Muscle force estimation in clinical gait analysis using AnyBody and OpenSim

Trinler, UK, Schwameder, H, Baker, RJ ORCID: https://orcid.org/0000-0003-4759-4216 and Alexander, N 2019, 'Muscle force estimation in clinical gait analysis using AnyBody and OpenSim' , Journal of Biomechanics, 86 (Mar 19) , pp. 55-63.

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Abstract

A variety of musculoskeletal models are applied in different modelling environments for estimating muscle forces during gait. Influence of different modelling assumptions and approaches on model outputs are still not fully understood, while direct comparisons of standard approaches have been rarely undertaken. This study seeks to compare joint kinematics, joint kinetics and estimated muscle forces of two standard approaches offered in two different modelling environments (AnyBody, OpenSim). It is hypothesised that distinctive differences exist for individual muscles, while summing up synergists show general agreement. Experimental data of 10 healthy participants (28 ± 5 years, 1.72 ± 0.08 m, 69 ± 12 kg) was used for a standard static optimisation muscle force estimation routine in AnyBody and OpenSim while using two gait-specific musculoskeletal models. Statistical parameter mapping paired t-test was used to compare joint angle, moment and muscle force waveforms in Matlab. Results showed differences especially in sagittal ankle and hip angles as well as sagittal knee moments. Differences were also found for some of the muscles, especially of the triceps surae group and the biceps femoris short head, which occur as a result of different anthropometric and anatomical definitions (mass and inertia of segments, muscle properties) and scaling procedures (static vs. dynamic). Understanding these differences and their cause is crucial to operate such modelling environments in a clinical setting. Future research should focus on alternatives to classical generic musculoskeletal models (e.g. implementation of functional calibration tasks), while using experimental data reflecting normal and pathological gait to gain a better understanding of variations and divergent behaviour between approaches.

Item Type: Article
Uncontrolled Keywords: Gait analysis, Muscle force estimation, Musculoskeletal modelling
Schools: Schools > School of Health and Society > Centre for Health Sciences Research
Journal or Publication Title: Journal of Biomechanics
Publisher: Elsevier
ISSN: 0021-9290
Related URLs:
SWORD Depositor: Publications Router
Depositing User: Publications Router
Date Deposited: 26 Feb 2019 08:44
Last Modified: 04 Apr 2019 13:45
URI: http://usir.salford.ac.uk/id/eprint/50222

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