single-rb.php

JRM Vol.25 No.1 pp. 162-171
doi: 10.20965/jrm.2013.p0162
(2013)

Paper:

Intuitive Operability Evaluation of Robotic Surgery Using Brain Activity Measurements to Clarify Immersive Reality

Satoshi Miura*, Yo Kobayashi**, Kazuya Kawamura**,
Masatoshi Seki*, Yasutaka Nakashima*, Takehiko Noguchi***,
Masahiro Kasuya*, Yuki Yokoo*, and Masakatsu G. Fujie**

*Graduate School of Advanced Science and Engineering, Waseda University, 2-2 Wakamatsu, Shinjuku, Tokyo 162-8480, Japan

**The Faculty of Science and Engineering, Waseda University, Japan

***Graduate School of Creative Science and Engineering, Waseda University, Japan

Received:
March 30, 2012
Accepted:
August 3, 2012
Published:
February 20, 2013
Keywords:
surgical robot, medical robot, intuitive operability, manipulability, brain activity measurement
Abstract
Surgical robots have undergone considerable improvement in recent years. But intuitive operability, which represents user interoperability, has not been quantitatively evaluated. With the aim of designing a robot with intuitive operability, we thus propose a method for measuring brain activity to determine intuitive operability. The purpose of this paper is to clarify the master configuration against the position of the monitor that best allows user to perceive the manipulator as part of his own body. We assume that the master configuration provides immersive reality to user as if he puts own arm into the monitor. In our experiments, subjects controlled the hand controller to position the tip of the virtual slave manipulator on a target in the surgical simulator and we measured brain activity using brain imaging devices. We carried out experiments a number of times with themastermanipulator configured in a variety of ways and the position of the monitor fixed. We found that the brain was significantly activated in all subjects when the master manipulator was located behind the monitor. We concluded that the master configuration produces immersive reality through body images related to visual and somatic sensory feedback.
Cite this article as:
S. Miura, Y. Kobayashi, K. Kawamura, M. Seki, Y. Nakashima, T. Noguchi, M. Kasuya, Y. Yokoo, and M. Fujie, “Intuitive Operability Evaluation of Robotic Surgery Using Brain Activity Measurements to Clarify Immersive Reality,” J. Robot. Mechatron., Vol.25 No.1, pp. 162-171, 2013.
Data files:
References
  1. [1] T. Osa, C. Staub, and A. Knoll, “Framework of automatic robotic surgery system using visual servoing,” in Proc. 2010 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, Taipei, Taiwan, 2010.
  2. [2] J. Leven, D. Burschka, R. Knmar, M. Choti, C. Hasser, and R. H. Taylor, “Da Vinci Canvas: a telerobotic surgical system with integrated, robot-assisted, laparoscopic ultrasound capability,” Medical Image Computing and Computer-Assisted Intervention (MICCAI), Vol.3749, pp. 811-818, 2005.
  3. [3] G. H. Ballantyne, “Robotic surgery, telerobotic surgery, telepresence, and telementoring – review of early clinical results,” Surg Endosc, Vol.16, pp. 1389-1402, 2002.
  4. [4] S. Tachi, H. Arai, and T. Maeda, “Development of an anthropomorphic tele-existence slave robot,” in Proc. Int. Conf. on Advanced Mechatronics, Ibaraki, Japan, pp. 385-390, 1989.
  5. [5] I. Suh, M. Mukherjee, D. Oleynikov, and K-C. Siu, “Training program for fundamental surgical skill in robotic laparoscopic surgery,” Int. J. Med. Robotics Computer Assist. Surg., Vol.7, pp. 327-333, 2011.
  6. [6] S. Taya, G. Maehara, and H. Kojima, “Hemodynamic changes in response to the stimulated visual quadrants: a study with 24-channel near-infrared spectroscopy,” Jpn. J. Psychonomic Sci., 2009.
