Dynamical characteristics of surface EMG signals of hand grasps via recurrence plot

Gaoxiang Ouyang; Xiangyang  Zhu; Zhaojie Ju; Honghai Liu

doi:10.1109/JBHI.2013.2261311

Dynamical characteristics of surface EMG signals of hand grasps via recurrence plot

Gaoxiang Ouyang, Xiangyang Zhu, Zhaojie Ju, Honghai Liu

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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.

Original language	English
Pages (from-to)	257-265
Number of pages	9
Journal	IEEE Journal of Biomedical and Health Informatics
Volume	18
Issue number	1
Early online date	15 May 2013
DOIs	https://doi.org/10.1109/JBHI.2013.2261311
Publication status	Published - 1 Jan 2014

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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.",

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AU - Ouyang, Gaoxiang

AU - Zhu, Xiangyang

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Dynamical characteristics of surface EMG signals of hand grasps via recurrence plot

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