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.

1. [1] MEXT, “Decision of the Target Sports on Summer Olympics,” http://www.mext.go.jp/b_menu/houdou/27/04/1356716.htm, [Accessed February 8, 2016]
2. [2] Y. Omae, Y. Kon, K. Sakai, H. Takahashi, T. Akiduki, C. Miyazi, Y. Sakurai, N. Ezaki, and K. Nakai, “Supporting Environment to Coach Swimmer by Data-driven Approach,” JSME Symp.: Sports engineering and Human Dynamics 2015, A-14, 2015.
3. [3] Y. Kon, Y. Omae, K. Sakai, H. Takahashi, T. Akiduki, C. Miyazi, Y. Sakurai, N. Ezaki, and K. Nakai, “Swimming Style Classification for Developing System of Swimming Performance and Technique Evaluation,” JSME Symp.: Sports engineering and Human Dynamics 2015, A-15, 2015.
4. [4] Y. Ohgi, K. Kaneda, and A. Takakura, “A swimming style prediction using the chest acceleration,” Symp. on Sports and Human Dynamics 2012, pp. 98-103, 2012.
5. [5] W. Choi, J. Oh, T. Park, S. Kang, M. Moon, U. Lee, I. Hwang, and J. Song, “Mobydick: An interactive multi-swimmer exergame,” The 12th ACM Conf. on Embedded Network Sensor Systems, pp. 76-90, 2014.
6. [6] U. Jensen, F. Prade, and B. M. Eskofier, “Classification of Kinematic Swimming Data with Emphasis on Resource Consumption,” 2013 IEEE Int. Conf. Body Sensor Networks (BSN), pp. 1-5, 2013.
7. [7] P. Siirtola, P. Laurinen, J. Roning, and H. Kinnunen, “Efficient Accelerometer-Based Swimming Exercise Tracking,” 2011 IEEE Symp. Computational Intelligence and Data Mining (CIDM), pp. 156-161, 2011.
8. [8] W. H. Liao, Z. X. Liao, and M. J. Liu, “Swimming Style Classification from Video Sequences,” The 16th IPPR Conf. on Computer Vision, Graphics and Image Processing, pp. 226-233, 2003.
9. [9] Sports Sensing Co., LTD., “9 Axis Waterproof Type Wireless Motion Sensor,” http://www.sports-sensing.com/products/motion/inertia/motionwp01.html, [Accessed February 9, 2016]
10. [10] Sony, Digital Video Camera HDR-CX720V, http://www.sony.jp/handycam/products/HDR-CX720V/spec.html, [Accessed February 17, 2016]
11. [11] L. Bao and S. S. Intille, “Activity Recognition from User-annotated Acceleration Data,” Pervasive Computing, pp. 1-17, 2004.
12. [12] N. Ravi, N. Dandekar, P. Mysore, and M. L. Littman, “Activity recognition from accelerometer data,” AAAI, Vol.5, pp. 1541-1546, 2005.
13. [13] T. Hastie, R. Tibshirani, J. Friedman, M. Sugiyama, T. Ide, T. Kamishima, T. Kurita, and E. Maeda, “The Elements of Statistical Learning: Data Mining, Inference, and Prediction,” Kyoritsu Publisher, 2014.