[1]YOU Bo,LI Zhongjie,HUANG Ling.Hand-motion recognition based on improved BP neural network[J].CAAI Transactions on Intelligent Systems,2018,13(5):848-854.[doi:10.11992/tis.201703018]
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Hand-motion recognition based on improved BP neural network

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 5 Page number: 848-854 Column: 学术论文—机器感知与模式识别 Public date: 2018-09-05
Title:
Hand-motion recognition based on improved BP neural network
Author(s):
YOU Bo;  LI Zhongjie;  HUANG Ling
School of Automation, Harbin University of Science and Technology, Harbin 150080, China
Keywords:
BP neural network; sEMG signal; cross entropy; hand-motion recognition; feature extraction; regularization; machine learning; pattern recognition
CLC:
TP391
DOI:
10.11992/tis.201703018
Abstract:
To achieve accurate pattern recognition of hand motions, in this study, we first denoised collected electromyogram (EMG) signals, and then used a variance algorithm in the time domain to extract features from the collected signals. After normalizing the characteristic signal, in the experiment, we found that the general BP neural network classifier has a slow learning rate, poor generalization ability, and large differences in its accuracy of recognizing diverse motions. To address the above problems, we propose an improved BP neural network that processes its input data by artificially increasing the dimensions. It then theoretically determines the reason for the slow network learning rate, and introduces a cross-entropy cost function to regularize it, thereby improving the network’s generalization ability and increasing its reaction speed. Experimental results show that the improved BP neural network has a better learning speed, generalization ability, and accuracy in hand motion classification than the ordinary neural network, with an average recognition accuracy of 94.34%.
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