Neighborhood components analysis in sEMG signal dimensionality reduction for gait phase pattern recognition

Conference proceedings article

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Publication Details

Author list: Manit J., Youngkong P.

Publisher: Hindawi

Publication year: 2011

Start page: 86

End page: 90

Number of pages: 5

ISBN: 9781467307680

ISSN: 0146-9428

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84863890695&doi=10.1109%2fIB2Com.2011.6217897&partnerID=40&md5=9088fa28a84020c5e874ce68f2d14bed

Languages: English-Great Britain (EN-GB)

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Abstract

Dimensionality reduction technique is an essential method for sEMG signal pattern recognition and classification, especially for real-time application such as prosthesis control. This technique can reduce the high dimension extracted feature into a lower dimension space feature which help the classifier works more properly. This paper presents an application of a dimensionality reduction technique called neighborhood components analysis (NCA). We evaluate the efficiency of NCA by comparing its class separability and the classification accuracy with other three algorithms: principle component analysis (PCA), linear discriminant analysis (LDA) and local preserving projection (LPP). The result shows that NCA outperform other algorithm in the class separability, and its classification accuracy is also slightly higher. ฉ 2011 IEEE.

Keywords

dimensionality reduction, gait phase, myoelectric, neighborhood components analysis (NCA), Pattern recognition, surface electromyography (sEMG)