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The EMG Pattern Recognition System Based on Generalized Dynamic Fuzzy Neural Network

Author: XiongHan
Tutor: HuangJian
School: Huazhong University of Science and Technology
Course: Control Engineering
Keywords: EMGs Feature Extraction Pattern Recognition Generalized Dynamic Fuzzy Neural Network
CLC: TP183
Type: Master's thesis
Year: 2011
Downloads: 36
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Abstract


In the modern field of rehabilitation medicine , the health care system needs an intelligent rehabilitation function increasingly accurate judgment of human motion information and intentions , and by the EMG signal is one of the prerequisites necessary to establish such a system . This paper aims to study the mechanism of EMG signal characteristics , collection methods , feature extraction methods , action recognition , through a variety of EMG feature extraction method comparative study combined with pattern recognition algorithm to establish a stable set of experimental platform , by measuring the electromyographic analysis validation data on the platform , the purpose of the multi - operation mode of the identification manpower . This paper first introduces the application status for EMG Pattern Recognition and its related fields . Then studied a variety of EMG analysis and extraction methods and explains their meaning , then the structural characteristics of fuzzy systems and neural networks , the operational mechanism are introduced and fuzzy neural network combined with the advantages of the two leads , introduced the network the calculation of the weights , the membership function select , the basic structure , focus on a dynamic adjustment of fuzzy neural network and analyze the network structure . Then introduce the system structure of the EMG signal acquisition device , described the design process of the device hardware and software components . Then the generalized dynamic fuzzy neural network pattern recognition algorithm criteria and the implementation process were elaborated . Seven kinds of feature extraction methods and the analysis and comparison, and pattern recognition results are summarized in the final stages of experimental . The experiments show that the recognition accuracy rate of up to 97% or more , and have some applicability in a variety of feature extraction method application using generalized dynamic fuzzy neural network algorithm for opponents of the seven kinds of actions . In this paper, the experimental platform for research and development , to achieve a multi-action pattern recognition based EMGs manpower . In future studies will continue to improve the EMG signal acquisition , research new feature extraction method , and at the same time improve recognition algorithm to improve the real-time nature of the system , and lay a good foundation for the future to be applied to the rehabilitation robot motion control .

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CLC: > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory > Artificial Neural Networks and Computing
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