Realistic motor unit placement in a cylindrical HD-sEMG generation model

Carriou, Vincent; Laforêt, Jérémy; Boudaoud, Sofiane; Harrach, Mariam Al

doi:10.1109/embc.2016.7591044

Cited by 11 publications

(10 citation statements)

References 8 publications

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“…Surface EMG signals were generated for different muscle cross-sectional area morphologies (circle, ring, pizza, and ellipse) and regionalization levels (no regionalization, medium level of regionalization, and high level of regionalization). The method for MU regionalization was based on a previous study 2 . Force-EMG relations were computed for each condition, and the best fit between the simulation outcomes and an experimental data 3 was calculated using the coefficient of determination (R 2 ).…”

Section: Resultsmentioning

confidence: 99%

A computer simulation study on the influence of motor unit regionalization on the generation of surface myoelectric signals

Molinari

Elias

2019

revpibic

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The present study aims to analyze the influence of motor unit regionalization within the muscle cross-sectional area on time- and frequency-domain properties of surface myoelectric EMG signal. Computer simulations were performed using a phenomenological model of the neuromuscular system. Different contraction intensities were simulated, and the RMS and median frequency of the EMG were calculated for different muscle cross-sectional area morphologies. The level of MU regionalization was adjusted in the model. Results showed that experimental force-EMG relations could be appropriately simulated by the model, irrespective of the muscle cross-sectional area morphology and the level of MU regionalization. However, the best fit between simulation and experimental data is influenced by the level of MU regionalization.

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Section: Resultsmentioning

confidence: 99%

A computer simulation study on the influence of motor unit regionalization on the generation of surface myoelectric signals

Molinari

Elias

2019

revpibic

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“…Those parts of MU territories that exceed the muscle territory must be cut off, and the question remains how to account for this loss in MU territory. Several approaches to solving the problem are conceivable (Rodriguez-Falces et al, 2012; Carriou et al, 2016b): All fibers belonging to the MU are placed in the remaining parts of the MU territory. This approach leads to an increase in the fiber density of boundary MUs, and hence also in the overall fiber density toward the muscle boundaries.The number of fibers innervated by the MU is reduced proportionally.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…This approach keeps the number of fibers and the fiber density at the desired values but likely leads to strongly increased overall fiber density toward the muscle center, due to many adjusted MU regions overlapping there.MUs with territories exceeding the muscle territory are rejected completely. This approach obviously removes the need for MU property adjustments, but without further modifications leads to reduced overall fiber density close to the muscle boundaries (Carriou et al, 2016b). …”

Section: Mathematical Modelmentioning

confidence: 99%

“…It is advisable to divide the muscle cross section into M parts of equal size, and then distribute n / M MUs uniformly in each part, to avoid an unrealistically high variability of the fiber density due to the random MU placement. Alternatively, MU centers could also be placed using an optimization algorithm such as the one proposed by Carriou et al ( 2016b ), which sequentially places MUs at the position that maximizes the distance to the already placed MUs.…”

Section: Mathematical Modelmentioning

confidence: 99%

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A Comprehensive Mathematical Model of Motor Unit Pool Organization, Surface Electromyography, and Force Generation

Petersen

Rostalski

2019

Front. Physiol.

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Neuromuscular physiology is a vibrant research field that has recently seen exciting advances. Previous publications have focused on thorough analyses of particular aspects of neuromuscular physiology, yet an integration of the various novel findings into a single, comprehensive model is missing. In this article, we provide a unified description of a comprehensive mathematical model of surface electromyographic (EMG) measurements and the corresponding force signal in skeletal muscles, both consolidating and extending the results of previous studies regarding various components of the neuromuscular system. The model comprises motor unit (MU) pool organization, recruitment and rate coding, intracellular action potential generation and the resulting EMG measurements, as well as the generated muscular force during voluntary isometric contractions. Mathematically, it consists of a large number of linear PDEs, ODEs, and various stochastic nonlinear relationships, some of which are solved analytically, others numerically. A parameterization of the electrical and mechanical components of the model is proposed that ensures a physiologically meaningful EMG-force relation in the simulated signals, in particular taking the continuous, size-dependent distribution of MU parameters into account. Moreover, a novel nonlinear transformation of the common drive model input is proposed, which ensures that the model force output equals the desired target force. On a physiological level, this corresponds to adjusting the rate coding model to the force generating capabilities of the simulated muscle, while from a control theoretic point of view, this step is equivalent to an exact linearizing transformation of the controlled neuromuscular system. Finally, an alternative analytical formulation of the EMG model is proposed, which renders the physiological meaning of the model more clear and facilitates a mathematical proof that muscle fibers in this model at no point in time represent a net current source or sink. A consistent description of a complete physiological model as presented here, including thorough justification of model component choices, will facilitate the use of these advanced models in future research. Results of a numerical simulation highlight the model's capability to reproduce many physiological effects observed in experimental measurements, and to produce realistic synthetic data that are useful for the validation of signal processing algorithms.

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“…Moreover, possible MU area superposition is also considered in the model. For this purpose, we used a contrained random algorithm presented in [10] which gives more realistic MUs positioning within the muscle. Moreover, boundaries of the muscle are fixed to mimic the Biceps Brachii muscle using MRI technique [11].…”

Section: Motor Unit and Fibers Specificationsmentioning

confidence: 99%

Sensitivity analysis of HD-sEMG amplitude descriptors relative to grid parameter variations of a cylindrical multilayered muscle model

Carriou¹,

Laforêt²,

Boudaoud³

et al. 2016

Biomed. Phys. Eng. Express

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The aim of this work is to perform a sensitivity analysis of a high density surface electromyogram (HD-sEMG) amplitude descriptors according to several grid parameters. This study is motivated by the fact that the electrode grid position and layout are crucial to record relevant electromyographic data. For this purpose, an analytical limb model is used, where the upper limb is modeled as a multilayered cylinder with three layers: muscle, fat tissue and skin tissue. Using this model, HD-sEMG signals are computed over the skin as a 2D surface along angular and longitudinal directions. Electrode recording is performed through a surface integration on the 2D surface according to the electrode shape. 16 simulations on 10 anatomies (350 Motor Units) with the same parameters were computed for 3 constant contraction levels: 30%, 50% and 70% of the Maximal Voluntary Contraction (MVC). Then, a global sensitivity analysis using the Elementary Effect Method is performed to explore the sensitivity of amplitude descriptors (Average Rectified Value, Root Mean Square value and High Order Statistics) relative to varying parameters from the electrode grid (inter-electrode distances, electrodes radius, position and rotation). From those grid definitions, monopolar, bipolar and laplacian signals are also computed to see the electrode arrangement sensitivity. The obtained results exposed a huge impact of the grid rotation on the studied criteria. They also showed that parameters specific to the electrode grid layout (inter-electrode distances) have the less impact. Moreover, they exhibited the laplacian arrangement as the most sensitive electrode arrangement to grid modifications.

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Realistic motor unit placement in a cylindrical HD-sEMG generation model

Cited by 11 publications

References 8 publications

A computer simulation study on the influence of motor unit regionalization on the generation of surface myoelectric signals

A computer simulation study on the influence of motor unit regionalization on the generation of surface myoelectric signals

A Comprehensive Mathematical Model of Motor Unit Pool Organization, Surface Electromyography, and Force Generation

Sensitivity analysis of HD-sEMG amplitude descriptors relative to grid parameter variations of a cylindrical multilayered muscle model

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