Ramakrishnan Swaminathan

In this work, an attempt has been made to analyze surface electromyography signals (sEMG) by estimating maximum singular value. sEMG signals are recorded from biceps brachii muscles of 50 healthy volunteers during repetitive elbow flexion and extension exercise. Maximum singular values are estimated from the signals. The results show a decrease in MSV at the point of first muscle discomfort experienced by subjects. For most of the subjects, the point of first discomfort occur in fourth and fifth regions of the time axis. It appears that this method can be used to analyze progress of muscle condition towards fatigue.

In this work, an attempt has been made to analyze the cyclostationarity of surface electromyography (sEMG) signals recorded during dynamic contraction of biceps brachii muscle. Twenty five healthy adult volunteers have participated in this study. The recorded signals are preprocessed and segmented into three zones, namely, non-fatigue zone, first muscle discomfort zone and fatigue zone. These signals are subjected to strip spectral correlation algorithm to estimate the spectral correlation density (SCD). Cyclic domain profile (CDP) is extracted from the normalized SCD magnitude. The area of CDP is calculated for all the three zones. The results show that strip spectral correlation algorithm based SCD estimation is able to demonstrate the cyclostationary property of sEMG signal in all three zones. It is also observed that the normalized SCD magnitude spectrum is distinct for all cases. The CDP area shows the significant variation in fatigue zone. Further it appears that cyclostationarity based on SCD can be used to differentiate different neuromuscular and pathological conditions.

In this work, an attempt has been made to utilise the time-frequency spectrum obtained using generalised Warblet transform (GWT) for fatigue analysis. Signals are acquired from the biceps brachii muscles of 20 healthy volunteers during isometric contractions. The first and last 500 ms lengths of a signal are assumed as non-fatigue and fatigue zones respectively. Further, the signals from these zones are subjected to GWT for the computation of time-frequency spectrum. Features such as instantaneous mean frequency (IMNF), instantaneous median frequency (IMDF), instantaneous spectral entropy (ISPEn), and instantaneous spectral skewness (ISSkw) are estimated. The results show that the IMNF, IMDF and ISPEn increased by 24%, 34% and 36% respectively in non-fatigue condition. In contrast, 22% higher ISSkw is observed for fatigue condition. The statistical analysis indicates that the features are significant with p < 0.001. It appears that the current method is useful in analysing muscle fatigue disorders using sEMG signals.

Muscle fatigue analysis finds significant applications in the areas of biomechanics, sports medicine and clinical studies. Surface electromyography (sEMG) signals have wide application because of its non invasiveness. By nature, signals recorded using surface electrodes from muscles are highly nonstationary and random. The objective of this work is to analyze muscle related fatigue using sEMG signals and polynomial chirplet transform (PCT). sEMG signals are acquired from biceps brachii muscles of twenty volunteers (Mean (sd): age, 23.5 (4.3) years) in isometric contractions. The initial 500 ms is considered as nonfatigue and final 500 ms of the signals are considered as fatigue zone. Then signals are subjected to polynomial chirplet transform to estimate the time-frequency spectrum. Four features, instantaneous mean frequency (IsMNF), instantaneous median frequency (IsMDF), instantaneous spectral entropy (ISpEn) and instantaneous spectral skewness (ISSkw) are extracted for further analysis. Results show that the PCT is able to characterize the nonstationary and multi component nature of sEMG signals. The IsMNF, IsMDF, ISpEn are found to be high in nonfatigue conditions. Further, all the features are very distinct in muscle nonfatigue and fatigue conditions (p<0.001). This technique can be used in analyzing different neuromuscular disorders.

Analysis of neuromuscular fatigue finds various applications ranging from clinical studies to biomechanics. Surface electromyography (sEMG) signals are widely used for these studies due to its non-invasiveness. During cyclic dynamic contractions, these signals are nonstationary and cyclostationary. In recent years, several nonstationary methods have been employed for the muscle fatigue analysis. However, cyclostationary based approach is not well established for the assessment of muscle fatigue. In this work, cyclostationarity associated with the biceps brachii muscle fatigue progression is analyzed using sEMG signals and Spectral Correlation Density (SCD) functions. Signals are recorded from fifty healthy adult volunteers during dynamic contractions under a prescribed protocol. These signals are preprocessed and are divided into three segments, namely, non-fatigue, first muscle discomfort and fatigue zones. Then SCD is estimated using fast Fourier transform accumulation method. Further, Cyclic Frequency Spectral Density (CFSD) is calculated from the SCD spectrum. Two features, namely, cyclic frequency spectral area (CFSA) and cyclic frequency spectral entropy (CFSE) are proposed to study the progression of muscle fatigue. Additionally, degree of cyclostationarity (DCS) is computed to quantify the amount of cyclostationarity present in the signals. Results show that there is a progressive increase in cyclostationary during the progression of muscle fatigue. CFSA shows an increasing trend in muscle fatiguing contraction. However, CFSE shows a decreasing trend. It is observed that when the muscle progresses from non-fatigue to fatigue condition, the mean DCS of fifty subjects increases from 0.016 to 0.99. All the extracted features found to be distinct and statistically significant in the three zones of muscle contraction (p < 0.05). It appears that these SCD features could be useful in the automated analysis of sEMG signals for different neuromuscular conditions.

