ReadySim: A computational framework for building explicit finite element musculoskeletal simulations directly from motion laboratory data

Abstract

Musculoskeletal modeling allows researchers insight into joint mechanics which might not otherwise be obtainable through in vivo or in vitro studies. Common musculoskeletal modeling techniques involve rigid body dynamics software which often employ simplified joint representations. These representations have proven useful but are limited in performing single‐framework deformable analyzes in structures of interest. Musculoskeletal finite element (MSFE) analysis allows for representation of structures in sufficient detail to obtain accurate solutions of the internal stresses and strains including complex contact conditions and material representations. Studies which performed muscle force optimization directly in a finite element framework were often limited in complexity to minimize computational time. Recent advances in computational efficiency and control schemes for muscle force prediction have made these solutions more practical. Yet, the formulation of subject‐specific simulations remains a challenging problem. The objectives of this work were to develop an open‐source computational framework to build and run simulations which (a) scale the size of MSFE models and efficiently estimate (b) joint kinematics and (c) muscle forces from human motion data collected in a typical gait laboratory. A computational framework was built using MATLAB and Python to interface with model input and output files. The software uses laboratory marker data to scale model segment lengths and estimate joint kinematics. Concurrent muscle force and tissue strain estimations are performed based on the estimated kinematics and ground reaction forces. This software will improve the usability and consistency of single‐framework MSFE simulations. Both software and template model are made freely available on SimTK.Novelty Statement Single framework musculoskeletal modeling directly in a finite element environment for muscle force estimation and tissue strain analysis. Open dissemination of unilateral musculoskeletal finite element model and software used in manuscript

Robust automatic hexahedral cartilage meshing framework enables population-based computational studies of the knee

“…We derived flexion and joint loading profiles from data reported from five patients with telemetric knee implants (Heinlein et al, 2007;Kutzner et al, 2010). Due to the large number of simulations required, we excluded material deformation from cartilage representations-instead using linear pressure-overclosure contact definitions to compensate for rigid cartilage elements within the patellofemoral and tibiofemoral joint complexes (Halloran et al, 2005;Fitzpatrick et al, 2010;Hume et al, 2020).…”

“…Frontiers in Bioengineering and Biotechnology frontiersin.org hundreds of simulations required for this analysis in a computationally efficient manner, we did not allow for material deformation of the cartilage tissues, instead using linear pressure-overclosure definitions to compensate for rigid cartilage representations (Halloran et al, 2005;Fitzpatrick et al, 2010;Hume et al, 2020). The computational cost of these rigid body simulations was an order of magnitude faster than their deformable counterparts.…”

Integration of neural architecture within a finite element framework for improved neuromusculoskeletal modeling

“…The design approach for the NMS model was to develop an accurate representation of nerve-muscle interaction that would mimic in vivo muscle activation. To do this, the slow motor unit model developed by Kim 19 in the NEURON simulation environment (version 7.7.2) was modified to generate a motor neuron pool consisting of 310 motor units and incorporated into a FE musculoskeletal model based upon a previously developed model 31 .…”

“…The model includes the soleus and tibialis anterior muscles represented as axial connectors positioned to run through the centroid of the muscle cross-sectional geometry. The model also includes the foot bones, tibia, and 3D articular cartilage 31 at the tibia-talus joint. Muscle contraction is controlled by applying the forces from the NEURON simulation calculations to the soleus axial connector.…”

A Conceptual Blueprint for Making Neuromusculoskeletal Models Clinically Useful

“…For some clinical problems where functional outcome depends on tissue-level stresses and strains, rigid body skeletal models controlled by lumped-parameter Hill-type muscle-tendon models will not be sufficient. To capture tissue-level effects in muscles, ligaments, bones, and/or articular cartilage, finite element (FE) models with deformable tissue properties are the logical choice [102,[253][254][255][256][257][258][259][260][261][262][263]. In muscles, for example, non-uniform tissue-level behavior could be important for predicting muscle hypertrophy or injury in response to exercise, the effects of aging and disuse, the progression of muscular dystrophy, and muscle function in microgravity.…”