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Osteoarthritis affects roughly 10% of people over the age of 60 and is the second leading cause of years lost to disability in America. While joint replacement is effective at improving quality of life for individuals with end-stage osteoarthritis, conservative interventions to delay the need for this invasive procedure are desirable. Increased contact forces in the joint are thought to accelerate the structural progression of osteoarthritis, so many conservative interventions seek to reduce these forces.
Project Goal
Demers et al. (2014) used simulations to investigate the sensitivity of joint loading to systematic changes in the coordination strategy used to achieve experimentally measured walking kinematics. The results of this study suggest that recruiting the soleus more heavily than the gastrocnemius could reduce knee loads (adapted image below). Kinematics were fixed in this study, so it is not clear what compensatory kinematic and coordination changes would occur when adopting a gastrocnemius avoidance gait. The purpose of our study was to experimentally reproduce Demers' simulation results by testing if walking with gastrocnemius avoidance gait does reduce late stance knee loads.
Simulation Approach
Reducing knee joint moments in all three planes is the objective of many kinematic gait retraining paradigms; however, since coordination retraining aims to reduce the muscular contribution to joint loading, inverse dynamics alone is insufficient to assess its effectiveness in reducing knee loads. Additionally, solutions to the muscle redundancy problem that are not informed by EMG are insufficient to characterize changes in knee loads that result from coordination changes. EMG has been incorporated into simulations in a variety of ways including EMG-informed CMC (Hamner et al., 2013, Demerican et al., 2015), EMG-driven forward dynamics (Arnold et al., 2013), calibrated, EMG-driven forward simulations (Lloyd and Besier, 2003), and EMG-driven forward simulation-static optimization hybrids (Sartori et al., 2014). The benefit of EMG-driven forward simulations is that the joint moments produced by the muscles from the simulation can be validated against inverse dynamics joint moments. In fact, the calibration step in calibrated EMG-driven simulations involves parameter optimization to match muscle joint moments to inverse dynamics joint moments for a variety of activities. We chose to use an EMG-driven forward simulation similar to Arnold et al. (2013) with the understanding that our forward dynamics and inverse dynamics joint moments would not match exactly, but that we would be able to tune them by hand or use a parameter optimization package such as CEINMS (Pizzolato et al., 2015) if necessary.
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Prescribed motion from the inverse kinematics results were incorporated into the scaled model for both gait cycles. Normalized EMG was added as an excitation control. For muscles that remained in the model from which we did not collect EMG, we made assumptions from surrounding muscles with similar function. For example, the vastus intermedius excitation was an average between lateralis EMG was used as excitation for the vastus medialis lateralis and vastus lateralis EMG signals intermedius and the tibialis posterior was assigned the same excitation as the soleus.
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The knee loads (tibiofemoral force) during late stance (where we are more confident with our model validity) are lower during the gastroc avoidance gait cycle than the more gastroc gait cycle. The magnitude and shape of the gastroc avoidance profile matches the simulation results from Demers et al. (2014). The early stance knee loads from the more gastroc case are less than one body weight, which along with the lack of early stance extension moment (plot above) indicate an under representation of the quadriceps muscles. This is confirmed in the plot of active fiber force along the tendon of the quadriceps muscles below.
Future Work
Further work is necessary to improve the agreement of the inverse dynamics joint moments and forward dynamics joint moments. Hand tuning parameters associated with converting EMG to control inputs (delays, scale factors) is a place to start, along with hand tuning optimal fiber force, optimal fiber length, and tendon slack length to make the joint moments match more closely. For example, shortening the optimal fiber length of the vasti could cause them to generate more passive force during early stance, solving one of our identified problems with the current results. For a more comprehensive tuning of parameters, we could use CEINMS to tune parameters using other motion trials before running the forward simulations of the motions we care about.
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