  7. [7] G. Maehara, S. Taya, and H. Kojima, “Changes in hemoglobin concentration in the lateral occipital regions during shape recognition: a near-infrared spectroscopy study,” J. of Biomedical Optics, Vol.12, No.6, 062109, 2007.
  8. [8] E. Watanabe et al., “Non-invasive assessment o language dominance with near-infrared spectroscopic mapping,” Neurosci. Lett., Vol.256, pp. 49-52, 1998.
  9. [9] J. Lee et al., “Origins of Spatial Working Memory Deficits in Schizophrenia: An Event-related fMRI and Near-infrared Spectroscopy Study,” PLoS ONE, Vol.3, e1760, 2008.
  10. [10] M. Munetaka et al., “Dynamic Cortical Activity during Spasms in Three Patients with West Syndrome: A Multichannel Near-infrared Spectroscopic Topography Study,” Epilepsia, Vol.45, pp. 1248-1257, 2004.
  11. [11] T. Tsujii et al., “Effects of sedative and non-sedative H1 antagonists on cognitive tasks: behavioral and near-infrared spectroscopy (NIRS) examinations,” Psychoparmacology, Vol.194, pp. 83-91, 2007.
  12. [12] Y. Otsuka et al., “Neural activation to upright and inverted faces in infants measured by near infrared spectroscopy,” NeuroImage, Vol.34, pp. 399-406, 2007.
  13. [13] A.-C. Ehlis et al., “Multi-channel near-infrared spectroscopy detects specific inferior-frontal activation during incongruent Stroop trials,” Biological Psychology, Vol.69, pp. 315-331, 2005.
  14. [14] A. Maravita and A. Iriki, “Tools for the body,” Trends Cogn. Sci., Vol.8, No.2, pp. 79-86, 2004.
  15. [15] C. Nabeshima, Y. Kuniyoshi, and M. Lungarella, “Adaptive Body Scheme for Robotic Tool-Use,” Advanced Robotics, Vol.20, No.10, pp. 1105-1126, 2006.
  16. [16] K. Harada et al., “Micro Manipulator and Forceps Naviation for Endoscopic Fetal Surgery,” J. of Mechatoronics, 2006.
  17. [17] K. Tadano et al., “Development of a Pneumatic Surgical Manupulator IBIS IV,” J. of Robotics and Mechatronics, 2010.
  18. [18] K. Kishi et al., “Dual-Armed Surgical Master-Slave Manipulator System with MR Compatiblity,” J. of Robotics and Mechatronics, 2005.
  19. [19] T. Yonemura et al., “Comparison of Pose Correspondence Methods of Master-Slave Manipulator for Neurosurgical Robotics Systems,” J. of Robotics and Mechatronics, 2011.
  20. [20] Y. Sekiguchi et al., “Development of a Tool Manipulator Driven by a Flexible Shaft for Single-Port Endoscopic Surgery,” J. of Robotics and Mechatronics, 2011.
  21. [21] I. Scott. MacKenzie, “Fitts’ Law as a Research and Design Tool in Human-Computer Interaction,” HUMAN-COMPUTER INTERACTION, 1992, Vol.7, pp. 91-139, 1992.
  22. [22] S. K. Card and T. P. Moran, “The Keystroke-Level Model for User Performance Time with Interactive Systems,” Communication of the ACM, Vol.23, No.7, pp. 396-410, Jul. 1980.
  23. [23] T. Yoshikawa, “Manipulability of Robotic Mechanisms,” The Int. J. of Robotics Research 1985.
  24. [24] A. C. B. Garcia, C. Maciel, and F. B. Pint, “A Quality Inspection Method to Evaluate E-Government Sites,” Electric Government 2005, Lecture Notes in Computer Science, Vol.3591, 198-209, 2005.
  25. [25] M. Winder and A. herts, “A new heuristic method for the flow shop sequencing problem,” European J. of Operational Research, Vol.41, pp. 186-193, 1988.
  26. [26] M. H. Blackmon, P. G. Polson, M. Kitajima, and C. Lewis, “Cognitive Walkthrough for the WEB,” in Proc. of the SIGCHI conf. on Human factors in computing systems: Changing our world, changing ourselves (CHI2002), Minneapolis, Minnesota, USA, April 20-25, 2002.
  27. [27] W. Wimmer, “The ECODESIGN Checklist Method: A Redesign Tool for Environment Product Improvements,” in proc. of Environmentally Conscious Design and Inverse Manufacturing, pp. 685-688, Feb. 1-3, 1999.
  28. [28] J. S. Michaelson, E. Halpern, and D. B. Kopans, “Breast Cancer: Computer Simulation Method for Estimating Optimal Intervals for Screening,” Radop;pgu 1999, 551-560, 1999.
