Background: Although walking speed is the most common measure of gait performance poststroke, improved walking speed following rehabilitation does not always indicate the recovery of paretic limb function. Over the last decade, the measure paretic propulsion (Pp, defined as the propulsive impulse generated by the paretic leg divided by the sum of the propulsive impulses of both legs) has been established as a measure of paretic limb output and recently targeted in poststroke rehabilitation paradigms. However, the literature lacks a detailed synthesis of how paretic propulsion, walking speed, and other biomechanical and neuromuscular measures collectively relate to post-stroke walking performance and motor recovery. Objective: The aim of this review was to assess factors associated with the ability to generate Pp and identify rehabilitation targets aimed at improving Pp and paretic limb function. Methods: Relevant literature was collected in which paretic propulsion was used to quantify and assess propulsion symmetry and function in hemiparetic gait. Results: Paretic leg extension during terminal stance is strongly associated with Pp. Both paretic leg extension and propulsion are related to step length asymmetry, revealing an interaction between spatiotemporal, kinematic and kinetic metrics that underlies hemiparetic walking performance. The importance of plantarflexor function in producing propulsion is highlighted by the association of an independent plantarflexor excitation module with increased Pp. Furthermore, the literature suggests that although current rehabilitation techniques can improve Pp, these improvements depend on the patient's baseline plantarflexor function. Significance: Pp provides a quantitative measure of propulsion symmetry and should be a primary target of post-stroke gait rehabilitation. The current literature suggests rehabilitation

With more than 29,000 OpenSim users, several musculoskeletal models with varying levels of complexity are available to study human gait. However, how different model parameters affect estimated joint and muscle function between models is not fully understood. The purpose of this study is to determine the effects of four OpenSim models (Gait2392, Lower Limb Model 2010, Full-Body OpenSim Model, and Full Body Model 2016) on gait mechanics and estimates of muscle forces and activations. Using OpenSim 3.1 and the same experimental data for all models, six young adults were scaled in each model, gait kinematics were reproduced, and static optimization estimated muscle function. Simulated measures differed between models by up to 6.5° knee range of motion, 0.012 Nm/Nm peak knee flexion moment, 0.49 peak rectus femoris activation, and 462 N peak rectus femoris force. Differences in coordinate system definitions between models altered joint kinematics, influencing joint moments. Muscle parameter and joint moment discrepancies altered muscle activations and forces. Additional model complexity yielded greater error between experimental and simulated measures; therefore, this study suggests Gait2392 is a sufficient model for studying walking in healthy young adults. Future research is needed to determine which model(s) is best for tasks with more complex motion.

Background Older adults and individuals with knee osteoarthritis (KOA) often exhibit reduced locomotor function and altered muscle activity. Identifying age- and KOA-related changes to the modular control of gait may provide insight into the neurological mechanisms underlying reduced walking performance in these populations. The purpose of this pilot study was to determine if the modular control of walking differs between younger and older adults without KOA and adults with end-stage KOA. Methods Kinematic, kinetic, and electromyography data were collected from ten younger (23.5 ± 3.1 years) and ten older (63.5 ± 3.4 years) adults without KOA and ten adults with KOA (64.0 ± 4.0 years) walking at their self-selected speed. Separate non-negative matrix factorizations of 500 bootstrapped samples determined the number of modules required to reconstruct each participant’s electromyography. One-way Analysis of Variance tests assessed the effect of group on walking speed and the number of modules. Kendall rank correlations (τb) assessed the association between the number of modules and self-selected walking speed. Results The number of modules required in the younger adults (3.2 ± 0.4) was greater than in the individuals with KOA (2.3 ± 0.7; p = 0.002), though neither cohorts’ required number of modules differed significantly from the unimpaired older adults (2.7 ± 0.5; p ≥ 0.113). A significant association between module number and walking speed was observed (τb = 0.350, p = 0.021) and individuals with KOA walked significantly slower (0.095 ± 0.21 m/s) than younger adults (1.24 ± 0.15 m/s; p = 0.005). Individuals with KOA also exhibited altered module activation patterns and composition (which muscles are associated with each module) compared to unimpaired adults. Conclusion These findings suggest aging alone may not significantly alter modular control; however, the combined effects of knee osteoarthritis and aging may together impair the modular control of gait.

Two optimization techniques, static optimization (SO) and computed muscle control (CMC), are often used in OpenSim to estimate the muscle activations and forces responsible for movement. Although differences between SO and CMC muscle function have been reported, the accuracy of each technique and the combined effect of optimization and model choice on simulated muscle function is unclear. The purpose of this study was to quantitatively compare the SO and CMC estimates of muscle activations and forces during gait with the experimental data in the Gait2392 and Full Body Running models. In OpenSim (version 3.1), muscle function during gait was estimated using SO and CMC in 6 subjects in each model and validated against experimental muscle activations and joint torques. Experimental and simulated activation agreement was sensitive to optimization technique for the soleus and tibialis anterior. Knee extension torque error was greater with CMC than SO. Muscle forces, activations, and co-contraction indices tended to be higher with CMC and more sensitive to model choice. CMC’s inclusion of passive muscle forces, muscle activation-contraction dynamics, and a proportional-derivative controller to track kinematics contributes to these differences. Model and optimization technique choices should be validated using experimental activations collected simultaneously with the data used to generate the simulation.