Skip to main content
SpringerLink
Log in

An Analysis of Case Representations for Marathon Race Prediction and Planning

  • Conference paper
  • First Online:
Case-Based Reasoning Research and Development (ICCBR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11156))

Included in the following conference series:

Abstract

We use case-based reasoning to help marathoners achieve a personal best for an upcoming race, by helping them to select an achievable goal-time and a suitable pacing plan. We evaluate several case representations and, using real-world race data, highlight their performance implications. Richer representations do not always deliver better prediction performance, but certain representational configurations do offer very significant practical benefits for runners, when it comes to predicting, and planning for, challenging goal-times during an upcoming race.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Mayer-Schönberger, V., Cukier, K.: Big data: a revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt, Boston (2013)

    Google Scholar 

  2. Peek, N., Combi, C., Marin, R., Bellazzi, R.: Thirty years of artificial intelligence in medicine (AIME) conferences: a review of research themes. Artif. Intell. Med. 65(1), 61–73 (2015)

    Article  Google Scholar 

  3. Buchanan, B.G., Shortliffe, E.H.: Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley Series in Artificial Intelligence). Addison-Wesley Longman Publishing Co., Inc., Boston (1984)

    Google Scholar 

  4. Wiesner, M., Pfeifer, D.: Health recommender systems: concepts, requirements, technical basics and challenges. Int. J. Environ. Res. Public Health 11(3), 2580–2607 (2014)

    Article  Google Scholar 

  5. Wiesner, M., Pfeifer, D.: Adapting recommender systems to the requirements of personal health record systems. In: Proceedings of the 1st ACM International Health Informatics Symposium, IHI 2010, New York, NY, USA, pp. 410–414. ACM (2010)

    Google Scholar 

  6. Leijdekkers, P., Gay, V.: Improving user engagement by aggregating and analysing health and fitness data on a mobile App. In: Geissbühler, A., Demongeot, J., Mokhtari, M., Abdulrazak, B., Aloulou, H. (eds.) ICOST 2015. LNCS, vol. 9102, pp. 325–330. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19312-0_30

    Chapter  Google Scholar 

  7. Möller, A., et al.: GymSkill: mobile exercise skill assessment to support personal health and fitness. In: 9th International Conference on Pervasive Computing, Pervasive: Video, CA, USA, San Francisco, p. 2011 (2011)

    Google Scholar 

  8. Hermens, H., op den Akker, H., Tabak, M., Wijsman, J., Vollenbroek-Hutten, M.: Personalized coaching systems to support healthy behavior in people with chronic conditions, vol. 24, no. 6, pp. 815–826 (2014). eemcs-eprint-25228

    Article  Google Scholar 

  9. Ohlin, F., Olsson, C.M.: Intelligent computing in personal informatics: key design considerations. In: Proceedings of the 20th International Conference on Intelligent User Interfaces, IUI 2015, New York, NY, USA, pp. 263–274. ACM (2015)

    Google Scholar 

  10. Geleijnse, G., Nachtigall, P., van Kaam, P., Wijgergangs, L.: A personalized recipe advice system to promote healthful choices. In: Proceedings of the 16th International Conference on Intelligent User Interfaces, IUI 2011, New York, NY, USA, pp. 437–438. ACM (2011)

    Google Scholar 

  11. Bichindaritz, I., Montani, S., Portinale, L.: Special issue on case-based reasoning in the health sciences. Appl. Intell. 28(3), 207–209 (2008)

    Article  Google Scholar 

  12. Lewis, M.: Moneyball: The Art of Winning an Unfair Game. WW Norton & Company, New York (2004)

    Google Scholar 

  13. Kelly, D., Coughlan, G.F., Green, B.S., Caulfield, B.: Automatic detection of collisions in elite level Rugby union using a wearable sensing device. Sports Eng. 15(2), 81–92 (2012)

