Excessive contact force is believed to contribute to the development of medial compartment knee osteoarthritis. The external knee adduction moment (KAM) has been identified as a surrogate measure for medial contact force during gait, with an abnormally large peak value being linked to increased pain and rate of disease progression. This study used in vivo gait data collected from a subject with a force-measuring knee implant to assess whether KAM decreases accurately predict corresponding decreases in medial contact force. Changes in both quantities generated via gait modification were analyzed statistically relative to the subject's normal gait. The two gait modifications were a ''medial thrust'' gait involving knee medialization during stance phase and a ''walking pole'' gait involving use of bilateral walking poles. Reductions in the first (largest) peak of the KAM (32-33%) did not correspond to reductions in the first peak of the medial contact force. In contrast, reductions in the second peak and angular impulse of the KAM (15-47%) corresponded to reductions in the second peak and impulse of the medial contact force (12-42%). Calculated reductions in both KAM peaks were highly sensitive to rotation of the shank reference frame about the superior-inferior axis of the shank. Both peaks of medial contact force were best predicted by a combination of peak values of the external KAM and peak absolute values of the external knee flexion moment (R 2 ¼ 0.93). Future studies that evaluate the effectiveness of gait modifications for offloading the medial compartment of the knee should consider the combined effect of these two knee moments. ß

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Musculoskeletal modeling and optimization theory are often used to determine muscle forces in vivo. However, convincing quantitative evaluation of these predictions has been limited to date. The present study evaluated model predictions of knee muscle forces during walking using in vivo measurements of joint contact loading acquired from an instrumented implant. Joint motion, ground reaction force, and tibial contact force data were recorded simultaneously from a single subject walking at slow, normal, and fast speeds. The body was modeled as an 8-segment, 21-degree-of-freedom articulated linkage, actuated by 58 muscles. Joint moments obtained from inverse dynamics were decomposed into leg-muscle forces by solving an optimization problem that minimized the sum of the squares of the muscle activations. The predicted knee muscle forces were input into a 3D knee implant contact model to calculate tibial contact forces. Calculated and measured tibial contact forces were in good agreement for all three walking speeds. The average RMS errors for the medial, lateral, and total contact forces over the entire gait cycle and across all trials were 140 AE 40 N, 115 AE 32 N, and 183 AE 45 N, respectively. Muscle coordination predicted by the model was also consistent with EMG measurements reported for normal walking. The combined experimental and modeling approach used in this study provides a quantitative framework for evaluating model predictions of muscle forces in human movement. ß

The ability to predict patient-specific joint contact and muscle forces accurately could improve the treatment of walking-related disorders. Muscle synergy analysis, which decomposes a large number of muscle electromyographic (EMG) signals into a small number of synergy control signals, could reduce the dimensionality and thus redundancy of the muscle and contact force prediction process. This study investigated whether use of subject-specific synergy controls can improve optimization prediction of knee contact forces during walking. To generate the predictions, we performed mixed dynamic muscle force optimizations (i.e., inverse skeletal dynamics with forward muscle activation and contraction dynamics) using data collected from a subject implanted with a forcemeasuring knee replacement. Twelve optimization problems (three cases with four subcases each) that minimized the sum of squares of muscle excitations were formulated to investigate how synergy controls affect knee contact force predictions. The three cases were: (1) CalibrateþMatch where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously matched, (2) Precalibrateþ Predict where experimental knee contact forces were predicted using precalibrated muscle model parameters values from the first case, and (3) Calibrateþ Predict where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously predicted, all while matching inverse dynamic loads at the hip, knee, and ankle. The four subcases used either 44 independent controls or five synergy controls with and without EMG shape tracking. For the CalibrateþMatch case, all four subcases closely reproduced the measured medial and lateral knee contact forces (R 2 ! 0.94, root-mean-square (RMS) error < 66 N), indicating sufficient model fidelity for contact force prediction. For the PrecalibrateþPredict and CalibrateþPredict cases, synergy controls yielded better contact force predictions (0.61 < R 2 < 0.90, 83 N < RMS error < 161 N) than did independent controls (-0.15 < R 2 < 0.79, 124 N < RMS error < 343 N) for corresponding subcases. For independent controls, contact force predictions improved when precalibrated model parameter values or EMG shape tracking was used. For synergy controls, contact force predictions were relatively insensitive to how model parameter values were calibrated, while EMG shape tracking made lateral (but not medial) contact force predictions worse. For the subject and optimization cost function analyzed in this study, use of subject-specific synergy controls improved the accuracy of knee contact force predictions, especially for lateral contact force when EMG shape tracking was omitted, and reduced prediction sensitivity to uncertainties in muscle model parameter values.

Persistent pain is an important cause of patient dissatisfaction after unicompartmental knee replacement (UKR) and has been correlated with localised tibial strain. However, the factors that influence these strains are not well understood. To address this issue, we created finite element models to examine the effect on tibial strain of: (1) muscle forces (estimated using instrumented knee data) acting on attachment sites on the proximal tibia, (2) UKR implantation, (3) loading position, and (4) changes in gait pattern. Muscle forces acting on the tibia had no significant influence on strains within the periprosthetic region, but UKR implantation increased strain by 20%. Strain also significantly increased if the region of load application was moved >3 mm medially. The strain within the periprosthetic region was found to be dependent on gait pattern and was influenced by both medial and lateral loads, with the medial load having a greater effect (regression coefficients: medial=0.74, lateral=0.30). These findings suggest that tibial strain is increased after UKR and may be a cause of pain. It may be possible to reduce pain through modification of surgical factors or through altered gait patterns.