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.
Bifurcation and chaos analysis for a discrete ecological developmental systems
