Segment-based musculoskeletal models allow the prediction of muscle, ligament, and joint forces without making assumptions regarding joint degrees-of-freedom (DOF). The dataset published for the “Grand Challenge Competition to Predict in vivo Knee Loads” provides directly measured tibiofemoral contact forces for activities of daily living (ADL). For the Sixth Grand Challenge Competition to Predict in vivo Knee Loads, blinded results for “smooth” and “bouncy” gait trials were predicted using a customized patient-specific musculoskeletal model. For an unblinded comparison, the following modifications were made to improve the predictions: further customizations, including modifications to the knee center of rotation; reductions to the maximum allowable muscle forces to represent known loss of strength in knee arthroplasty patients; and a kinematic constraint to the hip joint to address the sensitivity of the segment-based approach to motion tracking artifact. For validation, the improved model was applied to normal gait, squat, and sit-to-stand for three subjects. Comparisons of the predictions with measured contact forces showed that segment-based musculoskeletal models using patient-specific input data can estimate tibiofemoral contact forces with root mean square errors (RMSEs) of 0.48–0.65 times body weight (BW) for normal gait trials. Comparisons between measured and predicted tibiofemoral contact forces yielded an average coefficient of determination of 0.81 and RMSEs of 0.46–1.01 times BW for squatting and 0.70–0.99 times BW for sit-to-stand tasks. This is comparable to the best validations in the literature using alternative models.
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February 2016
Research-Article
In Vivo Knee Contact Force Prediction Using Patient-Specific Musculoskeletal Geometry in a Segment-Based Computational Model
Ziyun Ding,
Ziyun Ding
Department of Bioengineering,
Imperial College London,
London SW7 2AZ, UK
e-mail: z.ding@imperial.ac.uk
Imperial College London,
London SW7 2AZ, UK
e-mail: z.ding@imperial.ac.uk
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Daniel Nolte,
Daniel Nolte
Department of Bioengineering,
Imperial College London,
London SW7 2AZ, UK
e-mail: d.nolte@imperial.ac.uk
Imperial College London,
London SW7 2AZ, UK
e-mail: d.nolte@imperial.ac.uk
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Chui Kit Tsang,
Chui Kit Tsang
Department of Bioengineering,
Imperial College London,
London SW7 2AZ, UK
e-mail: chui.k.tsang@gmail.com
Imperial College London,
London SW7 2AZ, UK
e-mail: chui.k.tsang@gmail.com
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Daniel J. Cleather,
Daniel J. Cleather
School of Sport,
Health and Applied Science,
St Mary's University,
Waldegrave Road,
Twickenham TW1 4SX, UK
e-mail: daniel.cleather@stmarys.ac.uk
Health and Applied Science,
St Mary's University,
Waldegrave Road,
Twickenham TW1 4SX, UK
e-mail: daniel.cleather@stmarys.ac.uk
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Angela E. Kedgley,
Angela E. Kedgley
Department of Bioengineering,
Imperial College London,
London SW7 2AZ, UK
e-mail: akedgley@imperial.ac.uk
Imperial College London,
London SW7 2AZ, UK
e-mail: akedgley@imperial.ac.uk
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Anthony M. J. Bull
Anthony M. J. Bull
Department of Bioengineering,
Imperial College London,
London SW7 2AZ, UK
e-mail: a.bull@imperial.ac.uk
Imperial College London,
London SW7 2AZ, UK
e-mail: a.bull@imperial.ac.uk
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Ziyun Ding
Department of Bioengineering,
Imperial College London,
London SW7 2AZ, UK
e-mail: z.ding@imperial.ac.uk
Imperial College London,
London SW7 2AZ, UK
e-mail: z.ding@imperial.ac.uk
Daniel Nolte
Department of Bioengineering,
Imperial College London,
London SW7 2AZ, UK
e-mail: d.nolte@imperial.ac.uk
Imperial College London,
London SW7 2AZ, UK
e-mail: d.nolte@imperial.ac.uk
Chui Kit Tsang
Department of Bioengineering,
Imperial College London,
London SW7 2AZ, UK
e-mail: chui.k.tsang@gmail.com
Imperial College London,
London SW7 2AZ, UK
e-mail: chui.k.tsang@gmail.com
Daniel J. Cleather
School of Sport,
Health and Applied Science,
St Mary's University,
Waldegrave Road,
Twickenham TW1 4SX, UK
e-mail: daniel.cleather@stmarys.ac.uk
Health and Applied Science,
St Mary's University,
Waldegrave Road,
Twickenham TW1 4SX, UK
e-mail: daniel.cleather@stmarys.ac.uk
Angela E. Kedgley
Department of Bioengineering,
Imperial College London,
London SW7 2AZ, UK
e-mail: akedgley@imperial.ac.uk
Imperial College London,
London SW7 2AZ, UK
e-mail: akedgley@imperial.ac.uk
Anthony M. J. Bull
Department of Bioengineering,
Imperial College London,
London SW7 2AZ, UK
e-mail: a.bull@imperial.ac.uk
Imperial College London,
London SW7 2AZ, UK
e-mail: a.bull@imperial.ac.uk
1Corresponding author.
Manuscript received October 15, 2015; final manuscript received December 23, 2015; published online January 27, 2016. Editor: Beth A. Winkelstein.
J Biomech Eng. Feb 2016, 138(2): 021018 (9 pages)
Published Online: January 27, 2016
Article history
Received:
October 15, 2015
Revised:
December 23, 2015
Citation
Ding, Z., Nolte, D., Kit Tsang, C., Cleather, D. J., Kedgley, A. E., and Bull, A. M. J. (January 27, 2016). "In Vivo Knee Contact Force Prediction Using Patient-Specific Musculoskeletal Geometry in a Segment-Based Computational Model." ASME. J Biomech Eng. February 2016; 138(2): 021018. https://doi.org/10.1115/1.4032412
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