Swarmed Discriminant Analysis for Multifunction Prosthesis Control
One of the approaches enabling people with amputated
limbs to establish some sort of interface with the real world includes
the utilization of the myoelectric signal (MES) from the remaining
muscles of those limbs. The MES can be used as a control input to a
multifunction prosthetic device. In this control scheme, known as the
myoelectric control, a pattern recognition approach is usually utilized
to discriminate between the MES signals that belong to different
classes of the forearm movements. Since the MES is recorded using
multiple channels, the feature vector size can become very large. In
order to reduce the computational cost and enhance the generalization
capability of the classifier, a dimensionality reduction method is
needed to identify an informative yet moderate size feature set. This
paper proposes a new fuzzy version of the well known Fisher-s
Linear Discriminant Analysis (LDA) feature projection technique.
Furthermore, based on the fact that certain muscles might contribute
more to the discrimination process, a novel feature weighting scheme
is also presented by employing Particle Swarm Optimization (PSO)
for estimating the weight of each feature. The new method, called
PSOFLDA, is tested on real MES datasets and compared with other
techniques to prove its superiority.
[1] P. A. Parker, K. B. Englehart, and B. S. Hudgins, "Control of powered
upper limb prostheses", in Electromyography Physiology, Engineering,
and Noninvasive Applications, R. Merletti and P. Parker Eds. John Wiley
and Sons, 2004.
[2] Asghari Oskoei, M., & Hu, H. (2007). Myoelectric Control Systems-A
Survey. Biomedical Signal Processing and Control, 2, 275-294
[3] Englehart, K., Hudgins, B., Parker, P. A., Stevenson, M. (1998). Time-
Frequency Representation for Classification of the Ttransient Myoelectric
Signal. In: The 20th EMBS Annual International Conference, pp. 2627-
2630. China
[4] Chu, J. U., Moon, I., Mun, M. S. (2006). A Real-Time EMG Pattern
Recognition System based on Linear-Nonlinear Feature Projection for
a Multifunction Myoelectric Hand. IEEE Transactions on Biomedical
Engineering, 53, 2232-2239
[5] Chu, J. U., Moon, I., Mun, M. S. (2006). A Supervised Feature Projection
for Real-Time Multifunction Myoelectric Hand Control. In:The 28th
IEEE EMBS Annual International Conference, pp. 2417-2420. New York
City
[6] Ye, J., Janardan, R., Li, Q., Park, H. (2006). Feature Reduction via Generalized
Uncorrelated Linear Discriminant Analysis. IEEE Transactions
on Knowledge and Data Engineering, 18(10), 1312-1322
[7] Chan, A. D. C., & Green, G. C. (2007). Myoelectric Control Development
Toolbox. In. Proceedings of The 30-th Conference of the Canadian
Medical & Biological Engineering Society, Toronto, ON
[8] Watada, J., Tanaka, H., Asai, K. (1986). Fuzzy Discriminant Analysis in
Fuzzy Groups. Fuzzy Sets and Systems, 19, 261-271.
[9] Chen, Z. P., Jiang, J. H., Y. Li, Liang, Y. Z. and Yu, R. Q. (1999). Fuzzy
Linear Discriminant Analysis for Chemical Data Sets. Chemometrics and
Intelligent Laboratory Systems, 45(1-2), 295-302.
[10] Kwak, K. C. & Pedrycz, W. (2005). Face Recognition Using a Fuzzy
Fisherface Classifier. Pattern Recognition, 38, 1717-1732.
[11] Swets, D. L. & Weng, J. (1996). Using Discriminant Eigenfeatures
for Image Retrieval. IEEE Transactions Pattern Analysis and Machine
Intelligence, 18, 831-836.
[12] Wu, X.H. & Zhou, J. J. (2006). Fuzzy Discriminant Analysis with Kernel
Methods. Pattern Recognition, 39, 2236-2239.
[13] Lu, J., Plataniotis, K. N. & Venetsanopoulos, A. N. (2003). Regularized
Discriminant Analysis for the Small Sample Size Problem in Face
Recognition. Pattern Recognition Letters, 24, 3079-3087.
[14] Oliveira, J. V. d., Pedrycz, W. (2007): A Comprehensive, Coherent, and
in Depth Presentation of the State of the Art in Fuzzy Clustering. John
Wiley & Sons Ltd.
[15] Kennedy, J., Eberhart, R. C., Shi, Y. (2001). Swarm Intelligence. The
Morgan Kaufmann Series in Artificial Intelligence, Morgan Kaufmann
Publishers, London
[16] Ye, J. (2005). Characterization of a Family of Algorithms for Generalized
Discriminant Analysis on Undersampled Problems. Journal of
Machine Learning Research, 6, 483-502
[1] P. A. Parker, K. B. Englehart, and B. S. Hudgins, "Control of powered
upper limb prostheses", in Electromyography Physiology, Engineering,
and Noninvasive Applications, R. Merletti and P. Parker Eds. John Wiley
and Sons, 2004.
