2Segment-based musculoskeletal models allow the prediction of muscle, ligament and joint forces without making assumptions regarding joint degrees of freedom. The 4 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 6 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 8 musculoskeletal model. For an unblinded comparison the following modifications were made to improve the predictions: 10  further customisations, including modifications to the knee centre of rotation;  reductions to the maximum allowable muscle forces to represent known loss of 12 strength in knee arthroplasty patients; and  a kinematic constraint to the hip joint to address the sensitivity of the segment-14 based approach to motion tracking artefact.For validation, the improved model was applied to normal gait, squat and sit-to-stand 16 for three subjects. Comparisons of the predictions with measured contact forces showed that segment-based musculoskeletal models using patient-specific input data 18 can estimate tibiofemoral contact forces with root mean square errors (RMSEs)

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Accurate muscle geometry for musculoskeletal models is important to enable accurate subject-specific simulations. Commonly, linear scaling is used to obtain individualised muscle geometry. More advanced methods include non-linear scaling using segmented bone surfaces and manual or semi-automatic digitisation of muscle paths from medical images. In this study, a new scaling method combining non-linear scaling with reconstructions of bone surfaces using statistical shape modelling is presented. Statistical Shape Models (SSMs) of femur and tibia/fibula were used to reconstruct bone surfaces of nine subjects. Reference models were created by morphing manually digitised muscle paths to mean shapes of the SSMs using non-linear transformations and inter-subject variability was calculated. Subject-specific models of muscle attachment and via points were created from three reference models. The accuracy was evaluated by calculating the differences between the scaled and manually digitised models. The points defining the muscle paths showed large inter-subject variability at the thigh and shank - up to 26mm; this was found to limit the accuracy of all studied scaling methods. Errors for the subject-specific muscle point reconstructions of the thigh could be decreased by 9% to 20% by using the non-linear scaling compared to a typical linear scaling method. We conclude that the proposed non-linear scaling method is more accurate than linear scaling methods. Thus, when combined with the ability to reconstruct bone surfaces from incomplete or scattered geometry data using statistical shape models our proposed method is an alternative to linear scaling methods.

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