A LONGITUDINAL EXAMINATION OF THE EMERGENCE OF A HEALTHY CHAOTIC WALKING PATTERN IN NORMAL INFANT DEVELOPMENT Harbourne, R.T. 1, Kurz, M. 2, and DeJong, S. 1 1 University of Nebraska Medical Center, Omaha, Nebraska, 2 University of Nebraska at Omaha Purpose Our purpose was to explore the variability present during the development of normal infant gait. Chaos has been shown to be a central feature of human locomotion in adults, but less is known about child locomotion. This investigation examines the question of whether chaotic features change over time as the infant learns to walk independently. Subject  Our subject began this longitudinal investigation at the age of 11months and continued weekly sessions until he was 14.5 months old.  He is a typically developing child, and began walking independently at approximately 13 months old. Developmental changes that were significant during the time of data collection:  11mo - uses primarily belly crawl, but can attain hands and knees.  11m,2w - starting to crawl on hands and knees; still belly crawls.  11m,3w - crawling more on hands and knees than belly crawl.  12m,1w - attempting to stand independently; walking 1 hand held.  13m - taking a few independent steps, still primarily crawling.  14m,1w - walking independently over ground more than crawling. Methods Data was collected once weekly using 2-D video and the Peak Performance motion analysis system. Reflective markers were placed at the greater trochanter, knee, lateral malleolus and dorsum of the foot. The greater trochanter, knee and lateral malleolus markers defined the knee angle. The markers at the greater trochanter and lateral malleolus defined the leg pendulum. The treadmill speed was set at.4mph because that seemed to be a comfortable speed for the infant. The infant was lightly supported at the arms by the parent, and walking was continuous until at least 30 consecutive footfalls of the measured leg had occurred. Analysis The Lyapunov Exponent (LyE) was calculated to determine the chaotic time structure of the selected time series for each day (see Figure 1). The LyE is a measure of local stability in a time series. The LyE for a periodic signal (e.g., sine wave) is zero, indicating that the signal is predictable and repeats itself in the same way for each phase, and is predictable (Figure 2). The LyE can also be at the opposite extreme as a random signal (+0.469), which never repeats itself and is unpredictable (see Figure 2 and graph). In between the periodic and the random signal is chaos, where there is dynamic stability. It is well known that chaos is a feature of normal locomotion as well as other normal biological rhythms such as cardiac rhythm. Conclusion Prior to independent walking, the LyE may have been in the more periodic range because the child was allowing the motion of the treadmill to drive his locomotion. As the child began to have more control over his leg movements, the LyE shifted toward randomness. This can be explained by the child exploring the degrees of freedom of his body within the locomotive pattern, and the search for a workable strategy. This data supports the dynamic systems theory and the construct of emergent behavior when greater variability is present. Clinical Relevance As treadmill training for improvement of locomotion becomes more commonplace in the clinic, it is necessary to understand the importance of variability during the emergence of normal walking. Methods of measuring variability, particularly nonlinear techniques which further describe variability in terms of stability and complexity, are important tools for exploring the normal emergence of motor skill. These techniques need further examination as tools to help therapists in evaluation of motor problems and motor outcomes resulting from intervention. Results For both the leg pendulum and the knee angle, the LyE shifted over time from a periodic value toward a more chaotic value when the child was able to walk independently. During the week that first independent steps were taken, the LyE was high for knee angle, into the random range. This may indicate a shift was occurring in the control of that degree of freedom, with the child allowing more degrees of freedom and randomness during the movement until he could find a dynamically stable strategy. CHAOTIC MEASURES Lyapunov Exponent (LyE) was used to quantify the structure of the variability present in the joint angle time series (Fig. 1). Trajectory of Flow x(t) x(t)” x(t)’ LyE for a sine wave is zero indicating no divergence in the system and local stability (Fig. 2A). LyE for random white noise is indicating a high amount of divergence and instability (Fig. 2C). Chaotic systems lie between the two extremes (e.g. Lorenz Attractor = 0.1; Fig. 2B). Figure 1. LyE measures the exponential separation of the trajectories in the attractor over time. Conceptually, the larger the arrows, between the trajectories, the more variability in the system. A.A. B.B. C.C. Figure 2. Sine Wave Attractor (A), Projection of the Chaotic Lorenz Attractor (B), and Projection of the Random White Noise Attractor (C). Changes in the LyE value can be used to infer the local stability of the locomotive system. Shift towards periodicity indicates greater local stability, while shifts towards randomness indicate instability.