  29. [29] J. Ott, “Computer-simulation methods in human linkage analysis,” Proc. of the National Academy of Science of the Untied States of America (PNAS), Vol.86, pp. 4157-4178, Jun. 1989.
  30. [30] D. J. Kasik and H. G. George, “Toward Automatic Generation of Novice User Test Scripts,” Proc. of the SIGCHI conf. on Human factors in computing systems: common ground (CHI 96), Vancouver, BC Canada, pp. 244-251, 1996.
  31. [31] K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “a Method for Automatic Evaluation of Machine Transaction,” in Proc. of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia, pp. 311-318, Jul. 2002
  32. [32] N. Kuboyama et al., “The Effect of Maximal Finger Tapping on Cerebral Activation,” J. of physiological Anthropology and Applied human Science, Vol.23, pp. 105-110, 2004.
  33. [33] T. Suto et al, “Multichannel Near-infrared Spectroscopy in Depression and Schizophrenia: Cognitive Brain Activation Study,” Biol Psychiatry, Vol.55, pp. 501-511, 2004.
  34. [34] T. Ohtani et al., “Hemodynamic response of eye movement desensitization and reprocessing in posttraumatic stress disorder,” Neuroscience Research, Vol.65, pp. 375-383, 2009.
  35. [35] C. E. Colby and M. E. Golberg, “Space and attention in parietal cortex,” Annu. Rev. Neurosci., Vol.12, pp. 319-349, 1999.
  36. [36] R. A. Anderson, “Visual and eye movement functions of the posterior parietal cortex,” Annu. Rev. Neurosci., Vol.12, pp. 377-403, 1989.
  37. [37] J. C. Culham and N. G. Kanwisher, “Neuroimaging of cognitive functions in human parietal cortex,” Current Opinion in Neurobiology, Vol.11, pp. 157-163, 2001.
  38. [38] H. Head and G. Holmes, “Sensory disturbances from cerebral lesions,” Brain, Vol.34, pp. 102-245, 1911.
  39. [39] A. N. Fader and P. F. Escobar, “Laparoendoscopic single-site surgery (LESS) in gynecologic oncology: technique and initial report,” Gynecologic Oncology, Vol.114, 2009.
  40. [40] M. Miyazaki, M. Hiroshima, and D. Nozaki, “The Cutaneous Rabbit Hopping out of the Body,” J. Neurosci., Vol.30, No.5, pp. 1856-1860, 2010.
  41. [41] H. Imamizu, S. Miyauchi, T. Tamada, Y. Sasaki, R. Takino, B. Pz, T. Yoshioka, and M. Kawato, “Human Cerebellar Activity Reflecting an Acquired Internal Model of a New Tool,” Nature, Vol.403, pp. 192-195, 2000.
  42. [42] D. M. Clower and D. Boussaound, “Selective Use of Perceptual Recalibration Versus Visuomotor Skill Acquisition,” J. Neurophysiol., Vol.84, pp. 2703-2708, 2000.
  43. [43] S. Miura, Y. Kobayashi, M. Seki, T. Noguchi, M. Kasuya, Y. Yokoo, and M. G. Fujie, “Intuitive Operability Evaluation of Robotic Surgery using Brain Activity Measurement to Identify the Hand-Eye Coordination,” in proc. of the2012 IEEE Int. Conf. on Robotics and Automation (ICRA’12), 2012.
  44. [44] V. J. Santos and F. J. Valero-Cuevas, “Reported Anatomical Variability Naturally Leads to Multimodal Distributions of Denavit-Hartenberg Parameters for the Human Thumb,” IEEE T. Bio-med. Eng., Vol.53, No.2, pp. 155-163, 2006.
  45. [45] J. Paillard, “The Use of Tools by Human and Non-human Primates,” Oxford University Press, New York, 1993.
  46. [46] S. R. A. Fisher, “The Design of Experiments,” 1935.
  47. [47] R. W. Human, J. Herman, and P. Purdy, “Cerebral location of international 10-20 system electrode placement,” Electroen. Clin. Neuro., Vol.66, pp. 376-382, 1987.
  48. [48] M. Hoffman, H. G. Marques, A. H. Arieta, H. Sumioka, M. Lungarella, and R. Pefeifer, “Body Scheme in Robotics: a review,” IEEE Trans. Autonomous Mental Development, Vol.2, No.4, pp. 304-324, 2010.
  49. [49] E. Cassirer, “Philosophie der symbolischen Formen,” pp. 1923-1929, 1923.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 22, 2024