Identification of Gesture Based on Combination of Raw sEMG and sEMG Envelope Using Supervised Learning and Univariate Feature Selection-Reference-Cited by-同舟云学术

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Identification of Gesture Based on Combination of Raw sEMG and sEMG Envelope Using Supervised Learning and Univariate Feature Selection

Published:2019-07 Issue:4 Volume:16 Page:647-662
ISSN:1672-6529
Container-title:Journal of Bionic Engineering
language:en
Short-container-title:J Bionic Eng

Author:

Liang Shili,Wu Yansheng,Chen Jianfei,Zhang Ling,Chen Peipei,Chai Zongqian,Cao Chunlei

Publisher

Springer Science and Business Media LLC

Subject

Bioengineering,Biophysics,Biotechnology

Link

http://link.springer.com/content/pdf/10.1007/s42235-019-0052-1.pdf

Reference32 articles.

1. Xiao Z G, Menon C. Performance of forearm FMG and sEMG for estimating elbow, forearm and wrist positions. Journal of Bionic Engineering, 2017, 14, 284–295.

2. Kim K S, Choi H H, Moon C S, Mun C W. Comparison of k-nearest neighbor, quadratic discriminant and linear discriminant analysis in classification of electromyogram signals based on the wrist-motion directions. Current Applied Physics, 2011, 11, 740–745.

3. Liu J, Li X Y, Li G L, Zhou P. EMG feature assessment for myoelectric pattern recognition and channel selection: A study with incomplete spinal cord injury. Medical Engineering & Physics, 2014, 36, 975–980.

4. Phinyomark A, Phukpattaranont P, Limsakul C. Fractal analysis features for weak and single-channel upper-limb EMG signals. Expert Systems with Applications, 2012, 39, 11156–11163.

5. Yang D P, Zhao J D, Gu Y K, Wang X Q, Li N, Jiang L, Liu H, Huang H, Zhao D W. An anthropomorphic robot hand developed based on underactuated mechanism and controlled by EMG signals. Journal of Bionic Engineering, 2009, 6, 255–263.

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