Low-dimensional Feature Vector Extraction from Motion Capture Data by Phase Plane Analysis
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This paper proposes a method to obtain a low-dimensional feature vector appropriately representing the characteristics of a given motion-capture data stream. The feature vector is derived based on the concept of phase plane analysis. A set of phase plane trajectories are obtained from the temporal variation of the state variables representing the body-segment arrangement. The information on six motion-characteristic properties is extracted from the shapes of the trajectories, and used as the components of a six-dimensional feature vector. The experimental results showed the effectiveness and limitation of the proposed method.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.25(2017) (online)DOI http://dx.doi.org/10.2197/ipsjjip.25.884------------------------------
This paper proposes a method to obtain a low-dimensional feature vector appropriately representing the characteristics of a given motion-capture data stream. The feature vector is derived based on the concept of phase plane analysis. A set of phase plane trajectories are obtained from the temporal variation of the state variables representing the body-segment arrangement. The information on six motion-characteristic properties is extracted from the shapes of the trajectories, and used as the components of a six-dimensional feature vector. The experimental results showed the effectiveness and limitation of the proposed method.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.25(2017) (online)DOI http://dx.doi.org/10.2197/ipsjjip.25.884------------------------------
収録刊行物
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- 情報処理学会論文誌
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情報処理学会論文誌 58 (9), 2017-09-15
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詳細情報 詳細情報について
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- CRID
- 1050282812885177472
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- NII論文ID
- 170000148923
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- NII書誌ID
- AN00116647
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- ISSN
- 18827764
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- Web Site
- http://id.nii.ac.jp/1001/00183529/
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- 本文言語コード
- en
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- 資料種別
- journal article
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- データソース種別
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- IRDB
- CiNii Articles