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Prediction of lower extremities' movement by angle-angle diagrams and neural networks

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In contemporary science, the analysis of human walking is extensively used. The prediction of leg motion, as well as rehabilitation, can be usable for orthosis and prosthesis programing. Our work is focused on predicting of human walking by angle-angle diagrams, also called cyclograms. The applications of cyclograms in conjunction with artificial intelligence offers wide area of applications in medicine. But until now, this approach has not been studied or applied in practice.
Rocznik
Strony
57--65
Opis fizyczny
Bibliogr. 37 poz., rys., tab.
Twórcy
autor
autor
  • Faculty of Biomedical Engineering, Czech Technical University in Prague, Czech Republic, kutilek@fbmi.cvut.cz
Bibliografia
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  • [2] KERRIGAN D.C., SCHAUFELE M., WEN M.N., Gait Analysis. Rehabilitation Medicine: Principles and Practice, Lippincott Williams & Wilkins, Philadelphia, 1998, 167–187.
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  • [5] HERRERO-JARABA J.E., ORRITE-URUÑUELA C., BULDAIN PÉREZ J.D., ROY-YARZA A., Human Recognition by Gait Analysis Using Neural Networks, International Conference on Artificial Neural Networks, 2002, 364–369.
  • [6] LAI D.T.H., BEGG R.K., PALANISWAMI M., Computational intelligence in gait research: a perspective on current applications and future challenges, IEEE Transactions on Information Technology in Biomedicine, 2009, Vol. 13, Issue 5, 687–702.
  • [7] LIANG WANG, TIENIU TAN, HUAZHONG NING, WEIMING HU, Automatic gait recognition based on statistical shape analysis, IEEE Trans Image Processing, 2003, Vol. 12, Issue 9, 1120–1131.
  • [8] MIJAILOVIĆ N., GAVRILOVIĆ M., RAFAJLOVIĆ S., Gait phases recognition from accelerations and ground reaction forces: application of neural networks, Telfor Journal, 2009, Vol. 1, Issue 1, 34–36.
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  • [12] HELLER B.W., VELTLINK P.H., RIJKHOF N.J.M., RUTTEN W.L.C., ANDREWS B., Reconstructing muscle activation during normal walking: a comparison of symbolic and connectionist machine learning techniques, Biological Cybernetics, 1993, Vol. 69, Issue 4, 327–335.
  • [13] GRIEVE D.W., Gait patterns and the speed of walking, Biomedical Engineering, 1968, Vol. 3, Issue 3, 119–122.
  • [14] GRIEVE D.W., The assessment of gait, Physiotherapy, 1969, Vol. 55, Issue 11, 452–460.
  • [15] GOSWAMI A., Kinematics quantification of gait symmetry based on bilateral cyclograms, XIX Congress of the Internation Society of Biomechanics (ISB), Dunedin, New Zealand, 2003, 34–43.
  • [16] GOSWAMI A., New gait parameterization technique by means of cyclogram moments: application to human slope walking, Gait and Posture, 1998, Vol. 8, Issue 1, 15–26.
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  • [27] VAUGHAN C.L., DAVIS B.L., O’CONNOR J.C., Dynamics of Human Gait, 2nd Edition, Cape Town, Kiboho Publishers, 1999.
  • [28] YAM C.Y., NIXON M.S., CARTER J.N., Gait Recognition by Walking and Running: A Model-Based Approach, Proceedings of 5th Asian Conference on Computer Vision, 2002, 1–6.
  • [29] ENG J.J., WINTER D.A., Kinetic analysis of the lower limbs during walking: What information can be gained from a threedimensional model, Journal of Biomechanics, 1995, Vol. 28, Issue 6, 753–758.
  • [30] BELLMANN M., SCHMALZ T., BLUMENTRITT S., Comparative biomechanical analysis of current microprocessor-controlled prosthetic knee joints, Archives of physical medicine and rehabilitation, 2010, Vol. 91, Issue 4, 644–652.
  • [31] BRIAN J.H., LAURA L.W., NOELLE C.B., KATHERYN J.A., DOUGLAS G.S., Evaluation of function, performance, and preference as transfemoral amputees transition from mechanical to microprocessor control of the prosthetic knee, Archives of physical medicine and rehabilitation, 2010, Vol. 88, Issue 2, 207–217.
  • [32] BOIAN F.R., BURDEA C.G., DEUTSCH E.J., Robotics and Virtual Reality Applications in Mobility Rehabilitation. Rehabilitation Robotics, I-Tech Education and Publishing, Vienna, 2007, 27–42.
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  • [34] KANG H.G., DINGWELL J.B., Effects of walking speed, strength and range of motion on gait stability in healthy older adults, Journal of Biomechanics, 2008, Vol. 41, Issue 14, 2899–2905.
  • [35] OWINGS T.M., GRABINER M.D., Variability of step kinematics in young and older adults, Gait and Posture, 2004, Vol. 20, Issue 1, 26–29.
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  • [37] SUTTON R., BARTO A., Reinforcement Learning: An Introduction, Cambridge, MIT Press, 1998.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPBB-0002-0016
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