Adaptive wavelet extreme learning machine (Aw-Elm) for index finger recognition using two-channel Electromyography

Publication Type:
Conference Proceeding
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, 8834 pp. 471 - 478
Issue Date:
Filename Description Size
Thumbnailiconip2014_anam_official.pdfPublished Version226.09 kB
Adobe PDF
Full metadata record
© Springer International Publishing Switzerland 2014. 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-ELM0 and sigmoid (Sig-ELM) activation function. The experimental results demonstrate that the proposed AW-ELM performs better than W-ELM and Sig-ELM.
Please use this identifier to cite or link to this item: