Muscle force estimation in clinical gait analysis using AnyBody and OpenSim

Trinler, Ursula; Schwameder, Hermann; Baker, Richard; Alexander, Nathalie

doi:10.1016/j.jbiomech.2019.01.045

Cited by 80 publications

(56 citation statements)

References 32 publications

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“…Nevertheless, in line with the idea that simulation software in biomechanics should be validated in multiple ways (Hicks et al, 2015), providing similar tools but different in their approach allows the community to cross-validate the different implementation of the algorithms. For instance, two papers (Kim et al, 2018;Trinler et al, 2019) recently compared the outputs of Anybody and OpenSim and came to different results. Although the authors provided plausible explanations for these differences, due to the closed-source nature of Anybody, they had to assume that the implementation of the algorithms are flawless in both software.…”

Section: Statement Of Needmentioning

confidence: 99%

biorbd: A C++, Python and MATLAB library to analyze and simulate the human body biomechanics

Michaud¹,

Begon²

2021

JOSS

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Biomechanics is at the interface of several fields of science, such as mechanics, human physiology and robotics. Although this transdisciplinarity encourages the emergence of new ideas, the variety of data to analyze simultaneously can be overwhelming. Commonly biomechanical datasets are composed of skin markers trajectories (termed as markers), contact forces, electromyography (EMG) signal, inertial measurement units (IMU) kinematics, etc., which by nature are not straightforward to combine. It is at their meeting point-the body movementthat biorbd steps in; bio standing for biomechanics and rbd for rigid body dynamics. biorbd is a feature-based development library that targets the manipulation of biomechanical data in a comprehensive and accessible manner. For a given musculoskeletal model, it provides functions for inverse flow-i.e.

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Section: Statement Of Needmentioning

confidence: 99%

biorbd: A C++, Python and MATLAB library to analyze and simulate the human body biomechanics

Michaud¹,

Begon²

2021

JOSS

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“…Moreover, the different definitions of the joints in case of degree of freedom (DoF) or muscle positioning have been shown to influence the outcome (Valente et al, 2014(Valente et al, , 2015. A study comparing the outcome of an Anybody and OpenSim model relied the discrepancies mainly on the different muscle definitions and segmental coordinate systems (Trinler et al, 2019). Especially in a clinical environment, time consuming MSK modeling can hardly be performed, because the results need to be available soon after the gait analysis for, e.g., surgery or rehabilitation planning.…”

Section: State-of-the-artmentioning

confidence: 99%

“…Furthermore, models vary in muscle parameters as muscles' peak isometric force and affecting the calculation of muscle activation and forces during gait (Roelker et al, 2017). A previous study, which compared muscle force estimation between OpenSim and Anybody, reported that variations in muscle forces were mainly caused by dissimilar anatomical definitions, contrasts in calculated joint centers and segmental interactions of the models (Trinler et al, 2019). Further on, the calculation of joint contact forces and muscle forces appear to be more sensitive for changes in musculoskeletal definitions compared to joint angles and moments when varying body landmark positions, musculotendon geometry, or maximum muscle tension (Valente et al, 2014).…”

Section: Limitations Of Musculoskeletal Modelingmentioning

confidence: 99%

A Systematic Review of the Associations Between Inverse Dynamics and Musculoskeletal Modeling to Investigate Joint Loading in a Clinical Environment

Holder

Trinler

Meurer

et al. 2020

Front. Bioeng. Biotechnol.

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The assessment of knee or hip joint loading by external joint moments is mainly used to draw conclusions on clinical decision making. However, the correlation between internal and external loads has not been systematically analyzed. This systematic review aims, therefore, to clarify the relationship between external and internal joint loading measures during gait. A systematic database search was performed to identify appropriate studies for inclusion. In total, 4,554 articles were identified, while 17 articles were finally included in data extraction. External joint loading parameters were calculated using the inverse dynamics approach and internal joint loading parameters by musculoskeletal modeling or instrumented prosthesis. It was found that the medial and total knee joint contact forces as well as hip joint contact forces in the first half of stance can be well predicted using external joint moments in the frontal plane, which is further improved by including the sagittal joint moment. Worse correlations were found for the peak in the second half of stance as well as for internal lateral knee joint contact forces. The estimation of external joint moments is useful for a general statement about the peak in the first half of stance or for the maximal loading. Nevertheless, when investigating diseases as valgus malalignment, the estimation of lateral knee joint contact forces is necessary for clinical decision making because external joint moments could not predict the lateral knee joint loading sufficient enough. Dependent on the clinical question, either estimating the external joint moments by inverse dynamics or internal joint contact forces by musculoskeletal modeling should be used.

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“…Introducing experimentally derived ground reaction forces, the OpenSim model is basically an open loop. Thus, the differences between AnyBody and OpenSim will lessen (Kim et al, 2018;Trinler et al, 2019).…”

Section: Muscle Activation Patternsmentioning

confidence: 99%

A three-dimensional musculoskeletal model of the dog

Stark

Fischer

Hunt

et al. 2020

Preprint

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Dogs are an interesting object of investigation because of the wide range of body size, body mass, and physique. In the last several years, the number of clinical and biomechanical studies on dog locomotion has increased. However, the relationship between body structure and joint load during locomotion, as well as between joint load and degenerative diseases of the locomotor system (e.g. dysplasia), are not sufficiently understood. In vivo measurements/records of joint forces and loads or deep/small muscles are complex, invasive, and sometimes ethically questionable. The use of detailed musculoskeletal models may help in filling that knowledge gap. We describe here the methods we used to create a detailed musculoskeletal model with 84 degrees of freedom and 134 muscles. Our model has three key-features: Three-dimensionality, scalability, and modularity. We tested the validity of the model by identifying forelimb muscle synergies of a beagle at walk. We used inverse dynamics and static optimization to estimate muscle activations based on experimental data. We identified three muscle synergy groups by using hierarchical clustering. Predicted activation patterns exhibited good agreement with experimental data for most of the forelimb muscles. We expect that our model will speed up the analysis of how body size, physique, agility, and disease influence joint neuronal control and loading in dog locomotion.

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Muscle force estimation in clinical gait analysis using AnyBody and OpenSim

Cited by 80 publications

References 32 publications

biorbd: A C++, Python and MATLAB library to analyze and simulate the human body biomechanics

biorbd: A C++, Python and MATLAB library to analyze and simulate the human body biomechanics

A Systematic Review of the Associations Between Inverse Dynamics and Musculoskeletal Modeling to Investigate Joint Loading in a Clinical Environment

A three-dimensional musculoskeletal model of the dog

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