    Article  Google Scholar 

  14. Buttussi, F., Chittaro, L.: MOPET: a context-aware and user-adaptive wearable system for fitness training. Artif. Intell. Med. 42(2), 153–163 (2008)

    Article  Google Scholar 

  15. Vales-Alonso, J., et al.: Ambient intelligence systems for personalized sport training. Sensors 10(3), 2359–2385 (2010)

    Article  Google Scholar 

  16. de Oliveira, R., Oliver, N.: TripleBeat: enhancing exercise performance with persuasion. In: Proceedings of the 10th International Conference on Human Computer Interaction with Mobile Devices and Services, MobileHCI 2008, New York, NY, USA, pp. 255–264. ACM (2008)

    Google Scholar 

  17. Iyer, S.R., Sharda, R.: Prediction of athletes performance using neural networks: an application in cricket team selection. Expert Syst. Appl. 36(3), 5510–5522 (2009)

    Article  Google Scholar 

  18. Bartolucci, F., Murphy, T.B.: A finite mixture latent trajectory model for modeling ultrarunners’ behavior in a 24-hour race. J. Quant. Anal. Sport. 11(4), 193–203 (2015)

    Google Scholar 

  19. Smyth, B., Cunningham, P.: Running with cases: a CBR approach to running your best marathon. In: Case-Based Reasoning Research and Development - 25th International Conference, ICCBR 2017, Trondheim, Norway, 26–28 June 2017, Proceedings, pp. 360–374 (2017)

    Google Scholar 

  20. Smyth, B., Cunningham, P.: A novel recommender system for helping marathoners to achieve a new personal-best. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys 2017, Como, Italy, 27–31 August 2017, pp. 116–120 (2017)

    Google Scholar 

  21. Vickers, A.J., Vertosick, E.A.: An empirical study of race times in recreational endurance runners. BMC Sport. Sci., Med. Rehabil. 8(1), 26 (2016)

    Article  Google Scholar 

  22. Deaner, R.O.: More males run fast: a stable sex difference in competitiveness in us distance runners. Evol. Hum. Behav. 27(1), 63–84 (2006)

    Article  Google Scholar 

  23. March, D.S., Vanderburgh, P.M., Titlebaum, P.J., Hoops, M.L.: Age, sex, and finish time as determinants of pacing in the marathon. J. Strength Cond. Res. 25(2), 386–391 (2011)

    Article  Google Scholar 

  24. Trubee, N.W.: The effects of age, sex, heat stress, and finish time on pacing in the marathon. Ph.D. thesis, University of Dayton (2011)

    Google Scholar 

  25. Abbiss, C.R., Laursen, P.B.: Describing and understanding pacing strategies during athletic competition. Sports Med. 38(3), 239–252 (2008)

    Article  Google Scholar 

  26. Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)

    Article  Google Scholar 

Download references

Acknowledgments

Supported by Science Foundation Ireland through the Insight Centre for Data Analytics under grant number SFI/12/RC/2289 and by Accenture Labs, Dublin.

Author information

Authors and Affiliations

  1. Insight Centre for Data Analytics School of Computer Science, University College Dublin, Dublin, Ireland

    Barry Smyth & Pádraig Cunningham

Authors
  1. Barry Smyth
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pádraig Cunningham
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Barry Smyth .

Editor information

Editors and Affiliations

  1. Wright State University, Dayton, OH, USA

    Michael T. Cox

  2. Mälardalen University, Västeras, Sweden

    Peter Funk

  3. Mälardalen University, Västeras, Sweden

    Shahina Begum

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Smyth, B., Cunningham, P. (2018). An Analysis of Case Representations for Marathon Race Prediction and Planning. In: Cox, M., Funk, P., Begum, S. (eds) Case-Based Reasoning Research and Development. ICCBR 2018. Lecture Notes in Computer Science(), vol 11156. Springer, Cham. https://doi.org/10.1007/978-3-030-01081-2_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-01081-2_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01080-5

  • Online ISBN: 978-3-030-01081-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation