Discrimination Models of Swing Phase and Stance Phase using MediaPipe
Author:
Affiliation:
1. Department of Informatics Graduate School of Technology, Kogakuin University
Publisher
Japan Society of Kansei Engineering
Link
https://www.jstage.jst.go.jp/article/ijae/23/3/23_IJAE-D-23-00036/_pdf
Reference12 articles.
1. Aoyama, Y., & Tanaka, H. (2023). Automatic analysis of gait cycle using MediaPipe. Proceedings of the 9th International Symposium on Affective Science and Engineering, PM-1A-1. https://doi.org/10.5057/isase.2023-c000016
2. Arai, T., Shiba, Y., Watanabe, S., & Shibata, H. (2011). The relationship between the stride time variability, motor ability and fall in community-dwelling elderly people. Physical Therapy Japan, 38(3), 165-172. (in Japanese) https://www.jstage.jst.go.jp/article/rigaku/38/3/38_KJ00007296808/_article/-char/en
3. Castelli, A., Paolini, G., Cereatti, A., & Della Croce, U. (2015). A 2D markerless gait analysis methodology: Validation on healthy subjects. Computational and Mathematical Methods in Medicine, 2015(1), Article 186780. https://doi.org/10.1155/2015/186780
4. Kanko, R. M., Laende, E. K., Strutzenberger, G., Brown, M., Selbie, W. S., DePaul, V., Scott, S. H., & Deluzio, K. J. (2021). Assessment of spatiotemporal gait parameters using a deep learning algorithm-based markerless motion capture system. Journal of Biomechanics, 122, Article 110414. https://doi.org/10.1016/j.jbiomech.2021.110414
5. Nara, I., & Matsuo, Y. (2011). Hokō o Miru: Kansatsu kara hajimeru rigaku ryōhō jissen. Bunkōdō. (in Japanese)
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