Fuzzy Discriminant Analysis based Feature Projection in Myoelectric Control

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008, pp. 5049 - 5052
Issue Date:
2008-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2008003251.pdf278.02 kB
Adobe PDF
The myoelectric signal (MES) from human muscles is usually utilized as an input to the controller of a multifunction prosthetic hand. In such a system, a pattern recognition approach is usually employed to discriminate between the MES from different classes. Since the MES is recorded using multi 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 moderate size feature set. This paper proposes a novel feature projection technique based on a combination of Fisher's Linear Discriminant Analysis (LDA), and Fuzzy Logic. The new method, called FLDA, assigns different membership degrees to the data points thus reducing the effect of overlapping points in the discrimination process. Furthermore, the concept of Mutual Information (MI) is introduced in the fuzzy memberships in order to assign weights to the features (attributes) according to their contribution to the discrimination process. The FLDA method is tested on a seven classes MES dataset and compared with other feature projection techniques proving its superiority.
Please use this identifier to cite or link to this item: