Fuzzy Wavelet Packet based Feature Extraction Method for Multifunction Myoelectric Control
The myoelectric signal (MES) is one of the Biosignals
utilized in helping humans to control equipments. Recent approaches
in MES classification to control prosthetic devices employing pattern
recognition techniques revealed two problems, first, the classification
performance of the system starts degrading when the number of
motion classes to be classified increases, second, in order to solve the
first problem, additional complicated methods were utilized which
increase the computational cost of a multifunction myoelectric
control system. In an effort to solve these problems and to achieve a
feasible design for real time implementation with high overall
accuracy, this paper presents a new method for feature extraction in
MES recognition systems. The method works by extracting features
using Wavelet Packet Transform (WPT) applied on the MES from
multiple channels, and then employs Fuzzy c-means (FCM)
algorithm to generate a measure that judges on features suitability for
classification. Finally, Principle Component Analysis (PCA) is
utilized to reduce the size of the data before computing the
classification accuracy with a multilayer perceptron neural network.
The proposed system produces powerful classification results (99%
accuracy) by using only a small portion of the original feature set.
[1] K. Englehart, B. Hudgin, and P.A. Parker, "A wavelet-based
continuous classification scheme for multifunction myoelectric
control," IEEE Transactions on Biomedical Engineering, vol. 48, pp.
302 - 311, 2001.
[2] C. J. De Luca, "Physiology and mathematics of myoelectric signals,"
IEEE Transactions on Biomedical Engineering, vol. BME-26, pp. 313-
325, 1979.
[3] R. Merletti and P. Parker, "Electromyography physiology, Engineering,
and noninvasive applications," IEEE Press Engineering in Medicine
and Biology Society., 2004.
[4] J. Hunt, "A 3-degree-of-freedom myoelectric control suitable for easy
implementation in hardware," in Electrical and Computer Engineering,
vol. Master of Science. Fredericton, NB Canada: University of
NewBrunswick, 1998.
[5] B. Hudgins, P. Parker, and R. N. Scott, "A new strategy for
multifunction myoelectric control," IEEE Transactions on Biomedical
Engineering, vol. 40, pp. 82-94, 1993.
[6] C. M. Lighty, P. H. Chappelly, B. Hudgins, and K. Englehart,
"Intelligent multifunction myoelectric control of hand prostheses,"
Journal of Medical Engineering & Technology, vol. 26, pp. 139- 146,
2002.
[7] S. Leowinata, "A new strategy for multifunction myoelectric control
using an array of surface electrodes," in Electrical and Computer
Engineering, vol. Master: University of New Brunswick, 2000.
[8] M. Vuskovic and D. Sijiang, "Classification of prehensile EMG
patterns with simplified fuzzy ARTMAP networks," Proceedings of the
International Joint Conference on Neural Networks, IJCNN '02. , vol.
3, pp. 2539-2544, 2002.
[9] J. J. Im, D. H. Rho, Y. J. Jeon, N. B. Lee, and J. I. Chung, "Extraction
of parameters from EMG signals for the biofeedback electrical
stimulation," presented at Proceedings of the Second Joint
EMBS/BMES Conference, 2002.
[10] S-P. Lee, S-H. Park, J-S. Kim, and I.-J. Kim, "EMG pattern recognition
based on evidence accumulation for prosthesis control," 18th Annual
International Conference of the IEEE Engineering in Medicine and
Biology Society, Amsterdam, The Netherlands, pp. 1481-1483, 1996.
[11] Sang-Hui Park and S.-P. Lee, "EMG Pattern Recognition Based on
Artificial Intelligence Techniques," IEEE Transactions on
Rehabilitation Engineering, vol. 6, pp. 400-405, 1998.
[12] L. Seok-Pil, P. Sang-Hui, K. Jeong-Seop, and K. Ig-Jae, "EMG pattern
recognition based on evidence accumulation for prosthesis control,"
18th Annual International Conference of the IEEE Engineering in
Medicine and Biology Society, Amsterdam, vol. 4, pp. 1481-1483 vol.4,
1996.
[13] Jun-Uk Chu, Inhyuk Moon, Shin-Ki Kim, and M.-S. Mun., "Control of
multifunction myoelectric hand using a real-time EMG pattern
recognition," IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS 2005). pp. 3511 - 3516, 2005.
