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Applications of Haptics in Medicine

  • Angel R. Licona
  • Fei Liu
  • David Pinzon
  • Ali Torabi
  • Pierre Boulanger
  • Arnaud LelevéEmail author
  • Richard Moreau
  • Minh Tu Pham
  • Mahdi Tavakoli
Chapter

Abstract

Touch is one of the most important sensory inputs during the performance of surgery. However, the literature on kinesthetic and tactile feedback both called haptics in surgical training remains rudimentary. This rudimentary knowledge is partial since that haptic feedback is difficult to describe, as well as record and playback. This chapter aims at focusing on the use of haptics in the training of medical staff and also as a complementary tool for robotized and remote procedures. It provides an overview of the various available technologies to perform haptic feedback and details on how haptic guidance can enhance surgical skill acquisition. A critical review of available haptic interfaces vis-a-vis medical interventions to be performed is provided. The chapter ends with an illustration merging the advantages of usual supervised hands-on training and the ones offered by computer-based training: dual-user training simulators.

Notes

Acknowledgements

The authors acknowledge the financial support of the China Scholarship Council (CSC) and the Consejo Nacional de Ciencia y Tecnologia (CONACyT) in Mexico.

References

  1. 1.
    Adams, J.A.: A closed-loop theory of motor learning. Journal of Motor Behavior 3(2), 111–150 (1971). https://doi.org/10.1080/00222895.1971.10734898. PMID: 15155169CrossRefGoogle Scholar
  2. 2.
    Adams, J.A.: Historical review and appraisal of research on the learning, retention, and transfer of human motor skills. Psychological Bulletin 101(1), 41–74 (1987). https://doi.org/10.1037/0033-2909.101.1.41 CrossRefGoogle Scholar
  3. 3.
    Aggarwal, R., Moorthy, K., Darzi, A.: Laparoscopic skills training and assessment. British Journal of Surgery 91(12), 1549–1558 (2004)CrossRefGoogle Scholar
  4. 4.
    Anderson, R.J., Spong, M.W.: Bilateral control of teleoperators with time delay. In: Proceedings of the 27th IEEE Conference on Decision and Control, pp. 167–173 (1988).  https://doi.org/10.1109/CDC.1988.194290
  5. 5.
    Auricchio, F., Taylor, R.L., Lubliner, J.: Shape-memory alloys: macromodelling and numerical simulations of the superelastic behavior. Computer Methods in Applied Mechanics and Engineering 146(3), 281–312 (1997). https://doi.org/10.1016/S0045-7825(96)01232-7. URL http://www.sciencedirect.com/science/article/pii/S0045782596012327 CrossRefzbMATHGoogle Scholar
  6. 6.
    Bakr, M.M., Massey, W., Alexander, H.: Evaluation of simodont® haptic 3d virtual reality dental training simulator. International journal of dental clinics 5(4) (2013)Google Scholar
  7. 7.
    Bann, S., Darzi, A.: Selection of individuals for training in surgery. The American Journal of Surgery 190(1), 98–102 (2005)CrossRefGoogle Scholar
  8. 8.
    Basdogan, C., De, S., Kim, J., Muniyandi, M., Kim, H., Srinivasan, M.A.: Haptics in minimally invasive surgical simulation and training. IEEE Computer Graphics and Applications 24(2), 56–64 (2004)CrossRefGoogle Scholar
  9. 9.
    Berkelman, P.J., Hollis, R.L.: Lorentz magnetic levitation for haptic interaction: Device design, performance, and integration with physical simulations. The International Journal of Robotics Research 19(7), 644–667 (2000). https://doi.org/10.1177/027836490001900703 CrossRefGoogle Scholar
  10. 10.
    Beyl, P., Van Damme, M., Van Ham, R., Vanderborght, B., Lefeber, D.: Pleated pneumatic artificial muscle-based actuator system as a torque source for compliant lower limb exoskeletons. IEEE/ASME Transactions on Mechatronics 19(3), 1046–1056 (2014).  https://doi.org/10.1109/TMECH.2013.2268942 CrossRefGoogle Scholar
  11. 11.
