Proceedings ArticleDOI
Multifractal analysis of sEMG signals for fatigue assessment in dynamic contractions using Hurst exponents
Kiran Marri,Ramakrishnan Swaminathan +1 more
- pp 1-2
TLDR
In this article, surface EMG signals recorded from biceps brachii muscles of 30 subjects were analyzed in dynamic fatigue conditions using multifractal techniques and the results indicate strength of multifractality is very high in fatigue condition and highly significant (p>2.7E-6) as compared to nonfatigue condition.Abstract:
Multifractal analysis are useful to characterize complex physiological time-series. In this work, surface EMG signals recorded from biceps brachii muscles of 30 subjects are analyzed in dynamic fatigue conditions using multifractal techniques. The signals are segmented into six zones for time normalization. The first and last zones are considered as nonfatigue and fatigue conditions. The preprocessed signals are subjected to multifractal analysis and Hurst exponent function is computed. Three features, namely maximum and minimum exponent and strength of multifractality are used for analyzing nonfatigue and fatigue regions. The results indicate strength of multifractality is very high in fatigue condition and highly significant (p>2.7E-6) as compared to nonfatigue condition. The multifractal Hurst features are found to be useful in analyzing sEMG signal characteristics and this work can be extended for studying neuromuscular conditions.read more
Citations
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Proceedings ArticleDOI
Classification of muscle fatigue using surface electromyography signals and multifractals
TL;DR: An attempt is made to classify sEMG signals recorded from biceps brachii muscles in nonfatigue and fatigue using multifractal features, which appears to be useful in classifying s EMG signals in dynamic contraction.
Journal ArticleDOI
Analyzing Origin of Multifractality of Surface Electromyography Signals in Dynamic Contractions
TL;DR: In this paper, the origin of multifractality of surface electromyography (sEMG) signals during dynamic contraction in nonfatigue and fatigue conditions was analyzed and the results indicated that sEMG signals exhibit multifractal behavior.
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Journal ArticleDOI
Fatigue Analysis of Triceps Brachii Muscle using sEMG Signals and Recurrence Quantification Technique
TL;DR: It appears that RQA method may be a useful technique in differentiating fatigue and nonfatigue conditions under varied dynamic muscle contractions.
Proceedings ArticleDOI
An LSTM-Attention-based Method to Muscle Fatigue Detection by Integrating Multi-Source sEMG Signals
TL;DR: In this article, a fatigue detection method to muscle fatigue detection based on integrating multi-source sEMG signals is proposed, where long shortterm memories (LSTM) and one attention layer are used as an inference model.
References
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Journal ArticleDOI
Multifractal Detrended Fluctuation Analysis of Nonstationary Time Series
Jan W. Kantelhardt,Jan W. Kantelhardt,Stephan Zschiegner,Eva Koscielny-Bunde,Eva Koscielny-Bunde,Shlomo Havlin,Shlomo Havlin,Armin Bunde,H. Eugene Stanley +8 more
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Journal ArticleDOI
Multifractal detrended fluctuation analysis of nonstationary time series
Jan W. Kantelhardt,Stephan Zschiegner,Eva Koscielny-Bunde,Armin Bunde,Shlomo Havlin,H. Eugene Stanley +5 more
TL;DR: In this article, the authors developed a method for the multifractal characterization of nonstationary time series, which is based on a generalization of the detrended fluctuation analysis (DFA).
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Electromyography. Physiology, engineering and non invasive applications
TL;DR: This work focuses on the development of models for Surface EMG Signal Generation based on the principles of Structure--Based SEMG models, which were developed in the context of motor control and Muscle Contraction.
Journal ArticleDOI
Detrending moving average algorithm for multifractals.
Gao-Feng Gu,Wei-Xing Zhou +1 more
TL;DR: The backward MFDMA algorithm is applied to analyzing the time series of Shanghai Stock Exchange Composite Index and its multifractal nature is confirmed, and it is found that the backward M FDMA algorithm also outperforms the multifractional detrended fluctuation analysis.
Journal ArticleDOI
Can muscle coordination be precisely studied by surface electromyography
TL;DR: The appropriateness of using EMG recordings for studying muscle coordination from EMG signals is discussed, and the main intrinsic drawbacks of the EMG technique are described and discussed.