Maintenance of standing posture during multi-directional leaning demands the recruitment of task-specific motor units in the ankle plantarflexors

Cohen, Joshua W.; Vieira, Taian; Ivanova, Tanya D.; Cerone, Giacinto Luigi; Garland, S. Jayne

doi:10.1007/s00221-021-06154-0

Cited by 10 publications

(6 citation statements)

References 54 publications

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“…These results were possible because of the capability of HDsEMG to describe how neural excitation distributes and evolves over the muscle surface. In this regard, evidence on the regional excitation of skeletal muscles has been garnered over the past 20 years both at global 15 – 18 and at single MU level 19 – 22 . The often implicit assumption in these studies is that EMGs with high amplitude are associated with excitation of a spatially localised site underlying the electrode array 23 .…”

Section: Introductionmentioning

confidence: 99%

Physical and electrophysiological motor unit characteristics are revealed with simultaneous high-density electromyography and ultrafast ultrasound imaging

Carbonaro

Meiburger

Seoni

et al. 2022

Sci Rep

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Electromyography and ultrasonography provide complementary information about electrophysiological and physical (i.e. anatomical and mechanical) muscle properties. In this study, we propose a method to assess the electrical and physical properties of single motor units (MUs) by combining High-Density surface Electromyography (HDsEMG) and ultrafast ultrasonography (US). Individual MU firings extracted from HDsEMG were used to identify the corresponding region of muscle tissue displacement in US videos. The time evolution of the tissue velocity in the identified region was regarded as the MU tissue displacement velocity. The method was tested in simulated conditions and applied to experimental signals to study the local association between the amplitude distribution of single MU action potentials and the identified displacement area. We were able to identify the location of simulated MUs in the muscle cross-section within a 2 mm error and to reconstruct the simulated MU displacement velocity (cc > 0.85). Multiple regression analysis of 180 experimental MUs detected during isometric contractions of the biceps brachii revealed a significant association between the identified location of MU displacement areas and the centroid of the EMG amplitude distribution. The proposed approach has the potential to enable non-invasive assessment of the electrical, anatomical, and mechanical properties of single MUs in voluntary contractions.

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Section: Introductionmentioning

confidence: 99%

Physical and electrophysiological motor unit characteristics are revealed with simultaneous high-density electromyography and ultrafast ultrasound imaging

Carbonaro

Meiburger

Seoni

et al. 2022

Sci Rep

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“…However, our sEMG signals were likely not sensitive to changes in excitation of the proximal soleus region, which is covered by the gastrocnemius heads. Of more critical concern, though, is collecting sEMG sensitive to mediolateral differences in soleus excitation, which have been shown to change during quiet and perturbed standing (Cohen et al, 2020;Cohen et al, 2021). With our experimental protocol, sEMG signals were most sensitive and specific to the target muscles, minimizing both Types I and II errors (Vieira and Botter, 2021).…”

Section: Figurementioning

confidence: 95%

A real-time and convex model for the estimation of muscle force from surface electromyographic signals in the upper and lower limbs

Shirzadi

Marateb²,

Rojas-Martínez³

et al. 2023

Front. Physiol.

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Surface electromyography (sEMG) is a signal consisting of different motor unit action potential trains and records from the surface of the muscles. One of the applications of sEMG is the estimation of muscle force. We proposed a new real-time convex and interpretable model for solving the sEMG—force estimation. We validated it on the upper limb during isometric voluntary flexions-extensions at 30%, 50%, and 70% Maximum Voluntary Contraction in five subjects, and lower limbs during standing tasks in thirty-three volunteers, without a history of neuromuscular disorders. Moreover, the performance of the proposed method was statistically compared with that of the state-of-the-art (13 methods, including linear-in-the-parameter models, Artificial Neural Networks and Supported Vector Machines, and non-linear models). The envelope of the sEMG signals was estimated, and the representative envelope of each muscle was used in our analysis. The convex form of an exponential EMG-force model was derived, and each muscle’s coefficient was estimated using the Least Square method. The goodness-of-fit indices, the residual signal analysis (bias and Bland-Altman plot), and the running time analysis were provided. For the entire model, 30% of the data was used for estimation, while the remaining 20% and 50% were used for validation and testing, respectively. The average R-square (%) of the proposed method was 96.77 ± 1.67 [94.38, 98.06] for the test sets of the upper limb and 91.08 ± 6.84 [62.22, 96.62] for the lower-limb dataset (MEAN ± SD [min, max]). The proposed method was not significantly different from the recorded force signal (p-value = 0.610); that was not the case for the other tested models. The proposed method significantly outperformed the other methods (adj. p-value < 0.05). The average running time of each 250 ms signal of the training and testing of the proposed method was 25.7 ± 4.0 [22.3, 40.8] and 11.0 ± 2.9 [4.7, 17.8] in microseconds for the entire dataset. The proposed convex model is thus a promising method for estimating the force from the joints of the upper and lower limbs, with applications in load sharing, robotics, rehabilitation, and prosthesis control for the upper and lower limbs.

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“…The motor unit recruitment assessment during exercise and in different populations is of great interest since it can provide relevant information about physical capacity, the composition/function of different muscles and the effect of a clinical intervention [10,[13][14][15][16][17][18]. Moreover, its assessment is useful in providing information about standing balance [19], force levels during painful contractions [20] and lower limb mobility and strength [21].…”

Section: Basis Of Motor Unit Recruitment and Its Assessmentmentioning

confidence: 99%

“…Motor unit action potentials result in electric signals. Although they can be assessed using electromyography intramuscular electrodes, their invasive nature and small volume detection have resulted in more surface electromyography (sEMG) related studies [18,22] and their exclusion in teaching practical lessons. Motor units' recruitment has been assessed using sEMG by frequency and spectral analysis [13][14][15], but this type of analysis has been criticized due to a difficult association between fiber type and conduction velocity [23].…”

Section: Basis Of Motor Unit Recruitment and Its Assessmentmentioning

confidence: 99%

How to Work with Electromyography Decomposition in Practical Classes of Exercise Physiology and Biomechanics

Priego-Quesada

Goethel

Becker

et al. 2022

Life

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Concepts about motor unit recruitment are important learning contents in exercise physiology and biomechanics classes that are usually taught theoretically. In the last few years, great advances have occurred in the decomposition of surface electromyography, allowing the learning of theoretical contents in an experimental way. In this tutorial paper, we have described the decomposition of surface electromyography methodological aspects and examples to teach motor unit recruitment concepts in exercise physiology and biomechanics practical lessons. This work has the aim to facilitate physiology and biomechanics academics to introduce this technique in practical classes.

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Maintenance of standing posture during multi-directional leaning demands the recruitment of task-specific motor units in the ankle plantarflexors

Cited by 10 publications

References 54 publications

Physical and electrophysiological motor unit characteristics are revealed with simultaneous high-density electromyography and ultrafast ultrasound imaging

Physical and electrophysiological motor unit characteristics are revealed with simultaneous high-density electromyography and ultrafast ultrasound imaging

A real-time and convex model for the estimation of muscle force from surface electromyographic signals in the upper and lower limbs

How to Work with Electromyography Decomposition in Practical Classes of Exercise Physiology and Biomechanics

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