    Bholat, O.S., Haluck, R.S., Murray, W.B., Gorman, P.J., Krummel, T.M.: Tactile feedback is present during minimally invasive surgery. Journal of the American College of Surgeons 189(4), 349–355 (1999). https://doi.org/10.1016/S1072-7515(99)00184-2. URL http://www.sciencedirect.com/science/article/pii/S1072751599001842 CrossRefGoogle Scholar
  12. 12.
    Caldwell, D.G., Gosney, C.: Enhanced tactile feedback (tele-taction) using a multi-functional sensory system. In: [1993] Proceedings IEEE International Conference on Robotics and Automation, pp. 955–960 vol.1 (1993).  https://doi.org/10.1109/ROBOT.1993.292099
  13. 13.
    Chebbi, B., Lazaroff, D., Bogsany, F., Liu, P.X., Ni, L., Rossi, M.: Design and implementation of a collaborative virtual haptic surgical training system. In: IEEE International Conference Mechatronics and Automation, 2005, vol. 1, pp. 315–320 Vol. 1 (2005).  https://doi.org/10.1109/ICMA.2005.1626566
  14. 14.
    Chebbi, B., Lazaroff, D., Liu, P.X.: A collaborative virtual haptic environment for surgical training and tele-mentoring. International Journal of Robotics and Automation 22(1), 69–78 (2007).  https://doi.org/10.2316/Journal.206.2007.1.206-1007 CrossRefGoogle Scholar
  15. 15.
    Chellali, A., Dumas, C., Milleville-Pennel, I.: Haptic communication to support biopsy procedures learning in virtual environments. Presence: Teleoperators and Virtual Environments 21(4), 470–489 (2012).  https://doi.org/10.1162/PRES_a_00128 CrossRefGoogle Scholar
  16. 16.
    Chou, C.P., Hannaford, B.: Measurement and modeling of mckibben pneumatic artificial muscles. IEEE Transactions on Robotics and Automation 12(1), 90–102 (1996). https://doi.org/10.1109/70.481753 CrossRefGoogle Scholar
  17. 17.
    Colgate, J.E., Brown, J.M.: Factors affecting the z-width of a haptic display. In: Proceedings of the 1994 IEEE International Conference on Robotics and Automation, pp. 3205–3210 vol.4 (1994).  https://doi.org/10.1109/ROBOT.1994.351077
  18. 18.
    Delorme, S., Laroche, D., DiRaddo, R., F. Del Maestro, R.: Neurotouch: A physics-based virtual simulator for cranial microneurosurgery training. Neurosurgery 71 (suppl 1), 32–42 (2012)CrossRefGoogle Scholar
  19. 19.
    Ernst, M., Banks, M.: Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415(6870), 429–433 (2002)CrossRefGoogle Scholar
  20. 20.
    Feygin, D., Keehner, M., Tendick, R.: Haptic guidance: Experimental evaluation of a haptic training method for a perceptual motor skill. In: Proceedings of the 10th Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems. HAPTICS 2002, pp. 40–47. IEEE (2002)Google Scholar
  21. 21.
    Fitts, P., Peterson, J.: Information capacity of discrete motor responses. Journal of Experimental Psychology 67, 103–112 (1964)CrossRefGoogle Scholar
  22. 22.
    Fitts, P.M., Posner, M.I.: Human Performance, Belmont, CA edn. Brooks/Cole Pub. Co. (1967)Google Scholar
  23. 23.
    Fleming, N.: I’m different; not dumb. modes of presentation (vark) in the tertiary classroom. In: Proceedings of the 1995 Annual Conference of the Higher Education and Research Development Society of Australasia (HERDSA), Research and Development in Higher Education, vol. 18, pp. 308–313. HERDSA (1995). Zelmer,A., (ed.)Google Scholar
  24. 24.
