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Mathematical Problems in Engineering
Volume 2015, Article ID 329783, 9 pages
http://dx.doi.org/10.1155/2015/329783
Research Article

Combining Independent Component and Grey Relational Analysis for the Real-Time System of Hand Motion Identification Using Bend Sensors and Multichannel Surface EMG

Department of Electrical Engineering, Southern Taiwan University of Science and Technology, Tainan City 71005, Taiwan

Received 14 August 2014; Accepted 13 October 2014

Academic Editor: Teen-Hang Meen

Copyright © 2015 Pei-Jarn Chen and Yi-Chun Du. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. G. R. Naik, D. K. Kumar, V. P. Singh, and M. Palaniswami, “Hand gestures for HCI using ICA of EMG,” in Proceedings of the HCSNet Workshop on Use of Vision in Human-Computer Interaction (VisHCI '06), pp. 67–72, November 2006.
  2. M. R. Ahsan, M. I. Ibrahimy, and O. O. Khalifa, “Advances in electromyogram signal classification to improve the quality of life for the disabled and aged people,” Journal of Computer Science, vol. 6, no. 7, pp. 706–715, 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. T. Lorrain, N. Jiang, and D. Farina, “Influence of the training set on the accuracy of surface EMG classification in dynamic contractions for the control of multifunction prostheses,” Journal of NeuroEngineering and Rehabilitation, vol. 8, no. 1, article 25, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. J. Rafiee, M. A. Rafiee, F. Yavari, and M. P. Schoen, “Feature extraction of forearm EMG signals for prosthetics,” Expert Systems with Applications, vol. 38, no. 4, pp. 4058–4067, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. S. Polak, Y. Barniv, and Y. Baram, “Head motion anticipation for virtual-environment applications using kinematics and EMG energy,” IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, vol. 36, no. 3, pp. 569–576, 2006. View at Publisher · View at Google Scholar · View at Scopus
  6. C. Cipriani, C. Antfolk, M. Controzzi et al., “Online myoelectric control of a dexterous hand prosthesis by transradial amputees,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 19, no. 3, pp. 260–270, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. Z. Ren, J. Meng, and J. Yuan, “Depth camera based hand gesture recognition and its applications in Human-Computer-Interaction,” in Proceedings of the 8th International Conference on Information, Communications and Signal Processing (ICICS '11), pp. 1–5, IEEE, December 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. Y.-C. Du, L.-Y. Shyu, and W. Hu, “The effect of combining stationary wavelet transform and independent component analysis in the multichannel SEMGs hand motion identification system,” Journal of Medical and Biological Engineering, vol. 26, no. 1, pp. 9–14, 2006. View at Google Scholar · View at Scopus
  9. X. Zhang, X. Chen, Y. Li, V. Lantz, K. Wang, and J. Yang, “A framework for hand gesture recognition based on accelerometer and EMG sensors,” IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, vol. 41, no. 6, pp. 1064–1076, 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. R. Tidwell, S. Akumalla, S. Karlaputi, R. Akl, and K. Kavi, “Evaluating the feasibility of EMG and bend sensors for classifying hand gestures,” in Proceedings of the International Conference on Multimedia and Human Computer Interaction, vol. 63, pp. 1–8, 2013.
  11. F. Wei, C. Xiang, W. Wen-hui et al., “A method of hand gesture recognition based on multiple sensors,” in Proceedings of the 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE '10), pp. 1–4, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. Y.-C. Du, C.-H. Lin, L.-Y. Shyu, and T. Chen, “Portable hand motion classifier for multi-channel surface electromyography recognition using grey relational analysis,” Expert Systems with Applications, vol. 37, no. 6, pp. 4283–4291, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. X. Tang, Y. Liu, C. Lv, and D. Sun, “Hand motion classification using a multi-channel surface electromyography sensor,” Sensors, vol. 12, no. 2, pp. 1130–1147, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. G. R. Naik, D. K. Kumar, and M. Palaniswami, “Multi run ICA and surface EMG based signal processing system for recognising hand gestures,” in Proceedings of the IEEE 8th International Conference on Computer and Information Technology (CIT '08), pp. 700–705, Sydney, Australia, July 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. A. Phinyomark, H. Hu, P. Phukpattaranont, and C. Limsakul, “Application of linear discriminant analysis in dimensionality reduction for hand motion classification,” Measurement Science Review, vol. 12, no. 3, pp. 82–89, 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. I. Mesa, A. Rubio, I. Tubia, J. De No, and J. Diaz, “Channel and feature selection for a surface electromyographic pattern recognition task,” Expert Systems with Applications, vol. 41, no. 11, pp. 5190–5200, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. A. Phinyomark, P. Phukpattaranont, and C. Limsakul, “A review of control methods for electric power wheelchairs based on electromyography signals with special emphasis on pattern recognition,” IETE Technical Review, vol. 28, no. 4, pp. 316–326, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. T. Xuerui and L. Yuguang, “Using grey relational analysis to analyze the medical data,” Kybernetes, vol. 33, no. 2, pp. 355–362, 2004. View at Publisher · View at Google Scholar · View at Scopus
  19. L.-Y. Shyu, J.-Y. Chen, R.-W. Tatn, and W. Hu, “A new electrode system for hand action discrimination,” Journal of Medical and Biological Engineering, vol. 22, no. 4, pp. 211–217, 2002. View at Google Scholar · View at Scopus
  20. K. J. Lee and B. Lee, “Removing ECG artifacts from the EMG: a comparison between combining empirical-mode decomposition and independent component analysis and other filtering methods,” in Proceedings of the 13th International Conference on Control, Automation and Systems (ICCAS '13), pp. 181–184, IEEE, October 2013. View at Publisher · View at Google Scholar · View at Scopus
  21. A. Hyvärinen, “The FastICA MATLAB package,” http://www.cis.hut.fi/projects/ica/icademo/.
  22. J. L. Semmlow and W. Yuan, “Components of disparity vergence eye movements: application of independent component analysis,” IEEE Transactions on Biomedical Engineering, vol. 49, no. 8, pp. 805–811, 2002. View at Publisher · View at Google Scholar · View at Scopus
  23. E. D. Engeberg, “A physiological basis for control of a prosthetic hand,” Biomedical Signal Processing and Control, vol. 8, no. 1, pp. 6–15, 2013. View at Publisher · View at Google Scholar · View at Scopus