SVD-based Feature Extraction from Time-series Motion Data and Its Application to Gesture Recognition

Isao Hayashi; Yinlai Jiang; Shuoyu Wang

8th International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)

Research Article

SVD-based Feature Extraction from Time-series Motion Data and Its Application to Gesture Recognition

Cite: BibTeX Plain Text

@INPROCEEDINGS{10.4108/icst.bict.2014.257811,
    author={Isao Hayashi and Yinlai Jiang and Shuoyu Wang},
    title={SVD-based Feature Extraction from Time-series Motion Data and Its Application to Gesture Recognition},
    proceedings={8th International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)},
    publisher={ICST},
    proceedings_a={BICT},
    year={2015},
    month={2},
    keywords={feature extraction svd gesture recognition},
    doi={10.4108/icst.bict.2014.257811}
}

Isao Hayashi
Yinlai Jiang
Shuoyu Wang
Year: 2015
SVD-based Feature Extraction from Time-series Motion Data and Its Application to Gesture Recognition
BICT
ACM
DOI: 10.4108/icst.bict.2014.257811

Isao Hayashi¹, Yinlai Jiang^,*, Shuoyu Wang²

1: Kansai University
2: Kochi University of Technology

*Contact email: jiang@hi.mce.uec.ac.jp

Abstract

Singular value decomposition is used to extract features from time-series motion data. A matrix consisting of the time-series data is decomposed into left singular vectors which represent the patterns of the motion and singular values as a scalar, by which each corresponding left singular vector affects the matrix. Gesture recognition using the extracted features suggest the effectiveness of the method.

Keywords: feature extraction svd gesture recognition

Published: 2015-02-02
Publisher: ICST
Appears in: ACM Digital Library

: http://dx.doi.org/10.4108/icst.bict.2014.257811

SVD-based Feature Extraction from Time-series Motion Data and Its Application to Gesture Recognition

Abstract

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