Abstract
Athletes voluntarily change their sportive behavior in order to improve performance or to reduce load. If this process is guided by feedback loops, characteristics of adaptive systems are met. The occurring adaptive change is relevant to achieving a goal or objective. In a similar manner, smart sports equipment may alter its properties depending on environmental conditions. In order to automatically give feedback on how to continue exercising and/or to adjust the sports equipment during the physical activity, intelligent devices are required. These devices rely on models for recognition and classification of patterns in the motion or activity currently performed. Different methods and models, such as Neural Networks, Hidden Markov models or Support Vector Machines have proven to be applicable for this purpose. Examples from recreational running, mountain-biking, exercising on weight training machines and long distance running illustrate the principle.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Baca, A. (2003). Computer-science based feedback systems on sports performance. International Journal of Computer Science in Sport, 2, 20–30.
Baca, A., Dabnichki, P., Heller, M., & Kornfeind, P. (2009). Ubiquitous computing in sports: A review and analysis. Journal of Sports Sciences, 27, 1335–1346.
Baca, A., Kornfeind, P., Preuschl, E., Bichler, S., Tampier, M., & Novatchkov, H. (2010). A server-based mobile coaching system. Sensors, 10, 10640–10662.
Baca, A., & Kornfeind, P. (2012). Stability analysis of motion patterns in biathlon shooting. Human Movement Science, 31, 295–302.
Baca, A. (2013). Methods for recognition and classification of human motion patterns: A prerequisite for intelligent devices assisting in sports activities. In IFAC-PapersOnline: Mathematical Modelling, 7, 55–61.
Bartlett, R. (2006). Artificial intelligence in sports biomechanics: New dawn or false hope? Journal of Sports Science and Medicine, 5, 474–479.
Chen, V. C. (2004). Evaluation of Bayes, ICA, PCA and SVM methods for classification. In RTO SET symposium on target identification and recognition using RF systems (pp. 522–525).
Eskofier, B., Wagner, M., Munson, I., & Oleson, M. (2010). Embedded classification of speed and inclination during running. International Journal of Computer Science in Sport, 9, 4–19.
Hansmann, J., Mayer, D., Hanselka, H., Heller, M. & Baca, A. (2011). Environment for simulation and optimization of mechatronical-biomechanical coupled systems under consideration of usage profiles. In J.C. Samin, & P. Fisette (Eds.), Proceedings of multibody dynamics 2011—ECCOMAS thematic conference, Brussels.
Jiang, Y. (2010) An HMM based approach for video action recognition using motion trajectories. In Proceedings of international conference on intelligent control and information processing (pp. 359–364).
Kohavi, R., & John, G. H. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97, 273–324.
Novatchkov, H., & Baca, A. (2013). Artificial intelligence in sports on the example of weight training. Journal of Sports Science and Medicine, 12, 27–37.
Novatchkov, H., & Baca, A. (2013). Fuzzy logic in sports: A review and an illustrative case study in the field of strength training. International Journal of Computer Applications, 71, 8–14.
Perl, J. (2004). Artificial neural networks in motor control research. Clinical Biomechanics, 19, 873–875.
Perl, J. (2004). A neural network approach to movement pattern analysis. Human Movement Science, 23, 605–620.
Perl, J. (2004). PerPot—A meta-model and software tool for analysis and optimisation of load-performance-interaction. International Journal of Performance Analysis of Sport, 4, 61–73.
Perl, J. (2005). Dynamic simulation of performance development: Prediction and optimal scheduling. International Journal of Computer Science in Sport, 4, 28–37.
Perl, J. (2008). Physiologic adaptation by means of antagonistic dynamics. In M. Khosrow-Pour (ed.), Encyclopaedia of information science and technology (2nd ed.), vol. 6 (pp. 3086–3092).
Perl, J., & Endler, S. (2006). Training- and contest-scheduling in endurance sports by means of course profiles and PerPot-based analysis. International Journal of Computer Science in Sport, 5, 42–46.
Schöllhorn, W. I. (2004). Applications of artificial neural nets in clinical biomechanics. Clinical Biomechanics, 19, 876–898.
Tampier, M., Baca, A. & Novatchkov, H. (2012) E-Coaching in sports. In Y. Jiang & A. Baca (Eds.), Proceedings of the 2012 pre-olympic congress on sports science and computer science in sport (IACSS2012) (pp. 132–136). Edgbaston: World Academic Union.
Vales-Alonso, J., López-Matencio, P., Gonzalez-Castaño, F. J., Navarro-Hellín, H., Baños-Guirao, P. J., Pérez-Martínez, F. J., et al. (2010). Ambient intelligence systems for personalized sport training. Sensors, 10, 2359–2385.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Baca, A. (2014). Adaptive Systems in Sports. In: Pardalos, P., Zamaraev, V. (eds) Social Networks and the Economics of Sports. Springer, Cham. https://doi.org/10.1007/978-3-319-08440-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-08440-4_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08439-8
Online ISBN: 978-3-319-08440-4
eBook Packages: Business and EconomicsBusiness and Management (R0)