Study of Rehabilitation of Injured Knee Joint Applying Chaotic Theory in Human Body Motion

Article Preview

Abstract:

Gait analysis is essential to identify accurate cause and knee condition from patients who display abnormal walking. Traditional linear tools can, however, mask the true structure of motor variability, since biomechanical data from a few strides during the gait have limitation to understanding the system. Therefore, it is necessary to propose a more precise dynamic method. The chaos analysis, a nonlinear technique, focuses on understanding how variations in the gait pattern change over time. Healthy eight subjects walked on a treadmill for 100 seconds at 60 Hz. Three dimensional walking kinematic data were obtained using two cameras and KWON3D motion analyzer. The largest Lyapunov exponent from the measured knee angular displacement time series was calculated to quantify local stability. This study quantified the variability present in time series generated from gait parameter via chaos analysis. Gait pattern is found to be chaotic. The proposed Lyapunov exponent can be used in rehabilitation and diagnosis of recoverable patients.

You might also be interested in these eBooks

Info:

Periodical:

Key Engineering Materials (Volumes 342-343)

Pages:

581-584

Citation:

Online since:

July 2007

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2007 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Stergiou, N., Moraiti, C., Giakas, G., Ristanis, S. and Georgoulis, A. D., 2004, The effect of the walking speed on the stability of the anterior cruciate ligament deficient knee, Clin. Biomech, Vol. 19, No. 9, p.957~963.

DOI: 10.1016/j.clinbiomech.2004.06.008

Google Scholar

[2] Dingwell, J. B. and Cusumano, J. P., 2000, Nonlinear time series analysis of normal and pathological human walking, Chaos, Vol. 10, No. 4, p.848~863.

DOI: 10.1063/1.1324008

Google Scholar

[3] Abraham, N. B., Albano, A. M., Das, B., Guzman, G. D. and YongR, S., 1986, Calculating the dimension of attractors from small data sets, Phys. Lett. A, Vol. 114, No. 5, p.217~221.

DOI: 10.1016/0375-9601(86)90210-0

Google Scholar

[4] Jeong, J., Gore, J. C. and Peterson, B. S., 2001, Mutual information analysis of the EEG in patients with Alzheimer's disease, Clinical Neurophysiology, Vol. 114, No. 5, p.827~835.

DOI: 10.1016/s1388-2457(01)00513-2

Google Scholar

[5] Rhodes, C. and Morari, M., 1997, The false nearest neighbors algorithm: An overview, Computers & Chemical Engineering, Vol. 21, No. 1, p.1149~1154.

DOI: 10.1016/s0098-1354(97)87657-0

Google Scholar

[6] Wolf, A., Swift, J. B., Swinney, H. L. and Vastano, J. A., 1985, Determining Lyapunov exponents from a time series, Physica D: Nonlinear Phenomena, Vol. 16, No. 3, p.285~317.

DOI: 10.1016/0167-2789(85)90011-9

Google Scholar