The influence of gait speed on local dynamic stability of walking.

The focus of this study was to examine the role of walking velocity in stability during normal gait. Local dynamic stability was quantified through the use of maximum finite-time Lyapunov exponents, lambda(Max). These quantify the rate of attenuation of kinematic variability of joint angle data recorded as subjects walked on a motorized treadmill at 20%, 40%, 60%, and 80% of the Froude velocity. A monotonic trend between lambda(Max) and walking velocity was observed with smaller lambda(Max) at slower walking velocities. Smaller lambda(Max) indicates more stable walking dynamics. This trend was evident whether stride duration variability remained or was removed by time normalizing the data. This suggests that slower walking velocities lead to increases in stability. These results may reveal more detailed information on the behavior of the neuro-controller than variability-based analyses alone.

Stability of dynamic trunk movement.

STUDY DESIGN: Nonlinear systems analyses of trunk kinematics were performed to estimate control of dynamic stability during repetitive flexion and extension movements. OBJECTIVE: Determine whether movement pace and movement direction of dynamic trunk flexion and extension influence control of local dynamic stability. SUMMARY OF BACKGROUND DATA: Spinal stability has been previously characterized in static, but not in dynamic movements. Biomechanical models make inferences about static spinal stability, but existing analyses provide limited insight into stability of dynamic movement. Stability during dynamic movements can be estimated from Lyapunov analyses of empirical data. METHODS: There were 20 healthy subjects who performed repetitive trunk flexion and extension movements at 20 and 40 cycles per minute. Maximum Lyapunov exponents describing the expansion of the kinematic state-space were calculated from the measured trunk kinematics to estimate stability of the dynamic system. RESULTS: The complexity of torso movement dynamics required at least 5 embedded dimensions, which suggests that stability components of lumbar lordosis may be empirically measurable in addition to global stability of trunk dynamics. Repeated trajectories from fast paced movements diverged more quickly than slower movement, indicating that local dynamic stability is limited in fast movements. Movements in the midsagittal plane showed higher multidimensional kinematic divergence than asymmetric movements. CONCLUSION: Nonlinear dynamic systems analyses were successfully applied to empirically measured data, which were used to characterize the neuromuscular control of stability during repetitive dynamic trunk movements. Movement pace and movement direction influenced the control of spinal stability. These stability assessment techniques are recommended for improved workplace design and the clinical assessment of spinal stability in patients with low back pain.