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Research Papers

Update on Grand Challenge Competition to Predict in Vivo Knee Loads

[+] Author and Article Information
Allison L. Kinney

Department of Mechanical and Aerospace Engineering,
University of Florida,
Gainesville, FL 32611

Thor F. Besier

Auckland Bioengineering Institute,
University of Auckland,
Auckland 1142, New Zealand

Darryl D. D'Lima

Shiley Center for Orthopaedic Research and Education at Scripps Clinic,
La Jolla, CA 92037

Benjamin J. Fregly

Department of Mechanical and Aerospace Engineering,
University of Florida,
Gainesville, FL 32611;
Department of Biomedical Engineering,
University of Florida,
Gainesville, FL 32611;
Department of Orthopaedics and Rehabilitation,
University of Florida, Gainesville, FL 32611
e-mail: fregly@ufl.edu

1Corresponding author. Present address: Department of Mechanical and Aerospace Engineering, 231 MAE-A Building, P.O. Box 116250, University of Florida, Gainesville, FL 32611-6250.

Contributed by the Bioengineering Division of ASME for publication in the JOURNAL OF BIOMECHANICAL ENGINEERING. Manuscript received October 2, 2012; final manuscript received December 18, 2012; accepted manuscript posted December 26, 2012; published online February 7, 2013. Editor: Beth Winkelstein.

J Biomech Eng 135(2), 021012 (Feb 07, 2013) (4 pages) Paper No: BIO-12-1460; doi: 10.1115/1.4023255 History: Received October 02, 2012; Revised December 18, 2012; Accepted December 26, 2012

Validation is critical if clinicians are to use musculoskeletal models to optimize treatment of individual patients with a variety of musculoskeletal disorders. This paper provides an update on the annual Grand Challenge Competition to Predict in Vivo Knee Loads, a unique opportunity for direct validation of knee contact forces and indirect validation of knee muscle forces predicted by musculoskeletal models. Three competitions (2010, 2011, and 2012) have been held at the annual American Society of Mechanical Engineers Summer Bioengineering Conference, and two more competitions are planned for the 2013 and 2014 conferences. Each year of the competition, a comprehensive data set collected from a single subject implanted with a force-measuring knee replacement is released. Competitors predict medial and lateral knee contact forces for two gait trials without knowledge of the experimental knee contact force measurements. Predictions are evaluated by calculating root-mean-square (RMS) errors and R2 values relative to the experimentally measured medial and lateral contact forces. For the first three years of the competition, competitors used a variety of methods to predict knee contact and muscle forces, including static and dynamic optimization, EMG-driven models, and parametric numerical models. Overall, errors in predicted contact forces were comparable across years, with average RMS errors for the four competition winners ranging from 229 N to 312 N for medial contact force and from 238 N to 326 N for lateral contact force. Competitors generally predicted variations in medial contact force (highest R2 = 0.91) better than variations in lateral contact force (highest R2 = 0.70). Thus, significant room for improvement exists in the remaining two competitions. The entire musculoskeletal modeling community is encouraged to use the competition data and models for their own model validation efforts.

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Figures

Grahic Jump Location
Fig. 2

Medial, lateral, and total contact forces over the gait cycle for the two trials selected for each competition. Experimental forces were measured with a force-measuring knee replacement (black solid line) and predicted by the 2010 [5], 2011 [6], and 2012 [7,8] winners of the competition. In 2010 and 2011, competitors were asked to submit only blinded predictions (dark gray dashed line). In 2012, competitors were asked to submit blinded (dark gray dashed line) and unblinded (light gray solid line) predictions, and the judges selected two co-winners (2012a and 2012b).

Grahic Jump Location
Fig. 1

Overview of the experimental data available for past and future Grand Challenge Competitions. Four types of experimental data (shown in the center) are available for the six main categories of data collected (shown at the top and bottom).

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