Ramakrishnan Swaminathan

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.

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.