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Abstract
Musculoskeletal models enable non-invasive estimation of knee contact forces (KCFs) during functional movements. However, the redundant nature of the musculoskeletal system and uncertainty in model parameters necessitates that model predictions are critically evaluated. This study compared KCF and muscle activation patterns predicted using a scaled generic model and OpenSim static optimization tool against in vivo measurements from six patients in the CAMS-knee datasets during level walking and squatting. Generally, the total KCFs were under-predicted (RMS: 47.55%BW, R2: 0.92) throughout the gait cycle, but substiantially over-predicted (RMS: 105.7%BW, R2: 0.81) during squatting. To understand the underlying etiology of the errors, muscle activations were compared to electromyography (EMG) signals, and showed good agreement during level walking. For squatting, however, the muscle activations showed large descrepancies especially for the biceps femoris long head. Errors in the predicted KCF and muscle activation patterns were greatest during deep squat. Hence suggesting that the errors mainly originate from muscle represented at the hip and an associated muscle co-contraction at the knee. Furthermore, there were substaintial differences in the ranking of subjects and activities based on peak KCFs in the simulations versus measurements. Thus, future simulation study designs must account for subject-specific uncertainties in musculoskeletal predictions.
Highlights
Knowledge of the internal musculoskeletal forces acting on the knee joint during dynamic functional movements has significant potential for informing injury and degenerative disease prevention strategies,[48] improving the outcomes of orthopedic treatments,[31] enhancing implant designs,[3,24] and validating computational model predictions.[18,38] Since the 1970s, multibody musculoskeletal models of increasing complexity have been proposed to predict such internal knee joint loading conditions.[45]
The only exceptions were for subjects K3R and K8L, where the compressive contact forces were overestimated at the second peak (Supplementary material Fig. S1)
Musculoskeletal models allow the non-invasive estimation of muscle and joint contact forces, but previous studies have indicated that substantial errors are present, especially when generic models are used.[19,36,38]
Summary
Knowledge of the internal musculoskeletal forces acting on the knee joint during dynamic functional movements has significant potential for informing injury and degenerative disease prevention strategies,[48] improving the outcomes of orthopedic treatments,[31] enhancing implant designs,[3,24] and validating computational model predictions.[18,38] Since the 1970s, multibody musculoskeletal models of increasing complexity have been proposed to predict such internal knee joint loading conditions.[45]. Several musculoskeletal modeling software packages such as AnyBody,[7] LifeModeler,[25] SIMM,[9] BodyMech,[13] and OpenSim,[8] provide simulation tools for predicting joint loading. While standard motion analysis measurements and rigid body mechanics can directly determine inter-segmental joint loads and moments, distribution of these loads to muscles, ligaments, and articular contact surfaces remains complicated by the inherent redundancy within the musculoskeletal system,[11] particularily with regards to muscle co-contraction.[44]. In vivo validation remains a major obstacle in widespread acceptance of model predictions of knee loading and limits clinical translation of the technology. The Orthoload team have released datasets including knee contact forces, whole
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