Automatic Classification of Strike Techniques Using Limb Trajectory Data

  • Kasper M. W. Soekarjo
  • Dominic OrthEmail author
  • Elke Warmerdam
  • John van der Kamp
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11330)


The classification of trajectory data is required in a wide variety of movement tracking experiments. Automatic classification using machine learning techniques has the potential to greatly increase efficiency and reliability of these studies. Here, we apply supervised classification algorithms on a dataset obtained through a kickboxing experiment to classify the limb and technique that was used for each strike as well as the expertise of the person performing the strike. Beginner and expert kickboxers were asked to strike a boxing bag from several distances, producing a dataset of approximately 4000 strike trajectories. These trajectories were classified using the K-nearest neighbours (KNN) and multi-class linear support vector classification (SVC). We show that both of these algorithms are capable of correctly classifying the limb used for the strike with \(\sim \)99% prediction accuracy. Both algorithms could classify the techniques used with \(\sim \)86% accuracy. The accuracy of technique classification was improved even further by applying hierarchical classification, classifying techniques separately for each limb. Only 10% of the dataset was required as training set to approach the observed prediction accuracy. Finally, KNN was capable of classifying the strikes by skill level with 73.3% accuracy. These findings demonstrate the potential of using supervised classification on complex limb trajectory datasets.


Machine learning Strike technique classification Limb trajectory analysis Combat sport 


  1. 1.
    Ashker, S.E.: Technical and tactical aspects that differentiate winning and losing performances in boxing. Int. J. Perform. Anal. Sport 11(2), 356–364 (2011)CrossRefGoogle Scholar
  2. 2.
    Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)CrossRefGoogle Scholar
  3. 3.
    Hassanat, A.B., Abbadi, M.A., Altarawneh, G.A., Alhasanat, A.A.: Solving the problem of the K parameter in the KNN classifier using an ensemble learning approach. arXiv preprint arXiv:1409.0919 (2014)
  4. 4.
    Herault, R., Orth, D., Seifert, L., Boulanger, J., Lee, J.A.: Comparing dynamics of fluency and inter-limb coordination in climbing activities using multi-scale Jensen-Shannon embedding and clustering. Data Mining Knowl. Discov. 31(6), 1758–1792 (2017)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Hölbling, D., Preuschl, E., Hassmann, M., Baca, A.: Kinematic analysis of the double side kick in pointfighting, kickboxing. J. Sports Sci. 35(4), 317–324 (2017)CrossRefGoogle Scholar
  6. 6.
    Hsu, C.W., Lin, C.J.: A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Netw. 13(2), 415–425 (2002)CrossRefGoogle Scholar
  7. 7.
    Kasiri, S., Fookes, C., Sridharan, S., Morgan, S.: Fine-grained action recognition of boxing punches from depth imagery. Comput. Vis. Image Underst. 159, 143–153 (2017)CrossRefGoogle Scholar
  8. 8.
    Neto, O.P., Magini, M., Saba, M.M., Pacheco, M.T.T.: Comparison of force, power, and striking efficiency for a Kung Fu strike performed by novice and experienced practitioners: preliminary analysis. Percept. Mot. Skills 106(1), 188–196 (2008)CrossRefGoogle Scholar
  9. 9.
    Orth, D., van der Kamp, J., Memmert, D., Savelsbergh, G.J.: Creative motor actions as emerging from movement variability. Front. Psychol. 8, 1903 (2017)CrossRefGoogle Scholar
  10. 10.
    Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12(Oct), 2825–2830 (2011)Google Scholar
  11. 11.
    Song, Y., Huang, J., Zhou, D., Zha, H., Giles, C.L.: IKNN: informative k-nearest neighbor pattern classification. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 248–264. Springer, Heidelberg (2007). Scholar
  12. 12.
    Weston, J., Watkins, C.: Multi-class support vector machines. Technical report. Citeseer (1998)Google Scholar
  13. 13.
    Whiting, W.C., Gregor, R.J., Finerman, G.A.: Kinematic analysis of human upper extremity movements in boxing. Am. J. Sports Med. 16(2), 130–136 (1988)CrossRefGoogle Scholar
  14. 14.
    Zatsiorsky, V.M., Zaciorskij, V.M.: Kinetics of Human Motion. Human Kinetics, Champaign (2002)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Kasper M. W. Soekarjo
    • 1
  • Dominic Orth
    • 1
    • 2
    • 3
    Email author
  • Elke Warmerdam
    • 1
  • John van der Kamp
    • 1
    • 2
    • 3
  1. 1.Faculty of Behavioural and Movement SciencesVrije Universiteit AmsterdamAmsterdamNetherlands
  2. 2.Amsterdam Movement SciencesAmsterdamThe Netherlands
  3. 3.Institute of Brain and BehaviorAmsterdamThe Netherlands

Personalised recommendations