Hostname: page-component-7b7567c8c-7pwfn Total loading time: 0 Render date: 2026-11-12T17:07:17.008Z Has data issue: false hasContentIssue false
  • English
  • Français

Towards a virtual reality-assisted movement diagnostics - an outline

Published online by Cambridge University Press:  09 March 2009

Vladimir Medved
Vladimir Medved
Affiliation:
Faculty of Physical Education, University of Zagreb, Horvacanski zavoj 15, 41000 Zagreb (Croatia)
Get accessRights & Permissions [Opens in a new window]

Extract

The paper proposes a concept of a movement diagnostics system of enhanced performances. The existing system, using electromyographic (EMG) and ground reaction force signals as inputs, has been applied primarily to sports locomotion evaluation and testing, providing suitable quantitative criteria. The proposed enhancement relies on movement simulation facility, incorporating subject-specific anatomical and functional data on the neuro-musculo-skeletal system of extremities. Reflecting the principles of anthropomorphic robotics, the computer graphics simulation is conceptualized to generate artificial (synthesized) movement patterns in virtual reality.

By comparing synthesized and measured signals the suitable feedback information is provided that may be used to enhance the overall system's performance. Using the power of computer technology and computer graphics, a user-friendly system of high ergonomic potentials is thus devised, in order to enhance the quality and efficacy of diagnostic procedure. The bioengineering principles used and the merging of measurement with simulation in the process of operation define a powerful instrument applicable to both healthy and pathological movement assessment.

Keywords

Movement diagnosticsSportsSimulationVirtual reality.

Information

Type
Research Article
Information
Robotica , Volume 12 , Issue 1 , January 1994, pp. 55 - 57
Copyright
Copyright © Cambridge University Press 1994

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

1.Delp, S.L., Loan, J.P., Hoy, M.G. and Zajac, F.E., “An interactive graphics-based model of the lower extremity to study orthopaedic surgical procedures” IEEE Transaction on Biomedical Engineering 37 (8), 757–767 (1990).CrossRefGoogle ScholarPubMed
2.Sutherland, C.J., Bresina, S.J., Gayou, D.E. & Blair, V.P. III, “Application of computer-assisted engineering (CAE) software to orthopaedic surgical planning” SOMA Engineering for the Human Body, 49–56 (1990).Google Scholar
3.Yoon, I. and Thompson, D.E., “Planning tendon paths using an interactive graphic workstation” J. Biomechanical Engineering 112, 387–391 (1990).CrossRefGoogle ScholarPubMed
4.Morasso, P. and Solari, M., “Kinesis: a model-driven approach to human motion analysis” SPIE Close-Range Photogrammetry Meets Machine Vision 1395, 775–780 (1990).Google Scholar
5.Morasso, P., “Neural mechanisms of synergy formation” Human Movement Science 11, 169–180 (1992).CrossRefGoogle Scholar
6.Gaglio, S., Morasso, P., Sandini, G. and Tagliasco, V., “Anthropomorphic robotics” In: (Nicolini, C., editor) Modeling and Analysis in Biomedicine (World Scientific Publishing Singapore, 1984) pp. 391–445.Google Scholar
7.Yamaguchi, G.T., “Performing whole-body simulations of gait with 3-D, dynamic musculoskeletal models” In: (Winters, J.M., Woo, S.L.Y., editors) Multiple Muscle Systems: Biomechanics and Movement Organization (Springer-Verlag, Berlin, 1990) pp. 663–679.CrossRefGoogle Scholar
8.Medved, V., “An approach to bioelectric signal analysis and processing” In: Proceedings - World Congress on Medical Physics and Biomedical Engineering, Hamburg, (1982) p. 18.05.Google Scholar
9.Medved, V. and Tonkovic, S., “Method to evaluate the skill level in fast locomotion through myoelectric and kinetic signal analysis” Medical and Biological Engineering and Computing 29 406–412 (1991).CrossRefGoogle ScholarPubMed
10.Medved, V., “A new concept of virtual reality assisted locomotion evaluation instrument” Abstract. Association for the Advancement of Medical instrumentation (AAMI)'s 26th Annual Meeting and Exposition,Washington, D.C. (1991).Google Scholar
11.Medved, V., “Micro-computer based measurement and processing method for human locomotor system's take-off properties evaluation” In: (Wieringa, H., editor) Experimental Stress Analysis (Martinus Nijhoff Publishers, Dordrecht, 1986) pp. 117–122.CrossRefGoogle Scholar
12.Medved, V. and Tonkovic, S., “Locomotion diagnostics: some neuromuscular and robotic aspects” IEEE Engineering in Medicine and Biology Magazine, (1991) 10(2), 23–28 (1991).CrossRefGoogle ScholarPubMed
13.Bouisset, S., “EMG and muscle force in normal motor activities” In: (Desmedt, J.E., editor) New Developments in EMG and Clinical Neurophysiology (Karger, Basel, 1973) Vol. 1, pp. 547–583.Google Scholar
14.Grenander, U., Pattern Synthesis - Lectures in Pattern Theory (springer Verlag, Berlin, 1980).Google Scholar
15.Valencie, V., “Pattern recognition of paraparetics' gait” (in Slovene), Elektrotehnicki vjesnik 4, 135–139 (1992).Google Scholar
16.Kleissen, R.F.M., Hermens, H.J., Exter, Th. den, de Kreek, J.A. and Silvold, G., “Simultaneous measurement of surface EMG and movements for clinical use” Medical and Biological Engineering and Computing 27, 291–297 (1989).CrossRefGoogle ScholarPubMed
17.Brand, R.A., “Assessment of musculoskeletal disorders by locomotion analysis: a critical historical and epistemological review” Presentation at the Symposium on Biolocomotion: A Century of Research Using Moving Pictures, Formia (1989).Google Scholar
18.Leo, T. and Woltring, H.H., “The CAMARC project-An attempt to bridge the gap” Workshop on Gait Analysis: a Clinical tool, Bilthoven (1989).Google Scholar
19.Chen, A., “Neural networks: computing's next frontier” Microcomputer Solutions (A Publication of Intel Corporation, 03/04, 1991) pp. 3–6.Google Scholar