    Frisoli, A., Rocchi, F., Marcheschi, S., Dettori, A., Salsedo, F., Bergamasco, M.: A new force-feedback arm exoskeleton for haptic interaction in virtual environments. In: First Joint Eurohaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems. World Haptics Conference, pp. 195–201 (2005).  https://doi.org/10.1109/WHC.2005.15
  25. 25.
    Fry, D.E., Harris, W.E., Kohnke, E.N., Twomey, C.L.: Influence of double-gloving on manual dexterity and tactile sensation of surgeons. Journal of the American College of Surgeons 210(3), 325–330 (2010). https://doi.org/10.1016/j.jamcollsurg.2009.11.001. URL http://www.sciencedirect.com/science/article/pii/S1072751509015555 CrossRefGoogle Scholar
  26. 26.
    Gallager, A., O’Sullivan, G.: Fundamentals of surgical simulation. Principles and practice. Dorchert Heidelrgerg, Springer, London (2012)CrossRefGoogle Scholar
  27. 27.
    Gallagher, A.G., Ritter, E.M., Champion, H., Higgins, G., Fried, M.P., Moses, G., Smith, C.D., Satava, R.M.: Virtual reality simulation for the operating room: proficiency-based training as a paradigm shift in surgical skills training. Annals of surgery 241(2), 364–372 (2005). URL https://www.ncbi.nlm.nih.gov/pubmed/15650649 CrossRefGoogle Scholar
  28. 28.
    Ghorbanian, A., Rezaei, S., Khoogar, A., Zareinejad, M., Baghestan, K.: A novel control framework for nonlinear time-delayed dual-master/single-slave teleoperation. ISA Transactions 52(2), 268–277 (2013)CrossRefGoogle Scholar
  29. 29.
    Grange, S., Conti, F., Rouiller, P., Helmer, P., Baur, C.: The delta haptic device. Mecatronics (2001)Google Scholar
  30. 30.
    Greer, A.D., Newhook, P.M., Sutherland, G.R.: Human–machine interface for robotic surgery and stereotaxy. IEEE/ASME Transactions on Mechatronics 13(3), 355–361 (2008)CrossRefGoogle Scholar
  31. 31.
    Han, Y.M., Choi, S.B.: Force-feedback control of a spherical haptic device featuring an electrorheological fluid. Smart Materials and Structures 15(5), 1438–1446 (2006). https://doi.org/10.1088/0964-1726/15/5/033 CrossRefGoogle Scholar
  32. 32.
    Hayward, V., Gregorio, P., Astley, O., Greenish, S., Doyon, M., Lessard, L., McDougall, J., Sinclair, I., Boelen, S., Chen, X., Demers, J.G., Poulin, J., Benguigui, I., Almey, N., Makuc, B., Zhang, X.: Freedom-7: A high fidelity seven axis haptic device with application to surgical training. In: A. Casals, A.T. de Almeida (eds.) Experimental Robotics V, pp. 443–456. Springer Berlin Heidelberg, Berlin, Heidelberg (1998)CrossRefGoogle Scholar
  33. 33.
    Herzig, N., Moreau, R., Leleve, A., Pham, M.T.: Stiffness Control of Pneumatic Actuators to Simulate Human Behavior on Medical Haptic Simulators. In: IEEE (ed.) 2016 IEEE AIM. IEEE, IEEE, Banff, Canada (2016).  https://doi.org/10.1109/AIM.2016.7576997. URL https://hal.archives-ouvertes.fr/hal-01333383. A paraître. Nominé parmi les 5 meilleurs articles pour le prix du “Best Paper Award” de la conférence AIM 2016
  34. 34.
    Herzig, N., Moreau, R., Redarce, T., Abry, F., Brun, X.: Nonlinear position and stiffness Backstepping controller for a two Degrees of Freedom pneumatic robot. Control Engineering Practice 73, 26–39 (2018). https://doi.org/10.1016/j.conengprac.2017.12.007. URL https://hal.archives-ouvertes.fr/hal-01682127 CrossRefGoogle Scholar
  35. 35.
