Psychomotor Surgical Training in Virtual Reality

  • George Papagiannakis
  • Panos Trahanias
  • Eustathios Kenanidis
  • Eleftherios TsiridisEmail author


In this chapter, we present a novel s/w system aiming to disrupt the healthcare training industry with the first psychomotor virtual reality (VR) surgical training solution. We provide the means for performing surgical operations in VR, thereby facilitating training in a fail-safe environment that very accurately simulates reality and significantly reduces training costs, offering surgeons and the healthcare ecosystem a way to improve operation outcomes drastically.

With the presented system, we focus on a completed total knee arthroplasty (TKA) virtual reality operating module, opening the way for making available a full suite of virtual reality operations. Our methodology transforms medical training to a cost-effective and easily and broadly accessible process. The latter is accomplished by employing the latest VR, gamification and tracking technologies for virtual character-based, interactive 3D medical simulation training. It requires standard h/w (PCs, laptops) irrelevant of the operating system. For optimal user experience, a commodity VR head-mounted display (HMD) should be employed along with motion or other hand-controller sensors. The open ovidVR architecture supports all current and forthcoming VR HMDs and standard 3D content generation. Our novel technologies facilitate Presence that is the feeling of ‘being there’ and ‘acting there’ in the virtual world, thereby offering the means for unprecedented training.


  1. 1.
    Association of Surgeons of Great Britain and Ireland. The Impact of EWTD on Delivery of Surgical Services: A Consensus Statement; 2008.Google Scholar
  2. 2.
    Philbert I, Friedmann P, Williams WT, SCGME Work Group on Resident Duty Hours. Accreditation Council for Graduate Medical Education: new requirements for resident duty hours. JAMA. 2002;288:1112–4.CrossRefGoogle Scholar
  3. 3.
    Aggarwal R, Mytton OT, Derbrew M, Hananel D, Heydenburg M, Issenberg B, MacAulay C, Mancini ME, Morimoto T, Soper N, Ziv A, Reznick R. Training and simulation for patient safety. Qual Saf Health Care. 2010;19(Suppl 2):i34–43.CrossRefPubMedGoogle Scholar
  4. 4.
    Gaba DM. The future vision of simulation in health care. Qual Saf Health Care. 2004;13(Suppl 1):i2–i10.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Wiet GJ, Stredney D, Kerwin T, Hittle B, Kang DR. Simulation for training in resource-restricted countries: using a scalable temporal bone surgical simulator. Int J Med Educ. 2016;7:293–4. Scholar
  6. 6.
    Frank RM, Erickson B, Frank JM, Bush-Joseph CA, Bach BR, Cole BJ, Romero AA, Provencher MT, Verma NN. Utility of modern arthroscopic simulator training models. Arthroscopy. 2014;30(1):121–33.CrossRefPubMedGoogle Scholar
  7. 7.
    Tay C, Khajuria A, Gupte C. Simulation training: a systematic review of simulation in arthroscopy and proposal of a new competency-based training framework. Int J Surg. 2014;12(6):626–33.CrossRefPubMedGoogle Scholar
  8. 8.
    Dawe SR, Pena GN, Windsor JA, Broeders JAJL, Cregan PC, Hewett PJ, Maddern GJ. Systematic review of skills transfer after surgical simulation-based training. Br J Surg. 2014;101(9):1063–76.CrossRefPubMedGoogle Scholar
  9. 9.
    Sabri H, Cowan B, Kapralos B, Porte M, Backstein D, Dubrowskie A. Serious games for knee replacement surgery procedure education and training. Procedia Soc Behav Sci. 2010;2(2):3483–8. Scholar
  10. 10.
    de Ribaupierre S, Kapralos B, Haji F, Stroulia E, Dubrowski A, Eagleson R. Healthcare training enhancement through virtual reality and serious games. In: Virtual, augmented reality and serious games for healthcare 1, vol. 68. Berlin, Heidelberg: Springer; 2014. p. 9–27. Scholar
  11. 11.
    Sigalas M, Baltzakis H, Trahanias P. Gesture recognition based on arm tracking for human-robot interaction. Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ international conference, pp. 5424–5429 2010.Google Scholar
  12. 12.
    Sigalas M, Pateraki M, Trahanias P. Robust articulated upper body pose tracking under severe occlusions. International conference. Intelligent Robots and Systems, IROS2014, pp. 4104–4111, Chicago, 2014.Google Scholar
  13. 13.
    Papagiannakis G. Geometric algebra rotors for skinned character animation blending. Technical Brief, ACM SIG-GRAPH ASIA 2013, Hong Kong, November 2013, 2013.Google Scholar
  14. 14.
    Papagiannakis G, Papanikolaou P, Greassidou E, Trahanias PE. glGA: an OpenGL geometric application framework for a modern, shader-based computer graphics curriculum. Eurographics’14 (Education Papers Track). 2014, pp. 9–16.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • George Papagiannakis
    • 1
    • 2
  • Panos Trahanias
    • 1
    • 2
  • Eustathios Kenanidis
    • 3
  • Eleftherios Tsiridis
    • 4
    Email author
  1. 1.Department of Computer ScienceUniversity of CreteCreteGreece
  2. 2.Computational Vision and Robotics Laboratory, Institute of Computer Science, Foundation for Research & Technology—Hellas (FORTH), Science and Technology Park of CreteCreteGreece
  3. 3.Academic Orthopaedic UnitAristotle University Medical SchoolThessalonikiGreece
  4. 4.Academic Orthopaedic UnitPapageorgiou General Hospital, Aristotle University Medical SchoolThessalonikiGreece

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