Visualization of Features in Multivariate Gait Data: Use of a Deep Learning for the Visualization of Body Parts and Their Timing During Gait Training

Yusuke Osawa; Keiichi Watanuki; Kazunori Kaede; Keiichi Muramatsu

doi:10.1007/978-3-030-51194-4_132

Visualization of Features in Multivariate Gait Data: Use of a Deep Learning for the Visualization of Body Parts and Their Timing During Gait Training

Yusuke Osawa^*, Keiichi Watanuki, Kazunori Kaede, Keiichi Muramatsu

^*この研究の対応する著者

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

In this study, we aimed to examine the usefulness of gait classification and feature visualization based on multivariate data for the development of a gait feedback training system capable of considering the physical differences among the trainees. The multivariate data considered in this study were the joint angles and the ground reaction forces. In addition, all multivariate gait data were labeled as gait “rarely associated with stumbling” or “frequently associated with stumbling”. A convolutional neural network was used to learn the gait features. Furthermore, the feature parts of the multivariate gait data used for classification were visualized on a heat map created using Grad-CAM. As the results indicate, a heatmap is able to show the feature parts of a gait frequently associated with stumbling, through which the trainee can adjust their gait.

本文言語	English
ホスト出版物のタイトル	Advances in Industrial Design - Proceedings of the AHFE 2020 Virtual Conferences on Design for Inclusion, Affective and Pleasurable Design, Interdisciplinary Practice in Industrial Design, Kansei Engineering, and Human Factors for Apparel and Textile Engineering
編集者	Giuseppe Di Bucchianico, Cliff Sungsoo Shin, Scott Shim, Shuichi Fukuda, Gianni Montagna, Cristina Carvalho
出版社	Springer
ページ	1007-1013
ページ数	7
ISBN（印刷版）	9783030511937
DOI	https://doi.org/10.1007/978-3-030-51194-4_132
出版ステータス	Published - 2020
外部発表	はい
イベント	AHFE Virtual Conference on Design for Inclusion, the Virtual Conference on Interdisciplinary Practice in Industrial Design, the Virtual Conference on Affective and Pleasurable Design, the Virtual Conference on Kansei Engineering, and the Virtual Conference on Human Factors for Apparel and Textile Engineering, 2020 - San Diego, United States 継続期間: 2020 7月 16 → 2020 7月 20

出版物シリーズ

名前	Advances in Intelligent Systems and Computing
巻	1202 AISC
ISSN（印刷版）	2194-5357
ISSN（電子版）	2194-5365

Conference

Conference	AHFE Virtual Conference on Design for Inclusion, the Virtual Conference on Interdisciplinary Practice in Industrial Design, the Virtual Conference on Affective and Pleasurable Design, the Virtual Conference on Kansei Engineering, and the Virtual Conference on Human Factors for Apparel and Textile Engineering, 2020
国/地域	United States
City	San Diego
Period	20/7/16 → 20/7/20

ASJC Scopus subject areas

制御およびシステム工学
コンピュータサイエンス（全般）

文献へのアクセス

10.1007/978-3-030-51194-4_132

他のファイルとリンク

フィンガープリント

「Visualization of Features in Multivariate Gait Data: Use of a Deep Learning for the Visualization of Body Parts and Their Timing During Gait Training」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル

Osawa, Y., Watanuki, K., Kaede, K., & Muramatsu, K. (2020). Visualization of Features in Multivariate Gait Data: Use of a Deep Learning for the Visualization of Body Parts and Their Timing During Gait Training. In G. Di Bucchianico, C. S. Shin, S. Shim, S. Fukuda, G. Montagna, & C. Carvalho (Eds.), Advances in Industrial Design - Proceedings of the AHFE 2020 Virtual Conferences on Design for Inclusion, Affective and Pleasurable Design, Interdisciplinary Practice in Industrial Design, Kansei Engineering, and Human Factors for Apparel and Textile Engineering (pp. 1007-1013). (Advances in Intelligent Systems and Computing; Vol. 1202 AISC). Springer. https://doi.org/10.1007/978-3-030-51194-4_132

Visualization of Features in Multivariate Gait Data: Use of a Deep Learning for the Visualization of Body Parts and Their Timing During Gait Training. / Osawa, Yusuke; Watanuki, Keiichi; Kaede, Kazunori その他.
Advances in Industrial Design - Proceedings of the AHFE 2020 Virtual Conferences on Design for Inclusion, Affective and Pleasurable Design, Interdisciplinary Practice in Industrial Design, Kansei Engineering, and Human Factors for Apparel and Textile Engineering. ed. / Giuseppe Di Bucchianico; Cliff Sungsoo Shin; Scott Shim; Shuichi Fukuda; Gianni Montagna; Cristina Carvalho. Springer, 2020. p. 1007-1013 (Advances in Intelligent Systems and Computing; Vol. 1202 AISC).

研究成果: Conference contribution

Osawa, Y, Watanuki, K, Kaede, K & Muramatsu, K 2020, Visualization of Features in Multivariate Gait Data: Use of a Deep Learning for the Visualization of Body Parts and Their Timing During Gait Training. in G Di Bucchianico, CS Shin, S Shim, S Fukuda, G Montagna & C Carvalho (eds), Advances in Industrial Design - Proceedings of the AHFE 2020 Virtual Conferences on Design for Inclusion, Affective and Pleasurable Design, Interdisciplinary Practice in Industrial Design, Kansei Engineering, and Human Factors for Apparel and Textile Engineering. Advances in Intelligent Systems and Computing, vol. 1202 AISC, Springer, pp. 1007-1013, AHFE Virtual Conference on Design for Inclusion, the Virtual Conference on Interdisciplinary Practice in Industrial Design, the Virtual Conference on Affective and Pleasurable Design, the Virtual Conference on Kansei Engineering, and the Virtual Conference on Human Factors for Apparel and Textile Engineering, 2020, San Diego, United States, 20/7/16. https://doi.org/10.1007/978-3-030-51194-4_132

Osawa Y, Watanuki K, Kaede K, Muramatsu K. Visualization of Features in Multivariate Gait Data: Use of a Deep Learning for the Visualization of Body Parts and Their Timing During Gait Training. In Di Bucchianico G, Shin CS, Shim S, Fukuda S, Montagna G, Carvalho C, editors, Advances in Industrial Design - Proceedings of the AHFE 2020 Virtual Conferences on Design for Inclusion, Affective and Pleasurable Design, Interdisciplinary Practice in Industrial Design, Kansei Engineering, and Human Factors for Apparel and Textile Engineering. Springer. 2020. p. 1007-1013. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-51194-4_132

Osawa, Yusuke ; Watanuki, Keiichi ; Kaede, Kazunori その他. / Visualization of Features in Multivariate Gait Data : Use of a Deep Learning for the Visualization of Body Parts and Their Timing During Gait Training. Advances in Industrial Design - Proceedings of the AHFE 2020 Virtual Conferences on Design for Inclusion, Affective and Pleasurable Design, Interdisciplinary Practice in Industrial Design, Kansei Engineering, and Human Factors for Apparel and Textile Engineering. editor / Giuseppe Di Bucchianico ; Cliff Sungsoo Shin ; Scott Shim ; Shuichi Fukuda ; Gianni Montagna ; Cristina Carvalho. Springer, 2020. pp. 1007-1013 (Advances in Intelligent Systems and Computing).

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