
  71. 71.
    Sun, S., Bezemer, R., Long, X., Muehlsteff, X., and Aarts, R. M., Systolic blood pressure estimation using PPG and ECG during physical exercise, Physiol Meas, pp. 2154–2169, 2016.Google Scholar
  72. 72.
    Muehlsteff, J., Aubert, X. A., and Morren, G., Continuous cuff-less blood pressure monitoring based on the pulse arrival time approach: The impact of posture. In: Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE (pp. 1691–1694). IEEE, 2008.Google Scholar
  73. 73.
    Shaltis, P. A., Reisner, A. T., and Asada, H. H., Cuffless blood pressure monitoring using hydrostatic pressure changes. IEEE Trans. Biomed. Eng. 55(6):1775–1777, 2008.PubMedGoogle Scholar
  74. 74.
    Shaltis, P. A., Reisner, A., and Asada, H. H., Wearable, cuff-less PPG-based blood pressure monitor with novel height sensor. In Engineering in Medicine and Biology Society, 2006. EMBS'06. 28th Annual International Conference of the IEEE (pp. 908–911), 2006.Google Scholar
  75. 75.
    Pielmuş, A. G., Pflugradt, M., Tigges, T., Klum, M., Feldheiser, A., Hunsicker, O., and Orglmeister, R., Novel computation of pulse transit time from multi-channel PPG signals by wavelet transform. Current Directions in Biomedical Engineering 2(1):209–213, 2016.Google Scholar
  76. 76.
    Pinheiro, E., Postolache, O., and Girão, P., Blood pressure and heart rate variabilities estimation using ballistocardiography. In Proceedings of the 7th Conf. on. Telecom (pp. 125–128), 2009.Google Scholar
  77. 77.
    Holz, C., and Wang, E. J., Glabella: Continuously sensing blood pressure behavior using an unobtrusive wearable device. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1(3):58, 2017.Google Scholar
  78. 78.
    Ananth, S., and Sharath, S., Project milestone report for CS229: Blood Pressure detection from PPG, Retrieved: August 2018, 2014.
  79. 79.
    Ruiz-Rodríguez, J. C., Ruiz-Sanmartín, A., Ribas, V., Caballero, J., García-Roche, A., Riera, J. et al., Innovative continuous non-invasive cuffless blood pressure monitoring based on photoplethysmography technology. Intensive Care Med. 39(9):1618–1625, 2013.PubMedGoogle Scholar
  80. 80.
    Kurylyak, Y., Lamonaca, F., and Grimaldi, D., A neural network-based method for continuous blood pressure estimation from a PPG signal, Instrumentation and Measurement Technology Conference (I2MTC), 2013 IEEE International. IEEE 2013:280–283, 2013.Google Scholar
  81. 81.
    He, X., Goubran, R. A., and Liu, X. P., Secondary Peak Detection of PPG Signal for Continuous Cuffless Arterial Blood Pressure Measurement. IEEE Trans. Instrum. Meas. 63(6):1431–1439, 2014.Google Scholar
  82. 82.
    Liu, M., Po, L. M., and Fu, H., Cuffless Blood Pressure Estimation Based on Photoplethysmography Signal and Its Second Derivative. International Journal of Computer Theory and Engineering 9(3):202, 2017.Google Scholar
  83. 83.
    Hassan, M. K. B. A., Mashor, M. Y., Nasir, N. M., and Mohamed, S., Measuring of systolic blood pressure based on heart rate. In 4th Kuala Lumpur International Conference on Biomedical Engineering 2008 (pp. 595–598). Berlin: Springer, 2008.Google Scholar
  84. 84.
    Nemati, E., Deen, M. J., and Mondal, T., A wireless wearable ECG sensor for long-term applications. IEEE Communications Magazine, 50(1), 2012.Google Scholar
  85. 85.
    Nonlinear Analysis for the ECG and Blood Pressure Signals, Retrieved: August, 2018.
  86. 86.
    Monroy Estrada, G., Mendoza, L. E., and Molina, V., Relationship of blood pressure with the electrical signal of the heart using signal processing. Tecciencia 9(17):9–14, 2014.Google Scholar
  87. 87.
