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Research Papers

Prediction of In Vivo Knee Joint Loads Using a Global Probabilistic Analysis

[+] Author and Article Information
Alessandro Navacchia

Center for Orthopaedic Biomechanics,
Department of Mechanical and
Materials Engineering,
The University of Denver,
2390 S York Street,
Denver, CO 80208
e-mail: alessandro.navacchia@du.edu

Casey A. Myers

Mem. ASME
Center for Orthopaedic Biomechanics,
Department of Mechanical and
Materials Engineering,
The University of Denver,
2390 S York Street,
Denver, CO 80208
e-mail: casey.myers@du.edu

Paul J. Rullkoetter

Mem. ASME
Center for Orthopaedic Biomechanics,
Department of Mechanical and
Materials Engineering,
The University of Denver,
2390 S York Street,
Denver, CO 80208
e-mail: paul.rullkoetter@du.edu

Kevin B. Shelburne

Center for Orthopaedic Biomechanics,
Department of Mechanical and
Materials Engineering,
The University of Denver,
2390 S York Street,
Denver, CO 80208
e-mail: kevin.shelburne@du.edu

1Corresponding author.

Manuscript received July 3, 2015; final manuscript received, December 15, 2015; published online January 29, 2016. Assoc. Editor: Tammy L. Haut Donahue.

J Biomech Eng 138(3), 031002 (Jan 29, 2016) (12 pages) Paper No: BIO-15-1327; doi: 10.1115/1.4032379 History: Received July 03, 2015

Musculoskeletal models are powerful tools that allow biomechanical investigations and predictions of muscle forces not accessible with experiments. A core challenge modelers must confront is validation. Measurements of muscle activity and joint loading are used for qualitative and indirect validation of muscle force predictions. Subject-specific models have reached high levels of complexity and can predict contact loads with surprising accuracy. However, every deterministic musculoskeletal model contains an intrinsic uncertainty due to the high number of parameters not identifiable in vivo. The objective of this work is to test the impact of intrinsic uncertainty in a scaled-generic model on estimates of muscle and joint loads. Uncertainties in marker placement, limb coronal alignment, body segment parameters, Hill-type muscle parameters, and muscle geometry were modeled with a global probabilistic approach (multiple uncertainties included in a single analysis). 5–95% confidence bounds and input/output sensitivities of predicted knee compressive loads and varus/valgus contact moments were estimated for a gait activity of three subjects with telemetric knee implants from the “Grand Challenge Competition.” Compressive load predicted for the three subjects showed confidence bounds of 333 ± 248 N, 408 ± 333 N, and 379 ± 244 N when all the sources of uncertainty were included. The measured loads lay inside the predicted 5–95% confidence bounds for 77%, 83%, and 76% of the stance phase. Muscle maximum isometric force, muscle geometry, and marker placement uncertainty most impacted the joint load results. This study demonstrated that identification of these parameters is crucial when subject-specific models are developed.

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Figures

Grahic Jump Location
Fig. 1

Workflow of the study. Monte Carlo analyses were performed with opensim at each step of a pipeline that included inverse kinematics, inverse dynamics, muscle force prediction with static optimization and joint reaction analysis. Every probabilistic input was described as a normal distribution with standard deviations from the literature. Specific description of each uncertainty can be found in Table 1 (marker placement), Table 2 (Inertial parameters) and Table 3 (muscle paths and properties). Only uncertainty in tibiofemoral alignment (VV alignment) is not described in a table since it is a single distribution with null mean and std of 2.5 deg [47]. The propagation of the uncertainties was obtained by using output distributions of each step as input uncertainty for following steps. The final output of the workflow was knee compressive load and varus/valgus contact moment.

Grahic Jump Location
Fig. 2

(a) Effect of marker and limb coronal alignment uncertainties shown as 5–95% confidence bounds on joint angles for subject 1. The baseline results are represented by the black line. (b) Mean and standard deviation of 5–95% Bounds for each individual source of uncertainty that affected kinematics (subject 1).

Grahic Jump Location
Fig. 3

(a) Effect of marker, limb coronal alignment, and body segment parameters uncertainties shown as 5–95% confidence bounds on joint moments for subject 1. The baseline results are represented by the black line. (b) Mean and standard deviation of 5–95% Bounds for each individual source of uncertainty that affected inverse dynamics (subject 1).

Grahic Jump Location
Fig. 4

Effect of all sources of uncertainty shown as 5–95% confidence bounds on muscle activation predictions for subject 1. Activation confidence bounds are compared to processed EMG signals (black dashed line). Vastus medialis processed EMG signal was not included in the graph because it was not considered reliable.

Grahic Jump Location
Fig. 5

(a) Effect of all the uncertainties shown as 5–95% confidence bounds on knee compressive load for the three subjects. The baseline results are represented by the solid black line. Corresponding measured data are represented by the black dashed line. (b) Mean and standard deviation of 5–95% bounds for each individual source of uncertainty that affected compressive loads for the three subjects.

Grahic Jump Location
Fig. 6

(a) Effect of all the uncertainties shown as 5–95% confidence bounds on varus/valgus contact moment for the three subjects. The baseline results are represented by the solid black line. Corresponding measured data are represented by the black dashed line. (b) Mean and standard deviation of 5–95% bounds for each individual source of uncertainty that affected contact moment for the three subjects.

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