Skip to main content

Neural Networks Based System for the Supervision of Therapeutic Exercises

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7666)

Abstract

Present contribution describes application of the neural networks based models to detect incorrectly performed therapeutic exercises within the frameworks of wearable supervision system. Electronic accelerometers and gyroscopes attached to the human upper and lower limbs gather information about performed exercise in real time. Trained, on the data describing correctly done exercises, neural network based dynamic model of the limb is used to find the difference between the actual and ”ideal” performances and judge if exercises are performed in a correct way or not.

Keywords

  • Neural networks
  • dynamic model
  • NN-ANARX model
  • medical system
  • rehabilitation

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-642-34478-7_45
  • Chapter length: 8 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   99.00
Price excludes VAT (USA)
  • ISBN: 978-3-642-34478-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   131.00
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Friedrich, M., Cermak, T., Maderbacher, P.: The effect of brochure use versus therapist teaching on patients performing therapeutic exercise and on changes in impairment status. Physical Therapy (Journal of the American Physical Therapy Association) 76, 1082–1088 (1996)

    Google Scholar 

  2. Kotta, Ü., Chowdhury, F., Nõmm, S.: On realizability of neural networks-based input-output models in the classical state space form. Automatica 42(6), 1211–1216 (2006)

    MATH  CrossRef  Google Scholar 

  3. Nõmm, S., Petlenkov, E., Vain, J., Yoshimitsu, K., Ohnuma, K., Sadahiro, T., Miyawaki, F.: Nn-based anarx model of the surgeon’s hand for the motion recognition. In: Proceedings of the 4th COE Workshop on Human Adaptive Mechatronics (HAM), pp. 19–24. Tokyo Denki University, Tokyo (2007)

    Google Scholar 

  4. Zhou, H., Hu, H.: A Survey - Human Movement Tracking and Stroke Rehabilitation. Technical report, CSM-420, University of Essex (2004) ISSN 1744 - 8050

    Google Scholar 

  5. Lorincz, K., Chen, B., Welsh, M.: Mercury: a wearable sensor network platform for high-fidelity motion analysis. In: Proc of the 7th ACM Conference on Embedded Networked Sensor Systems, SenSys 2009, Bercley, pp. 183–196 (2009)

    Google Scholar 

  6. Kisner, C., Colby, L.: Therapeutic Exercise, Foundations and Techniques, 5th edn. F.A.Davis Company, Philadelfia (2007)

    Google Scholar 

  7. Nomm, S., Belikov, J.: Online identification of unknown plant by nn-based anarx model. In: 2011 International Conference on Adaptive and Intelligent Systems, Klagenfurt, Austria, pp. 5–15 (2011)

    Google Scholar 

  8. Nomm, S., Vassilejva, K., Beliokv, J., Petlenkov, E.: Structure identification of nn-anarx model by genetic algorithm with combined cross-correlation-test based evaluation function. In: 2011 9th IEEE International Conference on Control and Automation (ICCA), Santiago,Chile, pp. 65–70 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Nõmm, S., Kuusik, A., Ovsjanski, S., Malmberg, I., Parve, M., Orunurm, L. (2012). Neural Networks Based System for the Supervision of Therapeutic Exercises. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34478-7_45

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-34478-7_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34477-0

  • Online ISBN: 978-3-642-34478-7

  • eBook Packages: Computer ScienceComputer Science (R0)