Evaluating the Effects of Ankle-Foot Orthosis Mechanical Property Assumptions on Gait Simulation Muscle Force Results.

Musculoskeletal modeling and simulation techniques have been used to gain insights into movement disabilities for many populations, such as ambulatory children with cerebral palsy (CP). The individuals who can benefit from these techniques are often limited to those who can walk without assistive devices, due to challenges in accurately modeling these devices. Specifically, many children with CP require the use of ankle-foot orthoses (AFOs) to improve their walking ability, and modeling these devices is important to understand their role in walking mechanics. The purpose of this study was to quantify the effects of AFO mechanical property assumptions, including rotational stiffness, damping, and equilibrium angle of the ankle and subtalar joints, on the estimation of lower-limb muscle forces during stance for children with CP. We analyzed two walking gait cycles for two children with CP while they were wearing their own prescribed AFOs. We generated 1000-trial Monte Carlo simulations for each of the walking gait cycles, resulting in a total of 4000 walking simulations. We found that AFO mechanical property assumptions influenced the force estimates for all the muscles in the model, with the ankle muscles having the largest resulting variability. Muscle forces were most sensitive to assumptions of AFO ankle and subtalar stiffness, which should therefore be measured when possible. Muscle force estimates were less sensitive to estimates of damping and equilibrium angle. When stiffness measurements are not available, limitations on the accuracy of muscle force estimates for all the muscles in the model, especially the ankle muscles, should be acknowledged.

Original investigation correlated joint fluctuations can influence the selection of steady state gait patterns in the elderly.

This investigation utilized a Markov model to investigate the relationship of correlated lower extremity joint fluctuations and the selection of a steady state gait pattern in the young and elderly. Our model simulated the neuromuscular system by predicting the behavior of the joints for the next gait cycle based on the behavior exhibited in the preceding gait cycles. Such dependencies in the joint fluctuations have been noted previously in the literature. We speculated that compared to the young model, the characteristics of the correlated fluctuations in the elderly model would result in the selection of a different steady state gait pattern. The results of our simulation support the notion that correlated fluctuations in the joint kinematics influence the selection of a steady state gait pattern. The steady state gait pattern for the elderly model was dependent the ankle and hip. Conversely, the steady state gait pattern for the young control model was dependent on the behavior of the knee and hip. Based on our model, we suggested that the altered steady state gait patterns observed in the elderly may be due to an altered neuromuscular memory of prior joint behaviors.