Swarmed Discriminant Analysis for Multifunction Prosthesis Control

Authors: Rami N. Khushaba, Ahmed Al-Ani, Adel Al-Jumaily

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.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1072531

References:

[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