[14] Y. H. Kim, I. J. Shim, and G. T. Park, "A method of controlling
household electrical appliance by hand motion in LonWorks,"
Proceedings of the 41st SICE Annual Conference SICE. , vol. 5, pp.
2849-2854, 2002.
[15] B. Hannaford and S. Lehman, "Short time fourier analysis of the
electromyogram: fast movements and constant contraction," IEEE
Transactions on Biomedical Engineering, vol. BME-33, pp. 1173-
1181, 1986.
[16] S. Karlsson, Y. Jun, and M. Akay, "Time-frequency analysis of
myoelectric signals during dynamic contractions: a comparative study,"
IEEE Transactions on Biomedical Engineering, vol. 47, pp. 228-238,
2000.
[17] M. Zecca, S. Micera, M. C. Carrozza, and P. Dario, "Control of
multifunctional prosthetic hands by processing the electromyographic
signal," Critical Review in Biomedical Engineering, vol. 30, pp. 459-
485, 2002.
[18] K. Englehart, B. Hudgins, P. A. Parker, and M. Stevenson, "Improving
myoelectric signal classification using wavelet packets and principal
components analysis," IEEE Engineering in Medicine and Biology
Society, Atlanta, vol. 1, pp. 569, 1999.
[19] J.-U. Chu, I. Moon, and M.-S. Mun, "A real-time EMG pattern
recognition based on linear-nonlinear feature projection for
multifunction myoelectric hand," Proceedings of the IEEE 9th
International Conference on Rehabilitation Robotics, pp. 295-298,
2005.
[20] J. U. Chu, I. Moon, and M. S. Mun, "A real-time EMG pattern
recognition system based on linear-nonlinear feature projection for a
multifunction myoelectric hand," IEEE Transactions on Biomedical
Engineering, vol. 53, pp. 2232-2239, 2006.
[21] L. Hargrove, K. Englehart, and B. Hudgins, "A comparison of surface
and intramuscluar myoelectric signal classification," IEEE
Transactions on Biomedical Engineering, Accepted for future
publication.
[22] R. R. Coifman, Y. Meyer, and M. V. Wickerhauser, "Wavelet analysis
and signal processing," in Wavelets and Their Applications, M. B.
Ruskai, Ed. Boston: Jones and Bartlett, 1992.
[23] K. Englehart and B. Hudgins, "A robust, real-time control scheme for
multifunction myoelectric control," Biomedical Engineering, IEEE
Transactions on, vol. 50, pp. 848-854, 2003.
[24] L. Deqiang, W. Pedrycz, and N. J. Pizzi, "Fuzzy wavelet packet based
feature extraction method and its application to biomedical signal
classification," IEEE Transactions on Biomedical Engineering, vol. 52,
pp. 1132-1139, 2005.
[25] K. Englehart, "Signal representation for classification of the transient
myoelectric signal " in Electrical and Computer Engineering
Department., vol. PhD Dissertation: University of New Brunswick,
1998.
[26] I. Drummond and S. Sandri, "A clustering-based fuzzy classifier," in
Artificial Intelligence Research and Development, B. Lopez, Ed.: IOS
Press, 2005.
[27] Y. KO├çY─░─×─░T and M. KOR├£REK, "EMG signal classif─▒cation using
wavelet transform and fuzzy clustering algorithms," presented at Proc.
of ELECO'2003, Bursa, Turkey, 2003.
[28] B. Karlik, M. Osman Tokhi, and M. Alci, "A fuzzy clustering neural
network architecture for multifunction upper-limb prosthesis," IEEE
Transactions on Biomedical Engineering, vol. 50, pp. 1255-1261,
2003.
[29] A. D. Boca and D. C. Park, "Myoelectric signal recognition using fuzzy
clustering and artificial neural networks in real time," IEEE
International Conference on Neural Networks, vol. 5, pp. 3098-3103
1994.
[30] M. M. Trivedi and J. C. Bezdeck, "Low-level segmentation of aerial
images with fuzzy clustering," IEEE Transactions on Systems, Man and
Cybernetics, vol. SMC-16, pp. 589- 598, 1986.
[31] R. Boostani and M. H. Moradi, "Evaluation of the forearm EMG signal
features for the control of a prosthetic hand," Physiological
Measurement, vol. 24, pp. 309-319, 2003.
[1] K. Englehart, B. Hudgin, and P.A. Parker, "A wavelet-based
continuous classification scheme for multifunction myoelectric
control," IEEE Transactions on Biomedical Engineering, vol. 48, pp.