    Hu, T., Castellanos, A.E., Tholey, G., Desai, J.P.: Real-time haptic feedback in laparoscopic tools for use in gastro-intestinal surgery*. In: T. Dohi, R. Kikinis (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2002, pp. 66–74. Springer Berlin Heidelberg, Berlin, Heidelberg (2002)CrossRefGoogle Scholar
  36. 36.
    Jacobsen, S., Smith, F., Backman, D., Iversen, E.: High performance, high dexterity, force reflective teleoperator. In: ANS topical meeting on robotics and remote systems, pp. 24–27 (1991)Google Scholar
  37. 37.
    Kerr, R.: Intersensory integration: A kinesthetic bias. Perceptual and Motor Skills 79(3), 1068–1070 (1994).  https://doi.org/10.2466/pms.1994.79.3.1068 CrossRefGoogle Scholar
  38. 38.
    Khademian, B., Hashtrudi-Zaad, K.: Shared control architectures for haptic training: performance and coupled stability analysis. The International Journal of Robotics Research 30(13), 1627–1642 (2011)CrossRefGoogle Scholar
  39. 39.
    Kirkpatrick, A.E., Douglas, S.A.: Application-based evaluation of haptic interfaces. In: Proceedings 10th Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems. HAPTICS 2002, pp. 32–39 (2002).  https://doi.org/10.1109/HAPTIC.2002.998938 Google Scholar
  40. 40.
    Konstantinova, J., Li, M., Aminzadeh, V., Althoefer, K., Dasgupta, P.: Evaluating manual palpation trajectory patterns in tele-manipulation for soft tissue examination. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics, pp. 4190–4195 (2013).  https://doi.org/10.1109/SMC.2013.714
  41. 41.
    Krautter, M., Weyrich, P., Schultz, J.H., Buss, S.J., Maatouk, I., Jünger, J., Nikendei, C.: Effects of peyton’s four-step approach on objective performance measures in technical skills training: A controlled trial. Teaching and Learning in Medicine 23(3), 244–250 (2011). https://doi.org/10.1080/10401334.2011.586917. URL https://doi.org/10.1080/10401334.2011.586917. PMID: 21745059
  42. 42.
    Lazeroms, M., Villavicencio, G., Jongkind, W., Honderd, G.: Optical fibre force sensor for minimal-invasive-surgery grasping instruments. In: Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 1, pp. 234–235 vol.1 (1996).  https://doi.org/10.1109/IEMBS.1996.656931
  43. 43.
    Lederman, S.J., Klatzky, R.L.: Haptic perception: A tutorial. Attention, Perception, & Psychophysics 71(7), 1439–1459 (2009).  https://doi.org/10.3758/APP.71.7.1439. URL  https://doi.org/10.3758/APP.71.7.1439
  44. 44.
    Lee, D., Li, P.: Passive bilateral feedforward control of linear dynamically similar teleoperated manipulators. Robotics and Automation, IEEE Transactions on 19(3), 443–456 (2003)CrossRefGoogle Scholar
  45. 45.
    Lee, D., Spong, M.: Passive bilateral teleoperation with constant time delay. Robotics, IEEE Transactions on 22(2), 269–281 (2006)CrossRefGoogle Scholar
  46. 46.
    van der Linde, R., Lammertse, P.: Hapticmaster – a generic force controlled robot for human interaction. Industrial Robot: the international journal of robotics research and application 30(6), 515–524 (2003)CrossRefGoogle Scholar
  47. 47.
    Liu, C., Bar-Cohen, Y.: Scaling laws of microactuators and potential applications of electroactive polymers in mems. Proceedings of SPIE – The International Society for Optical Engineering 3669 (1999). https://doi.org/10.1117/12.349692
  48. 48.
