Three-Dimensional In-Vivo Kinematic Analysis of Finger Movement

  • Sandro Fioretti
Chapter
Part of the NATO ASI Series book series (NSSA, volume 256)

Abstract

A large part of biomechanics literature concerning the hand has focused its interest on the long fingers and in particular on the index. In fact, this latter is characterized by the highest mobility with respect to the other long fingers and has an important functional role in allowing the movement of opposition with the thumb. The joint that mostly provides the index finger with the mobility and the stability necessary to perform useful work is the metacarpophalangeal (MCP) joint. Many investigators have studied different aspects of this joint such as its anatomy or its kinematic and dynamic behaviour. But most of these studies have been performed in-vitro and, as far as kinematics is concerned, in-vivo studies were limited only to the determination of range of motion or to the assessment of finger orientation in static conditions.

Keywords

Translation Speed Helical Axis Calibration Object Metacarpal Head Instantaneous Axis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    B.D. Anderson, and J.B. Moore, “Optimal filtering”, Prentice-Hall, Englewood Cliffs (1979).Google Scholar
  2. 2.
    A. Cappozzo, Gait analysis methodology, Human Movement Science, 3:27 (1984).CrossRefGoogle Scholar
  3. 3.
    E.Y.S. Chao, K.N. An, W.P. Cooney, and R.D. Linsheid, “Biomechanics of the hand. A basic research study”, World Scientific Pub., Singapore (1989).CrossRefGoogle Scholar
  4. 4.
    A. de Lange, J.M.G. Kauer, R. Huiskes, and H.J. Woltring, Carpal bone motion axes and pivots in flexion and extension of the hand, in: “Proc. 32nd Ann. Orthopaedic Research Society Meeting”, New Orleans (1986).Google Scholar
  5. 5.
    J. Dimnet, and M. Guinguand, The finite displacements vector’s method: an application to the scoliotic spine, J.Biomech. 17:397 (1984).PubMedCrossRefGoogle Scholar
  6. 6.
    J. Dubousset, Anatomie fonctionnelle de l’appareil capsulo-ligamentaire des articulations des doigts (sauf le pouce), in: Traumatismes ostéoarticulaires de la main, Conférence du G.E.M., Masson, Paris (1974).Google Scholar
  7. 7.
    S. Fioretti, A. Germani, and T. Leo, Stereometry in very close-range stereophotogrammetry with non-metric cameras for human movement analysis,. J.Biomech. 18:11,831 (1985).PubMedCrossRefGoogle Scholar
  8. 8.
    S. Fioretti, and L. Jetto, Accurate derivative estimation from noisy data: a state-space approach, Int. J. Systems Sci. 20:1,33 (1989).CrossRefGoogle Scholar
  9. 9.
    S. Fioretti, L. Jetto, and T. Leo, Reliable in-vivo estimation of the instantaneous helical axis in human segmental movements, IEEE Trans, on BME, 37:4,398 (1990a).CrossRefGoogle Scholar
  10. 10.
    S. Fioretti, T. Leo, E. Pisani, and M.L. Corradini, A computer-aided movement analysis system, IEEE Trans, on BME, 37:8,812 (1990b).CrossRefGoogle Scholar
  11. 11.
    S.K. Ghosh, “Analytical Photogrammetry”, Pergamon Press, New York (1979).Google Scholar
  12. 12.
    R.W. Hakstian, and R. Tubiana, Ulnar deviation of the fingers. The role of joint structure and function, J. Bone Joint Surg. 49A:2,299 (1967).PubMedGoogle Scholar
  13. 13.
    G.L. Kinzel, B.M. Hillberry, A.S.Jr. Hall, D.C. Van Sickle, and W.M. Harvey, Measurement of the total motion between two body segments, II. Description of application, J.Biomech. 5:283 (1972).PubMedCrossRefGoogle Scholar
  14. 14.
    H. Lanshammar, On precision limits for derivatives numerically calculated from noisy data, J. Biomech. 15:459 (1982).PubMedCrossRefGoogle Scholar
  15. 15.
    T. Leo, and V. Macellari, An optoelectronic device-microcomputer system for automatized gait analysis”, in: “Changes in Health Care Instrumentation due to Microprocessor Technology”, F. Pinciroli and J. Anderson Ed., North-Holland, Amsterdam (1981).Google Scholar
  16. 16.
    V. Macellari, CoSTEL: a computer peripheral remote sensing device for 3-dimensional monitoring of human motion, Med. and Biol. Engng. and Comput. 21:311 (1983).CrossRefGoogle Scholar
  17. 17.
    E.M. Mikhail, “Observations and Least Squares”, IEP Dun-Donnelley, New York (1976).Google Scholar
  18. 18.
    V.A. Morozov, “Methods for Solving Incorrectly Posed Problems”, Springer-Verlag, New York (1984).CrossRefGoogle Scholar
  19. 19.
    M.M. Panjabi, M.H. Krag, and V.K. Goel, A technique for measurement and description of three-dimensional six degree-of-freedom motion of a body joint with an application to the human spine, J.Biomech. 14:447 (1981).PubMedCrossRefGoogle Scholar
  20. 20.
    C.W. Spoor, and F.E. Veldpaus, Rigid-body motion calculated from spatial coordinares of markers, J.Biomech. 13:4,391 (1980).PubMedCrossRefGoogle Scholar
  21. 21.
    H.J. Woltring, A Fortran package for generalized, cross-validatory spline smoothing and differentiation, Advances in Engineering Software, 8:2,104 (1986).CrossRefGoogle Scholar
  22. 22.
    H.J. Woltring, Representation and calculation of 3-D joint movement, in: Proceedings of the Workshop on: Assessment of Clinical Protocols, Ancona, October 16–17, 1989, Deliverable 6 of CAMARC project, CEC AIM Programme, Public Report (1989).Google Scholar
  23. 23.
    H.J. Woltring, Model and measurement error influences in data processing, in “Biomechanics of Human Movement: Applications in Rehabilitation, Sports and Ergonomics”, N.Berme and A. Cappozzo, ed., Bertec Corporation, Pub., Worthington (1990).Google Scholar
  24. 24.
    H.J. Woltring, and R. Huiskes, A statistically motivated approach to instantaneous helical axis estimation from noisy, sampled landmark coordinates, in: “Biomechanics IX-B”, D.A. Winter et al., eds, Human Kinetics Pub., Champaign (1985).Google Scholar
  25. 25.
    H.J. Woltring, R. Huiskes, and A. de Lange, Finite centroid and helical axis estimation from noisy landmark measurements in the study of human joint kinematics, J.Biomech. 18: 379 (1985).PubMedCrossRefGoogle Scholar
  26. 26.
    H.J. Woltring, A. de Lange, J.M.G. Kauer, and R. Huiskes, Instantaneous helical axis estimation via natural, cross-validated splines, in: “Biomechanics: Basic and Applied Research”, G. Bergmann, R. Kolbel, and A. Rohlmann, eds., Martinus Nijhoff, Dordrecht (1987).Google Scholar

Copyright information

© Springer Science+Business Media New York 1994

Authors and Affiliations

  • Sandro Fioretti
    • 1
  1. 1.Dipartimento di Elettronica ed AutomaticaUniversita’ di AnconaAnconaItaly

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