期刊
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
卷 18, 期 1, 页码 257-265出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2013.2261311
关键词
Hand grasp; nonlinear measures; recurrence plot (RP); surface electromyogram (sEMG)
类别
资金
- National Basic Research Program (973 Program) of China [2011CB013305]
- Leverhulme Trust [13754]
- U.K. National Engineering and Physical Scientific Research Council [EP/G041377/1]
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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