A passive dynamic walking robot that has a deterministic nonlinear gait.

There is a growing body of evidence that the step-to-step variations present in human walking are related to the biomechanics of the locomotive system. However, we still have limited understanding of what biomechanical variables influence the observed nonlinear gait variations. It is necessary to develop reliable models that closely resemble the nonlinear gait dynamics in order to advance our knowledge in this scientific field. Previously, Goswami et al. [1998. A study of the passive gait of a compass-like biped robot: symmetry and chaos. International Journal of Robotic Research 17(12)] and Garcia et al. [1998. The simplest walking model: stability, complexity, and scaling. Journal of Biomechanical Engineering 120(2), 281-288] have demonstrated that passive dynamic walking computer models can exhibit a cascade of bifurcations in their gait pattern that lead to a deterministic nonlinear gait pattern. These computer models suggest that the intrinsic mechanical dynamics may be at least partially responsible for the deterministic nonlinear gait pattern; however, this has not been shown for a physical walking robot. Here we use the largest Laypunov exponent and a surrogation analysis method to confirm and extend Garcia et al.'s and Goswami et al.'s original results to a physical passive dynamic walking robot. Experimental outcomes from our walking robot further support the notion that the deterministic nonlinear step-to-step variations present in gait may be partly governed by the intrinsic mechanical dynamics of the locomotive system. Furthermore the nonlinear analysis techniques used in this investigation offer novel methods for quantifying the nature of the step-to-step variations found in human and robotic gait.

The penguin waddling gait pattern has a more consistent step width than step length.

Previous research has indicated that the sagittal plane gait dynamics of humans are more stable and less dependent on active neural control, while the frontal plane dynamics are less stable and require greater neural control. The higher neural demands of the frontal plane dynamics are reflected in a more variable step width than step length. Greater variability in the step width occurs because humans modulate their foot placement for each step to ensure stability and prevent falls. Compared to other terrestrial animals, penguins appear to have excessive amount of frontal plane motion in their gait that is characterized as waddling. If excessive frontal plane motion requires additional neural control and is associated with falls, it would seem that evolutionary pressures would have eliminated such locomotive strategies. Here we measured the step length and width variability to determine if waddling results in a less stable gait. Remarkably, the variability of the step width was less than the variability of the step length. These results are directly opposite of what has been reported for humans. Hence, our data indicate that waddling may be an effective strategy for ensuring stability in the frontal plane dynamics.

A chronic mouse model of Parkinson's disease has a reduced gait pattern certainty.

The purpose of this investigation was to determine if a chronic Parkinson's disease mouse model will display less certainty in its gait pattern due to basal ganglia dysfunction. A chronic Parkinson's disease mouse model was induced by injecting male C57/BL mice with 10 doses of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (25mg/kg) (MPTP) and probenecid (250 mg/kg) (P) over 5 weeks. This chronic model produces a severe and persistent loss of nigrostriatal neurons resulting in dopamine depletion and locomotor impairment. The control mice were treated with probenecid alone. Fifteen weeks after the last MPTP/P treatment, the mice were videotaped in the sagittal plane with a digital camera (60 Hz) as they ran on a motorized treadmill at a speed of 10 m/min. The indices of gait and gait variability were calculated. Stride length was significantly (p=0.016) more variable in the chronic MPTP/P mice. Additionally, the chronic MPTP/P mice had a statistically less certain gait pattern when compared to the control mice (p=0.02). These results suggest that variability in the gait pattern can be used to evaluate changes in neural function. Additionally, our results imply that disorder of the basal ganglia results in less certainty in modulating the descending motor command that controls the gait pattern.