Skill grouping method: Mining and clustering skill differences from body movement BigData

Shinichi Yamagiwa, Yoshinobu Kawahara, Noriyuki Tabuchi, Yoshinobu Watanabe, Takeshi Naruo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Capturing human movement has become available in detail due to the advancement of motion sensor technology integrated by micro-machine and also due to the one of optical recording by high speed and high resolution image sensors. Therefore, we can easily record the human activity as the body movement BigData and analyze it to quest skill to become an expert of a target body movement. Especially, in the sports activity, the quest for becoming an expert athlete has been tried by using a mathematical model of an ideal body movement experienced from the biomechanics approach. The skill is discussed by comparing the differences from the predicted coordinates of body parts captured during the target performance. However, the approach potentially includes difficulties such as modeling the body control from the dynamics system for all human movements. And also the approach needs for adjusting jitters of the individual characteristics. Therefore, when applying the conventional approach, we must discuss a huge number of combinations of mathematical models and then we would find a model for the ideal body movement. To overcome the difficulty, this paper proposes an approach to visualize skill differences among experts and beginners from the BigData called the skill grouping method. It exploits the skill groups clustered by machine learning approach based on a kernel method. This paper shows applications of the skill grouping method from sports activities. Those show validities for finding the skill differences comparing to the BigData of skillful athletes, and also the one for managing skill transition of an athlete in a timeline.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015
EditorsFeng Luo, Kemafor Ogan, Mohammed J. Zaki, Laura Haas, Beng Chin Ooi, Vipin Kumar, Sudarsan Rachuri, Saumyadipta Pyne, Howard Ho, Xiaohua Hu, Shipeng Yu, Morris Hui-I Hsiao, Jian Li
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2525-2534
Number of pages10
ISBN (Electronic)9781479999255
DOIs
Publication statusPublished - Dec 22 2015
Externally publishedYes
Event3rd IEEE International Conference on Big Data, IEEE Big Data 2015 - Santa Clara, United States
Duration: Oct 29 2015Nov 1 2015

Publication series

NameProceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015

Conference

Conference3rd IEEE International Conference on Big Data, IEEE Big Data 2015
CountryUnited States
CitySanta Clara
Period10/29/1511/1/15

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All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Software

Cite this

Yamagiwa, S., Kawahara, Y., Tabuchi, N., Watanabe, Y., & Naruo, T. (2015). Skill grouping method: Mining and clustering skill differences from body movement BigData. In F. Luo, K. Ogan, M. J. Zaki, L. Haas, B. C. Ooi, V. Kumar, S. Rachuri, S. Pyne, H. Ho, X. Hu, S. Yu, M. H-I. Hsiao, ... J. Li (Eds.), Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015 (pp. 2525-2534). [7364049] (Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2015.7364049