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. 2013 Nov 15;46(16):2778-86.
doi: 10.1016/j.jbiomech.2013.09.005. Epub 2013 Sep 12.

Subject-specific knee joint geometry improves predictions of medial tibiofemoral contact forces

Affiliations

Subject-specific knee joint geometry improves predictions of medial tibiofemoral contact forces

Pauline Gerus et al. J Biomech. .

Abstract

Estimating tibiofemoral joint contact forces is important for understanding the initiation and progression of knee osteoarthritis. However, tibiofemoral contact force predictions are influenced by many factors including muscle forces and anatomical representations of the knee joint. This study aimed to investigate the influence of subject-specific geometry and knee joint kinematics on the prediction of tibiofemoral contact forces using a calibrated EMG-driven neuromusculoskeletal model of the knee. One participant fitted with an instrumented total knee replacement walked at a self-selected speed while medial and lateral tibiofemoral contact forces, ground reaction forces, whole-body kinematics, and lower-limb muscle activity were simultaneously measured. The combination of generic and subject-specific knee joint geometry and kinematics resulted in four different OpenSim models used to estimate muscle-tendon lengths and moment arms. The subject-specific geometric model was created from CT scans and the subject-specific knee joint kinematics representing the translation of the tibia relative to the femur was obtained from fluoroscopy. The EMG-driven model was calibrated using one walking trial, but with three different cost functions that tracked the knee flexion/extension moments with and without constraint over the estimated joint contact forces. The calibrated models then predicted the medial and lateral tibiofemoral contact forces for five other different walking trials. The use of subject-specific models with minimization of the peak tibiofemoral contact forces improved the accuracy of medial contact forces by 47% and lateral contact forces by 7%, respectively compared with the use of generic musculoskeletal model.

Keywords: Contact force; EMG-driven modeling; Knee joint model; Muscle force.

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Conflict of interest statement

Conflicts of interest statement

We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.

Figures

Figure 1
Figure 1
Anterior-posterior and superior-inferior translation of the tibia relative to the femur defined in OpenSim for the generic and subject-specific knee joint kinematic model using fluoroscopic data. The reference (0,0) is the initial position of the knee joint center, and the translation is the displacement of the knee joint center relative to its initial position.
Figure 2
Figure 2
As an example, a schematic of point contact model used to estimate the medial contact force. The external adduction/abduction moment about the lateral condyle ( MIDLC) estimated from OpenSim must be balanced by the moment produced by the muscles ( MMuscleLC) and an unknown contact force (FMC) acting at distance (dIC) equal to 40mm. riLC represents the muscle moment arms relative to the lateral contact point estimated by the OpenSim model and Fi represents the muscle-tendon forces estimated by the EMG-driven approach. The same approach was used to estimate the lateral contact force, except moment arms and moments were determined about the medial condyle contact point.
Figure 3
Figure 3
RMSerrors estimated on (A) the knee joint flexion/extension moments, (B) the medial tibiofemoral contact forces, and (C) the lateral tibiofemoral contact forces in the walking trials using the different cost functions and knee joint models. The RMSerrors are the mean and the error-bars represents 95% confidence interval for the five prediction trials.
Figure 4
Figure 4
Medial contact forces predicted from the (A) G-Geom & G-Kin, (B) G-Geom & SS-Kin, (C) SS-Geom & G-Kin and (D) SS-Geom & SS-Kin models and directly measured with the instrumented knee prosthesis (in vivo measured). Mean and standard deviations from five prediction trials are shown (shade regions).
Figure 5
Figure 5
Lateral contact forces predicted from the (A) G-Geom & G-Kin, (B) G-Geom & SS-Kin, (C) SS-Geom & G-Kin and (D) SS-Geom & SS-Kin models and directly measured with the instrumented knee prosthesis (in vivo measured). Mean and standard deviations from five prediction trials are shown (shade regions).
Figure 6
Figure 6
Moment arms of selected major muscles estimated by the G-Geom & G-Kin (dashed), the SS-Geom & SS-Kin models (solid), and those measured experimentally (Buford et al., 1997; Grood et al., 1984; Sobczak et al. 2013; Spoor et al. 1992) for knee flexion angles from 0° to 100°, and with hip and ankle joints each set at 0° flexion.
Figure 7
Figure 7
Example of the muscle forces from the SemiMem (A) and VastMed (C), and the corresponding muscle-tendon moment arms from the SemiMem (B) and VastMed (D) obtained from the calibration gait trials for each EMG-driven model using the different knee joint geometry and kinematic models employing the min [ΔMKFEF MCF LC]cost function.

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