Classification of Surface EMG Using Wavelet Packet Energy Analysis and a Genetic Algorithm-Based Support Vector Machine

• Datum: 2013
• Hauptverfasser: Rong, Y., Hao, D., Han, X., Zhang, Y., Zhang, J., Zeng, Y.
• Format: Artikel
• Sprache: English
• Veröffentlicht: Інститут фізіології ім. О.О. Богомольця НАН України 2013
• Schriftenreihe: Нейрофизиология
• Online Zugang: https://nasplib.isofts.kiev.ua/handle/123456789/148026
• Назва журналу: Digital Library of Periodicals of National Academy of Sciences of Ukraine
• Zitieren: Classification of Surface EMG Using Wavelet Packet Energy Analysis and a Genetic Algorithm-Based Support Vector Machine / Y. Rong, D. Hao, X. Han, Y. Zhang, J. Zhang, Y. Zeng // Нейрофизиология. — 2013. — Т. 45, № 1. — С. 44-54. — Бібліогр.: 30 назв. — англ.

The aim of our study was to recognize results of surface electromyography (sEMG) recorded under conditions of a maximum voluntary contraction (MVС) and fatigue states using wavelet packet transform and energy analysis. The sEMG signals were recorded in 10 young men from the right upper limb with a handgrip. sEMG signals were decomposed by wavelet packet transform, and the corresponding energies of certain frequencies were normalized as feature vectors. A back-propagation neural network, a support vector machine (SVM), and a genetic algorithm-based SVM (GA-SVM) worked as classifiers to distinguish muscle states. The results showed that muscle fatigue and MVC could be identified by level-4 wavelet packet transform and GA-SVM more accurately than when using other approaches. The classification correct rate reached 97.3% with sevenfold cross-validation. The proposed method can be used to adequately reflect the muscle activity.