[2] Asghari Oskoei, M., & Hu, H. (2007). Myoelectric Control Systems-A
Survey. Biomedical Signal Processing and Control, 2, 275-294
[3] Englehart, K., Hudgins, B., Parker, P. A., Stevenson, M. (1998). Time-
Frequency Representation for Classification of the Ttransient Myoelectric
Signal. In: The 20th EMBS Annual International Conference, pp. 2627-
2630. China
[4] Chu, J. U., Moon, I., Mun, M. S. (2006). A Real-Time EMG Pattern
Recognition System based on Linear-Nonlinear Feature Projection for
a Multifunction Myoelectric Hand. IEEE Transactions on Biomedical
Engineering, 53, 2232-2239
[5] Chu, J. U., Moon, I., Mun, M. S. (2006). A Supervised Feature Projection
for Real-Time Multifunction Myoelectric Hand Control. In:The 28th
IEEE EMBS Annual International Conference, pp. 2417-2420. New York
City
[6] Ye, J., Janardan, R., Li, Q., Park, H. (2006). Feature Reduction via Generalized
Uncorrelated Linear Discriminant Analysis. IEEE Transactions
on Knowledge and Data Engineering, 18(10), 1312-1322
[7] Chan, A. D. C., & Green, G. C. (2007). Myoelectric Control Development
Toolbox. In. Proceedings of The 30-th Conference of the Canadian
Medical & Biological Engineering Society, Toronto, ON
[8] Watada, J., Tanaka, H., Asai, K. (1986). Fuzzy Discriminant Analysis in
Fuzzy Groups. Fuzzy Sets and Systems, 19, 261-271.
[9] Chen, Z. P., Jiang, J. H., Y. Li, Liang, Y. Z. and Yu, R. Q. (1999). Fuzzy
Linear Discriminant Analysis for Chemical Data Sets. Chemometrics and
Intelligent Laboratory Systems, 45(1-2), 295-302.
[10] Kwak, K. C. & Pedrycz, W. (2005). Face Recognition Using a Fuzzy
Fisherface Classifier. Pattern Recognition, 38, 1717-1732.
[11] Swets, D. L. & Weng, J. (1996). Using Discriminant Eigenfeatures
for Image Retrieval. IEEE Transactions Pattern Analysis and Machine
Intelligence, 18, 831-836.
[12] Wu, X.H. & Zhou, J. J. (2006). Fuzzy Discriminant Analysis with Kernel
Methods. Pattern Recognition, 39, 2236-2239.
[13] Lu, J., Plataniotis, K. N. & Venetsanopoulos, A. N. (2003). Regularized
Discriminant Analysis for the Small Sample Size Problem in Face
Recognition. Pattern Recognition Letters, 24, 3079-3087.
[14] Oliveira, J. V. d., Pedrycz, W. (2007): A Comprehensive, Coherent, and
in Depth Presentation of the State of the Art in Fuzzy Clustering. John
Wiley & Sons Ltd.
[15] Kennedy, J., Eberhart, R. C., Shi, Y. (2001). Swarm Intelligence. The
Morgan Kaufmann Series in Artificial Intelligence, Morgan Kaufmann
Publishers, London
[16] Ye, J. (2005). Characterization of a Family of Algorithms for Generalized
Discriminant Analysis on Undersampled Problems. Journal of
Machine Learning Research, 6, 483-502
@article{"International Journal of Medical, Medicine and Health Sciences:58384", author = "Rami N. Khushaba and Ahmed Al-Ani and Adel Al-Jumaily", title = "Swarmed Discriminant Analysis for Multifunction Prosthesis Control", abstract = "One of the approaches enabling people with amputated
limbs to establish some sort of interface with the real world includes
the utilization of the myoelectric signal (MES) from the remaining
muscles of those limbs. The MES can be used as a control input to a
multifunction prosthetic device. In this control scheme, known as the
myoelectric control, a pattern recognition approach is usually utilized
to discriminate between the MES signals that belong to different
classes of the forearm movements. Since the MES is recorded using
multiple channels, the feature vector size can become very large. In
order to reduce the computational cost and enhance the generalization
capability of the classifier, a dimensionality reduction method is
needed to identify an informative yet moderate size feature set. This
paper proposes a new fuzzy version of the well known Fisher-s
Linear Discriminant Analysis (LDA) feature projection technique.
Furthermore, based on the fact that certain muscles might contribute
more to the discrimination process, a novel feature weighting scheme
is also presented by employing Particle Swarm Optimization (PSO)
for estimating the weight of each feature. The new method, called
PSOFLDA, is tested on real MES datasets and compared with other
techniques to prove its superiority.", keywords = "Discriminant Analysis, Pattern Recognition, SignalProcessing.", volume = "5", number = "3", pages = "116-8", }