title: Dynamical characteristics of surface EMG signals of hand grasps via recurrence plot.
abstract: Recognizing human hand grasp movements through surface electromyogram (sEMG) is a challenging task. In this paper, we investigated nonlinear measures based on recurrence plot, as a tool to evaluate the hidden dynamical characteristics of sEMG during four different hand movements. A series of experimental tests in this study show that the dynamical characteristics of sEMG data with recurrence quantification analysis (RQA) can distinguish different hand grasp movements. Meanwhile, adaptive neuro-fuzzy inference system (ANFIS) is applied to evaluate the performance of the aforementioned measures to identify the grasp movements. The experimental results show that the recognition rate (99.1%) based on the combination of linear and nonlinear measures is much higher than those with only linear measures (93.4%) or nonlinear measures (88.1%). These results suggest that the RQA measures might be a potential tool to reveal the sEMG hidden characteristics of hand grasp movements and an effective supplement for the traditional linear grasp recognition methods.
authors.forename: Gaoxiang
authors.initials: G
authors.lastname: Ouyang
authors.forename: Xiangyang
authors.initials: X
authors.lastname: Zhu
authors.forename: Zhaojie
authors.initials: Z
authors.lastname: Ju
authors.forename: Honghai
authors.initials: H
authors.lastname: Liu
mesh_terms: D000328:Adult; D000704:Analysis of Variance; D004576:Electromyography / Q000379:methods*; D005260:Female; D006225:Hand / Q000502:physiology; D018737:Hand Strength / Q000502:physiology*; D006801:Humans; D008297:Male; D009068:Movement / Q000502:physiology; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult