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A Review of Health Monitoring Systems Using Sensors on Bed or Cushion

  • Massimo Conti
  • Simone Orcioni
  • Natividad Martínez Madrid
  • Maksym Gaiduk
  • Ralf Seepold
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10814)

Abstract

How technology can answer the challenge that currently population ageing is facing to the healthcare system? In this work, systems and devices related to “smart” bed and cushion, that are commercially available or matter of research works, are reviewed.

Keywords

Smart bed Smart cushion Population ageing Smart-care 

References

  1. 1.
    European Commission: Population ageing in Europe, Facts, implications and policies, Luxembourg: Publications Office of the European Union (2014)Google Scholar
  2. 2.
    European Union (EU): People in the EU – Population Projections, June 2015. http://ec.europa.eu/eurostat/statistics-explained/
  3. 3.
    European Pressure Ulcer Advisory Panel. http://www.epuap.org/pu-guidelines/
  4. 4.
  5. 5.
  6. 6.
  7. 7.
  8. 8.
  9. 9.
  10. 10.
  11. 11.
  12. 12.
  13. 13.
    Xu, W., Huang, M., Amini, N., He, L., Sarrafzadeh, M.: eCushion: a textile pressure sensor array design and calibration for sitting posture analysis. IEEE Sens. J. 13(10), 3926–3934 (2013)CrossRefGoogle Scholar
  14. 14.
    Shu, L., Tao, X., Feng, D.D.: A new approach for readout of resistive sensor arrays for wearable electronic applications. IEEE Sens. J. 15(1), 442–452 (2015)CrossRefGoogle Scholar
  15. 15.
    Yang, L., Ge, Y., Li, W., Rao, W., Shen, W.: A home mobile healthcare system for wheelchair users. In: Proceedings of IEEE 18th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 609–614 (2014)Google Scholar
  16. 16.
    Arias, S., Cardiel, E., Garay, L., Tovar, B., Pla, M., Rogeli, P.: A pressure distribution measurement system for supporting areas of wheelchair users. In: 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4751–4754 (2013)Google Scholar
  17. 17.
    Xu, L., Chen, G., Wang, J., Shen, R., Zhao, S., A sensing cushion using simple pressure distribution sensors. In: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 451–456 (2012)Google Scholar
  18. 18.
    Chugo, D., Fujita, K., Sakaida, Y., Yokota, S., Hashimoto, H.: A depressurization motion assistance for a seated wheelchair user using pressure distribution estimation. In: 5th International Conference on Human System Interactions, pp. 75–80 (2012)Google Scholar
  19. 19.
    Dai, R., Sonenblum, S.E., Sprigle, S.: A robust wheelchair pressure relief monitoring system. In: Annual International Conference of IEEE Engineering in Medicine and Biology Society, pp. 6107–6110 (2012)Google Scholar
  20. 20.
    Liang, G., Cao, J., Liu, X.: Smart cushion: a practical system for fine-grained sitting posture recognition. In: IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp. 419–424 (2017)Google Scholar
  21. 21.
    Russell, L., Goubran, R., Kwamena, F.: Posture sensing using a low-cost temperature sensor array. In: IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 443–447 (2017)Google Scholar
  22. 22.
    Khan, A., Reuter, M., Phung, N., Hafeez, S.: Wireless solution to prevent decubitus ulcers: preventive weight shifting guide, monitor, and tracker app for wheel chair users with spinal cord injuries (phase II). In: IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom), pp. 1–6 (2016)Google Scholar
  23. 23.
    Ahn, B., Kim, T., Sain, M., Jeong, D.: Implemented of posture discriminate system through the seat weight distribution. In: IEEE Conf. on Wireless Sensors (ICWiSe), pp. 24–27 (2015)Google Scholar
  24. 24.
    Sazonov, E., Abele, R., Gerrity, A., May, P., Spradling, K., Kennamore, T., Chen, Y., Klebine, P.: Development of wheelchair cushion pressure monitoring system. In: IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 1–5 (2014)Google Scholar
  25. 25.
    Arias, S., Cardiel, E., Rogeli, P., Mori, T., Nakagami, G., Noguchi, H., Sanada, H.: An alternating pressure sequence proposal for an air-cell cushion for preventing pressure ulcers. In: Proceedings of Conference on IEEE Engineering in Medicine and Biology Society, pp. 3480–3483 (2014)Google Scholar
  26. 26.
    Kurihara, Y., Watanabe, K.: Suppression of artifacts in biomeasurement system by pneumatic filtering. IEEE Sens. J. 12(3), 416–422 (2012)CrossRefGoogle Scholar
  27. 27.
    Bao, J., Shou, X., Wang, H., Yang, H.: Study on heartbeat information acquired from pressure cushion based on body sensor network. In: IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, pp. 1103–1108 (2013)Google Scholar
  28. 28.
    Watanabe, K., Watanabe, T., Watanabe, H., Ando, H., Ishikawa, T., Kobayashi, K.: Noninvasive measurement of heartbeat, respiration, snoring and body movements of a subject in bed via a pneumatic method. IEEE Trans. Biomed. Eng. 52(12), 2100–2107 (2005)CrossRefGoogle Scholar
  29. 29.
    Tanaka, S., Matsumoto, Y., Wakimoto, K.: Unconstrained and non-invasive measurement of heart-beat and respiration periods using a phonocardiographic sensor. Med. Biomed. Eng. Comput. 40(2), 246–252 (2002)CrossRefGoogle Scholar
  30. 30.
    Gaiduk, M., Kuhn, I., Seepold, R., Ortega, J.A., Madrid, N.M.: A sensor grid for pressure and movement detection supporting sleep phase analysis. In: Rojas, I., Ortuño, F. (eds.) IWBBIO 2017. LNCS, vol. 10209, pp. 596–607. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-56154-7_53CrossRefGoogle Scholar
  31. 31.
    Baran Pouyan, M., Nourani, M., Pompeo, M.: Sleep state classification using pressure sensor mats. In: 37th Annual International Conference of IEEE Engineering in Medicine and Biology Society (EMBC), 26 August 2015, pp. 1207–1210 (2015). ISSN 1094-687XGoogle Scholar
  32. 32.
    Long, X., Fonseca, P., Foussier, J., Haakma, R., Aarts, R.: Sleep and wake classification with actigraphy and respiratory effort using dynamic warping. IEEE J. Biomed. Health Inf. 18, 1272–1284 (2013)CrossRefGoogle Scholar
  33. 33.
    Karlen, W., Mattiussi, C., Floreano, D.: Sleep and wake classification with ecg and respiratory effort signals. IEEE Trans. Biomed. Circuits Syst. 3(2), 71–78 (2009)CrossRefGoogle Scholar
  34. 34.
    Lewicke, A., Sazonov, E., Corwin, M.J., Neuman, M., Schuckers, S.: Sleep versus wake classification from heart rate variability using computational intelligence: consideration of rejection in classification models. IEEE Trans. Biomed. Eng. 55(1), 108–118 (2008)CrossRefGoogle Scholar
  35. 35.
    Lotjonen, J., Korhonen, I., Hirvonen, K., Eskelinen, S., Myllymaki, M., Partinen, M.: Automatic sleep-wake and nap analysis with a new wrist worn online activity monitoring device vivago WristCare. Sleep 26(1), 86–90 (2003)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Università Politecnica delle MarcheAnconaItaly
  2. 2.Reutlingen UniversityReutlingenGermany
  3. 3.HTWG KonstanzKonstanzGermany

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