The Influence of the sEMG Amplitude Estimation Technique on the EMG–Force Relationship

Ranaldi, Simone; Corvini, Giovanni; Marchis, Cristiano De; Conforto, Silvia

doi:10.3390/s22113972

Cited by 7 publications

(3 citation statements)

References 30 publications

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“…The cut-off frequency of 2.0 Hz was used in our study for envelope estimation. The sEMG amplitude during isometric and quasi-static contractions was shown to have a frequency below 5-10 Hz, and the optimal cut-off frequencies between 2-3 Hz were provided in the literature (Staudenmann et al, 2010;Ranaldi et al, 2022). Such a cut-off frequency compromises sEMG dynamics, muscle force difference, and the time lag between those signals (Staudenmann et al, 2010).…”

Section: The Performance Of the Proposed Methodsmentioning

confidence: 99%

“…Such a cut-off frequency compromises sEMG dynamics, muscle force difference, and the time lag between those signals (Staudenmann et al, 2010). Different cut-off frequencies could affect the smoothness resulting in correlations among estimated sEMG amplitude and any target signal, especially in non-slowly varying signals (Ranaldi et al, 2022). Moreover, non-causal digital filters were used in our study.…”

Section: The Performance Of the Proposed Methodsmentioning

confidence: 99%

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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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Section: The Performance Of the Proposed Methodsmentioning

confidence: 99%

Section: The Performance Of the Proposed Methodsmentioning

confidence: 99%

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.

View full text Add to dashboard Cite

show abstract

“…As the force of an isometric contraction is gradually increased, the number of active motor unit increases, larger units are progressively recruited (Henneman et al 1965), and already recruited units increase their firing rate (Milner-Brown et al 1973;Desmedt and Godaux 1978). Therefore, the overall amplitude of the sEMG signal increases with increasing force (Disselhorst-Klug et al, 2009;Ranaldi et al, 2022). However, several questions related to this increase remain unresolved:…”

Section: Introductionmentioning

confidence: 99%