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Adaptive Classifier based on Distance of Probabilistic Fuzzy Set foe EMG Robot

Volume 14, Number 9, September 2018, pp. 21752180
DOI: 10.23940/ijpe.18.09.p26.21752180

Wenjing Huanga, Yaoqing Renb, Kejun Lia, and Yihua Lic

aSchool of Materials Science and Engineering, Central South University of Forestry and Technology, Changsha, 410004, China
bSchool of Mathematics and Physics, Yancheng Institute of Technology, Yancheng, 224051, China
cSchool of Logistics and Traffic, Central South University of Forestry and Technology, Changsha, 410004, China

(Submitted on May 20, 2018; Revised on July 15, 2018; Accepted on August 17, 2018)

Abstract:

Surface electromyographic (sEMG) signals always change with the external and internal conditions of human beings. Such a time-varying characteristic leads to decreasing classification accuracy of fixed-parameter classifiers for EMG patterns with time. To design a control system for EMG-based artificial limbs with stable performance, it is necessary to introduce the adaptive mechanism in the classifiers for EMG patterns. In addition, there are many uncertainties in the process of EMG signal acquisition and grasp model recognition. In this paper, on the basis of a distance classifier based on probabilistic fuzzy set, we attempted to introduce the adaptive scheme to the classifiers for EMG patterns and then verified the application of the scheme in the classification of EMG patterns through experiments. The study shows that a self-enhancement distance classifier based on probabilistic fuzzy set can improve recognition accuracy.

 

References: 14

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