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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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