Swarm-wavelet based extreme learning machine for finger movement classification on transradial amputees

Publication Type:
Conference Proceeding
2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, 2014, pp. 4192 - 4195
Issue Date:
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail13230164.pdf Published version683.51 kB
Adobe PDF
© 2014 IEEE. The use of a small number of surface electromyography (EMG) channels on the transradial amputee in a myoelectric controller is a big challenge. This paper proposes a pattern recognition system using an extreme learning machine (ELM) optimized by particle swarm optimization (PSO). PSO is mutated by wavelet function to avoid trapped in a local minima. The proposed system is used to classify eleven imagined finger motions on five amputees by using only two EMG channels. The optimal performance of wavelet-PSO was compared to a grid-search method and standard PSO. The experimental results show that the proposed system is the most accurate classifier among other tested classifiers. It could classify 11 finger motions with the average accuracy of about 94 % across five amputees.
Please use this identifier to cite or link to this item: