Surface Electromyography Classification Method Based on Temporal Two-Dimensionalization and Convolution Feature Fusion

被引：0
|
作者：
Luo J. [1 ]
Wang W. [1 ]
Wang Z. [1 ]
Liu H. [2 ]
机构：
[1] College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou
[2] Intelligent Systems and Biomedical Robotics Group, School of Computing, University of Portsmouth
来源：
Wang, Wanliang (wwl@zjut.edu.cn) | 1600年 / Science Press卷 / 33期
基金：
中国国家自然科学基金;
关键词：
Capsule Network; Feature Fusion; Gesture Recognition; Gramian Angular Field(GAF); Surface Electromyography(sEMG);
D O I：
10.16451/j.cnki.issn1003-6059.202007002
中图分类号：
TN911 [通信理论];
学科分类号：
081002 ;
摘要：
The traditional pattern recognition methods are prone to ignore characteristics of non-linearity and timing in the classification of surface electromyography(sEMG). Aiming at this problem, a sEMG signal classification method based on temporal two-dimensionalization and convolution feature fusion is proposed. Temporal two-dimensionalization is realized by Gramian angular field conversion to preserve the time dependence and correlation of original time series of sEMG. To highlight the local information and fully retain details simultaneously, a capsule network and a convolutional neural network are introduced to extract features together. In addition, the feature fusion is performed to realize the gesture recognition under different conditions. Experimental results show that the proposed method is more robust than other classification methods and it effectively enhances the electrode offset and the overall recognition level of hand movements facing new objects. © 2020, Science Press. All right reserved.
引用
收藏
页码：588 / 599
页数：11
相关论文
共 31 条
  • [1] CORDELLA F, CIANCIO A L, SACCHETTI R, Et al., Literature Review on Needs of Upper Limb Prosthesis Users, Frontiers in Neuroscience, (2016)
  • [2] ZECCA M, MICERA S, CARROZZA M C, Et al., Control of Multifunctional Prosthetic Hands by Processing the Electromyographic Signal, Critical Reviews in Biomedical Engineering, 30, 4, pp. 459-485, (2002)
  • [3] WU D M, SUN X, ZHANG Z C, Et al., Feature Collection and Analysis of Surface Electromyography Signals, Journal of Clinical Rehabilitative Tissue Engineering Research, 14, 43, pp. 8073-8076, (2010)
  • [4] PHINYOMARK A, PHUKPATTARANONT P, LIMSAKUL C., Feature Reduction and Selection for EMG Signal Classification, Expert Systems with Applications, 39, 8, pp. 7420-7431, (2012)
  • [5] DOULAH A B M S U, FATTAH S A, ZHU W P, Et al., Wavelet Domain Feature Extraction Scheme Based on Dominant Motor Unit Action Potential of EMG Signal for Neuromuscular Disease Classification, IEEE Transactions on Biomedical Circuits and Systems, 8, 2, pp. 155-164, (2014)
  • [6] POTLURI C, KUMAR P, ANUGOLU M, Et al., Frequency Domain Surface EMG Sensor Fusion for Estimating Finger Forces, Proc of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5975-5978, (2010)
  • [7] YU Y P, FAN L C, KUANG S L, Et al., The Research of sEMG Movement Pattern Classification Based on Multiple Fused Wavelet Function, Proc of the IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, pp. 487-491, (2015)
  • [8] MATSUBARA T, MORIMOTO J., Bilinear Modeling of EMG Signals to Extract User-Independent Features for Multiuser Myoelectric Interface, IEEE Transactions on Biomedical Engineering, 60, 8, pp. 2205-2213, (2013)
  • [9] AL-FAIZ M Z, ALI A A, MIRY A H., A k-Nearest Neighbor Based Algorithm for Human Arm Movements Recognition Using EMG Signals, Proc of the 1st International Conference on Energy, Power and Control, pp. 159-167, (2010)
  • [10] PURUSHOTHAMAN G, VIKAS R., Identification of a Feature Selection Based Pattern Recognition Scheme for Finger Movement Recognition from Multichannel EMG Signals, Australasian Physical and Engineering Sciences in Medicine, 41, 2, pp. 549-559, (2018)
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