Comparative evaluation of motor unit architecture models

Navallas, Javier; Malanda, Armando; Gila, Luis; Rodríguez, Javier; Rodríguez, Isabelle

doi:10.1007/s11517-009-0526-0

Cited by 9 publications

(14 citation statements)

References 52 publications

(89 reference statements)

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“…2.1.3. -Placement of MUTs: MUTs are placed by means of an algorithm proposed by Schnetzer et al [54] that makes it possible to eliminate any edge effect, and so obtain a muscle architecture model with homogeneous properties across its entire muscle cross-section [48]. -Innervation of muscle fibers: the innervation pattern is random in that each muscle fiber selects the innervating motor unit at random from the set of MUs whose territories are covering the fiber's position [55].…”

Section: Model Basismentioning

confidence: 99%

“…anddðq k Þ is the input muscle fiber density at q k , the kth point of the grid, which is defined as the summation of the input MUFDs of the MUs covering this point [48]. For a generic point r,…”

Section: Placement Of Mutsmentioning

confidence: 99%

“…This algorithm ensures that MUTs are placed in such a way that the input muscle fiber density resulting from the overlapping of MUTs is almost constant [48,54], namelỹ…”

Section: Placement Of Mutsmentioning

confidence: 99%

“…The MUT placement algorithm also provides an almost constant muscle fiber density throughout the muscle crosssection [48,54]. As stated in (11), this ensures that each MU has an innervation probability…”

Section: Adjustment Of the Mufd Input Parametersmentioning

confidence: 99%

“…Some current models suffer from important limitations [48], including severe edge effects whereby MUs on the borders of a muscle cross-section have higher MUFDs than expected [47]. Furthermore, there is physiological evidence to suggest that MUFD is not the same for all MUs [1,2] and that different MU types have different MUFD distributions [31].…”

Section: Introductionmentioning

confidence: 99%

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A muscle architecture model offering control over motor unit fiber density distributions

Navallas

Malanda

Gila

et al. 2010

Med Biol Eng Comput

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The aim of this study was to develop a muscle architecture model able to account for the observed distributions of innervation ratios and fiber densities of different types of motor units in a muscle. A model algorithm is proposed and mathematically analyzed in order to obtain an inverse procedure that allows, by modification of input parameters, control over the output distributions of motor unit fiber densities. The model's performance was tested with independent data from a glycogen depletion study of the medial gastrocnemius of the rat. Results show that the model accurately reproduces the observed physiological distributions of innervation ratios and fiber densities and their relationships. The reliability and accuracy of the new muscle architecture model developed here can provide more accurate models for the simulation of different electromyographic signals.

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Section: Model Basismentioning

confidence: 99%

Section: Placement Of Mutsmentioning

confidence: 99%

“…This algorithm ensures that MUTs are placed in such a way that the input muscle fiber density resulting from the overlapping of MUTs is almost constant [48,54], namelỹ…”

Section: Placement Of Mutsmentioning

confidence: 99%

Section: Adjustment Of the Mufd Input Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A muscle architecture model offering control over motor unit fiber density distributions

Navallas

Malanda

Gila

et al. 2010

Med Biol Eng Comput

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A masked least-squares smoothing procedure for artifact reduction in scanning-EMG recordings

Corera

Eciolaza

Rubio

et al. 2018

Med Biol Eng Comput

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Scanning-EMG is an electrophysiological technique in which the electrical activity of the motor unit is recorded at multiple points along a corridor crossing the motor unit territory. Correct analysis of the scanning-EMG signal requires prior elimination of interference from nearby motor units. Although the traditional processing based on the median filtering is effective in removing such interference, it distorts the physiological waveform of the scanning-EMG signal. In this study, we describe a new scanning-EMG signal processing algorithm that preserves the physiological signal waveform while effectively removing interference from other motor units. To obtain a cleaned-up version of the scanning signal, the masked least-squares smoothing (MLSS) algorithm recalculates and replaces each sample value of the signal using a least-squares smoothing in the spatial dimension, taking into account the information of only those samples that are not contaminated with activity of other motor units. The performance of the new algorithm with simulated scanning-EMG signals is studied and compared with the performance of the median algorithm and tested with real scanning signals. Results show that the MLSS algorithm distorts the waveform of the scanning-EMG signal much less than the median algorithm (approximately 3.5 dB gain), being at the same time very effective at removing interference components. Graphical AbstractThe raw scanning-EMG signal (left figure) is processed by the MLSS algorithm in order to remove the artifact interference. Firstly, artifacts are detected from the raw signal, obtaining a validity mask (central figure) that determines the samples that have been contaminated by artifacts. Secondly, a least-squares smoothing procedure in the spatial dimension is applied to the raw signal using the not contaminated samples according to the validity mask. The resulting MLSS-processed scanning-EMG signal (right figure) is clean of artifact interference.

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