Advertisement

Haptic Modes for Multiparameter Control in Robotic Surgery

  • Philipp SchleerEmail author
  • Sergey Drobinsky
  • Tahany Hmaid
  • Klaus Radermacher
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11768)

Abstract

Accurate manual execution of a pre- or intraoperatively generated plan is an essential ability of surgeons and can be related to a successful outcome of a surgery. Therefore, surgeons regularly need to control multiple parameters simultaneously which increases control complexity, particularly if the information has to be derived and fused from multiple reference frames (e.g. displays). In master-slave or cooperative robotic settings haptic assistances can be provided to facilitate manual control of e.g. milling tasks. Haptic assistances present the information in the human hand reference frame and therefore can make mental transformation obsolete. Additionally, in contrast to autonomous robotic milling, the surgeon remains in the control loop and is able to customize the plan according to his expertise and intraoperative requirements. This paper experimentally investigates effects on usability of different haptic assistances in separate degrees of freedom during a multiple parameter control task. Subjects had to apply a force and follow a path with a constant velocity, while different levels of haptic assistance were provided. Results indicate that each assistance provides a statistically significant improvement with respect to the associated measure (i.e. force, position, velocity) and the task-associated perceived workload is reduced. Consequently, haptically assisted milling allows for an efficient control of milling parameters during surgery whose performance lies in between completely manual and autonomous robotic execution while keeping the surgeon in the control loop.

Keywords

Robotic surgery Haptics Virtual fixtures 

References

  1. 1.
    Troccaz, J., Peshkin, M., Davies, B.: Guiding systems for computer-assisted surgery: introducing synergistic devices and discussing the different approaches. Med. Image Anal. 2, 101–119 (1998)CrossRefGoogle Scholar
  2. 2.
    Radermacher, K.: Computerunterstützte Operationsplanung und-ausführung mittels individueller Bearbeitungsschablonen in der Orthopädie. Shaker (1999)Google Scholar
  3. 3.
    Luczak, H.: Prinzipien menschlicher Informationsverarbeitung-Analytik und Gestaltung informatorisch-mentaler Arbeit. In: Luczak, H. (ed.) Arbeitswissenschaft, pp. 126–213. Springer, Heidelberg (1993).  https://doi.org/10.1007/978-3-662-21634-7_7CrossRefGoogle Scholar
  4. 4.
    Wickens, C.D., Hollands, J.G., Banbury, S., Parasuraman, R.: Engineering Psychology and Human Performance. Pearson, London (2013)Google Scholar
  5. 5.
    Schleer, P., Drobinsky, S., Radermacher, K.: Evaluation of different modes of haptic guidance for robotic surgery. IFAC-PapersOnLine 51, 97–103 (2019).  https://doi.org/10.1016/j.ifacol.2019.01.035CrossRefGoogle Scholar
  6. 6.
    Cunha-Cruz, V., et al.: Robot-and computer-assisted craniotomy (CRANIO): from active systems to synergistic man—machine interaction. Proc. Inst. Mech. Eng. Part H: J. Eng. Med. 224, 441–452 (2010)CrossRefGoogle Scholar
  7. 7.
    Denis, K., et al.: Influence of bone milling parameters on the temperature rise, milling forces and surface flatness in view of robot-assisted total knee arthroplasty. In: International Congress Series, pp. 300–306. Elsevier (2001)Google Scholar
  8. 8.
    Engelhardt, M., Bast, P., Lauer, W., Rohde, V., Schmieder, K., Radermacher, K.: Manual vs. robotic milling parameters for development of a new robotic system in cranial surgery. In: International Congress Series, pp. 533–538. Elsevier (2004)Google Scholar
  9. 9.
    Fu, Y., Yin, H., Pan, B.: Fuzzy based velocity constraints of virtual fixtures in tele-robotic surgery. In: 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 2625–2630. IEEE (2014)Google Scholar
  10. 10.
    Kouskoulas, Y., Renshaw, D., Platzer, A., Kazanzides, P.: Certifying the safe design of a virtual fixture control algorithm for a surgical robot. In: Proceedings of the 16th International Conference on Hybrid Systems: Computation and Control, pp. 263–272. ACM (2013)Google Scholar
  11. 11.
    Pezzementi, Z.A., Okamura, A.M., Hager, G.D.: Dynamic guidance with pseudoadmittance virtual fixtures. In: ICRA, pp. 1761–1767 (2007)Google Scholar
  12. 12.
    Bowyer, S.A., Davies, B.L., Rodriguez, Y., Baena, F.: Active constraints/virtual fixtures: a survey. IEEE Trans. Robot. 30, 138–157 (2014).  https://doi.org/10.1109/tro.2013.2283410CrossRefGoogle Scholar
  13. 13.
    Franke, T., Attig, C., Wessel, D.: A personal resource for technology interaction: development and validation of the Affinity for Technology Interaction (ATI) scale. Int. J. Hum.–Comput. Interact. 35(6), 456–467 (2018)CrossRefGoogle Scholar
  14. 14.
    Enayati, N., De Momi, E., Ferrigno, G.: Haptics in robot-assisted surgery: challenges and benefits. IEEE Rev. Biomed. Eng. 9, 49–65 (2016)CrossRefGoogle Scholar
  15. 15.
    Allin, S., Matsuoka, Y., Klatzky, R.: Measuring just noticeable differences for haptic force feedback: implications for rehabilitation. In: Proceedings of the 10th Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, HAPTICS 2002, pp. 299–302. IEEE (2002)Google Scholar
  16. 16.
    Peon, A.R., Prattichizzo, D.: Reaction times to constraint violation in haptics: comparing vibration, visual and audio stimuli. In: World Haptics Conference (WHC), pp. 657–661. IEEE (2013)Google Scholar
  17. 17.
    Bast, P., Engelhardt, M., Lauer, W., Schmieder, K., Rohde, V., Radermacher, K.: Identification of milling parameters for manual cutting of bicortical bone structures. Comput. Aided Surg. 8, 257–263 (2003)CrossRefGoogle Scholar
  18. 18.
    Abbink, D.A., Mulder, M., Boer, E.R.: Haptic shared control: smoothly shifting control authority? Cogn. Technol. Work 14, 19–28 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Chair of Medical EngineeringHelmholtz Institute for Biomedical EngineeringAachenGermany

Personalised recommendations