The effect of data reduction by independent component analysis and principal component analysis in hand motion identification

Y. C. Du, W. C. Hu, L. Y. Shyu

研究成果: Conference article同行評審

10 引文 斯高帕斯(Scopus)

摘要

Both independent component analysis (ICA) and principal component analysis (PCA) were used in this study to evaluate their effects in data reduction in the hand motion identification using surface electromyogram (SEMG) and stationary wavelet transformation. The results indicate that both methods increase the number of training epochs of the artificial neural network. The unsupervised Fast ICA reduces the number of SEMG channels from 7 to 4. However the hand motion identification rate using the reduced channels is significantly lower (p < 0.05). On the other hand, the PCA reduces the size of neural network by more than 70%. Moreover, the results of discrimination rate and neural network training epochs show no significant difference as compared to the results before PCA reduction. The result of this study demonstrates that using wavelet and PCA are effective pre-processing for surface EMG analysis. It can efficiently reduce the size of neural network and increase the discrimination rate for different hand motions.

原文English
頁(從 - 到)84-86
頁數3
期刊Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
26 I
出版狀態Published - 2004
事件Conference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004 - San Francisco, CA, United States
持續時間: 2004 九月 12004 九月 5

All Science Journal Classification (ASJC) codes

  • 訊號處理
  • 生物醫學工程
  • 電腦視覺和模式識別
  • 健康資訊學

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