Design of patient-specific gait modifications for knee osteoarthritis rehabilitation.

Abstract-Gait modification is a nonsurgical approach for reducing the external knee adduction torque in patients with knee osteoarthritis (OA). The magnitude of the first adduction torque peak in particular is strongly associated with knee OA progression. While toeing out has been shown to reduce the second peak, no clinically realistic gait modifications have been identified that effectively reduce both peaks simultaneously. This study predicts novel patient-specific gait modifications that achieve this goal without changing the foot path. The modified gait motion was designed for a single patient with knee OA using dynamic optimization of a patient-specific, full-body gait model. The cost function minimized the knee adduction torque subject to constraints limiting how much the new gait motion could deviate from the patient's normal gait motion. The optimizations predicted a "medial-thrust" gait pattern that reduced the first adduction torque peak between 32% and 54% and the second peak between 34% and 56%. The new motion involved three synergistic kinematic changes: slightly decreased pelvis obliquity, slightly increased leg flexion, and slightly increased pelvis axial rotation. After gait retraining, the patient achieved adduction torque reductions of 39% to 50% in the first peak and 37% to 55% in the second one. These reductions are comparable to those reported after high tibial osteotomy surgery. The associated kinematic changes were consistent with the predictions except for pelvis obliquity, which showed little change. This study demonstrates that it is feasible to design novel patient-specific gait modifications with potential clinical benefit using dynamic optimization of patient-specific, full-body gait models. Further investigation is needed to assess the extent to which similar gait modifications may be effective for other patients with knee OA.

Correlation between the knee adduction torque and medial contact force for a variety of gait patterns.

The external knee adduction torque has been proposed as a surrogate measure for medial compartment load during gait. However, a direct link between these two quantities has not been demonstrated using in vivo measurement of medial compartment load. This study uses in vivo data collected from a single subject with an instrumented knee implant to evaluate this link. The subject performed five different overground gait motions (normal, fast, slow, wide, and toe-out) with simultaneous collection of instrumented implant, video motion, and ground reaction data. For each trial, the knee adduction torque was measured externally while the total axial force applied to the tibial insert was measured internally. Based on data collected from the same subject performing treadmill gait under fluoroscopic motion analysis, a regression equation was developed to calculate medial contact force from the implant load cell measurements. Correlation analyses were performed for the stance phase and entire gait cycle to quantify the relationship between the knee adduction torque and both the medial contact force and the medial to total contact force ratio. When the entire gait cycle was analyzed, R(2) for medial contact force was 0.77 when all gait trials were analyzed together and between 0.69 and 0.93 when each gait trial was analyzed separately (p < 0.001 in all cases). For medial to total force ratio, R(2) was 0.69 for all trials together and between 0.54 and 0.90 for each trial separately (p < 0.001 in all cases). When only the stance phase was analyzed, R(2) values were slightly lower. These results support the hypothesis that the knee adduction torque is highly correlated with medial compartment contact force and medial to total force ratio during gait.

Determination of patient-specific multi-joint kinematic models through two-level optimization.

Dynamic patient-specific musculoskeletal models have great potential for addressing clinical problems in orthopedics and rehabilitation. However, their predictive capability is limited by how well the underlying kinematic model matches the patient's structure. This study presents a general two-level optimization procedure for tuning any multi-joint kinematic model to a patient's experimental movement data. An outer level optimization modifies the model's parameters (joint position and orientations) while repeated inner level optimizations modify the model's degrees of freedom given the current parameters, with the goal of minimizing errors between model and experimental marker trajectories. The approach is demonstrated by fitting a 27 parameter, three-dimensional, 12 degree-of-freedom lower-extremity kinematic model to synthetic and experimental movement data for isolated joint (hip, knee, and ankle) and gait (full leg) motions. For noiseless synthetic data, the approach successfully recovered the known joint parameters to within an arbitrarily tight tolerance. When noise was added to the synthetic data, root-mean-square (RMS) errors between known and recovered joint parameters were within 10.4 degrees and 10 mm. For experimental data, RMS marker distance errors were reduced by up to 62% compared to methods that estimate joint parameters from anatomical landmarks. Optimized joint parameters found using a loaded full-leg gait motion differed significantly from those found using unloaded individual joint motions. In the future, this approach may facilitate the creation of dynamic patient-specific musculoskeletal models for predictive clinical applications.