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Computational Human Model as Robot Technology

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Robotics Research

Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 100))

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Abstract

The study of computational approach to human understanding has been the history of artificial intelligence. The robotics developments in algorithms and software have prepared the powerful research tools that were not available when the study of intelligence started from unembodied frameworks. The computational human model is a large field of research. The author and the colleagues have studied by focussing on behavioral modeling and anatomical modeling. The aims of study on human modeling are double faces of a coin. One side is to develop the technological foundation to predict human behaviors including utterance for robots communicating with the humans. The other side is to develop the quantitative methods to estimate the internal states of the humans. The former directly connected to the development of robotic applications in the aging societies. The latter finds fields of application in medicine, rehabilitation, pathology, gerontology, development, and sports science. This paper survey the recent research of the authors group on the anatomical approach to the computational human modeling.

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Notes

  1. 1.

    The distance between Quadriceps and spinal nerve ramus is 800 mm.

  2. 2.

    This delay is often observed as the latency of knee-jerk reflex.

  3. 3.

    We use the measured EMG to estimate the activity of Rectus Femoris, so its co-contraction during the jump and squat motions appears in the computed activities.

References

  1. M. Minsky, Semantic Information Processing (The MIT Press, 1969)

    Google Scholar 

  2. H.A. Simon, The Sciences of the Artificial, 2nd edn. (The MIT Press, 1968, 1981)

    Google Scholar 

  3. T. Deacon, Symbolic Species: The Co-evolution of Language and the Brain (W.W. Norton and Company Inc, 1997)

    Google Scholar 

  4. M. Donald, Origin of the Modern mind (Harvard University Press, Cambridge, 1991)

    Google Scholar 

  5. V. Gallese, A. Goldman, Mirror neuron and the simulation theory of mind-reading. Trends Cogn. Sci. 2(12), 493–501 (1998)

    Article  Google Scholar 

  6. G. Rizzolatti, L. Fogassi, and V. Gallese, Neurophysiological mechanisms underlying the understanding and imitation of action. Nat. Rev. 661–670 (2001)

    Google Scholar 

  7. H. Ezaki, T. Inamura, Y. Nakamura, I. Toshima, Imitation and primitive symbol acquisition of humanoids by the integrated mimesis loop, in Proceedings of the 18th IEEE International Conference on Robotics and Automation, pp. 4208–4213 (2001)

    Google Scholar 

  8. T. Inamura, I. Toshima, H. Tanie, Y. Nakamura, Embodied symbol emergence based on mimesis theory. Int. J. Robot. Res. 23(4), 363–377 (2004)

    Article  Google Scholar 

  9. W. Takano, K. Yamane, T. Sugihara, K. Yamamoto, Y. Nakamura, Primitive communication based on motion recognition and generation with hierarchical mimesis model, in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 3602–2609 (2006)

    Google Scholar 

  10. B. Janus, Y. Nakamura, Unsupervised probabilistic segmentation of motion data for mimesis modeling, in Proceedings of IEEE International Conference on Advanced Robotics, (2005), pp. 411–417

    Google Scholar 

  11. D. Kulic, Y. Nakamura, Scaffolding on-line segmentation of full body human motion patterns, in Proceedings of the IEEE/RSJ 2008 International Conference on Intelligent Robots and Systems, pp. 2860–2866 (2008)

    Google Scholar 

  12. D. Kulic, H. Imagawa, Y. Nakamura, Online acquisition and visualization of motion primitives for humanoid robots, in Proceedings of the 18th IEEE International Symposium on Robot and Human Interactive Communication, pp. 1210–1215 (2009)

    Google Scholar 

  13. W. Takano, D. Kulic, H. Imagawa, Y. Nakamura, What do you expect from a robot that tells your future? the crystal ball, in Proceedings of the IEEE International Conference on Robotics and Automation (2010)

    Google Scholar 

  14. W. Takano, Y. Nakamura, Incremental learning of integrated semiotics based on linguistic and behavioral symbols, in Proceedings of the IEEE International Conference on Intelligent Robots and Systems, pp. 2545–2550 (2009)

    Google Scholar 

  15. W. Takano, Y. Nakamura, Associative processes between behavioral symbols and a large scale language model, in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 2404–2409 (2010)

    Google Scholar 

  16. W. Takano, H. Imagawa, Y. Nakamura, Prediction of human behaviors in the future through symbolic inference, in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1970–1975 (2011)

    Google Scholar 

  17. T. Flash, N. Hogan, The coordination of arm movements: an experimentally confirmed mathematical model. J. Neurosci. 5, 1688–1703 (1985)

    Google Scholar 

  18. M. Katato, Y. Maeda, Y. Uno, R. Suzuki, Trahectory formation of arm movement by cascade neural network model based on minimum torque-change criterion. Biol. Cybern. 62(4), 275–288 (1990)

