Skip to main content

A Personalised Body Motion Sensitive Training System Based on Auditive Feedback

  • Conference paper
Mobile Computing, Applications, and Services (MobiCASE 2009)

Abstract

In this paper the architecture and functionality of a personalized body motion sensitive training system based on auditive feedback is discussed. The system supports recognition of body motion using body worn sensors and gives the user feedback about his or her current status in adaptively selecting audio files accompanying the speed and path of exercise.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Lukowicz, P., Anliker, U., Ward, J., Troester, G., Hirt, E., Neufelt, C.: Amon: a wearable medical computer for high risk patients. In: Wearable Computers (ISWC 2002). Proceedings of the Sixth International Symposium on Wearable Computers, pp. 133–134 (2002), ISBN: 0-7695-1816-8

    Google Scholar 

  2. Heinz, E.A., Kunze, K., Gruber, M., Bannach, D., Lukowicz, P.: Using wearable sensors for real-time recognition tasks in games of martial arts. In: Proceedings of the 2nd IEEE Symposium on Computational Intelligence and Games (CIG), pp. 98–102. IEEE Press, Los Alamitos (2006)

    Google Scholar 

  3. Lee, S.-W., Mase, K.: Recognition of walking behaviors for pedestrian navigation. In: Control Applications (CCA 2001). Proceedings of the 2001 IEEE International Conference on Control Applications, pp. 1152–1155 (2001), ISBN: 0-7803-6733-2

    Google Scholar 

  4. Lukowicz, P., Ward, J.A., Junker, H., Stäger, M., Tröster, G., Atrash, A., Starner, T.: Recognizing workshop activity using body worn microphones and accelerometers. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 18–32. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  5. Bao, L., Intille, S.S.: Activity recognition from user-annotated acceleration data. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  6. Lukowicz, P., Hanser, F., Szubski, C., Schobersberger, W.: Detecting and interpreting muscle activity with wearable force sensors. In: Fishkin, K.P., Schiele, B., Nixon, P., Quigley, A. (eds.) PERVASIVE 2006. LNCS, vol. 3968, pp. 101–116. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  7. Hay, J.G.: The biomechanics of sports techniques. Prentice-Hall, Englewood Cliffs (1978), ISBN: 0-13-077164-3

    Google Scholar 

  8. Beigl, M., Krohn, A., Zimmer, T., Decker, C.: Typical sensors needed in ubiquitous and pervasive computing. In: Proceedings of the First International Workshop on Networked Sensing Systems (INSS 2004), pp. 153–158 (2004)

    Google Scholar 

  9. Jensen, K., Andersen, T.: Beat estimation on the beat. In: Applications of Signal Processing to Audio and Acoustics, 2003 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, pp. 87–90 (2003), ISBN: 0-7803-7850-4

    Google Scholar 

  10. Hainsworth, S.W.: Techniques for the automated analysis of musical audio. Signal Proscessing Group, Department of Engineering, University of Cambridge, Tech. Rep. (December 2003)

    Google Scholar 

  11. Gemperle, F., Kasabach, C., Bauer, J.S.M., Martin, R.: Design for wearability. In: ISWC 1998: Proceedings of the 2nd IEEE International Symposium on Wearable Computers, pp. 116–122. IEEE Computer Society, Los Alamitos (1998)

    Google Scholar 

  12. Estrin, D., Culler, D., Pister, K., Sukhatme, G.: Connecting the physical world with pervasive networks. IEEE Pervasive Computing 1(1), 59–69 (2002)

    Article  Google Scholar 

  13. Dideles, M.: Bluetooth: a technical overview. Crossroads 9(4), 11–18 (2003)

    Article  Google Scholar 

  14. Krassi, B.A.: Reliability of bluetooth. In: Proceedings of the 12th Conference on Extreme Robotics, RTC, St. Petersburg (2001)

    Google Scholar 

  15. Andersen, T.H., Andersen, K.: “Mixxx” (2009), http://www.mixxx.org/

  16. Bringmann, B., Zimmermann, A.: Tree 2 - decision trees for tree structured data. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 46–58. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  17. Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13(1), 21–27 (1967)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering

About this paper

Cite this paper

Hoelzl, G. (2010). A Personalised Body Motion Sensitive Training System Based on Auditive Feedback. In: Phan, T., Montanari, R., Zerfos, P. (eds) Mobile Computing, Applications, and Services. MobiCASE 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 35. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12607-9_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-12607-9_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12606-2

  • Online ISBN: 978-3-642-12607-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics