Training Simulation with Nothing but Training Data - Simulating Performance based on Training Data Without the Help of Performance Diagnostics in a Laboratory

Melanie Ludwig, David Schaefer, Alexander Asteroth

Abstract

Analyzing training performance in sport is usually based on standardized test protocols and needs laboratory equipment, e.g., for measuring blood lactate concentration or other physiological body parameters. Avoiding special equipment and standardized test protocols, we show that it is possible to reach a quality of performance simulation comparable to the results of laboratory studies using training models with nothing but training data. For this purpose, we introduce a fitting concept for a performance model that takes the peculiarities of using training data for the task of performance diagnostics into account. With a specific way of data preprocessing, accuracy of laboratory studies can be achieved for about 50% of the tested subjects, while lower correlation of the other 50% can be explained.

References

  1. Balmer, J., Davison, R. R., and Bird, S. R. (2000). Peak power predicts performance power during an outdoor 16.1-km cycling time trial. Medicine and Science in Sports and exercise, 32(8):1485-1490.
  2. Busso, T., Candau, R., and Lacour, J.-R. (1994). Fatigue and fitness modelled from the effects of training on performance. European journal of applied physiology and occupational physiology, 69(1):50-54.
  3. Busso, T., Carasso, C., and Lacour, J.-R. (1991). Adequacy of a systems structure in the modeling of training effects on performance. Journal of Applied Physiology, 71(5):2044-2049.
  4. Busso, T., Denis, C., Bonnefoy, R., Geyssant, A., and Lacour, J.-R. (1997). Modeling of adaptations to physical training by using a recursive least squares algorithm. Journal of applied physiology, 82(5):1685- 1693.
  5. Calvert, T. W., Banister, E. W., Savage, M. V., and Bach, T. (1976). A systems model of the effects of training on physical performance. IEEE Transactions on Systems, Man and Cybernetics, (2):94-102.
  6. Hellard, P., Avalos, M., Lacoste, L., Barale, F., Chatard, J.- C., and Millet, G. P. (2006). Assessing the limitations of the banister model in monitoring training. Journal of sports sciences, 24(05):509-520.
  7. Krebs, P. and Duncan, D. T. (2015). Health app use among us mobile phone owners: A national survey. JMIR mHealth and uHealth, 3(4).
  8. Lee, J.-M., Kim, Y., and Welk, G. J. (2014). Validity of consumer-based physical activity monitors. Med Sci Sports Exerc, 46(9):1840-8.
  9. Mujika, I., Busso, T., Lacoste, L., Barale, F., Geyssant, A., and Chatard, J.-C. (1996). Modeled responses to training and taper in competitive swimmers. Medicine and science in sports and exercise, 28(2):251-258.
  10. Perl, J. (2000). Antagonistic adaptation systems: An example of how to improve understanding and simulating complex system behaviour by use of meta-models and on line-simulation. 16th IMACS Congress.
  11. Pfeiffer, M. (2008). Modeling the relationship between training and performance-a comparison of two antagonistic concepts. International journal of computer science in sport, 7(2):13-32.
  12. Schaefer, D., Asteroth, A., and Ludwig, M. (2015). Training plan evolution based on training models. In 2015 International Symposium on Innovation in Intelligent SysTems and Applications (INISTA) Proceedings, pages 141-148.
  13. Shrout, P. E. and Fleiss, J. L. (1979). Intraclass correlations: uses in assessing rater reliability. Psychological bulletin, 86(2):420.
  14. Tan, F. H. and Aziz, A. R. (2005). Reproducibility of outdoor flat and uphill cycling time trials and their performance correlates with peak power output in moderately trained cyclists. J Sports Sci Med, 4(3):278-284.
  15. Yang, R., Shin, E., Newman, M. W., and Ackerman, M. S. (2015). When fitness trackers don't 'fit': End-user difficulties in the assessment of personal tracking device accuracy. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 7815, pages 623-634, New York, NY, USA. ACM.
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Paper Citation


in Harvard Style

Ludwig M., Schaefer D. and Asteroth A. (2016). Training Simulation with Nothing but Training Data - Simulating Performance based on Training Data Without the Help of Performance Diagnostics in a Laboratory . In Proceedings of the 4th International Congress on Sport Sciences Research and Technology Support - Volume 1: icSPORTS, ISBN 978-989-758-205-9, pages 75-82. DOI: 10.5220/0006042900750082


in Bibtex Style

@conference{icsports16,
author={Melanie Ludwig and David Schaefer and Alexander Asteroth},
title={Training Simulation with Nothing but Training Data - Simulating Performance based on Training Data Without the Help of Performance Diagnostics in a Laboratory},
booktitle={Proceedings of the 4th International Congress on Sport Sciences Research and Technology Support - Volume 1: icSPORTS,},
year={2016},
pages={75-82},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006042900750082},
isbn={978-989-758-205-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Congress on Sport Sciences Research and Technology Support - Volume 1: icSPORTS,
TI - Training Simulation with Nothing but Training Data - Simulating Performance based on Training Data Without the Help of Performance Diagnostics in a Laboratory
SN - 978-989-758-205-9
AU - Ludwig M.
AU - Schaefer D.
AU - Asteroth A.
PY - 2016
SP - 75
EP - 82
DO - 10.5220/0006042900750082