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Author

Kiran Marri

Other affiliations: Indian Institutes of Technology
Bio: Kiran Marri is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Multifractal system & Muscle fatigue. The author has an hindex of 6, co-authored 12 publications receiving 64 citations. Previous affiliations of Kiran Marri include Indian Institutes of Technology.

Papers
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Journal Article
TL;DR: The calculated and experimental muscle discomfort zone closely matched in 72% of subjects indicating that multifractal technique may be a good method for detecting onset of fatigue.
Abstract: Prolonged and repeated fatigue conditions can cause muscle damage and adversely impact coordination in dynamic contractions. Hence it is important to determine the onset of muscle fatigue (OMF) in clinical rehabilitation and sports medicine. The aim of this study is to propose a method for analyzing surface electromyography (sEMG) signals and identify OMF using multifractal detrending moving average algorithm (MFDMA). Signals are recorded from biceps brachii muscles of twenty two healthy volunteers while performing standard curl exercise. The first instance of muscle discomfort during curl exercise is considered as experimental OMF. Signals are pre-processed and divided into 1-second epoch for MFDMA analysis. Degree of multifractality (DOM) feature is calculated from multifractal spectrum. Further, the variance of DOM is computed and OMF is calculated from instances of high peaks. The analysis is carried out by dividing the entire duration into six equal zones for time axis normalization. High peaks are observed in zones where subjects reported muscle discomfort. First muscle discomfort occurred in third and forth zones for majority of subjects. The calculated and experimental muscle discomfort zone closely matched in 72% of subjects indicating that multifractal technique may be a good method for detecting onset of fatigue. The experimental data may have an element of subjectivity in identifying muscle discomfort. This work can also be useful to analyze progressive changes in muscle dynamics in neuromuscular condition and co-contraction activity.

12 citations

Journal ArticleDOI
23 Jun 2016
TL;DR: It appears that these multifractal features extracted from the concentric and eccentric contractions can be useful in the assessment of surface electromyography signals in sports medicine and training and also in rehabilitation programs.
Abstract: Muscle contractions can be categorized into isometric, isotonic (concentric and eccentric) and isokinetic contractions. The eccentric contractions are very effective for promoting muscle hypertrophy and produce larger forces when compared to the concentric or isometric contractions. Surface electromyography signals are widely used for analyzing muscle activities. These signals are nonstationary, nonlinear and exhibit self-similar multifractal behavior. The research on surface electromyography signals using multifractal analysis is not well established for concentric and eccentric contractions. In this study, an attempt has been made to analyze the concentric and eccentric contractions associated with biceps brachii muscles using surface electromyography signals and multifractal detrended moving average algorithm. Surface electromyography signals were recorded from 20 healthy individuals while performing a single curl exercise. The preprocessed signals were divided into concentric and eccentric cycles and in turn divided into phases based on range of motion: lower (0°-90°) and upper (>90°). The segments of surface electromyography signal were subjected to multifractal detrended moving average algorithm, and multifractal features such as strength of multifractality, peak exponent value, maximum exponent and exponent index were extracted in addition to conventional linear features such as root mean square and median frequency. The results show that surface electromyography signals exhibit multifractal behavior in both concentric and eccentric cycles. The mean strength of multifractality increased by 15% in eccentric contraction compared to concentric contraction. The lowest and highest exponent index values are observed in the upper concentric and lower eccentric contractions, respectively. The multifractal features are observed to be helpful in differentiating surface electromyography signals along the range of motion as compared to root mean square and median frequency. It appears that these multifractal features extracted from the concentric and eccentric contractions can be useful in the assessment of surface electromyography signals in sports medicine and training and also in rehabilitation programs.

11 citations

Proceedings ArticleDOI
01 Aug 2015
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.
Abstract: Muscle fatigue is commonly experienced in both normal and subjects with neuromuscular disorders. Surface electromyography (sEMG) signals are useful technique for analyzing muscle fatigue. sEMG signals are highly nonstationary and exhibit complex nonlinear characteristic in dynamic contractions. In this work, an attempt is made to classify sEMG signals recorded from biceps brachii muscles in nonfatigue and fatigue using multifractal features. The signals are recorded from 26 healthy normal adult subjects while performing standard experimental protocol involving dynamic contraction. The preprocessed signals are divided into six segments. The first and last segments are considered as nonfatigue and fatigue conditions respectively. The signals are then subjected to multifractal detrended moving average algorithm and eight multifractal features are extracted from both conditions. Further, information gain (IG) based ranking is used for reducing the number of features. Three different classification algorithms are employed namely, k-Nearest Neighbor algorithm (kNN), Naive Bayes (NB) and logistic regression (LR) for classification. The results show that signals exhibit multifractal characteristics and the multifractal features such as, generalized Hurst exponent, degree of multifractality and scaling exponent slope are significantly different in fatigue condition. The Hurst exponent for small fluctuation and degree of multifractality are found to be very highly significant feature. The LR and kNN classifier performance gave an accuracy of 84% and 82% respectively. This method of using multifractal features appears to be useful in classifying sEMG signals in dynamic contraction. This study can also be extended to classify fatigue condition in various neuromuscular disorders.

