skip to main content
10.1145/2968219.2971408acmconferencesArticle/Chapter ViewAbstractPublication PagesubicompConference Proceedingsconference-collections
poster

Estimation of beautiful gait using an accelerometer

Published: 12 September 2016 Publication History

Abstract

Beautiful gait provides attractiveness and health, and some people want to know whether their gait is beautiful or not. Though methods of beautiful gait evaluation are proposed in previous works by utilizing 3D motion capture and force plates, they are not suitable for daily monitoring of beautiful gait. From this viewpoint, we propose a new method to estimate beautiful gait by utilizing machine learning classifiers with only one accelerometer on the body (chest, waist, or wrist). We conducted an experiment to verify the accuracy of our method with 41 subjects. Each subject wore accelerometers and walked on a straight path whose length was about 10 m 6 times. The accuracy of estimation in total 246 trials was calculated by two kinds of cross validation; leave-one-out cross validation and 41-fold cross validation. The former accuracy is 80.1% (chest), 82.1% (waist), and 82.9% (wrist). The latter accuracy is 73.2% (chest), 78.0% (waist), and 58.1% (wrist). Our contribution is to reveal the feasibility of estimation of beautiful gait using an accelerometer.

References

[1]
Kamiar Aminian, Farzin Dadashi, Benoit Mariani, Constanze Lenoble-Hoskovec, Brigitte Santos-Eggimann, and Christophe J Büla. 2014. Gait analysis using shoe-worn inertial sensors: how is foot clearance related to walking speed?. In Proceedings of UbiComp. ACM, 481--485.
[2]
Agata Brajdic and Robert Harle. 2013. Walk detection and step counting on unconstrained smartphones. In Proceedings of UbiComp. ACM, 225--234.
[3]
Rúben Gouveia, Evangelos Karapanos, and Marc Hassenzahl. 2015. How do we engage with activity trackers?: a longitudinal study of Habito. In Proceedings of UbiComp. ACM, 1305--1316.
[4]
Hsin-Liu Cindy Kao, Bo-Jhang Ho, Allan C Lin, and Hao-Hua Chu. 2012. Phone-based gait analysis to detect alcohol usage. In Proceedings of UbiComp. ACM, 661--662.
[5]
Kanako Miura, Yasuaki Ohtaki, and Hikaru Inooka. 2001. Impression analysis of various humanlike gait patterns. In Proceedings of ROMAN. IEEE, 568--573.
[6]
Tiago Ornelas, Ana Caraban, Rúben Gouveia, and Evangelos Karapanos. 2015. CrowdWalk: leveraging the wisdom of the crowd to inspire walking activities. In Adjunct Proceedings of UbiComp. ACM, 213--216.
[7]
Kyoko Sudo, Satoshi Shimada, Yukiyasu Iida, Yasuko Takahashi, and Sakuichi Ohtsuka. 2006. Quantitative evaluation of walking style from the temporal and spatial tracks of sole pressure points. Electronics and Communications in Japan 89, 4 (2006), 42--54.

Cited By

View all
  • (2023)Auto-GaitProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35808457:1(1-19)Online publication date: 28-Mar-2023

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct
September 2016
1807 pages
ISBN:9781450344623
DOI:10.1145/2968219
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 September 2016

Check for updates

Author Tags

  1. accelerometer
  2. beautiful gait
  3. wearable device

Qualifiers

  • Poster

Conference

UbiComp '16

Acceptance Rates

Overall Acceptance Rate 764 of 2,912 submissions, 26%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)13
  • Downloads (Last 6 weeks)0
Reflects downloads up to 11 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2023)Auto-GaitProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35808457:1(1-19)Online publication date: 28-Mar-2023

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media