AI approaches towards signal recognition include Artificial Neural Networks (ANN), dynamic recurrent neural networks (DRNN), and fuzzy logic system. Mathematical models include wavelet transform, time-frequency approaches, Fourier transform, Wigner-Ville Distribution (WVD), statistical measures, and higher-order statistics. Various mathematical techniques and Artificial Intelligence (AI) have received extensive attraction. Recent advances in technologies of signal processing and mathematical models have made it practical to develop advanced EMG detection and analysis techniques. There are still limitations in detection and characterization of existing nonlinearities in the surface electromyography (sEMG, a special technique for studying muscle signals) signal, estimation of the phase, acquiring exact information due to derivation from normality ( 1, 2) Traditional system reconstruction algorithms have various limitations and considerable computational complexity and many show high variance ( 1). The technology of EMG recording is relatively new. It is quite important to carry out an investigation to classify the actual problems of EMG signals analysis and justify the accepted measures. Few hardware implementations have been done for prosthetic hand control, grasp recognition, and human-machine interaction. ![]() So far, research and extensive efforts have been made in the area, developing better algorithms, upgrading existing methodologies, improving detection techniques to reduce noise, and to acquire accurate EMG signals. Once appropriate algorithms and methods for EMG signal analysis are readily available, the nature and characteristics of the signal can be properly understood and hardware implementations can be made for various EMG signal related applications. ![]() The shapes and firing rates of Motor Unit Action Potentials (MUAPs) in EMG signals provide an important source of information for the diagnosis of neuromuscular disorders. The field of management and rehabilitation of motor disability is identified as one of the important application areas. The main reason for the interest in EMG signal analysis is in clinical diagnosis and biomedical applications. Detection of EMG signals with powerful and advance methodologies is becoming a very important requirement in biomedical engineering. Moreover, the EMG detector, particularly if it is at the surface of the skin, collects signals from different motor units at a time which may generate interaction of different signals. EMG signal acquires noise while traveling through different tissues. Hence, the EMG signal is a complicated signal, which is controlled by the nervous system and is dependent on the anatomical and physiological properties of muscles. The nervous system always controls the muscle activity (contraction/relaxation). The EMG signal is a biomedical signal that measures electrical currents generated in muscles during its contraction representing neuromuscular activities. ![]() This signal is normally a function of time and is describable in terms of its amplitude, frequency and phase. Biomedical signal means a collective electrical signal acquired from any organ that represents a physical variable of interest.
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