Swimming Style Classification Based on Ensemble Learning and Adaptive Feature Value by Using Inertial Measurement Unit

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  • Omae Yuto
    Department of Electrical Engineering, National Institute of Technology, Tokyo College
  • Kon Yoshihisa
    Department of Information and Management Systems Engineering, Nagaoka University of Technology
  • Kobayashi Masahiro
    Department of Information and Management Systems Engineering, Nagaoka University of Technology
  • Sakai Kazuki
    Department of Information Science and Control Engineering, Nagaoka University of Technology
  • Shionoya Akira
    Department of Information and Management Systems Engineering, Nagaoka University of Technology
  • Takahashi Hirotaka
    Department of Information and Management Systems Engineering, Nagaoka University of Technology
  • Akiduki Takuma
    Toyohashi University of Technology
  • Nakai Kazufumi
    National Institute of Technology, Toba College
  • Ezaki Nobuo
    National Institute of Technology, Toba College
  • Sakurai Yoshihisa
    Sports Sensing Co., LTD.
  • Miyaji Chikara
    Department of Creative Informatics, The University of Tokyo

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<p>We have been constructing a swimming ability improvement support system. One of the issues to be addressed is the automatic classification of swimming styles (backstroke, breaststroke, butterfly, and front crawl). The mainstream swimming style classification technique of conventional researches is based on non-ensemble learning; in their classification, breaststroke and butterfly are mixed up with each other. To improve its generalization performance, we need to use better classifiers and more adaptive feature values than previously considered. Therefore, this research has introduced (1) random forest technique, one of ensemble learning techniques, and (2) feature values specific to breaststroke and butterfly to construct a four-swimming-style classifier that has resolved this issue. From subjects with 7 to 20 years history of swimming races, we have obtained their sensor data during swimming and have divided the data into learning data and test data. We have also converted them into feature values that represent their body motions. We have selected from those body-motion-representing feature values the important data to classify four swimming styles and feature values specific to breaststroke and butterfly. We have used the learning data to construct a swimming style classifier, and the test data to evaluate its classification accuracy. The evaluation results show that (1’) the introduction of ensemble learning has improved the mean value of F-measure for breaststroke and butterfly by 0.053, and (2’) the introduction of feature values specific to breaststroke and butterfly has improved the mean value of F-measure for breaststroke and butterfly by 0.121 as compared with (1’). The proposed swimming style classifier has performed a mean F-measure of 0.981 for the four swimming styles as well as good classification accuracies for front crawl and backstroke. Therefore, we have concluded that the swimming style classifier we have constructed has resolved the problem of mixing up breaststroke and butterfly, as well as can properly classify all different swimming styles.</p>

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