BJMB
Brazilian Journal of Motor Behavior
Current Opinion
!
Kainz, Falisse,
Pizzolato
2024
VOL.18
N.1
1 of 4
Neuromusculoskeletal modeling in health and disease
HANS KAINZ1 | ANTOINE FALISSE2 | CLAUDIO PIZZOLATO3
1 University of Vienna, Centre for Sport Science and University Sports, Department of Biomechanics, Kinesiology and Computer Science in Sport, Vienna, Austria
2 Stanford University, Department of Bioengineering, Stanford, USA
3 Griffith University, Griffith Centre of Biomedical and Rehabilitation Engineering, Australia
Correspondence to:!Ass.-Prof. Mag. Hans Kainz, MSc PhD
Head of the Neuromechanics Research Group, University of Vienna - University of Vienna, Department of Biomechanics, Kinesiology and Computer Science in Sport,
Auf der Schmelz 6a (USZ II), Room: 2.16a, 1150 Wien –
Phone: +43-1-4277-48887
email: hans.kainz@univie.ac.at
https://doi.org/10.20338/bjmb.v18i1.420
ABBREVIATIONS
EMG Electromyograms
PUBLICATION DATA
Received 01 03 2024
Accepted 19 03 2024
Published 27 04 2024
ABSTRACT
This opinion paper provides an overview of musculoskeletal modeling, revealing insights into muscle-
tendon kinematics, forces, and joint contact forces during dynamic movements, thereby advancing our
understanding of biomechanics. While subject-specific modeling poses challenges, emerging software
tools enable rapid personalization of musculoskeletal geometry and motor control, enhancing
physiological accuracy. Advanced predictive simulations and multi-scale modeling expand clinical
applications, facilitating surgery outcomes prediction and movement modification for joint diseases.
Collaborative interdisciplinary efforts are essential for overcoming challenges, refining workflows, and
ultimately enhancing clinical treatment outcomes.
KEYWORDS: Biomechanics | Musculoskeletal modeling | Gait analysis
BACKGROUND
Musculoskeletal modeling is a powerful tool for simulating and analyzing the intricate functioning of the musculoskeletal system.
By integrating a musculoskeletal model with an individual's movement data—such as marker trajectories obtained from a 3D motion
capture system—it becomes possible to estimate in-vivo muscle-tendon kinematics, muscle forces, and joint contact forces during
dynamic movements. These variables, otherwise challenging to directly measure non-invasively, offer a comprehensive understanding of
biomechanics. Computational musculoskeletal models also enable predictive simulations of movement (Fig. 1), which do not rely on
experimental data 1. These simulations generate novel movements based on optimization of a performance criterion, which typically
involves several terms such as metabolic energy rate and muscle activity. They allow addressing 'what-if' questions that are beyond the
reach of traditional experimental methods.
VIEW OF THE PAST
Musculoskeletal modeling has been refined over the last 50 years (summarized in two recent reviews 2,3). Initially, 2D
simulations were performed, and muscles were modelled as ideal actuators. The Hill-type muscle model was developed in the 1970s and
has since become the standard muscle model for the majority of simulations 2. Over the years, musculoskeletal properties in generic
models have been updated based on novel experimental data, mainly from cadaver and medical imaging studies. With advancements in
computational methods and increased processing power, it is now possible to simulate whole-body movement in 3D within minutes.
In the initial stages of clinically-oriented musculoskeletal modeling studies, the primary emphasis was on examining walking
impairments in children with cerebral palsy. Amongst others, it has been shown that femoral geometry influences walking capabilities 4,5,
crouch gait requires more knee extensor and less hip abductor strength compared to unimpaired gait 6, and Botulinum Toxin injections
have a minor impact on dynamic muscle forces 7. While enabling mechanistic insights, most simulations were still based on generic
models, neglecting subject-specific musculoskeletal geometry and motor control.
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Figure 1.!Predictive simulation of human walking. Blue spheres represent contact geometries. Muscles turn red when active (image adapted from De Groote and Falisse1
with permission).
CURRENT STATE
Personalized musculoskeletal modeling has significantly advanced over the last two decades. Models with subject-specific
musculoskeletal details can be created either directly from 3D medical images (e.g., magnetic resonance imaging or computer
tomography) or by modifying existing models. Medical imaging-based models offer the potential to accurately represent the specific
musculoskeletal geometry of participants. Yet, challenges remain for creating a complete personalized model from medical images;
critically, defining muscle origin and insertion points from medical images is a laborious, time-consuming, and, error prone-process.
Consequently, software tools 8–10 that enable pre-existing models to be personalized in a rapid way have been developed (Fig. 2).
