Journal of Robotic Surgery

, Volume 13, Issue 3, pp 413–421 | Cite as

Evaluation of a pneumatic surgical robot with dynamic force feedback

  • Dimitrios KarponisEmail author
  • Yokota Koya
  • Ryoken Miyazaki
  • Takahiro Kanno
  • Kenji Kawashima
Original Article


Robot-assisted surgery is limited by the lack of haptic feedback and increased operating times. Force scaling adjusts feedback transmitted to the operator through the use of scaling factors. Herein, we investigate how force scaling affects forces exerted in robotic surgery during simple and complex tasks, using a pneumatic surgical robot, IBIS VI. Secondary objectives were to test the effects of force scaling on operating time, depth of needle insertion and user satisfaction. Two novice males performed simple (modified block transfer) and complex (needle insertion) tasks under four scaling factors: 0.0, 0.5, 1.0 and 2.0. Single-blind experiments were repeated five times, with alternating scaling factors in random order. Increasing the scaling factor from 0.0 to 2.0 reduces forces in block transfer (p = 0.04). All feedback conditions reduce forces in needle insertion compared to baseline (0.5: p < 0.001, 1.0: p = 0.001, 2.0: p = 0.001). Time to complete block transfer is shorter for scaling factor 0.5 (p = 0.02), but not for 1.0 (p = 0.05) or 2.0 (p = 0.48), compared to baseline. Depth of needle insertion decreases consistently with incremental scaling factors (p < 0.001). Further reductions are observed upon augmenting feedback (0.5–2.0: p = 0.02). User satisfaction in block transfer is highest for intermediate scaling factors (0.0–1.0: p = 0.01), but no change is observed in needle insertion (p = 0.99). Increments in scaling factor reduce forces exerted, particularly in tasks requiring precision. Depth of needle insertion follows a similar pattern, but operating time and user satisfaction are improved by intermediate scaling factors. In summary, dynamic adjustment of force feedback can improve operative outcomes and advance surgical automation.


Robot-assisted surgery Force feedback Force scaling Pneumatic surgical robot Block transfer Needle insertion 



The authors thank Riverfield inc. for providing the infrastructure to conduct this research.


Part of this research is based on the Cooperative Research Project of the Research Centre for Biomedical Engineering. Author-DK was supported by the JASSO scholarship.

Compliance with ethical standards

Conflict of interest

Authors DK, YK, RM, TK and KK declare that they have no conflict of interest.

Supplementary material

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Supplementary material 1 (DOCX 185 KB)
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Supplementary material 5 (DOCX 113 KB)
11701_2018_878_MOESM6_ESM.docx (47 kb)
Supplementary material 6 (DOCX 47 KB)