10 citations

Proceedings ArticleDOI
23 May 2014
TL;DR: In this paper, the spectral correlation density function (SCD) was used to discriminate fatigue and non-fatigue conditions using cyclostationary analysis and two features namely sum and entropy were extracted from SCD coefficients.
Abstract: Fatigue is a phenomena associated with reduction of maximum force in muscles while performing functional activities. Assessment of fatigue is important in the field of clinical conditions, rehabilitation and sports. In this work, an attempt has been made to discriminate fatigue and non-fatigue conditions using cyclostationary analysis. The surface electromyography (sEMG) signals are recorded from triceps brachii muscles of 15 adult normal subjects during dynamic contractions. These signals are subjected to cyclostationary analysis using spectral correlation density function (SCD). Fast Fourier transform accumulation algorithm is adopted to estimate the SCD. To distinguish fatigue and non-fatigue conditions, two features namely sum and entropy are extracted from SCD coefficients. The results show that magnitude and number of peaks in SCD spectrum are higher in fatigue conditions. Moreover, the extracted features are distinct in muscle contraction zones. This method appears to be useful in analyzing the sEMG signals during muscle fatigue condition.

8 citations


Cited by
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Journal ArticleDOI
TL;DR: The proposed methods are found to be capable of handling the nonstationary and multicomponent variations of sEMG signals recorded in dynamic fatiguing contractions and the combination of EMBD- polynomial kernel based SVM could be used to detect the dynamic muscle fatigue conditions.

103 citations

Journal ArticleDOI
Qi Wang1, Dai Zhang1, Ying-Yu Zhao1, Hong Hai1, Yue-Wen Ma1 
TL;DR: HF-rTMS over the contralesional cortex was superior to low-frequency rTMS and sham stimulation in promoting motor recovery in patients with severe hemiplegic stroke by acting on contraleional cortex plasticity.

33 citations

Journal ArticleDOI
01 Nov 2019
TL;DR: In this paper, the authors discussed the EMG signal processing and the methods of detection muscles fatigue with three domains (time domain, frequency domain, and time-frequency domain) based on EMG signals that are collected from the muscles during dynamic and static movements.
Abstract: Muscle fatigue is described by the decline in muscle maximum force during contraction. The fatigue occurs in the nervous or muscle fibre cells. The nerves produce a high-frequency signal to gain the maximum contraction, but it cannot sustain the high frequency signal for a long time, and that leads to a decline in muscle force. The surface Electromyography (EMG) is the dominant method to detect muscle fatigue because the EMG signals give more information about the muscle's activities. This review discussed the EMG signal processing and the methods of detection muscles fatigue with three domains (time domain, frequency domain, and time-frequency domain) based on EMG signals that are collected from the muscles during dynamic and static movements.

29 citations

Journal ArticleDOI
TL;DR: This study will guide and direct new researchers to areas that remain hidden in the human triceps brachii muscle through surface electromyography (sEMG) observations and identify areas that require further in-depth research.

26 citations

Journal ArticleDOI
TL;DR: In this paper, the authors used symbolic transition networks (SNTN) to extract myoelectric signals from the biceps brachii muscle of 52 healthy participants during dynamic contractions.
Abstract: The measurement and analysis of the electrical activity of muscle provide information that aids in the control of assistive devices. The investigation of these signals under varied physiological conditions, such as fatigue, enables reliable control. Muscle fatigue is a muscular condition associated with loss of muscle function. The early detection of muscle fatigue using surface Electromyography (sEMG)-based electrical measurements is challenging due to the nonlinear variations of the signal. In this work, an attempt has been made to understand the effect of dynamic nonlinear variations in the characteristics of the signal to develop a reliable fatigue index. The methodology involves the acquisition of myoelectric signals from the biceps brachii muscle of 52 healthy participants during dynamic contractions. The acquired signals are preprocessed and are analyzed with symbolic transition networks. Features such as symbolic entropy, network entropy, uniformity, and, minimum and maximum effective degrees (EDs) are extracted for further analysis. Appropriate decision boundaries are established for each feature using receiver operator characteristics (ROCs) and machine learning algorithms. The results indicate a decrease in signal complexity with fatigue. All the extracted features show a statistically significant difference (p < 0.05) between both conditions. Symbolic entropy achieves an accuracy of 89%, and the maximum ED yields an accuracy of 90% based on thresholds estimated with ROC. Furthermore, only a marginal improvement is observed with the combination of these features and the Naive Bayes classifier. It appears that the proposed maximum ED could be used as a reliable fatigue index in real-time applications for the improvement of rehabilitation efficacy.

25 citations