Figure 2.!Morphological features of the femur (A) that have a big impact on muscle and joint contact forces estimated from musculoskeletal models 23 angles can be
measured from medical images (B) and used in freely available tools, e.g., the Torsion Tool 9, (D) to modify the geometry of a pre-existing generic model (C), resulting in a
more personalized model (E). In (D), two muscle lines-of-action, representing the middle parts of the gluteus medius, are depicted, emphasizing how manipulating the bony
geometry with the Torsion Tool leads to alterations in muscle paths.
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Personalizing muscle properties and motor control has become an essential part for state-of-the-art musculoskeletal modeling.
Due to muscle redundancy, the same movement can be performed using different muscle recruitment strategies, which may vary among
individuals and pathological populations 11–13. Electromyograms (EMG), which are electrical potentials associated to muscle contraction
and coordination, can be combined with musculoskeletal modeling to calibrate muscle-tendon parameters and simulate the effect of
individual-specific motor control on the muscle forces and joint contact forces 14. These EMG-informed simulations account for potential
co-contraction, which is common in various populations such as individuals with osteoarthritis, thereby enhancing the physiological
plausibility of the simulation results 12.
Predictive simulations based on musculoskeletal models can reveal principles of locomotion by elucidating cause-effect
relationships. Recent advancements in computational methods have enabled the generation of 3D muscle-driven simulations of walking
in less than 30 minutes, promising broader applications 15. Combining personalized musculoskeletal models with predictive simulations
allows for the assessment of cause-effect relationships between specific impairments and a patient's gait pattern. This facilitates the
identification of primary treatment targets tailored to individual patients 16. However, further refinement and validation of the modeling
workflow are necessary before predictive simulations can be routinely employed in clinical practice.
Multi-scale models integrate musculoskeletal simulations with finite element analysis, facilitating the estimation of tissue loads.
These cutting-edge modeling approaches have been used to estimate subject-specific stresses on growth plates, thereby advancing our
understanding of pathological bone growth 17,18. Additionally, sophisticated multi-scale simulations have enabled real-time estimation of
stresses on the Achilles tendon 19, paving the way for various clinical applications such as load monitoring during rehabilitation. However,
the widespread clinical application of these approaches is currently hindered by several factors. Firstly, it demands expertise across
various biomechanical disciplines. Additionally, it requires comprehensive and expensive data collection, including medical images and
3D motion capture data. While some of these challenges are being tackled by the adoption of wearable sensors and computer vision
approaches 20, further work is required to facilitate the broader adoption of multi-scale musculoskeletal modeling approaches in clinical
settings.
FUTURE PERSPECTIVE
Current research endeavors are dedicated to advancing musculoskeletal modeling and its clinical applications on multiple
fronts. Among ongoing developments, two emerging 'hot topics' hold promise for potential breakthroughs in the coming decade. One
focuses on predicting surgery outcomes, where personalized modeling and comprehensive experimental data collections may bring us
closer to this goal. The other area involves using musculoskeletal modeling to inform movement modification for joint diseases, potentially
offering non-invasive interventions 21. Personalized simulations could pinpoint how small adjustments to a person's gait can alleviate joint
loads, reduce pain, and slow the progression of degenerative diseases 13. Integration of subject-specific models with real-time simulations
using smart garments 22 may enable real-time quantification of individual internal loads in the future.
In summary, musculoskeletal modeling has evolved into a cornerstone of biomechanics, experiencing a significant surge in
popularity over the past two decades. Numerous research groups are actively developing new models and tools, sharing their
advancements with the community, thereby fostering rapid progress. With the increasing ease of model personalization and simulation,
collaborative research efforts involving multidisciplinary teams are crucial to address challenges, refine modeling workflows, validate
simulation results, facilitate clinical integration, and ultimately enhance clinical treatment outcomes.
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Citation: Kainz H, Falisse A, Pizzolato C. (2024). Neuromusculoskeletal modeling in health and disease. Brazilian Journal of Motor Behavior, 18(1):
Editor-in-chief: Dr Fabio Augusto Barbieri - São Paulo State University (UNESP), Bauru, SP, Brazil. !
Associate editors: Dr José Angelo Barela - São Paulo State University (UNESP), Rio Claro, SP, Brazil; Dr Natalia Madalena Rinaldi - Federal University of Espírito Santo
(UFES), Vitória, ES, Brazil; Dr Renato de Moraes – University of São Paulo (USP), Ribeirão Preto, SP, Brazil.
Section editors (Current Opinion): Dr Luis Augusto Teixeira - University of São Paulo (USP), São Paulo, SP, Brazil; Dr Tibor Hortobágyi - University of Groningen, The
Netherlands; Dr Renato de Moraes - University of São Paulo (USP), Ribeirão Preto, SP, Brazil.
Copyright:© 2024 Kainz, Falisse and Pizzolato and BJMB. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non
Commercial-No Derivatives 4.0 International License which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source
are credited.
Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Competing interests: The authors have declared that no competing interests exist.
DOI:!https://doi.org/10.20338/bjmb.v18i1.420
e420.