A Simple and Low Cost Device for Automatically Supervising Urine Output of Critical Patients

  • Abraham Otero
  • Francisco Palacios
  • Andrey Apalkov
  • Roemi Fernández
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 273)


Nowadays, patients admitted to critical care units have most of their physiological parameters sensed by sophisticated commercial monitoring devices. These devices also supervise whether the values of the parameters lie within a preestablished range of normality set by the clinician. If any of the parameters leaves its normality range, an alarm will be triggered. The automation of the sensing and supervision of physiological parameters discharges the healthcare staff of a considerable workload. It also avoids human errors, which are common in repetitive and monotonous tasks.

Urine output is a physiological parameter that, despite being of great relevance in the treatment of critical care patients, is still measured and supervised manually. This paper presents a device capable of sensing and supervising urine output automatically. The device uses reed switches that are activated by a magnet that is attached to a float in order to measure the amount of urine collected in two containers. An electronic unit sends the state of the reed switches to a PC, which supervises the achievement of therapeutic goals.


Biosensors Urine output Critical care Patient monitoring Fuzzy logic 


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  1. 1.
    Akinfiev, T., Apalkov, A., Otero, A., Palacios, F.: Device for measuring the amount of liquid that flows and procedure for its measurement. Patent Pending, eS 201031227 (2010)Google Scholar
  2. 2.
    Amaravadi, R., Dimick, J., Pronovost, P., Lipsett, P.: ICU nurse-to-patient ratio is associated with complications and resource use after esophagectomy. Intensive Care Medicine 26(12), 1857–1862 (2000)CrossRefGoogle Scholar
  3. 3.
    Barro, S., Marín, R., Palacios, F., Ruíz, R.: Fuzzy logic in a patient supervision systems. Artificial Intelligence in Medicine 21, 193–199 (2001)CrossRefGoogle Scholar
  4. 4.
    Dimick, J., Pronovost, P., Heitmiller, R., Lipsett, P.: Intensive care unit physician staffing is associated with decreased length of stay, hospital cost, and complications after esophageal resection. Critical Care Medicine 29(4), 753 (2001)CrossRefGoogle Scholar
  5. 5.
    Hande, A., Polk, T., Walker, W., Bhatia, D.: Self-powered wireless sensor networks for remote patient monitoring in hospitals. Sensors 6(9), 1102–1117 (2006)CrossRefGoogle Scholar
  6. 6.
    Hersch, M., Einav, S., Izbicki, G.: Accuracy and ease of use of a novel electronic urine output monitoring device compared with standard manual urinometer in the intensive care unit. Journal of Critical Care 24, 629–633 (2009)CrossRefGoogle Scholar
  7. 7.
    Ishida, S.: Liquid level indicator using laser beam. us patent 4938590 (1990)Google Scholar
  8. 8.
    Johnson, S.J.: Liquid level measurement device. United States Patent 3693445 (1978)Google Scholar
  9. 9.
    Jungk, A., Thull, B., Rau, G.: Intelligent Alarms for Anaesthesia Monitoring Based on Fuzzy Logic Approach, pp. 219–238. Physica-Verlag (2002)Google Scholar
  10. 10.
    Kaufmann, A., Gupta, M.: Introduction to Fuzzy Arithmetic. Van Nostrand Reinhold Company Inc. (1984)Google Scholar
  11. 11.
    Klenzak, J., Himmelfarb, J.: Sepsis and the kidney. Critical Care Clinics 21(2), 211–222 (2005)CrossRefGoogle Scholar
  12. 12.
    Knauf, R., Lichtig, L., Risen-McCoy, R., Singer, A., Wozniak, L.: Implementing nursing’s report card: a study of RN staffing, length of stay and patient outcomes. American Nurses Association, Washington (1997)Google Scholar
  13. 13.
    Kovner, C., Gergen, P.: Nurse staffing levels and adverse events following surgery in US hospitals. Journal of Nursing Scholarship 30(4), 315–321 (1998)CrossRefGoogle Scholar
  14. 14.
    Mitra, B., Fitzgerald, M., Cameron, P., Cleland, H.: Fluid resuscitation in major burns. ANZ Journal of Surgery 76, 35–38 (2006)CrossRefGoogle Scholar
  15. 15.
    Mora, F., Passariello, G., Carrault, G., Pichon, J.L.: Intelligent patient monitoring and management systems: A review. IEEE Engineering in Medicine and Biology 12, 23–33 (1993)CrossRefGoogle Scholar
  16. 16.
    Otero, A., Akinfiev, T., Fernández, R., Palacios, F.: A device for automatic measurement of critical care patient’s urine output. In: 6th IEEE International Symposium on Intelligent Signal Processing, pp. 169–174 (2009)Google Scholar
  17. 17.
    Otero, A., Akinfiev, T., Fernández, R., Palacios, F.: A device for automatically measuring and supervising the critical care patient’s urine output. Sensors 10(1), 934–951 (2010)CrossRefGoogle Scholar
  18. 18.
    Otero, A., Félix, P., Barro, S., Palacios, F.: Addressing the flaws of current critical alarms: a fuzzy constraint satisfaction approach. Artificial Intelligence in Medicine 47(3), 219–238 (2009)CrossRefGoogle Scholar
  19. 19.
    Otero, A., Panigrahi, B., Palacios, F., Akinfiev, T., Fernández, R.: A prototype device to measure and supervise urine output of critical patients, pp. 321–324. Intech (2009)Google Scholar
  20. 20.
    Pronovost, P., Angus, D., Dorman, T., Robinson, K., Dremsizov, T., Young, T.: Physician staffing patterns and clinical outcomes in critically ill patients: a systematic review. Jama 288(17), 2151 (2002)CrossRefGoogle Scholar
  21. 21.
    Pronovost, P., Needham, D., Waters, H., Birkmeyer, C., Calinawan, J., Birkmeyer, J., Dorman, T.: Intensive care unit physician staffing: Financial modeling of the Leapfrog standard*. Critical Care Medicine 32(6), 1247 (2004)CrossRefGoogle Scholar
  22. 22.
    Rivers, E., Nguyen, B., Havstad, S., Ressler, J., Muzzin, A., Knoblich, B., Peterson, E., Tomlanovich, M., Group, E.G.D.T.C.: Early goal-directed therapy in the treatment of severe sepsis and septic shock. New England Journal of Medicine 345, 1368–1377 (2001)CrossRefGoogle Scholar
  23. 23.
    Zadeh, L.: The concept of a linguistic variable and its application to approximate reasoning. Information Science 8, 199–249 (1975), part 1MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Abraham Otero
    • 1
  • Francisco Palacios
    • 2
  • Andrey Apalkov
    • 3
  • Roemi Fernández
    • 3
  1. 1.Department of Information Systems EngineeringUniversity San Pablo CEUMadridSpain
  2. 2.Critical Care UnitUniversity Hospital of GetafeMadridSpain
  3. 3.Centre for Automation and Robotics CSIC-UPMMadridSpain

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