Skip to main content

Motivating Track and Field Athletes by Visualizing Training Drills and Records: Extraction and Visualization of Activities of Athletes from Blog Articles

  • Conference paper
  • First Online:
Advances in Human Factors in Sports and Outdoor Recreation

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 496))

  • 773 Accesses

Abstract

The aim of this study is to support track and field athletes to keep their motivation. Our approach is based on collection, extraction, and visualization of athlete’s activities by using blog articles. We collect blog articles written by track and field athletes. We extract menus, quantity, and the detail of training menus from these articles. We use the hidden Markov model (HMM) to extract these data. We then visualize the training and record data as graphs and tables. We provide athletes with various graphs and tables such as the distance graph.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ota, N.: Rival significance in education—analyzing rival recognition. Psychology. Bull. Univ. Nagoya. 46, 309–310 (1999) Japan

    Google Scholar 

  2. Sano, M., Fukuhara, T., Masuda, H., Yamada, K.: Extraction of athlete’s activities from blog articles. In: The 29th Annual Conference of the Japanese Society for Artificial Intelligence, no. 29, pp. 1–4 (2015) (Japan)

    Google Scholar 

  3. Sermore, K., McCallum, A., Rosenfeld, R.: Learning Hidden Markov Model Structure for Information Extraction. AAAI Technical Report, WS-99–11 (1999)

    Google Scholar 

  4. Kita, K., Tsujii, J.: Statistical models for natural language processing, University of Tokyo Press. Comput. Linguist. 4, 101–125 (2008) (Japan)

    Google Scholar 

  5. Ageishi, R., Miura, T.: Part of speech tagging for english to extract named entity. DEWS E7-6, 1–6 (2008) (Japan)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Masakazu Sano .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing Switzerland

About this paper

Cite this paper

Sano, M., Masuda, H., Yamada, K., Fukuhara, T. (2017). Motivating Track and Field Athletes by Visualizing Training Drills and Records: Extraction and Visualization of Activities of Athletes from Blog Articles. In: Salmon, P., Macquet, AC. (eds) Advances in Human Factors in Sports and Outdoor Recreation. Advances in Intelligent Systems and Computing, vol 496. Springer, Cham. https://doi.org/10.1007/978-3-319-41953-4_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-41953-4_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41952-7

  • Online ISBN: 978-3-319-41953-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics