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Proceedings ArticleDOI

Analyzing the influence of curl speed in fatiguing biceps brachii muscles using sEMG signals and multifractal detrended moving average algorithm

01 Aug 2016-Vol. 2016, pp 3658-3661
TL;DR: Results show that multifractal features are able to differentiate sEMG signals in fatiguing conditions and the multifractality increased with faster curls as compared with slower curl speed by 12%.
Abstract: In this work, an attempt has been made to analyze surface electromyography (sEMG) signals of fatiguing biceps brachii muscles at different curl speeds using multifractal detrended moving average (MFDMA) algorithm. For this purpose, signals are recorded from fifty eight healthy subjects while performing curl exercise at their comfortable speed until fatigue. The signals of first and last curls are considered as nonfatigue and fatigue conditions, respectively. Further, the number of curls performed by each subject and the endurance time is used for computing the normalized curl speed. The signals are grouped into fast, medium and slow using curl speeds. The curl segments are subjected to MFDMA to derive degree of multifractality (DOM), maximum singularity exponent (MXE) and exponent length multifractality index (EMX). The results show that multifractal features are able to differentiate sEMG signals in fatiguing conditions. The multifractality increased with faster curls as compared with slower curl speed by 12%. High statistical significance is observed using EMX and DOM values between curl speed and fatigue conditions. It appears that this method of analyzing sEMG signals with curl speed can be useful in understanding muscle dynamics in varied neuromuscular conditions and sports medicine.
Citations
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Journal ArticleDOI
TL;DR: Evaluating different EHG segments for recognizing UCs using the convolutional neural network (CNN) could be used to determine the efficient EHg segments for recognize UC with the CNN.
Abstract: Background. Uterine contraction (UC) is the tightening and shortening of the uterine muscles which can indicate the progress of pregnancy towards delivery. Electrohysterogram (EHG), which reflects uterine electrical activities, has recently been studied for UC monitoring. In this paper, we aimed to evaluate different EHG segments for recognizing UCs using the convolutional neural network (CNN). Materials and Methods. In the open-access Icelandic 16-electrode EHG database (122 recordings from 45 pregnant women), 7136 UC and 7136 non-UC EHG segments with the duration of 60 s were manually extracted from 107 recordings of 40 pregnant women to develop a CNN model. A fivefold cross-validation was applied to evaluate the CNN based on sensitivity (SE), specificity (SP), and accuracy (ACC). Then, 1056 UC and 1056 non-UC EHG segments were extracted from the other 15 recordings of 5 pregnant women. Furthermore, the developed CNN model was applied to identify UCs using different EHG segments with the durations of 10 s, 20 s, and 30 s. Results. The CNN achieved the average SE, SP, and ACC of 0.82, 0.93, and 0.88 for a 60 s EHG segment. The EHG segments of 10 s, 20 s, and 30 s around the TOCO peak achieved higher SE and ACC than the other segments with the same duration. The values of SE from 20 s EHG segments around the TOCO peak were higher than those from 10 s to 30 s EHG segments on the same side of the TOCO peak. Conclusion. The proposed method could be used to determine the efficient EHG segments for recognizing UC with the CNN.

14 citations


Cites methods from "Analyzing the influence of curl spe..."

  • ...-erefore, nonlinear methods including time reversibility, sample entropy, Lyapunov exponents and delay vector variance [11], nonlinear interdependencies [10], and multifractal analysis [12, 13] are useful for EHG analysis....

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Journal ArticleDOI
TL;DR: In this article , the authors investigated the excitation of the biceps brachii and anterior deltoid during bilateral biceps curl performed using the straight vs. EZ barbell and with or without flexing the arms.
Abstract: The present study investigated the excitation of the biceps brachii and anterior deltoid during bilateral biceps curl performed using the straight vs. EZ barbell and with or without flexing the arms. Ten competitive bodybuilders performed bilateral biceps curl in non-exhaustive 6-rep sets using 8-RM in four variations: using the straight barbell flexing (STflex) or not flexing the arms (STno-flex) or the EZ barbell flexing (EZflex) or not flexing the arms (EZno-flex). The ascending and descending phases were separately analyzed using the normalized root mean square (nRMS) collected using surface electro-myography. For the biceps brachii, during the ascending phase, a greater nRMS was observed in STno-flex vs. EZno-flex (+1.8%, effect size [ES]: 0.74), in STflex vs. STno-flex (+17.7%, ES: 3.93) and in EZflex vs. EZno-flex (+20.3%, ES: 5.87). During the descending phase, a greater nRMS was observed in STflex vs. EZflex (+3.8%, ES: 1.15), in STno-flex vs. STflex (+2.8%, ES: 0.86) and in EZno-flex vs. EZflex (+8.1%, ES: 1.81). The anterior deltoid showed distinct excitation based on the arm flexion/no-flexion. A slight advantage in biceps brachii excitation appears when using the straight vs. EZ barbell. Flexing or not flexing the arms seems to uniquely excite the biceps brachii and anterior deltoid. Practitioners should consider including different bilateral biceps barbell curls in their routine to vary the neural and mechanical stimuli.