    Liu, F., Leleve, A., Eberard, D., Redarce, T.: A dual-user teleoperation system with adaptive authority adjustement for haptic training. In: Medical and Service Robots, Proceedings of 4th International Workshop on (2015)Google Scholar
  49. 49.
    Liu, F., Leleve, A., Eberard, D., Redarce, T.: A dual-user teleoperation system with online authority adjustment for haptic training. In: IEEE Engineering in Medicine and Biology Society (EMBC), Proceedings of 37th Annual International Conference of (2015)Google Scholar
  50. 50.
    Liu, F., Leleve, A., Eberard, D., Redarce, T.: An energy based approach for passive dual-user haptic training systems. In: Proceedings of the 2016 IEEE International Conference on Intelligent Robots and Systems (IROS 2016). Daejeon, South Corea (2016)Google Scholar
  51. 51.
    Lu, Z., Huang, P., Dai, P., Liu, Z., Meng, Z.: Enhanced transparency dual-user shared control teleoperation architecture with multiple adaptive dominance factors. International Journal of Control, Automation and Systems 15(5), 2301–2312 (2017). https://doi.org/10.1007/s12555-016-0467-y CrossRefGoogle Scholar
  52. 52.
    MacLean, K.E.: Haptic interaction design for everyday interfaces. Reviews of Human Factors and Ergonomics 4(1), 149–194 (2008). https://doi.org/10.1518/155723408X342826 CrossRefGoogle Scholar
  53. 53.
    Magee, R.: Medical practice and medical education 1500–2001: an overview. ANZ Journal of Surgery 74(4), 272–276 (2004). https://doi.org/10.1111/j.1445-2197.2004.02960.x. URL https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1445-2197.2004.02960.x CrossRefGoogle Scholar
  54. 54.
    Mason, M.L.: Significance of the american college of surgeons to progress of surgery in america. The American Journal of Surgery 51(1), 267–286 (1941). https://doi.org/10.1016/S0002-9610(41)90056-9. URL http://www.sciencedirect.com/science/article/pii/S0002961041900569 CrossRefGoogle Scholar
  55. 55.
    Massie, T.H., Salisbury, J.K.: The phantom haptic interface: A device for probing virtual objects. In: Proceedings of the ASME Dynamic Systems and Control Division, pp. 295–301 (1994)Google Scholar
  56. 56.
    Mayer, H., Nagy, I., Knoll, A., Braun, E.U., Bauernschmitt, R., Lange, R.: Haptic feedback in a telepresence system for endoscopic heart surgery. Presence: Teleoperators and Virtual Environments 16(5), 459–470 (2007).  https://doi.org/10.1162/pres.16.5.459 CrossRefGoogle Scholar
  57. 57.
    Meier, A.H., Rawn, C.L., Krummel, T.M.: Virtual reality: surgical application challenge for the new millennium11no competing interests declared. Journal of the American College of Surgeons 192(3), 372–384 (2001). https://doi.org/10.1016/S1072-7515(01)00769-4. URL http://www.sciencedirect.com/science/article/pii/S1072751501007694 CrossRefGoogle Scholar
  58. 58.
    Van der Meijden, O.A., Schijven, M.P.: The value of haptic feedback in conventional and robot-assisted minimal invasive surgery and virtual reality training: a current review. Surgical endoscopy 23(6), 1180–1190 (2009)CrossRefGoogle Scholar
  59. 59.
    Nguyen, L., Brunicardi, F.C., DiBardino, D.J., Scott, B.G., Awad, S.S., Bush, R.L., Brandt, M.L.: Education of the modern surgical resident: Novel approaches to learning in the era of the 80-hour workweek. World Journal of Surgery 30(6), 1120–1127 (2006). https://doi.org/10.1007/s00268-005-0038-5. URL https://doi.org/10.1007/s00268-005-0038-5
  60. 60.
