Ramakrishnan Swaminathan

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.

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.

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.

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.