Models of surface electromyography (sEMG) signals are extensively used in muscle biomechanics to analyze and validate clinical investigation in healthy and pathological muscles of adult and elderly populations. sEMG is a noninvasive technique to measure electrical activity of skeletal muscles. In this work, an attempt has been made to analyze biceps brachii muscle in normal and pathological conditions for elderly people using modified sEMG model. Age associated changes such as reduction in number of motor units and ratio of type II fibers are already reported in the earlier model proposed by Arjunan et al. However, experimentally it was found that there is an increase in motor unit action potential duration (MUAPD) due to aging. This effect is incorporated in this present work to model sEMG signal. Influence of physiological changes such as loss of muscle fibers, increased fiber diameter variation and decreased MUAPD are also included in this sEMG model in myopathyic conditions. Features namely, root mean square (RMS), median frequency (MDF), entropy and fractal dimension (FD) is extracted from the signal in normal and pathological conditions. Results show that the modified sEMG model gives distinct values for both normal aging muscles and myopathic muscles. The corresponding variations in feature values are significant (p<0.05). The values of RMS, MDF and FD are found to be higher in myopathic conditions compared to normal aging. The description of the sEMG model, the method of analysis and the observations are presented in this work. It appears that this sEMG model can be used to analyze myopathic conditions in elderly populations.

In this work an attempt has been made to analyze the surface Electromyography signals recorded during dynamic contractions using quadratic time frequency distribution. Surface EMG signals are recorded from biceps brachii muscle in 50 healthy volunteers. These signals are subjected to Zhao-Atlas-Marks based Quadratic Time-Frequency Distribution (QTFD). Instantaneous median frequency (IMDF) and instantaneous mean frequency (IMNF) are estimated from the time frequency domain. In addition, IMDF are interpolated with time by using linear regression technique. The result shows that IMDF and IMNF are distinct in fatigue and non fatigue conditions and these parameters reduce significantly in fatigue case. Further, it is observed that IMDF decreases with time.

Exercise-induced muscle damage is a condition which results in the loss of muscle function due to overexertion. Muscle fatigue is a precursor of this phenomenon. The characterization of muscle fatigue plays a crucial role in preventing muscle damage. In this work, an attempt is made to develop signal processing methods to understand the dynamics of the muscle’s electrical properties. Surface electromyography signals are recorded from 50 healthy adult volunteers under dynamic curl exercise. The signals are preprocessed, and the first difference signal is computed. Furthermore, ascending and descending slopes are used to generate a binary sequence. The binary sequence of various motif lengths is analyzed using features such as the average symbolic occurrence, modified Shannon entropy, chi-square value, time irreversibility, maximum probability of pattern and forbidden pattern ratio. The progression of muscle fatigue is assessed using trend analysis techniques. The motif length is optimized to maximize the rho value of features. In addition, the first and the last zones of the signal are compared with standard statistical tests. The results indicate that the recorded signals differ in both frequency and amplitude in both inter- and intra-subjects along the period of the experiment. The binary sequence generated has information related to the complexity of the signal. The presence of more repetitive patterns across the motif lengths in the case of fatigue indicates that the signal has lower complexity. In most cases, larger motif length resulted in better rho values. In a comparison of the first and the last zones, most of the extracted features are statistically significant with p < 0.05. It is observed that at the motif length of 13 all the extracted features are significant. This analysis method can be extended to diagnose other neuromuscular conditions.

In this paper, an attempt has been made to analyze surface electromyography (sEMG) signals under non-fatigue and fatigue conditions using time-frequency based features. The sEMG signals are recorded from biceps brachii muscle of 50 healthy volunteers under well-defined protocol. The pre-processed signals are divided into six equal epochs. The first and last segments are considered as non-fatigue and fatigue zones respectively. Further, these signals are subjected to B-distribution based quadratic time-frequency distribution (TFD). Time frequency based features such as instantaneous median frequency (IMDF) and instantaneous mean frequency (IMNF) are extracted. The expression of spectral entropy is modified to obtain instantaneous spectral entropy (ISPEn) from the time-frequency spectrum. The results show that all the extracted features are distinct in both conditions. It is also observed that the values of all features are higher in non-fatigue zone compared to fatigue condition. It appears that this method is useful in analysing various neuromuscular conditions using sEMG signals.

In this work, an attempt has been made to analyze progression of muscle fatigue in surface electromyography (sEMG) signals by estimating the complexity. The sEMG signals are acquired from biceps brachii of 50 healthy volunteers during dynamic contraction. The pre-processed signals are segmented into non-overlapping epochs of various sizes (500ms, 750ms and 1000ms) and Lempel-Ziv Complexity (LZC) is computed for each epoch. The linear regression technique is used to track the slope variations of LZC. The values of LZC show a decreasing trend during the progression of muscle fatigue. The magnitude of negative trend remained nearly constant irrespective of epoch size. Further, inter-subject variability of LZC measure is found to be minimum. The results shows that this method is useful in analyzing progression of muscle fatigue during dynamic contractions.