In this section
@article{Ding:2015:10.1115/1.4032412,
author = {Ding, Z and Nolte, D and Tsang, CK and Cleather, DJ and Kedgley, AE and Bull, AM},
doi = {10.1115/1.4032412},
journal = {Journal of Biomechanical Engineering-Transactions of the ASME},
title = {In Vivo Knee Contact Force Prediction Using Patient-Specific Musculoskeletal Geometry in a Segment-Based Computational Model.},
url = {http://dx.doi.org/10.1115/1.4032412},
volume = {138},
year = {2015}
}
TY - JOUR
AB - Segment-based musculoskeletal models allow the prediction of muscle, ligament and joint forces without making assumptions regarding joint degrees of freedom. 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. For the "Sixth Grand Challenge Competition to Predict In Vivo Knee Loads", blinded results for "smooth" and "bouncy" gait trials were predicted using a customised patient-specific musculoskeletal model. For an unblinded comparison the following modifications were made to improve the predictions: • further customisations, including modifications to the knee centre 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 artefact. 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. Tibiofemoral contact force patterns were estimated with an average coefficient of determination of 0.81 and with 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.
AU - Ding,Z
AU - Nolte,D
AU - Tsang,CK
AU - Cleather,DJ
AU - Kedgley,AE
AU - Bull,AM
DO - 10.1115/1.4032412
PY - 2015///
SN - 0148-0731
TI - In Vivo Knee Contact Force Prediction Using Patient-Specific Musculoskeletal Geometry in a Segment-Based Computational Model.
T2 - Journal of Biomechanical Engineering-Transactions of the ASME
UR - http://dx.doi.org/10.1115/1.4032412
UR - http://hdl.handle.net/10044/1/51695
VL - 138
ER -
Professor Anthony Bull
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