An Application of Extended Kalman Filter for the Pressure Estimation in Minimally Invasive Surgery

  • Van-Muot NguyenEmail author
  • Eike Christian Smolinski
  • Alexander Benkmann
  • Wolfgang Drewelow
  • Torsten Jeinsch
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 953)


The paper presents an application of the Extended Kalman Filter (EKF) as an observer method to estimate the pressure in the operation area for the controlled process of minimally invasive surgery (MIS). Via trocars and pipes, the inflow and outflow of the rinsing fluid at the operation area are controlled by a double roller pump (DRP). Additionally, those flows are affected by the inside pressure of the pipes. The pressure sensor in the operation area is not allowed to utilize for surgery on the real patients at the current stage. Therefore, it is necessary to reconstruct the state of pressure in the operation area of MIS. The estimated pressure from the EKF estimator is used to replace the measured feedback signal to control the pump. The EKF worked based on the input signals of the rinsing fluid flows and the observable signal from available pressure sensors at the double roller pump. The proposed method was successfully implemented on MATLAB Simulink. For the further verification, it was also applied on the real device simulator environment. The results from the research show that the estimated pressure gives a high precision. In addition, the noises from the measured states are effectively eliminated.


Extended Kalman Filter Minimally invasive surgery Double roller pump Estimator 



Research funding: This research and development project is funded by the German Federal Ministry of Education and Research (BMBF) within the program “Medizintechnische Lösungen für die digitale Gesundheitsversorgung” under the project number 13GW0164B and managed by the Project Management Agency VDI Technologiezentrum GmbH. The author is responsible for the contents of this publication. Conflict of interest: Authors state no conflict of interest. Ethical approval: The conducted research is not related to either human or animals use.


  1. 1.
    Davies, B.: Robotics in minimally invasive surgery. In: IEE Colloquium on Through the Keyhole: Micro-engineering in Minimally Invasive Surgery, pp. 5/1–5/2 (1995)Google Scholar
  2. 2.
    Smolinski, E., Benkmann, A., Westerhoff, P., Nguyen, V.M., Drewelow, W., Jeinsch, T.: A hardware-in-the-loop simulator for the development of medical therapy devices. IFAC 50, 15050–15055 (2017)Google Scholar
  3. 3.
    Nguyen, V.M., Jeinsch, T.: Pressure control in minimally invasive surgery. In: International Symposium on Automatic Control (2017)Google Scholar
  4. 4.
    Hsiao, M.S., Kusnezov, N., Sieg, R.N., Owens, B.D., Herzog, J.P.: Use of an irrigation pump system in arthroscopic procedures. Orthopedics 39(3), 474–478 (2016)CrossRefGoogle Scholar
  5. 5.
    Muellner, T., Menth-Chiari, W.A., Reihsner, W.A., Eberhardsteiner, R., Eberhardsteiner, J., Engebretsen, L.: Accuracy of pressure and flow capacities of four arthroscopic fluid management systems. Arthrosc. J. Arthrosc. Relat. Surg. Off. Publ. Arthrosc. Assoc. N. Am. Int. Arthrosc. Assoc. 17, 760–764 (2001)CrossRefGoogle Scholar
  6. 6.
    Bergstrom, R., Gillquist, J.: The use of an infusion pump in arthroscopy. Arthrosc. J. Arthrosc. Relat. Surg. 2, 4–45 (1986)CrossRefGoogle Scholar
  7. 7.
    Nguyen, V.-M., Smolinski, E.C., Benkmann, A., Drewelow, W., Jeinsch, T.: An application of pressure estimation in minimally invasive surgery. In: 4th International Conference on Green Technology and Sustainable Development, pp. 658–662. IEEE (2018)Google Scholar
  8. 8.
    Brown, R.G., Hwang, P.Y.C.: Introduction to Random Signals and Applied Kalman Filtering. Wiley, New York (2012)zbMATHGoogle Scholar
  9. 9.
    Romaniuk, S., Ambroziak, L., Gosiewski, Z., Isto, P.: Real time localization system with Extended Kalman Filter for indoor applications. In: 21st International Conference on Methods and Models in Automation and Robotics, pp. 42–47. IEEE (2016)Google Scholar
  10. 10.
    Shyam, M.M., Naik, N., Gemson, R.M.O., Ananthasayanam, M.R.: Introduction to the Kalman Filter and tuning its statistics for near optimal estimates and Cramer Rao bound (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Van-Muot Nguyen
    • 1
    Email author
  • Eike Christian Smolinski
    • 1
  • Alexander Benkmann
    • 1
  • Wolfgang Drewelow
    • 1
  • Torsten Jeinsch
    • 1
  1. 1.Institute of Automation, University of RostockRostockGermany

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