    Nuño, E., Basañez, L., R., O.: Passivity-based control for bilateral teleoperation: A tutorial. Automatica 47(3), 485–495 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  61. 61.
    Nudehi, S., Mukherjee, R., Ghodoussi, M.: A shared-control approach to haptic interface design for minimally invasive telesurgical training. Control Systems Technology, IEEE Transactions on 13(4), 588–592 (2005)CrossRefGoogle Scholar
  62. 62.
    Ostoja-Starzewski, M., Skibniewski, M.: A master-slave manipulator for excavation and construction tasks. Robotics and Autonomous Systems 4, 333–337 (1989). https://doi.org/10.1016/0921-8890(89)90032-8 CrossRefGoogle Scholar
  63. 63.
    Panait, L., Akkary, E., Bell, R., Roberts, K., Dudrick, S., Duffy, A.: The role of haptic feedback in laparoscopic simulation training. Journal of Surgical Research 156(2), 312–316 (2009)CrossRefGoogle Scholar
  64. 64.
    Peyton, J.: Teaching in the theatre., chap. Teaching and learning in medical practice. Manticore Europe (1998)Google Scholar
  65. 65.
    Razi, K., Hashtrudi-Zaad, K.: Analysis of coupled stability in multilateral dual-user teleoperation systems. Robotics, IEEE Transactions on 30(3), 631–641 (2014)CrossRefGoogle Scholar
  66. 66.
    Reznick, R.K., MacRae, H.: Teaching surgical skills–changes in the wind. New England Journal of Medicine 355(25), 2664–2669 (2006)CrossRefGoogle Scholar
  67. 67.
    Rogers, D.A., Elstein, A.S., Bordage, G.: Improving continuing medical education for surgical techniques: Applying the lessons learned in the first decade of minimal access surgery. Annals of Surgery 233(2), 159–166 (2001)CrossRefGoogle Scholar
  68. 68.
    Sachdeva, A.K.: The changing paradigm of residency education in surgery: a perspective from the american college of surgeons. The American surgeon 73(2), 120 (2007)Google Scholar
  69. 69.
    Salisbury Jr, J.K., Madhani, A.J., Guthart, G.S., Niemeyer, G.D., Duval, E.F.: Master having redundant degrees of freedom (2004). US Patent 6,684,129Google Scholar
  70. 70.
    Schmidt, R.A., Lee, T.D., Winstein, C., Wulf, G., Zelaznik, H.N.: Motor Control and Learning: A Behavioral Emphasis. Human Kinetics (2018)Google Scholar
  71. 71.
    Schmidt, R.A., Young, D.E.: Methodology for Motor Learning: A Paradigm for Kinematic Feedback, vol. 23:1. Routledge (1991). https://doi.org/10.1080/00222895.1991.9941590 CrossRefGoogle Scholar
  72. 72.
    Shahbazi, M., Talebi, H., Atashzar, S., Towhidkhah, F., Patel, R., Shojaei, S.: A new set of desired objectives for dual-user systems in the presence of unknown communication delay. In: Advanced Intelligent Mechatronics (AIM), 2011 IEEE/ASME International Conference on, pp. 146–151 (2011).  https://doi.org/10.1109/AIM.2011.6027064
  73. 73.
    Shamaei, K., Kim, L., Okamura, A.: Design and evaluation of a trilateral shared-control architecture for teleoperated training robots. In: Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, pp. 4887–4893 (2015).  https://doi.org/10.1109/EMBC.2015.7319488
  74. 74.
    Singapogu, R.B., Long, L.O., Smith, D.E., Burg, T.C., Pagano, C.C., Prabhu, V.V., Burg, K.J.L.: Simulator-based assessment of haptic surgical skill: A comparative study. Surgical Innovation 22(2), 183–188 (2015). https://doi.org/10.1177/1553350614537119. PMID: 25053621CrossRefGoogle Scholar
  75. 75.
