Classification of MMG Signal Based on EMD

Mechanomyography (MMG) signal is the sound from the surface of a muscle when the muscle is contracted. The traditional filtering algorithms for the processing of MMG signal would make most useful signal filtered when they are used to remove noise. According to MMG signal’s characteristics, a new signal filtering method is presented in this paper based on combining empirical mode decomposition with digital filter, which has a better performance on MMG signal filtering processing in experimental analysis. With extracting the energy feature of wavelet packet coefficient as the feature of classifier, the BP neural network classifier gets a better classification results. The average classification results showed that the best performance for recognizing hand gestures with the energy feature of wavelet packet coefficient features was achieved by BP neural network with the accuracy of 86.41%. This work was accomplished by introducing the new signal filtering method for the recognition of different hand gestures; And suggesting basing on combining empirical mode decomposition with digital filter as a new filtering method in MG-based hand gesture classification.