2 citations

Journal ArticleDOI
TL;DR: In this article , the authors applied multifractal detrended fluctuation analysis (MFDFA) to surface EMG to detect neuromuscular changes after realistic warm-up procedures that was followed by various stretching exercises.
Abstract: ABSTRACT Introduction This study aimed to apply multifractal detrended fluctuation analysis (MFDFA) to surface EMG to detect neuromuscular changes after realistic warm-up procedures that was followed by various stretching exercises. Methods Sixteen volunteers conducted two experimental sessions. Testing included two maximal voluntary contractions before, after a standardized warm-up, and after a stretching exercise (static or neurodynamic nerve gliding technique). EMG was registered on biceps femoris and semitendinosus muscles. EMG was analyzed using different parameters obtained from the singularity Hurst exponent function and multifractal power spectrum (both obtained from the multifractal detrended fluctuation analysis). Results The Hurst exponent, α maximum, and peak value of the multifractal spectrum significantly decreased after warm-up as compared with baseline for both biceps femoris (P = 0.003, P = 0.006, and P = 0.003, respectively) and semitendinosus (P = 0.006, P = 0.013 and P = 0.01, respectively) muscles. No further alteration was obtained after static or neurodynamic nerve gliding stretching as compared with post-warm-up (P = 1.0). No significant difference was obtained for Hurst exponent range, width, and asymmetry of the multifractal spectrum (P > 0.05). Conclusions From the present results, EMG depicted multifractal features sensitive to detect neuromuscular changes after a warm-up procedure. An increase in multiscale complexity is revealed after warm-up without any further alteration after stretching. The multifractal spectrum depicted dominant small fluctuations that shifted toward slightly larger fluctuations that could be attributed to motor unit recruitment.

1 citations

Journal ArticleDOI
TL;DR: In this article , a green tea extract from Camellia sinensis was used to prevent muscle damage and preserve neuromuscular activity in a condition of cumulative fatigue, which is an unwanted result of consecutive days of exercise.

1 citations

Journal ArticleDOI
01 Mar 2023-Sports
TL;DR: In this paper , the authors analyzed the excitation of biceps brachii, brachioradialis, and anterior deltoid during bilateral biceps curl performed with different handgrips.
Abstract: The current study analyzed the excitation of biceps brachii, brachioradialis, and anterior deltoid during bilateral biceps curl performed with different handgrips. Ten competitive bodybuilders performed bilateral biceps curl in non-exhaustive 6-rep sets using 8-RM with the forearm in supinated, pronated, and neutral positions. The ascending and descending phase of each variation was separately analyzed using the normalized root mean square collected using surface electromyography. During the ascending phase, (i) biceps brachii excitation was greater with the supinated compared to the pronated [+19(7)%, ES: 2.60] and neutral handgrip [+12(9)%, ES: 1.24], (ii) the brachioradialis showed greater excitation with the supinated compared to the pronated [+5(4)%, ES: 1.01] and neutral handgrip [+6(5)%, ES: 1.10], (iii) the anterior deltoid excitation was greater with the pronated and neutral handgrip compared to the supinated condition [+6(3)% and +9(2)%, ES: 2.07 and 3.18, respectively]. During the descending phase, the anterior deltoid showed greater excitation in the pronated compared to the supinated handgrip [+5(4)%, ES: 1.02]. Changing the handgrips when performing biceps curl induces specific variations in biceps brachii and brachioradialis excitation and requires different anterior deltoid interventions for stabilizing the humeral head. Practitioners should consider including different handgrips in the biceps curl routine to vary the neural and mechanical stimuli.
References
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Journal ArticleDOI
TL;DR: A common body of knowledge has been created on SEMG sensors and sensor placement properties as well as practical guidelines for the proper use of SEMG.