    Singapogu, R.B., Long, L.O., Smith, D.E., Burg, T.C., Pagano, C.C., Prabhu, V.V., Burg, K.J.L.: Simulator-based assessment of haptic surgical skill: A comparative study. Surgical Innovation 22(2), 183–188 (2015). https://doi.org/10.1177/1553350614537119. PMID: 25053621CrossRefGoogle Scholar
  76. 76.
    Smith, R.: Smart Material Systems: Model Development. No. 32 in Frontiers in Applied Mathematics. SIAM (2005)Google Scholar
  77. 77.
    Staab, H., Sonnenburg, A., Hieger, C.: The dohelix-muscle: A novel technical muscle for bionic robots and actuating drive applications. In: 2007 IEEE International Conference on Automation Science and Engineering, pp. 306–311 (2007).  https://doi.org/10.1109/COASE.2007.4341812
  78. 78.
    Stadler, W.: Analytical robotics and mechatronics, international ed edn. McGraw-Hill Education, New York, USA (1995)Google Scholar
  79. 79.
    Stocco, L.J., Salcudean, S.E., Sassani, F.: Optimal kinematic design of a haptic pen. IEEE/ASME Transactions on Mechatronics 6(3), 210–220 (2001). https://doi.org/10.1109/3516.951359 CrossRefGoogle Scholar
  80. 80.
    Swinnen, S.: Advances in Motor learning and control, chap. Information feedback for motor skill learning. Human Kinetics, Champaign (1996)Google Scholar
  81. 81.
    Talasaz, A., Trejos, A.L., Patel, R.V.: The role of direct and visual force feedback in suturing using a 7-dof dual-arm teleoperated system. IEEE Transactions on Haptics 10(2), 276–287 (2017).  https://doi.org/10.1109/TOH.2016.2616874 CrossRefGoogle Scholar
  82. 82.
    Tan, H.Z., Pang, X.D., Durlach, N.I., et al.: Manual resolution of length, force, and compliance. Advances in Robotics 42, 13–18 (1992)Google Scholar
  83. 83.
    Tao, R., Roy, G.D.: Electrorheological Fluids. WORLD SCIENTIFIC (1994). https://doi.org/10.1142/2245. URL https://www.worldscientific.com/doi/abs/10.1142/2245
  84. 84.
    Tavakoli, M., Aziminejad, A., Patel, R., Moallem, M.: High-fidelity bilateral teleoperation systems and the effect of multimodal haptics. IEEE Transactions on Systems, Man and Cybernetics – Part B 37(6), 1512–1528 (2007)CrossRefGoogle Scholar
  85. 85.
    Tavakoli, M., Patel, R.V., Moallem, M.: A haptic interface for computer-integrated endoscopic surgery and training. Virtual Reality 9(2-3), 160–176 (2006). https://doi.org/10.1007/s10055-005-0017-z CrossRefGoogle Scholar
  86. 86.
    Tedman, S., Thornton, E., Baker, G.: Development of a scale to measure core beliefs and perceived self efficacy in adults with epilepsy. Seizure: the journal of the British Epilepsy Association 4, 221–31 (1995). https://doi.org/10.1016/S1059-1311(05)80065-2 CrossRefGoogle Scholar
  87. 87.
    Tobergte, A., Helmer, P., Hagn, U., Rouiller, P., Thielmann, S., Grange, S., Albu-Schäffer, A., Conti, F., Hirzinger, G.: The sigma. 7 haptic interface for mirosurge: A new bi-manual surgical console. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3023–3030. IEEE (2011)Google Scholar
  88. 88.
    Tondu, B., Ippolito, S., Guiochet, J., Daidié, A.: A Seven-degrees-of-freedom Robot-arm Driven by Pneumatic Artificial Muscles for Humanoid Robots. International Journal of Robotics Research 24(4), p.257–274 (2005). https://doi.org/10.1177/0278364905052437. URL https://hal.archives-ouvertes.fr/hal-01292939 CrossRefGoogle Scholar
  89. 89.
