Skip to main content
Springer Nature Link
Log in

Discovery of Skills from Motion Data

  • Conference paper
  • First Online:
New Frontiers in Artificial Intelligence (JSAI 2003, JSAI 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3609))

  • 633 Accesses

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  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. 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. 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)

    Article  Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  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. 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. 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. 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)

    Chapter  Google Scholar 

  12. Rissanen, J.: Stochastic Complexity in Statistical Inquiry. World Scientific Publishing, Singapore (1989)

    MATH  Google Scholar 

  13. Shasha, D., Wang, T.: New Techniques for Best-Match Retrieval. ACM Trans. Information Systems 8(2), 140–158 (1990)

    Article  Google Scholar 

  14. Gower, J.C.: Some Distance Properties of Latent Root and Vector Methods Used in Multivariate Analysis. Biometrica 53, 325–328 (1966)

    Article  MathSciNet  Google Scholar 

  15. Kruskal, J.B., Wish, M.: Multidimensional Scaling. Sage Publication, Thousand Oaks (1978)

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Graduate School of Science and Technology, Kobe University, Japan

    Kosuke Makio, Yoshiki Tanaka & Kuniaki Uehara

Authors
  1. Kosuke Makio
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yoshiki Tanaka
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kuniaki Uehara
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Akito Sakurai Kôiti Hasida Katsumi Nitta

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer Berlin Heidelberg

About this paper

Cite this paper

Makio, K., Tanaka, Y., Uehara, K. (2007). Discovery of Skills from Motion Data. In: Sakurai, A., Hasida, K., Nitta, K. (eds) New Frontiers in Artificial Intelligence. JSAI JSAI 2003 2004. Lecture Notes in Computer Science(), vol 3609. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71009-7_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-71009-7_23

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71008-0

  • Online ISBN: 978-3-540-71009-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics