Research Papers

In Vivo Knee Contact Force Prediction Using Patient-Specific Musculoskeletal Geometry in a Segment-Based Computational Model

[+] Author and Article Information
Ziyun Ding

Department of Bioengineering,
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

Chui Kit Tsang

Department of Bioengineering,
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

Angela E. Kedgley

Department of Bioengineering,
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

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 138(2), 021018 (Jan 27, 2016) (9 pages) Paper No: BIO-15-1518; doi: 10.1115/1.4032412 History: Received October 15, 2015; Revised December 23, 2015

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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Grahic Jump Location
Fig. 1

Blinded model predictions of medial, lateral, and total tibiofemoral contact forces compared with in vivo measurements obtained during two different gait trials

Grahic Jump Location
Fig. 2

Unblinded model predictions of medial, lateral, and total tibiofemoral contact forces compared with in vivo measurements obtained during two different gait trials

Grahic Jump Location
Fig. 3

Comparison of the predicted muscle forces in blinded and unblinded models and the corresponding active/inactive state for muscles of adductor brevis (AdB), gluteus maximus (GMax), gracilis (Gra), semimembranosus (SemM), biceps femoris long head (BF), vastus medialis (VasMed), vastus lateralis (VasLat), RF, gastrocnemius medialis (GasMed), sartorius (Sar), tibialis anterior (TibA), and soleus (Sol)



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