Evidence of movement variability patterns during a repetitive pointing task until exhaustion

Savin, Jonathan; Gaudez, Clarisse; Gilles, M.; Padois, Vincent; Bidaud, Philippe

doi:10.1016/j.apergo.2021.103464

Cited by 8 publications

(10 citation statements)

References 47 publications

(85 reference statements)

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“…The VIP value of the activity of the median and anterior deltoids ranged between 0.73-1.61, which is qualitatively smaller than the VIP value of median frequency, mobility, Higuchi fractal dimension, spectral entropy, and fuzzy entropy (1.26-2.28) for these muscles. Similarly to activation level, there were changes in activity for a large proportion of the participants in the upper trapezius (90%), although this muscle is not supposed to fatigue [72,73]. Thus, our results further confirm that amplitude related MMF indicators are not relevant f or assessing muscle fatigue.…”

Section: Activation Level Activity Approximate Sample and Multiscale ...supporting

confidence: 75%

“…Interestingly, our PLSR approach not only provided better prediction of RPE, but also physiologically meaningful results. Indeed, the highest VIP values were observed for the anterior and medial deltoids that are the muscles known to fatigue the most during the RPT used in the present study [72,73] and for the median frequency, a commonly well recognized indicator of muscle fatigue [108–113].…”

Section: Discussionmentioning

confidence: 88%

“…Median frequency was the MMF indicator that had the highest VIP value (2.28) and changed for a very large proportion of the participants (>85%) between RPT initiation and termination in the muscles known to fatigue the most during the RPT used in the present study, i.e., anterior and medial deltoids [72,73]. This result agrees with Goubault et al, [24], that also found that when correlating six MMF indicators to RPE, mobility and spectral entropy were among the MMF indicators that showed the highest R-square values.…”

Section: Discussionmentioning

confidence: 99%

“…This task has been shown to fatigue the anterior and medial deltoid [72][73][74]. Every 30 seconds, participants reported their RPE using the CR-10 Borg scale (RPE) [62] without interrupting the RPT movement.…”

Section: Experimental Protocolmentioning

confidence: 99%

“…In a recent study, Ni et al, [68] showed that time, frequency, and time- Consequently, the aim of the present study was to determine the best MMF indicators, f rom 15 MMF indicators identified in the literature as potentially relevant to assess muscle fatigue during a repetitive pointing task (RPT) performed with the upper limb. We hypothesized that for the anterior and medial deltoids, which are the muscles showing the largest signs of fatigue during the upper limb repetitive task used in the present studies [72][73][74], the mobility, median frequency, spectral entropy, fuzzy entropy, multiscale entropy [24,28,41,75,76], and degree of multifractality [40,47,49,53] will have greater importance to predict the evolution of the RPE. In addition, these MMF indicators will significantly change for a large proportion of the participants in the anterior and medial deltoid in comparison to approximate and sample entropy, recurrence quantification analysis, correlation dimension, and the largest Lyapunov exponent [28,40,46,55].…”

Section: Introductionmentioning

confidence: 99%

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What are the best indicators of myoelectric manifestation of fatigue?

Ndam

Goubault

Moyen-Sylvestre

et al. 2023

Preprint

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The myoelectric manifestation of fatigue (MMF) is predominantly assessed using median frequency and amplitude of electromyographic (EMG) signals. However, EMG has complex features so that fractals, correlation, entropy, and chaos MMF indicators were introduced to detect alteration of EMG features caused by muscle fatigue that may not be detected by linear indicators. The aim of this study was to determine the best MMF indicators. Twenty-four participants were equipped with EMG sensors on 9 shoulder muscles and performed a repetitive pointing task. They reported their rate of perceived exertion every 30 seconds and were stopped when they reached 8 or higher on the CR10 Borg scale. Partial least square regression was used to predict perceived exertion through 15 MMF indicators. In addition, the proportion of participants with a significant change between task initiation and termination was determined for each MMF indicator and muscle. The PLSR model explained 73% of the perceived exertion variance. Median frequency, mobility, spectral entropy, fuzzy entropy, and Higuchi fractal dimension had the greatest importance to predict perceived exertion and changed for 83.5% participants on average between task initiation and termination for the anterior and medial deltoids. The amplitude, activity, approximate, sample, and multiscale entropy, degree of multifractality, percent determinism and recurrent, correlation dimension, and largest Lyapunov exponent analysis MMF indicators were not efficient to assess MMF. Mobility, spectral entropy, fuzzy entropy, and Higuchi fractal dimension should be further considered to assess muscle fatigue and their combination with median frequency may further improve the assessment of muscle fatigue.

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Section: Activation Level Activity Approximate Sample and Multiscale ...supporting

confidence: 75%