Non-invasive Wireless Bio Sensing

  • Artur ArsenioEmail author
  • João Andrade
  • Andreia Duarte
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 574)


Wireless sensing technologies are increasingly being employed on Health systems, aiming to improve the data communication flow between patients and clinical experts. This is especially important for patients located at remote locations or facing mobility constraints. In order to fully exploit the advantages of wireless communications, it is necessary biosensors that collect data about user’s health, possibly integrated on a personal wireless sensor network. With this goal in mind, a wireless solution is described that presents an innovative wireless heart rate device, as well as user interface technologies for enabling real-time data visualization on mobile devices by patients and medical experts.


Non-invasive Bio-sensing Wireless communications Remote monitoring Personal networks 



Part of this work was supported by Harvard Medical School Portugal Collaborative Research Award HMSP-CT/SAU-ICT/0064/2009: Improving perinatal decision-making: development of complexity-based dynamical measures and novel acquisition systems. Artur Arsenio has also been partially funded by CMU-Portuguese program through Fundação para Ciência e Tecnologia, AHA-Augmented Human Assistance project, AHA, CMUP-ERI/HCI/0046/2013.


  1. 1.
    Campbell, A.T., Eisenman, S.B., Lane, N.D., Miluzzo, E., Peterson, R.A., Lu, H.L.H., Zheng, X.Z.X.: The rise of people-centric sensing. In: IEEE Internet Computing, pp. 12–21. IEEE Computer Society, doi: 10.1109/MIC (2008)
  2. 2.
    Lane, N.D., Miluzzo, E., Lu, H.L.H., Peebles, D., Choudhury, T., Campbell, A.T.: A survey of mobile phone sensing. IEEE Commun. Mag. 48, 140–150 (2010)CrossRefGoogle Scholar
  3. 3.
    Zhang, D., Guo, B., Li, B., Yu, Z.: Extracting social and community intelligence from digital footprints: an emerging research area. In: Yu, Z., Liscano, R., Chen, G., Zhang, D., Zhou, X. (eds.) UIC 2010. LNCS, vol. 6406, pp. 4–18. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Miluzzo, E., Lane, N.D., Fodor, K., Peterson, R., Lu, H., Musolesi, M., Eisenman, S.B., Zheng, X., Campbell, A.T.: Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application. Archit. Des. 10, 337–350 (2008). doi: 10.1145/1460412.1460445 Google Scholar
  5. 5.
    Abdelzaher, T., Anokwa, Y., Boda, P., Burke, J., Estrin, D., Guibas, L., Kansal, A., Madden, S., Reich, J.: Mobiscopes for human spaces. IEEE Pervasive Comput. 6(2), 20–29 (2007). doi: 10.1109/MPRV.2007.38 CrossRefGoogle Scholar
  6. 6.
    Graham, E.M., Ruis, K., Hartman, A.L., Northington, F.J., Fox, H.E.: A systematic review of the role of intrapartum hypoxiaischemia in the causation of neonatal encephalopathy. Am. J. Obstet. Gynecol. 199(6), 587–595 (2008)CrossRefGoogle Scholar
  7. 7.
    Devoe, L.D.: Electronic fetal monitoring: does it really lead to better outcomes? Am. J. Obstet. Gynecol. 204(6), 455–456 (2011)CrossRefGoogle Scholar
  8. 8.
    Jenkins, H.: Technical progress in fetal electrocardiography - a review. J. Perinat. Med. 14, 365–377 (1986)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Banta, D.H., Thacker, S.B.: Historical controversy in health technology assessment: the case of electronic fetal monitoring. Obstet. Gynecol. Surv. 56(11), 707–719 (2001)CrossRefGoogle Scholar
  10. 10.
    Alfirevic, Z., Devane, D., Gyte, G.M.L.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Syst. Rev. 3, CD006066 (2006)Google Scholar
  11. 11.
