title: The analysis of hand movement distinction based on relative frequency band energy method.
abstract: For the purpose of successfully developing a prosthetic control system, many attempts have been made to improve the classification accuracy of surface electromyographic (SEMG) signals. Nevertheless, the effective feature extraction is still a paramount challenge for the classification of SEMG signals. The relative frequency band energy (RFBE) method based on wavelet packet decomposition was proposed for the prosthetic pattern recognition of multichannel SEMG signals. Firstly, the wavelet packet energy of SEMG signals in each subspace was calculated by using wavelet packet decomposition and the RFBE of each frequency band was obtained by the wavelet packet energy. Then, the principal component analysis (PCA) and the Davies-Bouldin (DB) index were used to perform the feature selection. Lastly, the support vector machine (SVM) was applied for the classification of SEMG signals. Our results demonstrated that the RFBE approach was suitable for identifying different types of forearm movements. By comparing with other classification methods, the proposed method achieved higher classification accuracy in terms of the classification of SEMG signals.
authors.affiliation: The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, 28 Xianning West Road, Xi'an 710049, China.
authors.forename: Yanyan
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, 28 Xianning West Road, Xi'an 710049, China.
authors.forename: Gang
authors.identifier: 0000-0001-5859-3724
authors.initials: G
authors.lastname: Wang
authors.affiliation: The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, 28 Xianning West Road, Xi'an 710049, China.
authors.forename: Chaolin
authors.initials: C
authors.lastname: Teng
authors.affiliation: The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, 28 Xianning West Road, Xi'an 710049, China.
authors.forename: Zhongjiang
authors.initials: Z
authors.lastname: Sun
authors.affiliation: The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, 28 Xianning West Road, Xi'an 710049, China.
authors.forename: Jue
authors.initials: J
authors.lastname: Wang
mesh_terms: D000328:Adult; D000465:Algorithms; D004576:Electromyography; D006225:Hand / Q000502:physiology*; D006801:Humans; D009068:Movement / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine