Erratum to: An Optimization Approach to Inverse Dynamics Provides Insight as to the Function of the Biarticular Muscles During Vertical Jumping

Cleather, Daniel J.; Goodwin, Jon E.; Bull, Anthony M.J.

doi:10.1007/s10439-011-0340-3

Cited by 17 publications

(25 citation statements)

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“…This was predominantly because of a later onset of muscular activation in the estimated muscle forces than was seen in the EMG envelopes. We have previously identified that modelling the function of the biarticular muscles is a key challenge for the musculoskeletal modelling community [ 9 , 10 ]. In particular, the benefits derived from the biarticular muscles transferring power between joints [ 37 ] may not be entirely captured by this modelling approach.…”

Section: Discussionmentioning

confidence: 99%

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The development of a segment-based musculoskeletal model of the lower limb: introducing F ree B ody

Cleather

Bull

2015

R. Soc. open sci.

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Traditional approaches to the biomechanical analysis of movement are joint-based; that is the mechanics of the body are described in terms of the forces and moments acting at the joints, and that muscular forces are considered to create moments about the joints. We have recently shown that segment-based approaches, where the mechanics of the body are described by considering the effect of the muscle, ligament and joint contact forces on the segments themselves, can also prove insightful. We have also previously described a simultaneous, optimization-based, musculoskeletal model of the lower limb. However, this prior model incorporates both joint- and segment-based assumptions. The purpose of this study was therefore to develop an entirely segment-based model of the lower limb and to compare its performance to our previous work. The segment-based model was used to estimate the muscle forces found during vertical jumping, which were in turn compared with the muscular activations that have been found in vertical jumping, by using a Geers' metric to quantify the magnitude and phase errors. The segment-based model was shown to have a similar ability to estimate muscle forces as a model based upon our previous work. In the future, we will evaluate the ability of the segment-based model to be used to provide results with clinical relevance, and compare its performance to joint-based approaches. The segment-based model described in this article is publicly available as a GUI-based Matlab® application and in the original source code (at ).

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Section: Discussionmentioning

confidence: 99%

“…This is akin to the assumptions made in a joint-based approach. Equally, for the intermediate segment of a biarticular muscle that does not attach to the segment,

captures the rotational effects of the joint reaction forces created by tension in the muscle [ 10 ].…”

Section: Methodsmentioning

confidence: 99%

The development of a segment-based musculoskeletal model of the lower limb: introducing F ree B ody

Cleather

Bull

2015

R. Soc. open sci.

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“…The mechanical data captured in this study was analysed using FreeBody [14][15][16][17][18] a publicly available musculoskeletal model of the lower limb. FreeBody permits the estimation of the muscle and joint contact forces seen during movement and has been extensively tested in terms of its sensitivity to key modelling assumptions [18][19][20][21], its validity [14,22,23] and its reliability [24].…”

Section: Discussionmentioning

confidence: 99%

Muscular coordination during vertical jumping

Cleather¹,

Cushion²

2019

Preprint

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The musculoskeletal system has a relatively large number of mechanical degrees of freedom. In order to permit coordinated movement, it is thought that these degrees of freedom are constrained, reducing the complexity of the motor control problem. For this reason, principal component analysis can be a powerful technique for understanding the organisation of movement by calculating the number of functional degrees of freedom present in a particular movement task. In this study, we used principal component analysis to find the number of functional degrees of freedom exhibited during vertical jumping. We applied the technique to both the inter-segmental moments and the muscle forces (that were estimated based upon a publicly available musculoskeletal model of the lower limb). We found that over 90% of the variance in the 3 dimensional inter-segmental moments could be described by 3 principal components, suggesting the presence of 3 functional degrees of freedom. Similarly, only 4 principal components were required to capture 90% of the variance in muscles forces. These results suggest that there is a marked inter-individual similarity in both moments and muscle forces during vertical jumping.

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“…In order to generate the training and test data for this study, a publicly available model of the lower limb (FreeBody) was used to estimate the muscle and joint contact forces exhibited during jumping and landing from the motion capture and force plate data. The development and testing of FreeBody has been previously described in great detail [5,7,[10][11][12][13][14][15][16][17] and so only a brief sketch of the model is provided here. In short, the equations of motion of a chain of 5 rigid segments representing the pelvis and right lower limb are posed using wrench notation.…”

Section: Discussionmentioning

confidence: 99%

Neural network based approximation of muscle and joint contact forces during jumping and landing

Cleather¹

2019

Preprint

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Musculoskeletal models have been used to estimate the muscle and joint contact forces expressed during movement. One limitation of this approach, however, is that such models are computationally demanding, which limits the possibility of using them for real-time feedback. One solution to this problem is to train a neural network to approximate the performance of the model, and then to use the neural network to give real-time feedback. In this study, neural networks were trained to approximate the FreeBody musculoskeletal model for jumping and landing tasks. The neural networks were better able to approximate jumping than landing, which was probably a result of the greater variability in the landing data set used in this study. In addition, a neural network that was based on a reduced set of inputs was also trained to approximate the outputs of FreeBody during a landing task. These results demonstrate the feasibility of using neural networks to approximate the results of musculoskeletal models in order to provide real-time feedback. In addition, these neural networks could be based upon a reduced set of kinematic variables taken from a 2-dimensional video record, making the implementation of mobile applications a possibility.

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Erratum to: An Optimization Approach to Inverse Dynamics Provides Insight as to the Function of the Biarticular Muscles During Vertical Jumping

Cited by 17 publications

References 1 publication

The development of a segment-based musculoskeletal model of the lower limb: introducing F ree B ody

The development of a segment-based musculoskeletal model of the lower limb: introducing F ree B ody

Muscular coordination during vertical jumping

Neural network based approximation of muscle and joint contact forces during jumping and landing

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