Determination of muscle fatigue in SEMG signal using empirical mode decomposition

Chowdhury, Rubana H.; Reaz, Mamun Bin Ibne; Ali, Mohd

doi:10.1109/iecbes.2014.7047649

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…Due to its advantages, EMD has been successfully used in the analysis of non-linear and non-stationary biomedical signals including EMG [31,33,34]. These resultant IMFs can also be considered as different scales of the original signal for multi-scale analysis [25].…”

Section: Empirical Mode Decompositionmentioning

confidence: 99%

Fatiguing Effects on the Multi-Scale Entropy of Surface Electromyography in Children with Cerebral Palsy

Hong

Zhang

Ma³

et al. 2016

Entropy

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Abstract:The objective of this study was to investigate the effects of muscle fatigue on the multi-scale entropy of surface electromyography (EMG) in children with cerebral palsy (CP) and typical development (TD). Sixteen CP children and eighteen TD children participated in experiments where they performed upper limb cyclic lifting tasks following a muscle fatiguing process, while the surface EMG signals were recorded from their upper trapezius muscles. Multi-scale entropy (MSE) analyses of the surface EMG were applied by calculating sample entropy (SampEn) on individual intrinsic mode functions (IMFs) adaptively generated by empirical mode decomposition (EMD) of the original signal. The declining degree of the resultant MSE curve was found to reflect muscle fatigue level for all subjects, with its slope (purposely calculated over the first four scales) increasing significantly as the fatigue level increased. Further, such a slope increase was less significant for CP children as compared with TD children. Our findings confirmed that the decrease of muscle fiber conduction velocity (MFCV) and the increase of motor unit synchronization may be two possible factors induced by muscle fatigue, and further indicated that there appear to be some neuromuscular changes (such as MFCV decrease, motor unit synchronization increase, motor unit firing rates reduction, selective loss of larger motor units) that occur as a result of cerebral palsy. These changes may account for experimentally observed difference in fatiguing effects between subject groups. Our study provides an investigative tool to assess muscle fatigue as well as to help reveal complex neuropathological changes underlying the motor impairments of CP children.

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Section: Empirical Mode Decompositionmentioning

confidence: 99%

Fatiguing Effects on the Multi-Scale Entropy of Surface Electromyography in Children with Cerebral Palsy

Hong

Zhang

Ma³

et al. 2016

Entropy

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“…The results of the above studies indicate that the way to induce muscle fatigue may count, and it is better to select an endurance task similar to the test motion. The conclusion seems to be the common practice for the sEMG-related muscle fatigue studies, such as the studies that induced fatigue by running on a treadmill [29,30]/overground [31], walking [32], and cycling [33][34][35]. In this paper, we study the effect of muscle fatigue in walking, so long walking time on a treadmill was selected as the method to induce fatigue.…”

Section: Related Workmentioning

confidence: 99%

The Muscle Fatigue’s Effects on the sEMG-Based Gait Phase Classification: An Experimental Study and a Novel Training Strategy

Zhu

Wei

et al. 2021

Applied Sciences

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Surface Electromyography (sEMG) enables an intuitive control of wearable robots. The muscle fatigue-induced changes of sEMG signals might limit the long-term usage of the sEMG-based control algorithms. This paper presents the performance deterioration of sEMG-based gait phase classifiers, explains the deterioration by analyzing the time-varying changes of the extracted features, and proposes a training strategy that can improve the classifiers’ robustness against muscle fatigue. In particular, we first select some features that are commonly used in fatigue-related studies and use them to classify gait phases under muscle fatigue. Then, we analyze the time-varying characteristics of extracted features, with the aim of explaining the performance of the classifiers. Finally, we propose a training strategy that effectively improves the robustness against muscle fatigue, which contributes to an easy-to-use method. Ten subjects performing prolonged walking are recruited. Our study contributes to a novel perspective of designing gait phase classifiers under muscle fatigue.

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“…Fatigue in the muscles occurs when the cell lacks the energy to continue its activation [1]. Muscle fatigue can be defined as the inability to provide a certain amount of force required to complete a task [2]. Fatigue can also occur due to Parkinson's disease, endocrine disturbances, carcinoma, malnutrition, and immobilization [3].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Muscle Fatigue Using Electromyography Signals in Gastrocnemius Muscle during Isometric Plantar Flexion

Patel¹,

Makaram²,

Balasubramanian³

et al. 2018

IJBBB

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Fatigue during plantar flexion and the gastrocnemius muscles causes an increase in mediolateral sway and postural control impairment. The purpose of this study is to analyze the variation in the surface electromyography (sEMG) characteristics during isometric plantar flexion. For this, sEMG signals are recorded from the medial and lateral gastrocnemius muscle from 8 subjects and a minimum of 3 trials each, leading to a total of 25 trials per leg. The subjects performed isometric plantar flexion and are asked to hold the position until fatigue. The result shows that the endurance time of each subject vary. On comparing the endurance between legs, a marginal difference is observed. The recorded signal is nonstationary in nature. On visual inspection, the signals show an increase in amplitude with fatigue. The features such as RMS show an increase in value with 20.31% in right leg and 13.93% in the left leg for the signals from medial and lateral gastrocnemius. The proposed muscle fatigue index is a co-contraction based feature and provides better separation between non-fatigue and fatigue. The P-values obtained indicate that the proposed feature performs better than the conventional RMS value with a significance level of P<0.01. This study can be extended to analyze other neuromuscular conditions.

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Determination of muscle fatigue in SEMG signal using empirical mode decomposition

Cited by 6 publications

References 13 publications

Fatiguing Effects on the Multi-Scale Entropy of Surface Electromyography in Children with Cerebral Palsy

Fatiguing Effects on the Multi-Scale Entropy of Surface Electromyography in Children with Cerebral Palsy

The Muscle Fatigue’s Effects on the sEMG-Based Gait Phase Classification: An Experimental Study and a Novel Training Strategy

Analysis of Muscle Fatigue Using Electromyography Signals in Gastrocnemius Muscle during Isometric Plantar Flexion

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