Agreement of measured and calculated muscle activity during highly dynamic movements modelled with a spherical knee joint

Dupré, Thomas; Dietzsch, Mareike; Komnik, Igor; Potthast, Wolfgang; David, Sina

doi:10.1016/j.jbiomech.2018.12.013

Cited by 20 publications

(13 citation statements)

References 43 publications

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“…We calculated muscle forces using the AnyBody 6.0 (AnyBody Technology, Aalborg, DK) musculoskeletal model simulation software from the motion capture and the ground reaction force (GRF) data. The validity of muscle forces estimated using this musculoskeletal model simulation software has been confirmed in previous studies [17][18][19] . Participants walked on a 9-m walkway and stepped on the force plate with their right foot.…”

Section: Participants and Methodssupporting

confidence: 70%

Contribution of hip and knee muscles to lateral knee stability during gait

et al. 2020

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Section: Participants and Methodssupporting

confidence: 70%

Contribution of hip and knee muscles to lateral knee stability during gait

et al. 2020

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“…The dataset used for this study pools data of different studies [ 10 , 12 , 37 , 38 ]. All data were collected at the German Sport University Cologne.…”

Section: Methodsmentioning

confidence: 99%

Artificial Neural Networks in Motion Analysis—Applications of Unsupervised and Heuristic Feature Selection Techniques

Mundt

Koeppe

Bamer

et al. 2020

Sensors

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The use of machine learning to estimate joint angles from inertial sensors is a promising approach to in-field motion analysis. In this context, the simplification of the measurements by using a small number of sensors is of great interest. Neural networks have the opportunity to estimate joint angles from a sparse dataset, which enables the reduction of sensors necessary for the determination of all three-dimensional lower limb joint angles. Additionally, the dimensions of the problem can be simplified using principal component analysis. Training a long short-term memory neural network on the prediction of 3D lower limb joint angles based on inertial data showed that three sensors placed on the pelvis and both shanks are sufficient. The application of principal component analysis to the data of five sensors did not reveal improved results. The use of longer motion sequences compared to time-normalised gait cycles seems to be advantageous for the prediction accuracy, which bridges the gap to real-time applications of long short-term memory neural networks in the future.

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“…Scaling methods based on optoelectronic data are used in many studies (Hamner and Delp, 2013;Dupr e et al 2018;Muller et al 2019a) since they require less required time, knowledge and money.…”

Section: Introductionmentioning

confidence: 99%

Accuracy and kinematics consistency of marker-based scaling approaches on a lower limb model: a comparative study with imagery data

Puchaud

Sauret

Muller

et al. 2019

Computer Methods in Biomechanics and Biomedical Engineering

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Medical images are not typically included in protocol of motion laboratories. Thus, accurate scaling of musculoskeletal models from optoelectronic data are important for any biomechanical analysis. The aim of the current study was to identify a scaling method based on optoelectronic data, inspired from literature, which could offer the best trade-off between accurate geometrical parameters (segment lengths, orientation of joint axes, marker coordinates) and consistent inverse kinematics outputs (kinematic error, joint angles). The methods were applied on 26 subjects and assessed with medical imagery building EOS-based models, considered as a reference. The main contribution of this paper is to show that the marker-based scaling followed by an optimisation of orientation joint axes and markers local coordinates, gives the most consistent scaling and joint angles with EOS-based models. Thus, when a non-invasive mean with an optoelectronic system is considered, a marker-based scaling is preliminary needed to get accurate segment lengths and to optimise joint axes and marker local coordinates to reduce kinematic errors.

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Agreement of measured and calculated muscle activity during highly dynamic movements modelled with a spherical knee joint

Cited by 20 publications

References 43 publications

Contribution of hip and knee muscles to lateral knee stability during gait

Contribution of hip and knee muscles to lateral knee stability during gait

Artificial Neural Networks in Motion Analysis—Applications of Unsupervised and Heuristic Feature Selection Techniques

Accuracy and kinematics consistency of marker-based scaling approaches on a lower limb model: a comparative study with imagery data

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