Analysis of Biceps Brachii Muscles in Dynamic Contraction Using sEMG Signals and Multifractal DMA Algorithm

Marri, Kiran; Swaminathan, Ramakrishnan

doi:10.12720/ijsps.4.1.79-85

Cited by 8 publications

(18 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The signals are found to be highly complex in nonfatigue stage and there is a reduction in complexity during fatigue conditions. The result of reducing complexity in fatigue conditions concurs with previous work reported on biceps brachii muscles [25]. The 3-D visual plots are generated with DET, VMAX and complexity features for representing the nonfatigue and fatigue trends.…”

Section: Discussionsupporting

confidence: 83%

Fatigue Analysis of Triceps Brachii Muscle using sEMG Signals and Recurrence Quantification Technique

Marri¹,

Swaminathan²

2016

JOLST

View full text Add to dashboard Cite

Analysis of surface electromyography (sEMG) signals under fatigue conditions in dynamic contraction is gaining clinical relevance in the field of rehabilitation, ergonomics and sports performance. In this work, an attempt is made to analyze the sEMG signals recorded from triceps brachii muscles using Recurrence Quantification Analysis (RQA). The signals are recorded from 21 healthy adults during dynamic contraction involving dumbbell curl exercise. The sEMG signals are pre-processed and segmented into six equal zones along the time scale. The first zone and sixth zone are considered as nonfatigue and fatigue condition respectively. The signals are then subjected to RQA for further analysis. Two standard RQA features namely determinism (DET) and maximum length of the vertical line (V MAX) are computed for analyzing nonfatigue and fatigue conditions. A new RQA feature known as complexity (CPX) is also introduced for sEMG signal analysis that is derived from determinism. The results of RQA features are statistically verified using ANOVA. All the three RQA features namely, DET, complexity and V MAX are found to be statistically highly significant. In the case of fatigue condition, DET and V MAX increased by 17% and 30% respectively. It appears that RQA method may be a useful technique in differentiating fatigue and nonfatigue conditions under varied dynamic muscle contractions. 

show abstract

Section: Discussionsupporting

confidence: 83%

Fatigue Analysis of Triceps Brachii Muscle using sEMG Signals and Recurrence Quantification Technique

Marri¹,

Swaminathan²

2016

JOLST

View full text Add to dashboard Cite

show abstract

“…The values generalized Hurst exponent for nonfatigue and fatigue conditions are represented. H q in both nonfatigue and fatigue conditions exhibits decreasing trends with increasing order and this indicates multifractal characteristics of sEMG signal [9]. In the case of fatigue condition, H MAX increased and H MIN decreased indicating an increase of smaller fluctuations and reduction of large fluctuations respectively.…”

Section: Resultsmentioning

confidence: 88%

“…The multifractal technique is scale invariant analysis of time series [6]. Multifractal analysis of sEMG signals have been reported in previous studies using spectral features [7]- [9]. In this work, sEMG signals are analyzed using multifractal detrended moving average (MFDMA) algorithm with Hurst exponent features [10].…”

Section: Introductionmentioning

confidence: 99%

Multifractal analysis of sEMG signals for fatigue assessment in dynamic contractions using Hurst exponents

Marri

Swaminathan

2015

2015 41st Annual Northeast Biomedical Engineering Conference (NEBEC)

View full text Add to dashboard Cite

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.

show abstract

“…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%

What are the best indicators of myoelectric manifestation of fatigue?

Ndam

Goubault

Moyen-Sylvestre

et al. 2023

Preprint

View full text Add to dashboard Cite

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.

show abstract

Analysis of Biceps Brachii Muscles in Dynamic Contraction Using sEMG Signals and Multifractal DMA Algorithm

Cited by 8 publications

References 24 publications

Fatigue Analysis of Triceps Brachii Muscle using sEMG Signals and Recurrence Quantification Technique

Fatigue Analysis of Triceps Brachii Muscle using sEMG Signals and Recurrence Quantification Technique

Multifractal analysis of sEMG signals for fatigue assessment in dynamic contractions using Hurst exponents

What are the best indicators of myoelectric manifestation of fatigue?

Contact Info

Product

Resources

About