Adaptive Wavelet Extreme Learning Machine (AW-ELM) for Index Finger Recognition Using Two-Channel Electromyography

Publisher:
Springer International Publishing
Publication Type:
Conference Proceeding
Citation:
Lecture Notes in Computer Science: 21st International Conference, ICONIP 2014, Kuching, Malaysia, November 3-6, 2014. Proceedings, Part I, 2014, 8834 pp. 471 - 478 (8)
Issue Date:
2014-11
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This paper proposes a new structure of wavelet extreme learning machine, i.e. an adaptive wavelet extreme learning machine (AW-ELM) for finger motion recognition using only two EMG channels. The adaptation mechanism is performed by adjusting the wavelet shape based on the input information. The performance of the proposed method is compared to ELM using wavelet (W-ELM) and sigmoid (Sig-ELM) activation function. The experimental results demonstrate that the proposed AW-ELM performs better than W-ELM and Sig-ELM.
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