sEMG Techniques to Detect and Predict Localised Muscle Fatigue

Almulla, Mohammed; Sepúlveda, Francisco; Colley, Martin

doi:10.5772/25678

Cited by 13 publications

(4 citation statements)

References 121 publications

(135 reference statements)

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“…On this basis, it can be concluded that in the last second of the measurement, the frequency values reach the fatigue threshold which the organism cannot exceed. Therefore, it is possible to develop the reports of authors Al-Mulla et al 8 who believe that there is an individual variability of muscle features and it is not possible to simply determine the load and time function precisely specifying the muscle fatigue threshold.…”

Section: Discussionmentioning

confidence: 99%

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Training induced fatigability assessed by sEMG in Pre-Olympic ice-skaters

Pakosz

Konieczny

2020

Sci Rep

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the aim of this study was to investigate the size of the change and asymmetry in fatigability of gluteus maximus muscles during endurance training in short-track. the research has taken into account the position of athletes during skating and the problem of fatigue and pain in these muscles. the research covered involved eight female athletes of the polish national team in short track, which had been prepared to the olympic Games in pyeongchang. the surface electromyography (seMG) system was used to measure fatigue of right and left gluteus maximus muscles, in the modified Biering-Sorensen test. The test was conducted five times during the training: before training, after warmup, and after each of 3 series of the endurance training. Comparing the mean frequency of the surface electromyography power spectrum of the test, statistically significant reduction of the average frequency value of the right muscle from 55.61 ± 7.08 to 48.64 ± 4.48 Hz and left muscle from 58.78 ± 4.98 to 53.18 ± 4.62 Hz was reported, which prove the muscle fatigue. In subsequent series tests, the sEMG signal frequency of begin decrease more than the end of the each measurement, which determines the fatigue threshold. The size of the d Cohen effect in fatigue drops along with subsequent five tests during the training. Skaters has higher frequency reduction of the right lower limb, which indicates its greater fatigue during skateing. the fatigue and asymmetry in muscle observed in short-track has implications for training and performance. Speed skating has two disciplines that differ in track length and skating technique. In short track, the distance of one lap is 111 m, which is less than one third of the skating track on a long track (400 m). Such a short distance forces the competitors to go along an oval track with high speed and small turn radiuses. During a race at a distance of 1,000 m the short track competitor, when turning, generates twice as large forces as on a long track 1. In order to obtain the best possible result in short track, a successful athlete should reduce his fatiguability. In addition, what also matters are the race tactics, psychological and technical aspects, maximum voluntary contraction force, reaction time and the quality of lower limb muscle coordination 2-4. General body fatigue can affect psychological and physiological aspects. One of the factors of physiological fatigue is muscle fatigue, which is defined as reduction in muscle capacity to perform work, after prior physical effort 5,6. Local muscle fatigue is concentrated mainly on a decrease in contraction force, that is inability of the muscle to generate proper capacity 7. This is an inevitable process, and attempts to reduce it are constantly made. In the literature analysing muscle fatigue, many variants of this process can be found 8 , which also uses surface electromyography and analysis of the sEMG signal frequency of the power spectrum. This method provides useful information on local muscle fatigue 9-12. When the muscle fatigue increases, the num...

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Section: Discussionmentioning

confidence: 99%

“…The average value of frequency (Hz) of gluteus maximus muscles of all series, measured for all athletes(1)(2)(3)(4)(5)(6)(7)(8).…”

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confidence: 99%

Training induced fatigability assessed by sEMG in Pre-Olympic ice-skaters

Pakosz

Konieczny

2020

Sci Rep

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“…Feature extraction is the stage where the dimensionality from the segments is reduced into useful information for classifiers, especially for the sEMG signals that have a stochastic nature [52]. The features analyzed in this work are presented in Table 1 with their respective parameters for implementation.…”

Section: Feature Extractionmentioning

confidence: 99%

Analysis of Influence of Segmentation, Features, and Classification in sEMG Processing: A Case Study of Recognition of Brazilian Sign Language Alphabet

