Muscle fatigue is a neuromuscular condition where muscle performance decreases due to sustained or intense contraction. It is experienced by both normal and abnormal subjects. In this work, an attempt has been made to analyze the progression of muscle fatigue in biceps brachii muscles using surface electromyography (sEMG) signals. The sEMG signals are recorded from fifty healthy volunteers during dynamic contractions under well defined protocol. The acquired signals are preprocessed and segmented in to six equal parts for further analysis. The features, such as activity, mobility, complexity, sample entropy and spectral entropy are extracted from all six zones. The results are found showing that the extracted features except complexity feature have significant variations in differentiating non-fatigue and fatigue zone respectively. Thus, it appears that, these features are useful in automated analysis of various neuromuscular activities in normal and pathological conditions.
Recognition of the emotions demonstrated by human beings plays a crucial role in healthcare and human-machine interface. This paper reports an attempt to classify emotions using a spectral feature from facial electromyography (facial EMG) signals in the valence affective dimension. For this purpose, the facial EMG signals are obtained from the DEAP dataset. The signals are subjected to Short-Time Fourier Transform, and the peak frequency values are extracted from the signal in intervals of one second. Support vector machine (SVM) classifier is used for the classification of the features extracted. The extracted feature can classify the signals in the valence dimension with an accuracy of 61.37%. The proposed feature could be used as an added feature for emotion recognition, and this method of analysis could be extended to myoelectric control applications.
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.
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