Discovery of Skills from Motion Data

  • Kosuke Makio
  • Yoshiki Tanaka
  • Kuniaki Uehara
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3609)


In this paper, we discuss how to discover “skills” from motion data. Being able to understand how a skilled person moves enables beginners to make better use of their bodies and to become experts easier. However, only few attempts have so far been made for discovering skills from human motion data. To extract skills from motion data, we employ three approaches. As a first approach, we present association rule approach which extracts the dependency among the body parts to find the movement of the body parts performed by the experts. The second is an approach that extracts frequent patterns (motifs) from motion data. Recently, many researchers propose algorithms for discovering motifs. However, these algorithms require that users define the length of the motifs in advance. Our algorithm uses the MDL principle to overcome this problem so as to discover motifs with optimal length. Finally, we compare the motions of skilled tennis players and beginners, and discuss why skilled players can better serve.


Association Rule Time Series Data Motion Data Frequent Pattern Dynamic Time Warping 
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.


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  1. 1.
    Gavrila, D.M., Davis, L.S.: 3D Model-based Tracking of Humans in Action: A Multi-view Approach. In: Proc. of Computer Vision and Pattern Recognition, pp. 73–80 (1996)Google Scholar
  2. 2.
    Bradski, G.R., Davis, J.: Motion Segmentation and Pose Recognition with Motion History Gradients. In: Proc. of IEEE Workshop on Detection and Recognition of Events in Video, IEEE Computer Society Press, Los Alamitos (2001)Google Scholar
  3. 3.
    Bobick, A.F., Davis, J.: The Recognition of Human Movement Using Temporal Templates. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(3), 257–267 (2001)CrossRefGoogle Scholar
  4. 4.
    Widmer, G., Dixon, S., Goebl, W., Pampalk, E.: In Search of the Horowitz Factor. AI Magazine 24(3), 111–130 (2003)Google Scholar
  5. 5.
    Osaki, R., Shimada, M., Uehara, K.: A Motion Recognition Method by Using Primitive Motions. In: Proc. of 5th IFIP 2.6 Working Conference on Visual Database Systems, pp. 117–128 (2000)Google Scholar
  6. 6.
    Mori, T., Uehara, K.: Extraction of Primitive Motion and Discovery of Association Rules from Human Motion. In: Proc. of 10th IEEE International Workshop on Robot and Human Communication, pp. 200–206. IEEE Computer Society Press, Los Alamitos (2001)Google Scholar
  7. 7.
    Tanaka, Y., Uehara, K.: Discover Motifs in Multi Dimensional Time-Series Using the Principal Component Analysis and the MDL Principle. In: Perner, P., Rosenfeld, A. (eds.) MLDM 2003. LNCS, vol. 2734, pp. 252–265. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  8. 8.
    Tanaka, Y., Iwamoto, K., Uehara, K.: Discovery of Time-Series Motif from Multi-Dimensional Data Based on MDL Principle. Machine Learning (to appear)Google Scholar
  9. 9.
    Lin, J., Keogh, E., Lonardi, S., Patel, P.: Finding Motifs in Time Series. In: Proc. of the 2nd Workshop on Temporal Data Mining, pp. 53–68 (2002)Google Scholar
  10. 10.
    Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A Symbolic Representation of Time Series with Implications for Streaming Algorithms. In: Proc. of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, ACM Press, New York (2003)Google Scholar
  11. 11.
    Heras, D.B., Cabaleiro, J.C., Perez, V.B., Costas, P., Rivera, F.F.: Principal Component Analysis on Vector Computers. In: Palma, J.M.L.M., Dongarra, J.J. (eds.) VECPAR 1996. LNCS, vol. 1215, pp. 416–428. Springer, Heidelberg (1997)Google Scholar
  12. 12.
    Rissanen, J.: Stochastic Complexity in Statistical Inquiry. World Scientific Publishing, Singapore (1989)zbMATHGoogle Scholar
  13. 13.
    Shasha, D., Wang, T.: New Techniques for Best-Match Retrieval. ACM Trans. Information Systems 8(2), 140–158 (1990)CrossRefGoogle Scholar
  14. 14.
    Gower, J.C.: Some Distance Properties of Latent Root and Vector Methods Used in Multivariate Analysis. Biometrica 53, 325–328 (1966)zbMATHMathSciNetGoogle Scholar
  15. 15.
    Kruskal, J.B., Wish, M.: Multidimensional Scaling. Sage Publication, Thousand Oaks (1978)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Kosuke Makio
    • 1
  • Yoshiki Tanaka
    • 1
  • Kuniaki Uehara
    • 1
  1. 1.Graduate School of Science and Technology, Kobe UniversityJapan

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