title: Feature extraction of the first difference of EMG time series for EMG pattern recognition.
abstract: This paper demonstrates the utility of a differencing technique to transform surface EMG signals measured during both static and dynamic contractions such that they become more stationary. The technique was evaluated by three stationarity tests consisting of the variation of two statistical properties, i.e., mean and standard deviation, and the reverse arrangements test. As a result of the proposed technique, the first difference of EMG time series became more stationary compared to the original measured signal. Based on this finding, the performance of time-domain features extracted from raw and transformed EMG was investigated via an EMG classification problem (i.e., eight dynamic motions and four EMG channels) on data from 18 subjects. The results show that the classification accuracies of all features extracted from the transformed signals were higher than features extracted from the original signals for six different classifiers including quadratic discriminant analysis. On average, the proposed differencing technique improved classification accuracies by 2-8%.
authors.affiliation: GIPSA Laboratory, CNRS UMR 5216, Control System Department, SAIGA Team, University Joseph Fourier, Grenoble, France; LIG Laboratory, CNRS UMR 5217, University of Grenoble, Grenoble, France. Electronic address: angkoon.p@hotmail.com.
authors.forename: Angkoon
authors.initials: A
authors.lastname: Phinyomark
authors.affiliation: GIPSA Laboratory, CNRS UMR 5216, Control System Department, SAIGA Team, University Joseph Fourier, Grenoble, France. Electronic address: franck.quaine@gipsa-lab.grenoble-inp.fr.
authors.forename: Franck
authors.initials: F
authors.lastname: Quaine
authors.affiliation: GIPSA Laboratory, CNRS UMR 5216, Control System Department, SAIGA Team, University Joseph Fourier, Grenoble, France. Electronic address: sylvie.charbonnier@gipsa-lab.grenoble-inp.fr.
authors.forename: Sylvie
authors.initials: S
authors.lastname: Charbonnier
authors.affiliation: GIPSA Laboratory, CNRS UMR 5216, Control System Department, SAIGA Team, University Joseph Fourier, Grenoble, France. Electronic address: christine.serviere@gipsa-lab.grenoble-inp.fr.
authors.forename: Christine
authors.initials: C
authors.lastname: Serviere
authors.affiliation: LIG Laboratory, CNRS UMR 5217, University of Grenoble, Grenoble, France. Electronic address: franck.tarpin-bernard@imag.fr.
authors.forename: Franck
authors.initials: F
authors.lastname: Tarpin-Bernard
authors.affiliation: LIG Laboratory, CNRS UMR 5217, University of Grenoble, Grenoble, France. Electronic address: yann.laurillau@imag.fr.
authors.forename: Yann
authors.initials: Y
authors.lastname: Laurillau
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D004576:Electromyography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D009068:Movement / Q000502:physiology*; D009119:Muscle Contraction / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D055815:Young Adult
keywords: Differencing technique; Dynamic motions; Electromyography (EMG); Muscle–computer interface; Non-stationary signal