Results: 2

1.
Figure 1

Figure 1. From: GAIT VARIABILITY OF PATIENTS WITH INTERMITTENT CLAUDICATION IS SIMILAR BEFORE AND AFTER THE ONSET OF CLAUDICATION PAIN.

A graphical representation of the state space of knee joint angle time series and the calculation of the Largest Lyapunov exponent. (A) An original knee flexion-extension time series from a control subject. (B) A two-dimensional state space created by the position and velocity time series from the same knee angle. (C) A section of the state space where the divergence of neighboring trajectories is outlined. In our study the state space was reconstructed using 10 embedding dimensions. The number of embedding dimensions from our data was calculated according to procedures outlined at Stergiou et al(24). The divergence from one cycle to the next is outlined for several points. The Largest Lyapunov exponent is calculated as the slope of the average logarithmic divergence of the neighboring trajectories.(24)

Sara A. Myers, et al. Clin Biomech (Bristol, Avon). ;26(7):729-734.
2.
Figure 2

Figure 2. From: GAIT VARIABILITY OF PATIENTS WITH INTERMITTENT CLAUDICATION IS SIMILAR BEFORE AND AFTER THE ONSET OF CLAUDICATION PAIN.

A graphical comparison of knee joint angle variability between a (A) Control subject, (B) Intermittent Claudication (IC) patient in the pain free condition, and (C) IC patient in the pain condition. Graphs A, B, and C are the time series and graphs D, E, and F are two-dimensional state spaces created by plotting the position (X(t)) versus the velocity (X’ (t)) from the corresponding signals. The largest Lyapunov exponent (LyE) value of each time series is also given. It is clear that there is little or no difference between variability in the pain free and pain conditions of the IC patient. Additionally, it is apparent that PAD patients have more divergence and the healthy control graph traces nearly the same cycle with each stride.

Sara A. Myers, et al. Clin Biomech (Bristol, Avon). ;26(7):729-734.

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