5,044 citations


"Analyzing the influence of curl spe..." refers methods in this paper

  • ...Two surface electrodes (Ag/Ag-Cl; 2 cm inter-electrode distance; 1 cm diameter) which was placed on the belly of biceps brachii muscles and one reference electrode on the elbow region as per SENIAM standards [11]....

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Journal ArticleDOI
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).
Abstract: We develop a method for the multifractal characterization of nonstationary time series, which is based on a generalization of the detrended fluctuation analysis (DFA). We relate our multifractal DFA method to the standard partition function-based multifractal formalism, and prove that both approaches are equivalent for stationary signals with compact support. By analyzing several examples we show that the new method can reliably determine the multifractal scaling behavior of time series. By comparing the multifractal DFA results for original series with those for shuffled series we can distinguish multifractality due to long-range correlations from multifractality due to a broad probability density function. We also compare our results with the wavelet transform modulus maxima method, and show that the results are equivalent.

2,967 citations


"Analyzing the influence of curl spe..." refers background or methods in this paper

  • ...The multifractal detrended fluctuation analysis (MFDFA) is based on polynomial selection for analyzing multifractal properties [6]....

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  • ...This step handles the small and large fluctuations in the time series which is represented as negative and positive orders [6]....

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Journal ArticleDOI
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).
Abstract: We develop a method for the multifractal characterization of nonstationary time series, which is based on a generalization of the detrended fluctuation analysis (DFA). We relate our multifractal DFA method to the standard partition function-based multifractal formalism, and prove that both approaches are equivalent for stationary signals with compact support. By analyzing several examples we show that the new method can reliably determine the multifractal scaling behavior of time series. By comparing the multifractal DFA results for original series to those for shuffled series we can distinguish multifractality due to long-range correlations from multifractality due to a broad probability density function. We also compare our results with the wavelet transform modulus maxima (WTMM) method, and show that the results are equivalent.

