Researchers have explored a variety of neurorehabilitation approaches to restore normal walking function following a stroke. However, there is currently no objective means for prescribing and implementing treatments that are likely to maximize recovery of walking function for any particular patient. As a first step toward optimizing neurorehabilitation effectiveness, this study develops and evaluates a patient-specific synergy-controlled neuromusculoskeletal simulation framework that can predict walking motions for an individual post-stroke. The main question we addressed was whether driving a subject-specific neuromusculoskeletal model with muscle synergy controls (5 per leg) facilitates generation of accurate walking predictions compared to a model driven by muscle activation controls (35 per leg) or joint torque controls (5 per leg). To explore this question, we developed a subject-specific neuromusculoskeletal model of a single high-functioning hemiparetic subject using instrumented treadmill walking data collected at the subject’s self-selected speed of 0.5 m/s. The model included subject-specific representations of lower-body kinematic structure, foot–ground contact behavior, electromyography-driven muscle force generation, and neural control limitations and remaining capabilities. Using direct collocation optimal control and the subject-specific model, we evaluated the ability of the three control approaches to predict the subject’s walking kinematics and kinetics at two speeds (0.5 and 0.8 m/s) for which experimental data were available from the subject. We also evaluated whether synergy controls could predict a physically realistic gait period at one speed (1.1 m/s) for which no experimental data were available. All three control approaches predicted the subject’s walking kinematics and kinetics (including ground reaction forces) well for the model calibration speed of 0.5 m/s. However, only activation and synergy controls could predict the subject’s walking kinematics and kinetics well for the faster non-calibration speed of 0.8 m/s, with synergy controls predicting the new gait period the most accurately. When used to predict how the subject would walk at 1.1 m/s, synergy controls predicted a gait period close to that estimated from the linear relationship between gait speed and stride length. These findings suggest that our neuromusculoskeletal simulation framework may be able to bridge the gap between patient-specific muscle synergy information and resulting functional capabilities and limitations.

Computational walking simulations could facilitate the development of improved treatments for clinical conditions affecting walking ability. Since an effective treatment is likely to change a patient's foot-ground contact pattern and timing, such simulations should ideally utilize deformable foot-ground contact models tailored to the patient's foot anatomy and footwear. However, no study has reported a deformable modeling approach that can reproduce all six ground reaction quantities (expressed as three reaction force components, two center of pressure (CoP) coordinates, and a free reaction moment) for an individual subject during walking. This study proposes such an approach for use in predictive optimizations of walking. To minimize complexity, we modeled each foot as two rigid segments-a hindfoot (HF) segment and a forefoot (FF) segment-connected by a pin joint representing the toes flexion-extension axis. Ground reaction forces (GRFs) and moments acting on each segment were generated by a grid of linear springs with nonlinear damping and Coulomb friction spread across the bottom of each segment. The stiffness and damping of each spring and common friction parameter values for all springs were calibrated for both feet simultaneously via a novel three-stage optimization process that used motion capture and ground reaction data collected from a single walking trial. The sequential three-stage process involved matching (1) the vertical force component, (2) all three force components, and finally (3) all six ground reaction quantities. The calibrated model was tested using four additional walking trials excluded from calibration. With only small changes in input kinematics, the calibrated model reproduced all six ground reaction quantities closely (root mean square (RMS) errors less than 13 N for all three forces, 25 mm for anterior-posterior (AP) CoP, 8 mm for medial-lateral (ML) CoP, and 2 N·m for the free moment) for both feet in all walking trials. The largest errors in AP CoP occurred at the beginning and end of stance phase when the vertical ground reaction force (vGRF) was small. Subject-specific deformable foot-ground contact models created using this approach should enable changes in foot-ground contact pattern to be predicted accurately by gait optimization studies, which may lead to improvements in personalized rehabilitation medicine.

Background The tibial tubercle to trochlear groove (TT-TG) distance is used for screening patients with a variety of patellofemoral joint disorders to determine who may benefit from patellar medialization using a tibial tubercle osteotomy. Clinically, the TT-TG distance is predominately based on static imaging with the knee in full extension. Yet, the predictive ability of this measure for dynamic patellar tracking patterns is unknown. Purpose The aim of this study is to determine if the static TT-TG distance can predict dynamic lateral displacement of the patella. Study Design Case control Methods The static TT-TG distance was measured at full extension for 70 skeletally mature subjects with (n=32) and without (n=38) patellofemoral pain. The dynamic patellar tracking patterns were assessed from approximately 45° to 0° of knee flexion using dynamic cine-phase contrast magnetic resonance imaging. For each subject the value of dynamic lateral tracking corresponding to the exact knee angle measured in the static images for that subject was identified. Linear regression analysis determined the predictive ability of static TT-TG distance for dynamic patellar lateral displacement for each cohort. Results The static TT-TG distance measured with the knee in full extension cannot accurately predict dynamic lateral displacement of the patella. There was weak predictive ability among subjects with patellofemoral pain (r2=0.18, p=0.02) and no predictive capability among controls. Among subjects with patellofemoral pain and static TT-TG distances ≥15 mm, 8/13 (62%) demonstrated neutral or medial patellar tracking patterns. Conclusions The static TT-TG distance cannot accurately predict dynamic lateral displacement of the patella. A large percentage of patients with patellofemoral pain and pathologically large TT-TG distances may have neutral to medial maltracking patterns.

Background The distance between the tibial tubercle (TT) and trochlear groove (TT-TG distance) is known to be greater in patients with patellar instability. However, the potential role and prevalence of pathological TT-TG distances in a large cohort of skeletally mature patients with isolated patellofemoral pain (PFP) are not clear. Purpose To determine if the mean TT-TG distance is greater in patients with PFP, who lack a history of patellar dislocations, knee trauma, or osteoarthritis, relative to healthy controls. Study Design Cross-sectional study; Level of evidence, 3. Methods A total of 50 knees (38 patients) with PFP and 60 knees (56 controls) without PFP formed the basis of this study. Magnetic resonance imaging was used to determine the TT-TG distance from 3-dimensional static scans. Results The cohort with PFP demonstrated a significantly greater mean TT-TG distance, relative to asymptomatic controls (13.0 vs 10.8 mm, respectively; P = 0.001). Among the cohort with PFP, 15 knees (30%) demonstrated TT-TG distances ≥15 mm, and 3 knees (6%) demonstrated TT-TG distances ≥ 20 mm. Conclusion Most adult patients with isolated PFP have elevated TT-TG distances compared with controls, which likely contributes to the force imbalance surrounding the knee..