Automatic classification of strike techniques using limb trajectory data

Kasper M.W. Soekarjo, Dominic Orth, Elke Warmerdam, John van der Kamp

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

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 ∼99% prediction accuracy. Both algorithms could classify the techniques used with ∼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.

Original languageEnglish
Title of host publicationMachine Learning and Data Mining for Sports Analytics
Subtitle of host publication5th International Workshop, MLSA 2018, Co-located with ECML/PKDD 2018, Proceedings
EditorsUlf Brefeld, Jan Van Haaren, Albrecht Zimmermann, Jesse Davis
PublisherSpringer Verlag
Pages131-141
Number of pages11
ISBN (Electronic)9783030172749
ISBN (Print)9783030172732
DOIs
Publication statusPublished - 2019
Event5th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2018, colocated with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2018 - Dublin, Ireland
Duration: 10 Sep 201810 Sep 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11330 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2018, colocated with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2018
CountryIreland
CityDublin
Period10/09/1810/09/18

Fingerprint

Trajectories
Trajectory
Supervised Classification
Nearest Neighbor
Classify
Hierarchical Classification
Support Vector
Prediction
Multi-class
Classification Algorithm
Expertise
Experiment
Machine Learning
Person
Learning systems
Experiments
Demonstrate

Keywords

  • Combat sport
  • Limb trajectory analysis
  • Machine learning
  • Strike technique classification

Cite this

Soekarjo, K. M. W., Orth, D., Warmerdam, E., & van der Kamp, J. (2019). Automatic classification of strike techniques using limb trajectory data. In U. Brefeld, J. Van Haaren, A. Zimmermann, & J. Davis (Eds.), Machine Learning and Data Mining for Sports Analytics: 5th International Workshop, MLSA 2018, Co-located with ECML/PKDD 2018, Proceedings (pp. 131-141). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11330 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-17274-9_11
Soekarjo, Kasper M.W. ; Orth, Dominic ; Warmerdam, Elke ; van der Kamp, John. / Automatic classification of strike techniques using limb trajectory data. Machine Learning and Data Mining for Sports Analytics: 5th International Workshop, MLSA 2018, Co-located with ECML/PKDD 2018, Proceedings. editor / Ulf Brefeld ; Jan Van Haaren ; Albrecht Zimmermann ; Jesse Davis. Springer Verlag, 2019. pp. 131-141 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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abstract = "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 ∼99{\%} prediction accuracy. Both algorithms could classify the techniques used with ∼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.",
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author = "Soekarjo, {Kasper M.W.} and Dominic Orth and Elke Warmerdam and {van der Kamp}, John",
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Soekarjo, KMW, Orth, D, Warmerdam, E & van der Kamp, J 2019, Automatic classification of strike techniques using limb trajectory data. in U Brefeld, J Van Haaren, A Zimmermann & J Davis (eds), Machine Learning and Data Mining for Sports Analytics: 5th International Workshop, MLSA 2018, Co-located with ECML/PKDD 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11330 LNAI, Springer Verlag, pp. 131-141, 5th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2018, colocated with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2018, Dublin, Ireland, 10/09/18. https://doi.org/10.1007/978-3-030-17274-9_11

Automatic classification of strike techniques using limb trajectory data. / Soekarjo, Kasper M.W.; Orth, Dominic; Warmerdam, Elke; van der Kamp, John.

Machine Learning and Data Mining for Sports Analytics: 5th International Workshop, MLSA 2018, Co-located with ECML/PKDD 2018, Proceedings. ed. / Ulf Brefeld; Jan Van Haaren; Albrecht Zimmermann; Jesse Davis. Springer Verlag, 2019. p. 131-141 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11330 LNAI).

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

TY - GEN

T1 - Automatic classification of strike techniques using limb trajectory data

AU - Soekarjo, Kasper M.W.

AU - Orth, Dominic

AU - Warmerdam, Elke

AU - van der Kamp, John

PY - 2019

Y1 - 2019

N2 - 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 ∼99% prediction accuracy. Both algorithms could classify the techniques used with ∼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.

AB - 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 ∼99% prediction accuracy. Both algorithms could classify the techniques used with ∼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.

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SP - 131

EP - 141

BT - Machine Learning and Data Mining for Sports Analytics

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PB - Springer Verlag

ER -

Soekarjo KMW, Orth D, Warmerdam E, van der Kamp J. Automatic classification of strike techniques using limb trajectory data. In Brefeld U, Van Haaren J, Zimmermann A, Davis J, editors, Machine Learning and Data Mining for Sports Analytics: 5th International Workshop, MLSA 2018, Co-located with ECML/PKDD 2018, Proceedings. Springer Verlag. 2019. p. 131-141. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-17274-9_11