Surface EMG signals and deep transfer learning-based physical action classification-Reference-Cited by-同舟云学术

Surface EMG signals and deep transfer learning-based physical action classification

Published:2019-10-18 Issue:12 Volume:31 Page:8455-8462
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Demir Fatih,Bajaj Varun^ORCID,Ince Melih C.,Taran Sachin,Şengür Abdulkadir

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/content/pdf/10.1007/s00521-019-04553-7.pdf

Reference26 articles.

1. Durandau G, Sartori M, Bortole M, Moreno JC, Pons JL, Farina D (2016) EMG-driven models of human–machine interaction in individuals wearing the H2 exoskeleton. IFAC-PapersOnLine 49(32):200–203

2. Furui A, Hayashi H, Nakamura G, Chin T, Tsuji T (2017) An artificial EMG generation model based on signal-dependent noise and related application to motion classification. PLoS ONE 12(6):e0180112. https://doi.org/10.1371/journal.pone.0180112

3. Sezgin N (2012) Analysis of EMG signals in aggressive and normal activities by using higher-order spectra. Sci World J, Volume 2012, Article ID 478952, 5 pages. https://doi.org/10.1100/2012/478952

4. Wang L, Ge KD, Wu JY, Ye Y, Wei W (2018) A novel approach for the pattern recognition of hand movements based on EMG and VPMCD. J Mech Med Biol 18(01):1750115

5. Podrug E, Subasi A (2015) Surface EMG pattern recognition by using DWT feature extraction and SVM classifier. In The 1st conference of medical and biological engineering in Bosnia and Herzegovina (CMBEBIH 2015), 13–15 March 2015, Sarajevo

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