Shoulder electromyography-based indicators to assess manifestation of muscle fatigue during laboratory-simulated manual handling task

Goubault, Étienne; Martinez, Romain; Bouffard, Jason; Dowling-Medley, Jennifer; Begon, Mickaël; Maso, Fabien Dal

doi:10.1080/00140139.2021.1958013

Cited by 10 publications

(10 citation statements)

References 74 publications

(128 reference statements)

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“…Activation levels were obtained from 9 Hz low-pass filtering of the full-wave rectified EMG signals normalized by the maximum voluntary muscle activation [24] obtained using the average of the maximum 2-sec non-consecutive window across all MVIC tests.…”

Section: ∑ 𝑇𝐹𝑅(𝑡) 𝑙𝑜𝑔 [𝑇𝐹𝑅 (𝑡)] 𝑛 𝑡=1mentioning

confidence: 99%

“…However, as stated by Rampichini et al, [28], future standardized fatiguing protocols are needed to confirm whether the largest Lyapunov exponent could be an To this end, an interesting approach is to predict the rate of perceived exertion (RPE), which is known to increase with muscle fatigue [61], from MMF indicators. Interestingly, several studies have also shown a close relationship between the RPE [62] and MMF indicators [24,[63][64][65][66][67][68]. For instance, Goubault et al, [24] used correlation analyses between six MMF indicators and RPE scores assessed using the CR-10 Borg scale [69] during a laboratory simulated manual handling task.…”

Section: Introductionmentioning

confidence: 99%

“…Interestingly, several studies have also shown a close relationship between the RPE [62] and MMF indicators [24,[63][64][65][66][67][68]. For instance, Goubault et al, [24] used correlation analyses between six MMF indicators and RPE scores assessed using the CR-10 Borg scale [69] during a laboratory simulated manual handling task. They showed that spectral entropy, median f requency, and mobility were the MMF indicators that explained the largest percentage of the RPE variance, with R-square ranging from 11% to 39%.…”

Section: Introductionmentioning

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: ∑ 𝑇𝐹𝑅(𝑡) 𝑙𝑜𝑔 [𝑇𝐹𝑅 (𝑡)] 𝑛 𝑡=1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

What are the best indicators of myoelectric manifestation of fatigue?

Ndam

Goubault

Moyen-Sylvestre

et al. 2023

Preprint

Self Cite

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“…With the occurrence of muscle fatigue, the time domain features of sEMG generally show an upward trend over time ( Goubault et al, 2022 ). RMS and iEMG not only reflect the amplitude changes of the sEMG signal in the time domain but also clearly reflect the biomechanical properties and muscle energy changes in the exercise process ( Silvetti et al, 2017 ; Wu et al, 2017 ).…”

Section: Time-domain Feature Analysismentioning

confidence: 99%

Application of Surface Electromyography in Exercise Fatigue: A Review

Sun

Liu

Lin

et al. 2022

Front. Syst. Neurosci.

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Exercise fatigue is a common physiological phenomenon in human activities. The occurrence of exercise fatigue can reduce human power output and exercise performance, and increased the risk of sports injuries. As physiological signals that are closely related to human activities, surface electromyography (sEMG) signals have been widely used in exercise fatigue assessment. Great advances have been made in the measurement and interpretation of electromyographic signals recorded on surfaces. It is a practical way to assess exercise fatigue with the use of electromyographic features. With the development of machine learning, the application of sEMG signals in human evaluation has been developed. In this article, we focused on sEMG signal processing, feature extraction, and classification in exercise fatigue. sEMG based multisource information fusion for exercise fatigue was also introduced. Finally, the development trend of exercise fatigue detection is prospected.

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