Abstract
Wavelet transform feature extraction has become one of the most powerful techniques to improve the classification accuracy. In this paper, we are investigating the multi-level Daubechies wavelet reconstruction parameters. The EMG signal after performing the Daubechies wavelet was further processed by using one of the most successful features which is MAV. RES index statistical measurement was used to evaluate the class reparability of the features. The optimal results are obtained by using the seventh order of Daubechies with the level 1 and level 2 details components after performing wavelet reconstruction.
Original language | English |
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Title of host publication | Proceedings of the 2013 IEEE 8th Conference on Industrial Electronics and Applications, ICIEA 2013 |
Pages | 1492-1495 |
Number of pages | 4 |
DOIs | |
Publication status | Published - 2013 |
Event | IEEE Conference on Industrial Electronics and Applications 2013 - Melbourne, Australia Duration: 19 Jun 2013 → 21 Jun 2013 Conference number: 8th http://ieeeiciea.org/2013/index.html http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001289 (Proceedings) |
Conference
Conference | IEEE Conference on Industrial Electronics and Applications 2013 |
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Abbreviated title | ICIEA 2013 |
Country/Territory | Australia |
City | Melbourne |
Period | 19/06/13 → 21/06/13 |
Internet address |
Keywords
- Daubechies wavelet
- Electromyography signal
- EMG
- feature extraction