Mendes

Freitas

Campos

et al. 2020

Sensors

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Sign Language recognition systems aid communication among deaf people, hearing impaired people, and speakers. One of the types of signals that has seen increased studies and that can be used as input for these systems is surface electromyography (sEMG). This work presents the recognition of a set of alphabet gestures from Brazilian Sign Language (Libras) using sEMG acquired from an armband. Only sEMG signals were used as input. Signals from 12 subjects were acquired using a MyoTM armband for the 26 signs of the Libras alphabet. Additionally, as the sEMG has several signal processing parameters, the influence of segmentation, feature extraction, and classification was considered at each step of the pattern recognition. In segmentation, window length and the presence of four levels of overlap rates were analyzed, as well as the contribution of each feature, the literature feature sets, and new feature sets proposed for different classifiers. We found that the overlap rate had a high influence on this task. Accuracies in the order of 99% were achieved for the following factors: segments of 1.75 s with a 12.5% overlap rate; the proposed set of four features; and random forest (RF) classifiers.

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“…Parameters such as the amplitude of the root mean square of the EMG signal increase with fatigue, as more motor units are recruited for the same amount of force produced (Merletti et al, 2004). The mean or median frequency of the power spectrum density decreases, as the velocity of action potentials is slowed down (Edwards and Lippold, 1956;Lindström et al, 1977;Al-Mulla et al, 2012). A complication with EMG is the quality of the signal to be assessed.…”

Section: Introductionmentioning

confidence: 99%

Central and Peripheral Shoulder Fatigue Pre-screening Using the Sigma–Lognormal Model: A Proof of Concept

Laurent

Plamondon

Begon

2020

Front. Hum. Neurosci.

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Background: Clinical tests for detecting central and peripheral shoulder fatigue are limited. The discrimination of these two types of fatigue is necessary to better adapt recovery intervention. The Kinematic Theory of Rapid Human Movements describes the neuromotor impulse response using lognormal functions and has many applications in pathology detection. The ideal motor control is modeled and a change in the neuromuscular system is reflected in parameters extracted according to this theory. Objective: The objective of this study was to assess whether a shoulder neuromuscular fatigue could be detected through parameters describing the theory, if there is the possibility to discriminate central from peripheral fatigue, and which handwriting test gives the most relevant information on fatigue. Methods: Twenty healthy participants performed two sessions of fast stroke handwriting on a tablet, before and after a shoulder fatigue. The fatigue was in internal rotation for one session and in external rotation during the other session. The drawings consisted of simple strokes, triangles, horizontal, and vertical oscillations. Parameters of these strokes were extracted according to the Sigma-Lognormal model of the Kinematic Theory. The evolution of each participant was analyzed through a U-Mann-Whitney test for individual comparisons. A Hotelling's T 2-test and a U-Mann-Whitney test were also performed on all participants to assess the group evolution after fatigue. Moreover, a correlation among parameters was calculated through Spearman coefficients to assess intrinsic parameters properties of each handwriting test. Results: Central and peripheral parameters were statistically different before and after fatigue with a possibility to discriminate them. Participants had various responses to fatigue. However, when considering the group, parameters related to the motor program execution showed significant increase in the handwriting tests after shoulder fatigue. The test of simple strokes permits to know more specifically where the fatigue comes from, whereas the oscillations tests were the most sensitive to fatigue.

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sEMG Techniques to Detect and Predict Localised Muscle Fatigue

Cited by 13 publications

References 121 publications

Training induced fatigability assessed by sEMG in Pre-Olympic ice-skaters

Training induced fatigability assessed by sEMG in Pre-Olympic ice-skaters

Analysis of Influence of Segmentation, Features, and Classification in sEMG Processing: A Case Study of Recognition of Brazilian Sign Language Alphabet

Central and Peripheral Shoulder Fatigue Pre-screening Using the Sigma–Lognormal Model: A Proof of Concept

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