Gait Phase Partitioning and Footprint Detection Using Mutually Constrained Piecewise Linear Approximation with Dynamic Programming
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- YASUKAWA Makoto
- Osaka University
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- MAKIHARA Yasushi
- Osaka University
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- HOSOI Toshinori
- NEC Corporation
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- KUBO Masahiro
- NEC Corporation
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- YAGI Yasushi
- Osaka University
抄録
<p>Human gait analysis has been widely used in medical and health fields. It is essential to extract spatio-temporal gait features (e.g., single support duration, step length, and toe angle) by partitioning the gait phase and estimating the footprint position/orientation in such fields. Therefore, we propose a method to partition the gait phase given a foot position sequence using mutually constrained piecewise linear approximation with dynamic programming, which not only represents normal gait well but also pathological gait without training data. We also propose a method to detect footprints by accumulating toe edges on the floor plane during stance phases, which enables us to detect footprints more clearly than a conventional method. Finally, we extract four spatial/temporal gait parameters for accuracy evaluation: single support duration, double support duration, toe angle, and step length. We conducted experiments to validate the proposed method using two types of gait patterns, that is, healthy and mimicked hemiplegic gait, from 10 subjects. We confirmed that the proposed method could estimate the spatial/temporal gait parameters more accurately than a conventional skeleton-based method regardless of the gait pattern.</p>
収録刊行物
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- IEICE Transactions on Information and Systems
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IEICE Transactions on Information and Systems E104.D (11), 1951-1962, 2021-11-01
一般社団法人 電子情報通信学会
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詳細情報 詳細情報について
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- CRID
- 1390289920606326144
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- NII論文ID
- 130008110001
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- ISSN
- 17451361
- 09168532
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可