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. 2020 Apr;48(4):1430-1440.
doi: 10.1007/s10439-020-02465-5. Epub 2020 Jan 30.

The Capacity of Generic Musculoskeletal Simulations to Predict Knee Joint Loading Using the CAMS-Knee Datasets

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Free PMC article

The Capacity of Generic Musculoskeletal Simulations to Predict Knee Joint Loading Using the CAMS-Knee Datasets

Zohreh Imani Nejad et al. Ann Biomed Eng. .
Free PMC article

Erratum in

Abstract

Musculoskeletal models enable non-invasive estimation of knee contact forces (KCFs) during functional movements. However, the redundant nature of the musculoskeletal system and uncertainty in model parameters necessitates that model predictions are critically evaluated. This study compared KCF and muscle activation patterns predicted using a scaled generic model and OpenSim static optimization tool against in vivo measurements from six patients in the CAMS-knee datasets during level walking and squatting. Generally, the total KCFs were under-predicted (RMS: 47.55%BW, R2: 0.92) throughout the gait cycle, but substiantially over-predicted (RMS: 105.7%BW, R2: 0.81) during squatting. To understand the underlying etiology of the errors, muscle activations were compared to electromyography (EMG) signals, and showed good agreement during level walking. For squatting, however, the muscle activations showed large descrepancies especially for the biceps femoris long head. Errors in the predicted KCF and muscle activation patterns were greatest during deep squat. Hence suggesting that the errors mainly originate from muscle represented at the hip and an associated muscle co-contraction at the knee. Furthermore, there were substaintial differences in the ranking of subjects and activities based on peak KCFs in the simulations versus measurements. Thus, future simulation study designs must account for subject-specific uncertainties in musculoskeletal predictions.

Keywords: CAMS-knee; EMG; Instrumented knee implants; Knee contact force; Level walking; Musculoskeletal modeling; OpenSim; Squat.

Figures

Figure 1
Figure 1
The CAMS-Knee datasets were used to validate musculoskeletal simulation predictions of KCFs and muscle activations for six total knee replacement (TKR) subjects perfoming level walking and squatting. The OpenSim platform was used to scale a generic model, perform inverse kinematics, inverse dynamics, static optimization, and joint reaction force analysis to calculate the KCFs.
Figure 2
Figure 2
The predicted (dashed) and measured (solid) KCFs for all subjects performing level walking (black) and squatting (red). The bold lines represent the mean across all subjects and all trials, while the shaded areas represent ± 1SD.
Figure 3
Figure 3
The percent error in the predicted total KCF plotted against the hip and knee angles during level walking and squatting. The bold dashed black line represents the mean of all subjects. The colored lines represent the mean of all trials for each single subject. In the level walking plots, the red circles represent heel strike and the arrows designate the direction of the gait cycle. In the squat plot, the initial standing pose is shown to the left, and the final standing pose to the right.
Figure 4
Figure 4
Comparison of the predicted and measured peak KCF for each subject averaged across all trials of walking and squatting.
Figure 5
Figure 5
Predicted and measured peak contact forces for level walking and squat for each subject. The open circles and dashed lines represent the mean of the simulations for all trials, the closed circles and solid lines represent the mean of the measurements. The error bars indicate the range of all trials.
Figure 6
Figure 6
The predicted (dashed) and measured (solid) muscle activity for all subjects performing level walking (black) and squatting (red). The bold lines represent the mean across all subjects and all trials, while the shaded areas represent ± 1SD. The data is presented only for the leg with the instrumented implant.

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References

    1. Anderson FC, Pandy MG. Dynamic optimization of human walking. J. Biomech. Eng. 2001;123:381–390. doi: 10.1115/1.1392310. - DOI - PubMed
    1. Arnold EM, Ward SR, Lieber RL, Delp SL. A model of the lower limb for analysis of human movement. Ann. Biomed. Eng. 2010;38:269–279. doi: 10.1007/s10439-009-9852-5. - DOI - PMC - PubMed
    1. Bergmann G, Bender A, Graichen F, Dymke J, Rohlmann A, Trepczynski A, Heller MO, Kutzner I. Standardized loads acting in knee implants. PLoS ONE. 2014;9:e86035. doi: 10.1371/journal.pone.0086035. - DOI - PMC - PubMed
    1. Besier TF, Fredericson M, Gold GE, Beaupre GS, Delp SL. Knee muscle forces during walking and running in patellofemoral pain patients and pain-free controls. J. Biomech. 2009;42:898–905. doi: 10.1016/j.jbiomech.2009.01.032. - DOI - PMC - PubMed
    1. Blemker SS, Delp SL. Three-dimensional representation of complex muscle architectures and geometries. Ann. Biomed. Eng. 2005;33:661–673. doi: 10.1007/s10439-005-1433-7. - DOI - PubMed

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