302 - 311, 2001.
[2] C. J. De Luca, "Physiology and mathematics of myoelectric signals,"
IEEE Transactions on Biomedical Engineering, vol. BME-26, pp. 313-
325, 1979.
[3] R. Merletti and P. Parker, "Electromyography physiology, Engineering,
and noninvasive applications," IEEE Press Engineering in Medicine
and Biology Society., 2004.
[4] J. Hunt, "A 3-degree-of-freedom myoelectric control suitable for easy
implementation in hardware," in Electrical and Computer Engineering,
vol. Master of Science. Fredericton, NB Canada: University of
NewBrunswick, 1998.
[5] B. Hudgins, P. Parker, and R. N. Scott, "A new strategy for
multifunction myoelectric control," IEEE Transactions on Biomedical
Engineering, vol. 40, pp. 82-94, 1993.
[6] C. M. Lighty, P. H. Chappelly, B. Hudgins, and K. Englehart,
"Intelligent multifunction myoelectric control of hand prostheses,"
Journal of Medical Engineering & Technology, vol. 26, pp. 139- 146,
2002.
[7] S. Leowinata, "A new strategy for multifunction myoelectric control
using an array of surface electrodes," in Electrical and Computer
Engineering, vol. Master: University of New Brunswick, 2000.
[8] M. Vuskovic and D. Sijiang, "Classification of prehensile EMG
patterns with simplified fuzzy ARTMAP networks," Proceedings of the
International Joint Conference on Neural Networks, IJCNN '02. , vol.
3, pp. 2539-2544, 2002.
[9] J. J. Im, D. H. Rho, Y. J. Jeon, N. B. Lee, and J. I. Chung, "Extraction
of parameters from EMG signals for the biofeedback electrical
stimulation," presented at Proceedings of the Second Joint
EMBS/BMES Conference, 2002.
[10] S-P. Lee, S-H. Park, J-S. Kim, and I.-J. Kim, "EMG pattern recognition
based on evidence accumulation for prosthesis control," 18th Annual
International Conference of the IEEE Engineering in Medicine and
Biology Society, Amsterdam, The Netherlands, pp. 1481-1483, 1996.
[11] Sang-Hui Park and S.-P. Lee, "EMG Pattern Recognition Based on
Artificial Intelligence Techniques," IEEE Transactions on
Rehabilitation Engineering, vol. 6, pp. 400-405, 1998.
[12] L. Seok-Pil, P. Sang-Hui, K. Jeong-Seop, and K. Ig-Jae, "EMG pattern
recognition based on evidence accumulation for prosthesis control,"
18th Annual International Conference of the IEEE Engineering in
Medicine and Biology Society, Amsterdam, vol. 4, pp. 1481-1483 vol.4,
1996.
[13] Jun-Uk Chu, Inhyuk Moon, Shin-Ki Kim, and M.-S. Mun., "Control of
multifunction myoelectric hand using a real-time EMG pattern
recognition," IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS 2005). pp. 3511 - 3516, 2005.
[14] Y. H. Kim, I. J. Shim, and G. T. Park, "A method of controlling
household electrical appliance by hand motion in LonWorks,"
Proceedings of the 41st SICE Annual Conference SICE. , vol. 5, pp.
2849-2854, 2002.
[15] B. Hannaford and S. Lehman, "Short time fourier analysis of the
electromyogram: fast movements and constant contraction," IEEE
Transactions on Biomedical Engineering, vol. BME-33, pp. 1173-
1181, 1986.
[16] S. Karlsson, Y. Jun, and M. Akay, "Time-frequency analysis of
myoelectric signals during dynamic contractions: a comparative study,"
IEEE Transactions on Biomedical Engineering, vol. 47, pp. 228-238,
2000.
[17] M. Zecca, S. Micera, M. C. Carrozza, and P. Dario, "Control of
multifunctional prosthetic hands by processing the electromyographic
signal," Critical Review in Biomedical Engineering, vol. 30, pp. 459-
485, 2002.
[18] K. Englehart, B. Hudgins, P. A. Parker, and M. Stevenson, "Improving
myoelectric signal classification using wavelet packets and principal
components analysis," IEEE Engineering in Medicine and Biology
Society, Atlanta, vol. 1, pp. 569, 1999.
