Dynamic contraction and fatigue analysis in biceps brachii muscles using synchrosqueezed wavelet transform and singular value features

In this study, the dynamic contractions and the associated fatigue condition in biceps brachii muscle are analysed using Synchrosqueezed Wavelet Transform (SST) and singular value features of surface Electromyography (sEMG) signals. For this, the recorded signals are decomposed into time-frequency matrix using SST. Two analytic functions namely Morlet and Bump wavelets are utilised for the analysis. Singular Value Decomposition method is applied to this time-frequency matrix to derive the features such as Maximum Singular Value (MSV), Singular Value Entropy (SVEn) and Singular Value Energy (SVEr). The results show that both these wavelets are able to characterise nonstationary variations in sEMG signals during dynamic fatiguing contractions. Increase in values of MSV and SVEr with the progression of fatigue denotes the presence of nonstationarity in the sEMG signals. The lower values of SVEn with the progression of fatigue indicate the randomness in the signal. Thus, it appears that the proposed approach could be used to characterise dynamic muscle contractions under varied neuromuscular conditions.

Параметры фазовой синхронизации в электроэнцефалографических паттернах как маркеры когнитивных нарушений

The differences in phase synchronization between intermittent photostimulation and bioelectrical activity of the brain in the form of electroencephalographic patterns in two groups of people with chronically elevated blood pressure, with and without moderate manifestations of cognitive functions are investigated. It was found that the phase synchronization parameters calculated on the basis of the synchrosqueezed wavelet transform of the electroencephalographic patterns and photostimulus can be markers of mild cognitive impairment.

A new acoustic emission damage localization method using synchrosqueezed wavelet transforms picker and time-order method

Acoustic emission technique, as a passive structural health monitoring technique, has been widely applied to detecting and locating the structural damage. The time difference of arrival and the wave velocity are the key factors in most of the acoustic emission localization methods, and the accuracy of these two factors will affect the accuracy of damage localization. To improve the accuracy of damage localization, this article proposes a new damage localization method based on the synchrosqueezed wavelet transform picker and the time-order method. The synchrosqueezed wavelet transform picker, which picks the time–frequency similar point based on time–frequency similarity theory in the low-noise interval of time–frequency matrix, can improve the accuracy and robustness of calculating time difference of arrival. Meanwhile, the time-order method not only measures the wave velocity in real time but also reduces the computing time by appropriately arranging the distribution of acoustic emission sensors. These advantages improve the accuracy and robustness of acoustic emission localization, which was verified by experiments. Furthermore, the new localization method was employed to study the energy distribution in the embedded section of steel bar during the pull-out test of steel bar and concrete, and the results show the types of resistance between steel bar and concrete.