    Article  Google Scholar 

  19. S.L. Delp, J.P. Loan, A computational framework for simulating and analyzing human and animal movement. IEEE Comput. Sci. Eng. 2, 46–55 (2000)

    Article  Google Scholar 

  20. T. Komura, P. Prokopow, A. Nagano, Evaluation of the influence of muscle deactivation on other muscles and joints during gait motion. J. Biomech. 37(4), 425–436 (2004)

    Article  Google Scholar 

  21. F.C. Anderson, M.G. Pandy, Static and dynamic optimization solutions for gait are practically equivalent. J. Biomech. 34, 153–161 (2001)

    Article  Google Scholar 

  22. Y. Nakamura, K. Yamane, Y. Fujita, I. Suzuki, Somatosensory computation for man-machine interface from motion capture data and musculoskeletal human model. IEEE Trans. Rob. 21(1), 58–66 (2005)

    Article  Google Scholar 

  23. Y. Nakamura, K. Yamane, A. Murai. “Macroscopic Modeling and Identification of the Human Neuromuscular Network, in Proceedings of the 28th IEEE EMBS Annual International Conference, pp. 99–105 (2006)

    Google Scholar 

  24. K. Yamane, Y. Fujita, Y. Nakamura, Estimation of physically and physiologically valid somatosensory information, in Proceedings of IEEE International Conference on Robotics and Automation, pp. 2635–2641, Barcelona, Spain, April 2005

    Google Scholar 

  25. A.V. Hill, The heat of shortening and the dynamic constants of muscle. Proc. Royal Soc. Lond. B126, 136–195 (1938)

    Article  Google Scholar 

  26. A. Murai, K. Yamane, Y. Nakamura, Modeling and Identification of the Human Neu- romusculoskeletal Model’s Somatic Refrex Network, in Proceeding of 2007 JSME Conference on Robotics and Mechatronics (ROBOMEC’07) (2007) (in Japanese)

    Google Scholar 

  27. A. Hyva¨rinen, J. Karhunen, E. Oja, Independent Component Analysis (Wiley, 2001)

    Google Scholar 

  28. A. Murai, K. Yamane, Y. Nakamura, Effects of Nerve Signal Transmission Delay in Somatosensory Reflex Modeling Based on Inverse Dynamics and Optimization, in Proceeding of IEEE International Conference on Robotics and Automation, Anchorage, USA (2010)

    Google Scholar 

  29. A. Hill, The heat of shortening and the dynamic constants of muscle. Proc. Royal Soc. Lond. B126, 136–195 (1938)

    Article  Google Scholar 

  30. S. Stroeve, Impedance characteristics of a neuro-musculoskeletal model of the human arm I: posture control. J. Biol. Cybern. 81, 475–494 (1999)

    Article  MATH  Google Scholar 

  31. Y. Nakamura, K. Yamane, Y. Fujita, I. Suzuki, Somatosensory computation for man-machine interface from motion capture data and musculoskeletal human model. IEEE Trans. Rob. 21, 58–66 (2005)

    Article  Google Scholar 

  32. J.H. Warfel, The Extremities: Muscles and Motor Points (Lea & Febiger, Philadelphia, 1974)

    Google Scholar 

  33. J. Erlanger, H.S. Gasser, Electrical Signs of Nervous Activity (University Press, Philadelphia, 1937)

    Google Scholar 

  34. D.P.C. Lloyd, C.C. Hunt, A.K. McIntyre, Transmission in fractionated monosynaptic spinak reflex system. J. Gen. Physiol. 38, 789–799 (1955)

    Article  Google Scholar 

  35. A. Prochazka, M. Gorassini, Models of ensemble firing of muscle spindle afferents recorded during normal locomotion in cats. J. Physiol. 507, 277–291 (1998)

    Article  Google Scholar 

  36. C.D. Clemente, Gray’s Anatomy ed 30 (Lea & Febiger, Phyladellphia, 1985)

    Google Scholar 

  37. A.M.R. Agur, Grant’s Atlas of Anatomy (Williams & Wilkins, Baltimore, 1991)

    Google Scholar 

  38. A.E. Bryson, Yu-Chi Ho, Applied Optimal Control (Blaisdell, New York, 1969)

    Google Scholar 

  39. Y. Ibuka, A. Murai, Y. Nakamura, Modeling of Somatic Reflex Network with Cutaneous Sensation,in Proceeding of JSME Robotics and Mechatronics Conference (ROBOMECH) (2011)

    Google Scholar 

  40. F.H. Netter, Atlas of Human Anatomy, 4th edn. (Elsevier, 2006)

    Google Scholar 

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Correspondence to Yoshihiko Nakamura .

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Nakamura, Y. (2017). Computational Human Model as Robot Technology. In: Christensen, H., Khatib, O. (eds) Robotics Research . Springer Tracts in Advanced Robotics, vol 100. Springer, Cham. https://doi.org/10.1007/978-3-319-29363-9_31

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  • DOI: https://doi.org/10.1007/978-3-319-29363-9_31

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