    He, X., Goubran, R. A., and Liu, X. P., Using Eulerian video magnification framework to measure pulse transit time. In Medical Measurements and Applications (MeMeA), 2014 IEEE International Symposium on (pp. 1–4). IEEE, 2014.Google Scholar
  88. 88.
    Al-Shaqi, R., Mourshed, M., and Rezgui, Y., Progress in ambient assisted systems for independent living by the elderly. SpringerPlus 5(1):624, 2016.PubMedPubMedCentralGoogle Scholar
  89. 89.
    Linskell, J., Smart home technology and special needs reporting UK activity and sharing implemention experiences from Scotland. In Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2011 5th International Conference on (pp. 287–291). IEEE, 2011.Google Scholar
  90. 90.
    Liu, H., Ivanov, K., Wang, Y., and Wang, L., Toward a smartphone application for estimation of pulse transit time. Sensors 15(10):27303–27321, 2015.PubMedGoogle Scholar
  91. 91.
    Chandrasekhar, A., Kim, C. S., Naji, M., Natarajan, K., Hahn, J. O., and Mukkamala, R., Smartphone-based blood pressure monitoring via the oscillometric finger-pressing method. Science Translational Medicine, 10(431), eaap8674, 2018.Google Scholar
  92. 92.
    Majumder, S., Mondal, T., and Deen, M. J., Wearable sensors for remote health monitoring. Sensors 17(1):130, 2017.Google Scholar
  93. 93.
    Pantelopoulos, A., and Bourbakis, N. G., A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 40(1):1–12, 2010.Google Scholar
  94. 94.
    Zhang, Q., Zeng, X., Hu, W., and Zhou, D., A machine learning-empowered system for long-term motion-tolerant wearable monitoring of blood pressure and heart rate with Ear-ECG/PPG. IEEE Access 5:10547–10561, 2017.Google Scholar
  95. 95.
    Shahriyar, R., Bari, M. F., Kundu, G., Ahamed, S. I., and Akbar, M. M., Intelligent mobile health monitoring system (IMHMS). In: International Conference on Electronic Healthcare (pp. 5–12). Berlin: Springer, 2009.Google Scholar
  96. 96.
    Wannenburg, J., and Malekian, R., Body sensor network for mobile health monitoring, a diagnosis and anticipating system. IEEE Sensors J. 15(12):6839–6852, 2015.Google Scholar
  97. 97.
    Wood, A. D., Stankovic, J. A., Virone, G., Selavo, L., He, Z., Cao, Q., … and Stoleru, R., Context-aware wireless sensor networks for assisted living and residential monitoring. IEEE Network, 22(4), 2008.Google Scholar
  98. 98.
    Espina, J., Falck, T., Muehlsteff, J., Jin, Y., Adán, M. A., and Aubert, X., Wearable body sensor network towards continuous cuff-less blood pressure monitoring. In: Medical Devices and Biosensors, 2008. ISSS-MDBS 2008. 5th International Summer School and Symposium on (pp. 28–32). IEEE, 2008.Google Scholar
  99. 99.
    Mouradian, V., Poghosyan, A., and Hovhannisyan, L., Noninvasive continuous mobile blood pressure monitoring using novel PPG optical sensor. In Biomedical Wireless Technologies, Networks, and Sensing Systems (BioWireleSS), 2015 IEEE Topical Conference on (pp. 1–3). IEEE, 2015.Google Scholar
  100. 100.
    Ilango, S., and Sridhar, P., A non-invasive blood pressure measurement using android smart phones. IOSR J Dent Med Sci 13:28–31, 2014.Google Scholar
  101. 101.
    Theodor, M., Fiala, J., Ruh, D., Foerster, K., Heilmann, C., Beyersdorf, F. et al., Implantable accelerometer system for the determination of blood pressure using reflected wave transit time. Sensors Actuators A Phys. 206:151–158, 2014.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Aleksandra Stojanova
    • 1
    Email author
  • Saso Koceski
    • 1
  • Natasa Koceska
    • 1
  1. 1.Faculty of Computer ScienceUniversity Goce Delcev – StipŠtipRepublic of Macedonia

Personalised recommendations