1,891 citations

BookDOI
28 Jan 2005
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.
Abstract: Introduction. Contributors. 1 BASIC PHYSIOLOGY AND BIOPHYSICS OF EMG SIGNAL GENERATION (T. Moritani, D. Stegeman, R. Merletti). 1.1 Introduction. 1.2 Basic Physiology of Motor Control and Muscle Contraction. 1.3 Basic Electrophysiology of the Muscle Cell Membrane. References. 2 NEEDLE AND WIRE DETECTION TECHNIQUES (J. V. Trontelj, J. Jabre, M. Mihelin). 2.1 Anatomical and Physiological Background of Intramuscular Recording. 2.2 Recording Characteristics of Needle Electrodes. 2.3 Conventional Needle EMG. 2.4 Special Needle Recording Techniques. 2.5 Physical Characteristics of Needle EMG Signals. 2.6 Recording Equipment. References. 3 DECOMPOSITION OF INTRAMUSCULAR EMG SIGNALS (D. W. Stashuk, D. Farina, K. Sogaard). 3.1 Introduction. 3.2 Basic Steps for EMG Signal Decomposition. 3.3 Evaluation of Performance of EMG Signal Decomposition Algorithms. 3.4 Applications of Results of the Decomposition of an Intramuscular EMG Signal. 3.5 Conclusions. References. 4 BIOPHYSICS OF THE GENERATION OF EMG SIGNALS (D. Farina, R. Merletti, D. F. Stegeman). 4.1 Introduction. 4.2 EMG Signal Generation. 4.3 Crosstalk. 4.4 Relationships between Surface EMG Features and Developed Force. 4.5 Conclusions. References. 5 DETECTION AND CONDITIONING OF THE SURFACE EMG SIGNAL (R. Merletti, H. Hermens). 5.1 Introduction. 5.2 Electrodes: Their Transfer Function. 5.3 Electrodes: Their Impedance, Noise, and dc Voltages. 5.4 Electrode Configuration, Distance, Location. 5.5 EMG Front--End Amplifiers. 5.6 EMG Filters: Specifications. 5.7 Sampling and A/D Conversion. 5.8 European Recommendations on Electrodes and Electrode Locations. References. 6 SINGLE--CHANNEL TECHNIQUES FOR INFORMATION EXTRACTION FROM THE SURFACE EMG SIGNAL (E. A. Clancy, D. Farina, G. Filligoi). 6.1 Introduction. 6.2 Spectral Estimation of Deterministic Signals and Stochastic Processes. 6.3 Basic Surface EMG Signal Models. 6.4 Surface EMG Amplitude Estimation. 6.5 Extraction of Information in Frequency Domain from Surface EMG Signals. 6.6 Joint Analysis of EMG Spectrum and Amplitude (JASA). 6.7 Recurrence Quantification Analysis of Surface EMG Signals. 6.8 Conclusions. References. 7 MULTI--CHANNEL TECHNIQUES FOR INFORMATION EXTRACTION FROM THE SURFACE EMG (D. Farina, R. Merletti, C. Disselhorst--Klug). 7.1 Introduction. 7.2 Spatial Filtering. 7.3 Spatial Sampling. 7.4 Estimation of Muscle--Fiber Conduction Velocity. 7.5 Conclusions. References. 8 EMG MODELING AND SIMULATION (D. F. Stegeman, R. Merletti, H. J. Hermens). 8.1 Introduction. 8.2 Phenomenological Models of EMG. 8.3 Elements of Structure--Based SEMG Models. 8.4 Basic Assumptions. 8.5 Elementary Sources of Bioelectric Muscle Activity. 8.6 Fiber Membrane Activity Profiles, Their Generation, Propagation, and Extinction. 8.7 Structure of the Motor Unit. 8.8 Volume Conduction. 8.9 Modeling EMG Detection Systems. 8.10 Modeling Motor Unit Recruitment and Firing Behavior. 8.11 Inverse Modeling. 8.12 Modeling of Muscle Fatigue. 8.13 Other Applications of Modeling. 8.14 Conclusions. References. 9 MYOELECTRIC MANIFESTATIONS OF MUSCLE FATIGUE (R. Merletti, A. Rainoldi, D. Farina). 9.1 Introduction. 9.2 Definitions and Sites of Neuromuscular Fatigue. 9.3 Assessment of Muscle Fatigue. 9.4 How Fatigue Is Reflected in Surface EMG Variables. 9.5 Myoelectric Manifestations of Muscle Fatigue in Isometric Voluntary Contractions. 9.6 Fiber Typing and Myoelectric Manifestations of Muscle Fatigue. 9.7 Factors Affecting Surface EMG Variable. 9.8 Repeatability of Estimates of EMG Variables and Fatigue Indexes. 9.9 Conclusions. References. 10 ADVANCED SIGNAL PROCESSING TECHNIQUES (D. Zazula, S. Karlsson, C. Doncarli). 10.1 Introduction. 10.2 Theoretical Background. 10.3 Decomposition of EMG Signals. 10.4 Applications to Monitoring Myoelectric Manifestations of Muscle Fatigue. 10.5 Conclusions. Acknowledgment. References. 11 SURFACE MECHANOMYOGRAM (C. Orizio). 11.1 The Mechanomyogram (MMG): General Aspects during Stimulated and Voluntary Contraction. 11.2 Detection Techniques and Sensors Comparison. 11.3 Comparison between Different Detectors. 11.4 Simulation. 11.5 MMG Versus Force: Joint and Adjunct Information Content. 11.6 MMG Versus EMG: Joint and Adjunct Information Content. 11.7 Area of Application. References. 12 SURFACE EMG APPLICATIONS IN NEUROLOGY (M. J. Zwarts, D. F. Stegeman, J. G. van Dijk). 12.1 Introduction. 12.2 Central Nervous System Disorders and SEMG. 12.3 Compound Muscle Action Potential and Motor Nerve Conduction. 12.4 CMAP Generation. 12.5 Clinical Applications. 12.6 Pathological Fatigue. 12.7 New Avenues: High--Density Multichannel Recording. 12.8 Conclusion. References. 13 APPLICATIONS IN ERGONOMICS (G. M. Hagg, B. Melin, R. Kadefors). 13.1 Historic Perspective. 13.2 Basic Workload Concepts in Ergonomics. 13.3 Basic Surface EMG Signal Processing. 13.4 Load Estimation and SEMG Normalization and Calibration. 13.5 Amplitude Data Reduction over Time. 13.6 Electromyographic Signal Alterations Indicating Muscle Fatigue in Ergonomics. 13.7 SEMG Biofeedback in Ergonomics. 13.8 Surface EMG and Musculoskeletal Disorders. 13.9 Psychological Effects on EMG. References. 14 APPLICATIONS IN EXERCISE PHYSIOLOGY (F. Felici). 14.1 Introduction. 14.2 A Few "Tips and Tricks". 14.3 Time and Frequency Domain Analysis of sEMG: What Are We Looking For? 14.4 Application of sEMG to the Study of Exercise. 14.5 Strength and Power Training. 14.6 Muscle Damage Studied by Means of sEMG. References. 15 APPLICATIONS IN MOVEMENT AND GAIT ANALYSIS (C. Frigo, R. Shiavi). 15.1 Relevance of Electromyography in Kinesiology. 15.2 Typical Acquisition Settings. 15.3 Study of Motor Control Strategies. 15.4 Investigation on the Mechanical Effect of Muscle Contraction. 15.5 Gait Analysis. 15.6 Identification of Pathophysiologic Factors. 15.7 Workload Assessment in Occupational Biomechanics. 15.8 Biofeedback. 15.9 The Linear Envelope. 15.10 Information Enhancement through Multifactorial Analysis. References. 16 APPLICATIONS IN REHABILITATION MEDICINE AND RELATED FIELDS (A. Rainoldi, R. Casale, P. Hodges, G. Jull). 16.1 Introduction. 16.2 Electromyography as a Tool in Back and Neck Pain. 16.3 EMG of the Pelvic Floor: A New Challenge in Neurological Rehabilitation. 16.4 Age--Related Effects on EMG Assessment of Muscle Physiology. 16.5 Surface EMG and Hypobaric Hipoxia. 16.6 Microgravity Effects on Neuromuscular System. References. 17 BIOFEEDBACK APPLICATIONS (J. R. Cram). 17.1 Introduction. 17.2 Biofeedback Application to Impairment Syndromes. 17.3 SEMG Biofeedback Techniques. 17.4 Summary. References. 18 CONTROL OF POWERED UPPER LIMB PROSTHESES (P. A. Parker, K. B. Englehart, B. S. Hudgins). 18.1 Introduction. 18.2 Myoelectric Signal as a Control Input. 18.3 Conventional Myoelectric Control. 18.4 Emerging MEC Strategies. 18.5 Summary. References. Index.