[19] J.-U. Chu, I. Moon, and M.-S. Mun, "A real-time EMG pattern
recognition based on linear-nonlinear feature projection for
multifunction myoelectric hand," Proceedings of the IEEE 9th
International Conference on Rehabilitation Robotics, pp. 295-298,
2005.
[20] J. U. Chu, I. Moon, and M. S. Mun, "A real-time EMG pattern
recognition system based on linear-nonlinear feature projection for a
multifunction myoelectric hand," IEEE Transactions on Biomedical
Engineering, vol. 53, pp. 2232-2239, 2006.
[21] L. Hargrove, K. Englehart, and B. Hudgins, "A comparison of surface
and intramuscluar myoelectric signal classification," IEEE
Transactions on Biomedical Engineering, Accepted for future
publication.
[22] R. R. Coifman, Y. Meyer, and M. V. Wickerhauser, "Wavelet analysis
and signal processing," in Wavelets and Their Applications, M. B.
Ruskai, Ed. Boston: Jones and Bartlett, 1992.
[23] K. Englehart and B. Hudgins, "A robust, real-time control scheme for
multifunction myoelectric control," Biomedical Engineering, IEEE
Transactions on, vol. 50, pp. 848-854, 2003.
[24] L. Deqiang, W. Pedrycz, and N. J. Pizzi, "Fuzzy wavelet packet based
feature extraction method and its application to biomedical signal
classification," IEEE Transactions on Biomedical Engineering, vol. 52,
pp. 1132-1139, 2005.
[25] K. Englehart, "Signal representation for classification of the transient
myoelectric signal " in Electrical and Computer Engineering
Department., vol. PhD Dissertation: University of New Brunswick,
1998.
[26] I. Drummond and S. Sandri, "A clustering-based fuzzy classifier," in
Artificial Intelligence Research and Development, B. Lopez, Ed.: IOS
Press, 2005.
[27] Y. KO├çY─░─×─░T and M. KOR├£REK, "EMG signal classif─▒cation using
wavelet transform and fuzzy clustering algorithms," presented at Proc.
of ELECO'2003, Bursa, Turkey, 2003.
[28] B. Karlik, M. Osman Tokhi, and M. Alci, "A fuzzy clustering neural
network architecture for multifunction upper-limb prosthesis," IEEE
Transactions on Biomedical Engineering, vol. 50, pp. 1255-1261,
2003.
[29] A. D. Boca and D. C. Park, "Myoelectric signal recognition using fuzzy
clustering and artificial neural networks in real time," IEEE
International Conference on Neural Networks, vol. 5, pp. 3098-3103
1994.
[30] M. M. Trivedi and J. C. Bezdeck, "Low-level segmentation of aerial
images with fuzzy clustering," IEEE Transactions on Systems, Man and
Cybernetics, vol. SMC-16, pp. 589- 598, 1986.
[31] R. Boostani and M. H. Moradi, "Evaluation of the forearm EMG signal
features for the control of a prosthetic hand," Physiological
Measurement, vol. 24, pp. 309-319, 2003.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:62080", author = "Rami N. Khushaba and Adel Al-Jumaily", title = "Fuzzy Wavelet Packet based Feature Extraction Method for Multifunction Myoelectric Control", abstract = "The myoelectric signal (MES) is one of the Biosignals
utilized in helping humans to control equipments. Recent approaches
in MES classification to control prosthetic devices employing pattern
recognition techniques revealed two problems, first, the classification
performance of the system starts degrading when the number of
motion classes to be classified increases, second, in order to solve the
first problem, additional complicated methods were utilized which
increase the computational cost of a multifunction myoelectric
control system. In an effort to solve these problems and to achieve a
feasible design for real time implementation with high overall
accuracy, this paper presents a new method for feature extraction in
MES recognition systems. The method works by extracting features
using Wavelet Packet Transform (WPT) applied on the MES from
multiple channels, and then employs Fuzzy c-means (FCM)
algorithm to generate a measure that judges on features suitability for
classification. Finally, Principle Component Analysis (PCA) is
utilized to reduce the size of the data before computing the
classification accuracy with a multilayer perceptron neural network.
The proposed system produces powerful classification results (99%
accuracy) by using only a small portion of the original feature set.", keywords = "Biomedical Signal Processing, Data mining andInformation Extraction, Machine Learning, Rehabilitation.", volume = "2", number = "1", pages = "79-9", }
