In this work, an attempt has been made to analyze surface electromyography (sEMG) signals in dynamic contraction using multifractal detrending moving average algorithm (MFDMA). The signals are recorded from biceps brachii muscles of twenty two healthy participants using a standard experimental protocol. The recorded sEMG signals are pre-processed and normalized by dividing the time axis into six equal segments. The first segment and sixth segment are considered as nonfatigue and fatigue conditions for analysis. The signals are subjected to MFDMA and verified to test multifractal properties of biceps brachii muscles using scaling exponent, generalized Hurst exponent and multifractal spectrum in both nonfatigue and fatigue conditions. Each multifractal spectrum is characterized by calculating three features namely peak exponent (PEV), degree of multifractality (DOM) and mean multifractal spectral exponent (MSE). The variation of multifractal spectral features in fatigue conditions are analyzed using ANOVA and Tukey test. The results of scaling exponent function and generalized Hurst exponent function indicated multifractal characteristics for sEMG signals in dynamic contractions. DOM increased from 0.56 to 0.96 and MSE increased from 0.54 to 0.75 in nonfatigue and fatigue conditions respectively. It appears that this method is useful in analyzing fatigue and nonfatigue conditions associated with muscle mechanics using non-invasive sEMG recordings. This study can be useful in field of clinical studies, rehabilitation, prosthetics control and sports medicine. 

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.

Muscle fatigue is a neuromuscular condition experienced during daily activities. This phenomenon is generally characterized using surface electromyography (sEMG) signals and has gained a lot of interest in the fields of clinical rehabilitation, prosthetics control, and sports medicine. sEMG signals are complex, nonstationary and also exhibit self-similarity fractal characteristics. In this work, an attempt has been made to differentiate sEMG signals in nonfatigue and fatigue conditions during dynamic contraction using multifractal analysis. sEMG signals are recorded from biceps brachii muscles of 42 healthy adult volunteers while performing curl exercise. The signals are preprocessed and segmented into nonfatigue and fatigue conditions using the first and last curls, respectively. The multifractal detrended moving average algorithm (MFDMA) is applied to both segments, and multifractal singularity spectrum (SSM) function is derived. Five conventional features are extracted from the singularity spectrum. Twenty-five new features are proposed for analyzing muscle fatigue from the multifractal spectrum. These proposed features are adopted from analysis of sEMG signals and muscle fatigue studies performed in time and frequency domain. These proposed 25 feature sets are compared with conventional five features using feature selection methods such as Wilcoxon rank sum, information gain (IG) and genetic algorithm (GA) techniques. Two classification algorithms, namely, k-nearest neighbor (k-NN) and logistic regression (LR), are explored for differentiating muscle fatigue. The results show that about 60% of the proposed features are statistically highly significant and suitable for muscle fatigue analysis. The results also show that eight proposed features ranked among the top 10 features. The classification accuracy with conventional features in dynamic contraction is 75%. This accuracy improved to 88% with k-NN-GA combination with proposed new feature set. Based on the results, it appears that the multifractal spectrum analysis with new singularity features can be used for clinical evaluation in varied neuromuscular conditions, and the proposed features can also be useful in analyzing other physiological time series.

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.

The application of surface electromyography (sEMG) technique for muscle fatigue studies is gaining importance in the field of clinical rehabilitation and sports medicine. These sEMG signals are highly nonstationary and exhibit scale-invariant self-similarity structure. The fractal analysis can estimate the scale invariance in the form of fractal dimension (FD) using monofractal (global single FD) or multifractal (local varying FD) algorithms. A comprehensive study of sEMG signal for muscle fatigue using both multifractal and monofractal FD features have not been established in the literature. In this work, an attempt has been made to differentiate sEMG signals recorded nonfatigue and fatigue conditions using monofractal and multifractal algorithms, and machine learning methods. For this purpose, sEMG signals have been recorded from biceps brachii muscles of fifty eight healthy subjects using a standard protocol. The signals of nonfatigue and fatigue region were subjected to eight monofractal (Higuchi, Katz, Petrosian, Sevcik, box counting, multi-resolution length, Hurst and power spectrum density) and two multifractal (detrended fluctuating and detrended moving average) algorithms and 28 FD features were extracted. The features were ranked using conventional and genetic algorithms, and a subset of FD features were further subjected to Naïve Bayes (NB), Logistic Regression (LR) and Multilayer Perceptron (MLP) classifiers. The results show that all fractal features are statistically significant. The classification accuracy using feature subset of conventional method is observed to be from 83% to 88%. The highest accuracy of 93.96% was achieved using genetic algorithm and LR classifier combination. The result demonstrated that the performance of multifractal FD features to be more suitable for sEMG signals as compared to monofractal FD features. The fractal analysis of sEMG signals appears to be a very promising biomarker for muscle fatigue classification and can be extended to detection of fatigue onset in varied neuromuscular conditions.