1,078 citations


"Analyzing the influence of curl spe..." refers methods in this paper

  • ...Surface electromyography (sEMG) is a popular noninvasive technique for analyzing muscle fatigue [3]....

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Journal ArticleDOI
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.
Abstract: The detrending moving average (DMA) algorithm is a widely used technique to quantify the long-term correlations of nonstationary time series and the long-range correlations of fractal surfaces, which contains a parameter $\ensuremath{\theta}$ determining the position of the detrending window. We develop multifractal detrending moving average (MFDMA) algorithms for the analysis of one-dimensional multifractal measures and higher-dimensional multifractals, which is a generalization of the DMA method. The performance of the one-dimensional and two-dimensional MFDMA methods is investigated using synthetic multifractal measures with analytical solutions for backward $(\ensuremath{\theta}=0)$, centered $(\ensuremath{\theta}=0.5)$, and forward $(\ensuremath{\theta}=1)$ detrending windows. We find that the estimated multifractal scaling exponent $\ensuremath{\tau}(q)$ and the singularity spectrum $f(\ensuremath{\alpha})$ are in good agreement with the theoretical values. In addition, the backward MFDMA method has the best performance, which provides the most accurate estimates of the scaling exponents with lowest error bars, while the centered MFDMA method has the worse performance. It is found that the backward MFDMA algorithm also outperforms the multifractal detrended fluctuation analysis. The one-dimensional backward MFDMA method is applied to analyzing the time series of Shanghai Stock Exchange Composite Index and its multifractal nature is confirmed.

374 citations


"Analyzing the influence of curl spe..." refers methods in this paper

  • ...The MFDMA algorithm has been introduced to extract the multiple fractal dimensions within the time series [12]....

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