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Axis Visualizer: Enjoy Core Torsion and Be Healthy for Health Promotion Community Support

  • Takuichi NishimuraEmail author
  • Zilu Liang
  • Satoshi Nishimura
  • Tomoka Nagao
  • Satoko Okubo
  • Yasuyuki Yoshida
  • Kazuya Imaizumi
  • Hisae Konosu
  • Hiroyasu Miwa
  • Kanako Nakajima
  • Ken Fukuda
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10091)

Abstract

In Japan, the ratio of people with lifestyle-related diseases has increased to approximately 30%. Individuals as well as the Nation are getting more and more health-conscious, and special attention has been made to body trunk because it is vital for injury prevention, physical strength, and beauty. Various training methods have been proposed to increase the muscle mass of body trunk. However, for sports that emphasize somatoform such as dance, the strength of the trunk is mainly decided by smooth use of the trunk rather than its muscle mass. In this paper, in order to evaluate the use of the trunk torsion movement, we proposed a new trunk torsion model for the purpose of evaluating two trunk torsion standard movements. We also developed a mobile application named “Axis Visualizer” based on the proposed trunk torsion model analyzing sensor data in the device. Axis Visualizer generates higher score when a user rotates the shoulders or hips smoothly with axis fixed and high frequencies. This application can support trainers and coaches to visualize the use of customers’ trunk and to increase the training effect.

Keywords

Movement modelling Core strength Health promotion 

Notes

Acknowledgment

This study was partly supported by Japanese METI’s “Robotic Care Equipment Development and Introduction Project”, “Future AI and Robot Technology Research and Development Project” commissioned by the New Energy and Industrial Technology Development Organization (NEDO) and JSPS KAKENHI Grant Numbers 24500676 and 25730190. We would also like to thank the member of the health promotion project in Odaiba and Tsukuba for their kind support.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Takuichi Nishimura
    • 1
    Email author
  • Zilu Liang
    • 1
    • 6
  • Satoshi Nishimura
    • 1
  • Tomoka Nagao
    • 1
  • Satoko Okubo
    • 1
  • Yasuyuki Yoshida
    • 2
  • Kazuya Imaizumi
    • 3
  • Hisae Konosu
    • 4
  • Hiroyasu Miwa
    • 5
  • Kanako Nakajima
    • 5
  • Ken Fukuda
    • 1
  1. 1.AI Research CenterNational Institute of Advanced Industrial Science and TechnologyToyotaJapan
  2. 2.Graduate School of Decision Science and TechnologyTokyo Institute of TechnologyToyotaJapan
  3. 3.Faculty of HealthcareTokyo Healthcare UniversityToyotaJapan
  4. 4.Japan Dance Sport FederationToyotaJapan
  5. 5.Human Information Research InstituteNational Institute of Advanced Industrial Science and TechnologyToyotaJapan
  6. 6.Department of Computing and Information SystemsUniversity of MelbourneMelbourneAustralia

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