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A Method for Recognizing Postures and Gestures Using Foot Pressure Sensors
https://ipsj.ixsq.nii.ac.jp/records/195515
https://ipsj.ixsq.nii.ac.jp/records/19551507657c76-d8c7-49a9-8b58-4c757763877a
名前 / ファイル | ライセンス | アクション |
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Copyright (c) 2019 by the Information Processing Society of Japan
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オープンアクセス |
Item type | Journal(1) | |||||||||||
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公開日 | 2019-04-15 | |||||||||||
タイトル | ||||||||||||
タイトル | A Method for Recognizing Postures and Gestures Using Foot Pressure Sensors | |||||||||||
タイトル | ||||||||||||
言語 | en | |||||||||||
タイトル | A Method for Recognizing Postures and Gestures Using Foot Pressure Sensors | |||||||||||
言語 | ||||||||||||
言語 | eng | |||||||||||
キーワード | ||||||||||||
主題Scheme | Other | |||||||||||
主題 | [一般論文] foot pressure, posture recognition, gesture recognition, insole, shoes device | |||||||||||
資源タイプ | ||||||||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||
資源タイプ | journal article | |||||||||||
著者所属 | ||||||||||||
Graduate School of Engineering, Kobe University | ||||||||||||
著者所属 | ||||||||||||
Graduate School of Engineering, Kobe University/PREST, JST | ||||||||||||
著者所属 | ||||||||||||
Graduate School of Engineering, Kobe University | ||||||||||||
著者所属(英) | ||||||||||||
en | ||||||||||||
Graduate School of Engineering, Kobe University | ||||||||||||
著者所属(英) | ||||||||||||
en | ||||||||||||
Graduate School of Engineering, Kobe University / PREST, JST | ||||||||||||
著者所属(英) | ||||||||||||
en | ||||||||||||
Graduate School of Engineering, Kobe University | ||||||||||||
著者名 |
Ayumi, Ohnishi
× Ayumi, Ohnishi
× Tsutomu, Terada
× Masahiko, Tsukamoto
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著者名(英) |
Ayumi, Ohnishi
× Ayumi, Ohnishi
× Tsutomu, Terada
× Masahiko, Tsukamoto
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論文抄録 | ||||||||||||
内容記述タイプ | Other | |||||||||||
内容記述 | In this paper, we propose a method for recognizing postures and gestures by using foot pressure sensors, and we investigate optimal positions for pressure sensors on soles from the viewpoint of motion recognition accuracy. In experiments, the recognition accuracies of 22 kinds of daily postures and gestures were evaluated from foot-pressure sensor values. Furthermore, the optimum measurement points for high recognition accuracy were examined by evaluating combinations of two foot pressure measurement areas on a round-robin basis. As a result, when selecting the optimum two points for each user, the recognition accuracy was about 94.5% on average. The recognition accuracy of the averaged combinations of the best two combinations for all subjects was classified with an accuracy of about 91.9% on average. As a result of an evaluation to raise versatility, the average recognition accuracy in a three-point evaluation was 98.4%, which was almost the same with the recognition accuracy when using all 105 points. In anticipation of the applicability of this research result, two types of pressure sensing shoes were developed. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.27(2019) (online) DOI http://dx.doi.org/10.2197/ipsjjip.27.348 ------------------------------ |
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論文抄録(英) | ||||||||||||
内容記述タイプ | Other | |||||||||||
内容記述 | In this paper, we propose a method for recognizing postures and gestures by using foot pressure sensors, and we investigate optimal positions for pressure sensors on soles from the viewpoint of motion recognition accuracy. In experiments, the recognition accuracies of 22 kinds of daily postures and gestures were evaluated from foot-pressure sensor values. Furthermore, the optimum measurement points for high recognition accuracy were examined by evaluating combinations of two foot pressure measurement areas on a round-robin basis. As a result, when selecting the optimum two points for each user, the recognition accuracy was about 94.5% on average. The recognition accuracy of the averaged combinations of the best two combinations for all subjects was classified with an accuracy of about 91.9% on average. As a result of an evaluation to raise versatility, the average recognition accuracy in a three-point evaluation was 98.4%, which was almost the same with the recognition accuracy when using all 105 points. In anticipation of the applicability of this research result, two types of pressure sensing shoes were developed. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.27(2019) (online) DOI http://dx.doi.org/10.2197/ipsjjip.27.348 ------------------------------ |
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書誌レコードID | ||||||||||||
収録物識別子タイプ | NCID | |||||||||||
収録物識別子 | AN00116647 | |||||||||||
書誌情報 |
情報処理学会論文誌 巻 60, 号 4, 発行日 2019-04-15 |
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ISSN | ||||||||||||
収録物識別子タイプ | ISSN | |||||||||||
収録物識別子 | 1882-7764 |