  1. 1.
    Hashizume M, Tsugawa K (2004) Robotic surgery and cancer: the present state, problems and future vision. Jpn J Clin Oncol 34(5):227–237. CrossRefGoogle Scholar
  2. 2.
    Nio D, Bemelman WA, Busch OR, Vrouenraets BC, Gouma DJ (2004) Robot-assisted laparoscopic cholecystectomy versus conventional laparoscopic cholecystectomy: a comparative study. Surg Endosc 18(3):379–382CrossRefGoogle Scholar
  3. 3.
    Roh HF, Nam SH, Kim JM (2018) Robot-assisted laparoscopic surgery versus conventional laparoscopic surgery in randomized controlled trials: a systematic review and meta-analysis. PLoS One 13(1):e0191628. CrossRefGoogle Scholar
  4. 4.
    Breitenstein S, Nocito A, Puhan M, Held U, Weber M, Clavien PA (2008) Robotic-assisted versus laparoscopic cholecystectomy: outcome and cost analyses of a case-matched control study. Ann Surg 247(6):987–993. CrossRefGoogle Scholar
  5. 5.
    Wright JD, Ananth CV, Lewin SN et al (2013) Robotically assisted vs laparoscopic hysterectomy among women with benign gynecologic disease. JAMA 309(7):689–698. CrossRefGoogle Scholar
  6. 6.
    Enayati N, De Momi E, Ferrigno G (2016) Haptics in robot-assisted surgery: challenges and benefits. IEEE Rev Biomed Eng 9:49–65. CrossRefGoogle Scholar
  7. 7.
    Hannaford B, Okamura AM (2008) Haptics. In: Siciliano B, Khatib O (eds) Springer handbook of robotics. Springer, Berlin, pp 719–739CrossRefGoogle Scholar
  8. 8.
    Puangmali P, Althoefer K, Seneviratne LD, Murphy D, Dasgupta P (2008) State-of-the-art in force and tactile sensing for minimally invasive surgery. IEEE Sens J 8(4):371–381. CrossRefGoogle Scholar
  9. 9.
    Johansson RS, Westling G (1984) Roles of glabrous skin receptors and sensorimotor memory in automatic control of precision grip when lifting rougher or more slippery objects. Exp Brain Res 56(3):550–564. CrossRefGoogle Scholar
  10. 10.
    King CH, Culjat MO, Franco ML et al (2009) Tactile feedback induces reduced grasping force in robot-assisted surgery. IEEE Trans Haptics 2(2):103–110. CrossRefGoogle Scholar
  11. 11.
    Wottawa CR, Genovese B, Nowroozi BN et al (2016) Evaluating tactile feedback in robotic surgery for potential clinical application using an animal model. Surg Endosc 30(8):3198–3209. CrossRefGoogle Scholar
  12. 12.
    Roy J, Rothbaum DL, Whitcomb LL (2002) Haptic feedback augmentation through position based adaptive force scaling: theory and experiment. IEEE RSJ Int Conf Intell Robots Syst 3:2911–2919. Google Scholar
  13. 13.
    Rizun P, Gunn D, Cox B, Sutherland G (2006) Mechatronic design of haptic forceps for robotic surgery. Int J Med Robot 2(4):341–349. CrossRefGoogle Scholar
  14. 14.
    Tadano K, Kawashima K, Kojima K, Tanaka N (2009) Development of a pneumatically driven forceps manipulator IBIS IV. 2009 ICCAS-SICE conference. Fukuoka, Japan, pp 179–188Google Scholar
  15. 15.
    Kasahara Y, Kawana H, Usuda S, Ohnishi K (2012) Telerobotic-assisted bone-drilling system using bilateral control with feed operation scaling and cutting force scaling. Int J Med Robot 8(2):221–229. CrossRefGoogle Scholar
  16. 16.
    Sariyildiz E, Ohnishi K (2014) An adaptive reaction force observer design. IEEE ASME Trans Mechatron 20(2):750–760. CrossRefGoogle Scholar
  17. 17.
    Peddamatham S, Peine W, Tan H (2008) Assessment of vibrotactile feedback in a needle-insertion task using a surgical robot. 2008 symposium on haptic interfaces for virtual environment and teleoperator systems. Reno, Nevada, pp 93–99. CrossRefGoogle Scholar
  18. 18.
    Gwilliam JC, Mahvash M, Vagvolgyi B, Vacharat A, Yuh DD, Okamura AM (2009) Effects of haptic and graphical force feedback on teleoperated palpation. 2009 IEEE international conference on robotics and automation. Kobe, Japan, pp 677–682. CrossRefGoogle Scholar
  19. 19.
    Deml B, Ortmaier T, Weiss H (2004) Minimally invasive surgery: empirical comparison of manual and robot assisted force feedback surgery. EuroHaptics, Munich, Germany, pp 403–406Google Scholar
  20. 20.
    Wagner C, Howe R (2007) Force feedback benefit depends on experience in multiple degree of freedom robotic surgery task. IEEE Trans Robot 23(6):1235–1240. CrossRefGoogle Scholar
  21. 21.
    Yaxley JW, Coughlin GD, Chambers SK et al (2016) Robot-assisted laparoscopic prostatectomy versus open radical retropubic prostatectomy: early outcomes from a randomised controlled phase 3 study. Lancet 388(10049):1057–1066. CrossRefGoogle Scholar
  22. 22.
    Cundy TP, Harling L, Hughes-Hallett A et al (2014) Meta-analysis of robot-assisted vs conventional laparoscopic and open pyeloplasty in children. BJU Int 114(4):582–594. CrossRefGoogle Scholar
  23. 23.
    Wagner C, Stylopoulos N, Howe R (2002) The role of force feedback in surgery: analysis of blunt dissection. Haptics. Google Scholar
  24. 24.
    Van der Schatte Olivier RH, Van’t Hullenaar CD, Ruurda JP, Broeders IA (2009) Ergonomics, user comfort, and performance in standard and robot-assisted laparoscopic surgery. Surg Endosc 23(6):1365–1371. CrossRefGoogle Scholar
  25. 25.
    Nakagawa S (2004) A farewell to the Bonferroni: the problems of low statistical power and publication bias. Behav Ecol 15(6):1044. CrossRefGoogle Scholar
  26. 26.
    Shademan A, Decker RS, Opfermann JD, Leonard S, Krieger A, Kim PC (2016) Supervised autonomous robotic soft tissue surgery. Sci Transl Med 8(337):337ra64. CrossRefGoogle Scholar
  27. 27.
    Haidegger T, Benyó B, Kovács L, Benyó Z (2009) Force sensing and force control for surgical robots. IFAC Proc Vol. 42(12):401–406 CrossRefGoogle Scholar
  28. 28.
    Modi HN, Singh H, Orihuela-Espina F et al (2018) Temporal stress in the operating room: brain engagement promotes “coping” and disengagement prompts “choking”. Ann Surg 267(4):683–691. CrossRefGoogle Scholar
  29. 29.
    Davies BL, Harris SJ, Lin WJ, Hibberd RD, Middleton R, Cobb JC (1997) Active compliance in robotic surgery—the use of force control as a dynamic constraint. Proc Inst Mech Eng H 211(4):285–292. CrossRefGoogle Scholar
  30. 30.
    Cobb J, Henckel J, Gomes P et al (2006) Hands-on robotic unicompartmental knee replacement: a prospective, randomised controlled study of the acrobot system. J Bone Joint Surg Br 88(2):188–197. CrossRefGoogle Scholar
  31. 31.
    Meccariello G, Faedi F, AlGhamdi S et al (2016) An experimental study about haptic feedback in robotic surgery: may visual feedback substitute tactile feedback? J Robot Surg 10(1):57–61. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Biomaterials and BioengineeringTokyo Medical and Dental UniversityTokyoJapan
  2. 2.School of MedicineImperial College LondonLondonUK

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