    Torabi, A., Khadem, M., Zareinia, K., Sutherland, G.R., Tavakoli, M.: Manipulability of teleoperated surgical robots with application in design of master/slave manipulators. In: 2018 International Symposium on Medical Robotics (ISMR), pp. 1–6 (2018).  https://doi.org/10.1109/ISMR.2018.8333307
  90. 90.
    Torabi, A., Khadem, M., Zareinia, K., Sutherland, G.R., Tavakoli, M.: Application of a redundant haptic interface in enhancing soft-tissue stiffness discrimination. IEEE Robotics and Automation Letters 4(2), 1037–1044 (2019).  https://doi.org/10.1109/LRA.2019.2893606 CrossRefGoogle Scholar
  91. 91.
    Trejos, A.L., Patel, R.V., Naish, M.D.: Force sensing and its application in minimally invasive surgery and therapy: A survey. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 224(7), 1435–1454 (2010). https://doi.org/10.1243/09544062JMES1917 Google Scholar
  92. 92.
    Ueberle, M., Mock, N., Buss, M.: VISHARD10, a novel hyper-redundant haptic interface. In: Proceedings – 12th International Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, HAPTICS, pp. 58–65 (2004).  https://doi.org/10.1109/HAPTIC.2004.1287178
  93. 93.
    Wagner, C.R., Stylopoulos, N., Jackson, P.G., Howe, R.D.: The benefit of force feedback in surgery: Examination of blunt dissection. Presence: Teleoperators and Virtual Environments 16(3), 252–262 (2007).  https://doi.org/10.1162/pres.16.3.252 CrossRefGoogle Scholar
  94. 94.
    Winter, S.H., Bouzit, M.: Use of magnetorheological fluid in a force feedback glove. IEEE Transactions on Neural Systems and Rehabilitation Engineering 15(1), 2–8 (2007).  https://doi.org/10.1109/TNSRE.2007.891401 CrossRefGoogle Scholar
  95. 95.
    Yamamoto, T., Abolhassani, N., Jung, S., Okamura, A.M., Judkins, T.N.: Augmented reality and haptic interfaces for robot-assisted surgery. The International Journal of Medical Robotics and Computer Assisted Surgery 8(1), 45–56 (2012)CrossRefGoogle Scholar
  96. 96.
    Yang, X., Bischof, W.F., Boulanger, P.: Validating the performance of haptic motor skill training. In: 2008 Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, pp. 129–135 (2008).  https://doi.org/10.1109/HAPTICS.2008.4479929
  97. 97.
    Yiannakopoulou, E., Nikiteas, N., Perrea, D., Tsigris, C.: Virtual reality simulators and training in laparoscopic surgery. International Journal of Surgery 13(9), 60–64 (2014)Google Scholar
  98. 98.
    Yoshikawa, T.: Manipulability of Robotic Mechanisms. The International Journal of Robotics Research 4(2), 3–9 (1985)MathSciNetCrossRefGoogle Scholar
  99. 99.
    Zakerimanesh, A., Hashemzadeh, F., Ghiasi, A.R.: Dual-user nonlinear teleoperation subjected to varying time delay and bounded inputs. ISA Transactions 68, 33–47 (2017). https://doi.org/10.1016/j.isatra.2017.02.010 CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Angel R. Licona
    • 1
  • Fei Liu
    • 1
  • David Pinzon
    • 2
  • Ali Torabi
    • 3
  • Pierre Boulanger
    • 2
  • Arnaud Lelevé
    • 1
    Email author
  • Richard Moreau
    • 1
  • Minh Tu Pham
    • 1
  • Mahdi Tavakoli
    • 3
  1. 1.Laboratoire Ampère (UMR 5005)INSA Lyon, University of LyonLyonFrance
  2. 2.Department of Computing ScienceUniversity of AlbertaEdmontonCanada
  3. 3.Department of Electrical and Computer EngineeringUniversity of AlbertaEdmontonCanada

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