    American College of Obstetricians and Gynecologists: ACOG Practice Bulletin No. 106: Intrapartum fetal heart rate monitoring: nomenclature, interpretation, and general management principles. Obstet. Gynecol. 114(1), 192–202 (2009)CrossRefGoogle Scholar
  12. 12.
    Piéri, J.F., Crowe, J.A., Hayes-Gill, B.R., Spencer, C.J., Bhogal, K., James, D.K.: Compact long-term recorder for the transabdominal foetal and maternal electrocardiogram. Med. Biol. Eng. Comput. 39(1), 118–125 (2001)CrossRefGoogle Scholar
  13. 13.
    Crowe, J.A., Harrison, A., Hayes-Gill, B.R.: The feasibility of long-term fetal heart rate monitoring in the home environment using maternal abdominal electrodes. Physiol. Meas. 16(3), 195–202 (1995)CrossRefGoogle Scholar
  14. 14.
    Graatsma, E.M., Jacod, B.C., Van, Egmond L.A.J., Mulder, E.J.H., Visser, G.H.A.: Fetal electrocardiography: feasibility of long-term fetal heart rate recordings. BJOG: Int. J. Obstet. Gynaecol. 116(2), 334–337 (2009). discussion, pp. 337–338CrossRefGoogle Scholar
  15. 15.
    Karvounis, E.C., Tsipouras, M.G., Papaloukas, C., Tsalikakis, D.G., Naka, K.K., Fotiadis, D.I.: A non-invasive methodology for fetal monitoring during pregnancy. Methods Inf. Med. 49(3), 238–253 (2010)CrossRefGoogle Scholar
  16. 16.
    Taylor, M.J.O., Smith, M.J., Thomas, M., Green, A.R., Cheng, F., Oseku-Afful, S., Wee, L.Y., Fisk, N.M., Gardiner, H.M.: Non-invasive fetal electrocardiography in singleton and multiple pregnancies. BJOG: Int. J. Obstet. Gynaecol. 110(7), 668–678 (2003)CrossRefGoogle Scholar
  17. 17.
    Thomas, M.J., Cleal, J.K., Hanson, M.A., Green, L.R., Gardiner, H.M.: Non-invasive fetal electrocardiography: validation and interpretation. In: 4th IET International Conference on Advances in Medical, Signal and Information Processing MEDSIP, pp. 1–4 (2008)Google Scholar
  18. 18.
    CESDI 7th Annual Report - CTG Education Survey. Maternal and Child Health Research Consortium, London, Technical report (2000)Google Scholar
  19. 19.
    Lu, H., Pan, W., Lane, N.D., Choudhury, T., Campbell, A.T.: SoundSense: scalable sound sensing for people-centric applications on mobile phones. In: Architecture, pp. 165–178 (2009)Google Scholar
  20. 20.
    Zapata, B., Hernandez Ninirola, A., Fernandez-Aleman, J., Toval, A.: Assessing the privacy policies in mobile personal health records. In: 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4956–4959 (2014)Google Scholar
  21. 21.
    Lomotey, R., Deters, R.: Mobile-based medical data accessibility in mHealth. In: 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud), pp. 91–100 (2014)Google Scholar
  22. 22.
    Gandhi, O.P., Morgan, L.L., De Salles, A.A., Han, Y.-Y., Herberman, R.B., Davis, D.L.: Exposure limits: the underestimation of absorbed cell phone radiation, especially in children. Electromagn. Biol. Med. 31(1), 34–51 (2012)CrossRefGoogle Scholar
  23. 23.
    Kansal, A., Goraczko, M., Zhao, F.: Building a sensor network of mobile phones. In: Proceedings of the 6th International Conference on Information Processing in Sensor Networks, IPSN 2007 (2007)Google Scholar
  24. 24.
    Andrade, J., Arsenio, A.: Epidemic estimation over social networks using large scale biosensors. Publication at Advanced Research on Hybrid Intelligent Techniques and Applications, pp. 287–320. IGI Global, Hershey (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Universidade da Beira Interior, IST-ID and YDreams RoboticsCovilhãPortugal
  2. 2.Instituto Superior TécnicoUniversidade de LisboaPorto SalvoPortugal

Personalised recommendations