Refining muscle geometry and wrapping in the TLEM 2 model for improved hip contact force prediction

Abstract

Musculoskeletal models represent a powerful tool to gain knowledge on the internal forces acting at the joint level in a non-invasive way. However, these models can present some errors associated with the level of detail in their geometrical representation. For this reason, a thorough validation is necessary to prove the reliability of their predictions. This study documents the development of a generic musculoskeletal model and proposes a working logic and simulation techniques for identifying specific model features in need of refinement; as well as providing a quantitative validation for the prediction of hip contact forces (HCF). The model, implemented in the AnyBody Modeling System and based on the cadaveric dataset TLEM 2.0, was scaled to match the anthropometry of a patient fitted with an instrumented hip implant and to reproduce gait kinematics based on motion capture data. The relative contribution of individual muscle elements to the HCF and joint moments was analyzed to identify critical geometries, which were then compared to muscle magnetic resonance imaging (MRI) scans and, in case of inconsistencies, were modified to better match the volumetric scans. The predicted HCF showed good agreement with the overall trend and timing of the measured HCF from the instrumented prosthesis. The average root mean square error (RMSE), calculated for the total HCF was found to be 0.298*BW. Refining the geometries of the muscles thus identified reduced RMSE on HCF magnitudes by 17% (from 0.359*BW to 0.298*BW) over the whole gait cycle. The detailed study of individual muscle contributions to the HCF succeeded in identifying muscles with incorrect anatomy, which would have been difficult to intuitively identify otherwise. Despite a certain residual over-prediction of the final hip contact forces in the stance phase, a satisfactory level of geometrical accuracy of muscle paths has been achieved with the refinement of this model.

Current Preclinical Testing of New Hip Arthroplasty Technologies Does Not Reflect Real-World Loadings: Capturing Patient-Specific and Activity-Related Variation in Hip Contact Forces

“…Full datasets for 1 representative high-functioning patient and 1 lower-functioning patient demonstrating this variability are available at https://doi.org/10.5518/319. Previous studies have demonstrated that applications of musculoskeletal models can be used to reliably predict contact forces for a large cohort of patients during gait [26,27]. It was previously shown that different patient characteristics influence both kinematics [28] and loads experienced at hip [27], with patient's overall functionality being a highly influential factor in determining variability in kinematics and kinetics during gait.…”

“…Musculoskeletal simulations were performed using a commercially available software (AnyBody Modeling System, version 7.1, Aalborg, Denmark). A detailed musculoskeletal model of the lower limb [26] based on a cadaveric dataset [31] was scaled to match the anthropometrics of each patient based on marker data collected during a static trial [32]. Marker trajectories and GRF data from each gait trial served as input to an inverse dynamics analysis, based on a third-order-polynomial muscle recruitment criterion, to calculate muscle forces and HCFs.…”

“…Due to the inherently invasive nature of in vivo HCF measurement via instrumented implants, data are only available for a small number of patients and thus has not captured the variation which exists in larger populations. Advances in computational techniques such as musculoskeletal modeling have shown potential for estimating accurate HCFs noninvasively [26], and these techniques are much better suited to describing the load variability observed in larger populations [27].…”

Muscle coordination retraining inspired by musculoskeletal simulations: a study on reducing knee loading

“…We modified the model by calibrating the each muscle's passive muscle force-length curves so that joint moments generated by passive muscle forces more closely matched experimental data 55 . We also altered the muscle paths of the hip abductor musculature to more closely match moment arms estimated from experiments 56,57 , finite element models 58 , and MRI 59 (See Supplemental Material). The modified model was scaled to match the anthropometric measurements taken from the static trial, and virtual markers on the model were moved to match experimental marker locations during this trial.…”

Functional and Structural Moment Arm Validation for Musculoskeletal Models: A Study of the Human Forearm and Hand

“…The effort to create valid muscle-driven MS models spans dozens of years in the context of noninvasive analysis of gait, posture, and reaching movements (Arnold et al, 2010;Carbone et al, 2015;De Pieri et al, 2018;Delp et al, 1990;Gritsenko et al, 2016;Horsman, 2007;Rajagopal et al, 2016;Saul et al, 2015b). The models are typically developed using minimalistic sets of parameters and with additional testing of model performance against experimental observations (Kirchner, 2006).…”