Complexity as Interplay Between Science and Sport

Chapter
Part of the Lecture Notes in Economics and Mathematical Systems book series (LNE, volume 675)

Abstract

Complexity is a concept that clearly escapes to a general definition and any universal metric. In this work, it is argued about the relevance of this concept in modern science with particular emphasis to the interplay with sport and some related applications. In particular, it is claimed that suitable measures of complexity can be of help in analyzing and monitoring the performance of athletes as well as to predict possible injuries during exercise. As a matter of fact, some electro-physiological signals that can be easily acquired through noninvasive procedures may contain relevant information about the status of the muscles of athletes both during exercise and at rest. In particular, the analysis of the electromyogram (EMG), whose amplitude has been used for researches on myo-electrically controlled elbows, wrists, and hands, can also yield important clues in biofeedback applications, in ergonomic assessment. For example, in biomechanics, it is used to estimate the torque produced about a joint. Complexity can detect the level of compromising of muscle force production with fatigue. Thus, the extracted information can be useful for predicting muscular injuries and to properly guide athletes’ recovery. In this paper, it is highlighted the role of complexity measures carried out on the related physiological time-series.

Keywords

Complexity Alzheimer's Disease EEG EMG Rehabilitation Engineering 

Notes

Acknowledgment

This contribution represents an extended version of the talk given by the author at the 8th International Meeting of the Royal Academy of Economic and Financial Sciences, held in Barcelona, Spain. The author specially thanks Professor Jaime Gil Aluja for yielding this opportunity.

References

  1. 1.
    F.C. Morabito, M. Cacciola, G. Occhiuto, in 2011 International Joint Conference on Neural Networks, pp. 2387–2394 (2011)Google Scholar
  2. 2.
    G. Simone, F. Morabito, R. Polikar, J. Appl. Electromagn. Mech. 15, 291–294 (2002)Google Scholar
  3. 3.
    A.L. Goldberger, L.A.N. Amaral, J.M. Hausdorff, P.C. Ivanov, C.-K. Peng, H.E. Stanley, Proc. Natl. Acad. Sci. U.S.A. 99, 2466–2472 (2002)CrossRefGoogle Scholar
  4. 4.
    G. Tononi, O. Sporns, G.M. Edelman, Proc. Natl. Acad. Sci. U.S.A. 91, 5033–5037 (1994)CrossRefGoogle Scholar
  5. 5.
    L. Lipsitz, A.L. Goldberger, JAMA 267, 1806–1809 (1992)CrossRefGoogle Scholar
  6. 6.
    F.C. Morabito, D. Labate, F. La Foresta, A. Bramanti, G. Morabito, I. Palamara, Entropy 14(7), 1186–1202 (2012)CrossRefGoogle Scholar
  7. 7.
    C. Bandt, B. Pompe, Phys. Rev. Lett. 88, 174102 (2002)CrossRefGoogle Scholar
  8. 8.
    X. Delbeuck, M. Van Der Linden, F. Collette, Neuropsychol. Rev. 13(1), 79–92 (2003)CrossRefGoogle Scholar
  9. 9.
    J. Jeong, Clin. Neurophysiol. 115, 1490–1505 (2004)CrossRefGoogle Scholar
  10. 10.
    J.-H. Park, S. Kim, C.-H. Kim, A. Cichocki, K. Kim, Fractals Interdiscipl. J. Complex Geom. Nat. 15, 399 (2007)Google Scholar
  11. 11.
    F. Morabito, D. Labate, A. Bramanti, F. La Foresta, G. Morabito, I. Palamara, H. Szu, IEEE Sensor. J. 13(9), 3255–3262 (2013)CrossRefGoogle Scholar
  12. 12.
    D. Labate, F. La Foresta, G. Morabito, I. Palamara, F. Morabito, IEEE Sensor. J. 13(9), 3284–3292 (2013)CrossRefGoogle Scholar
  13. 13.
    E.A. Clancy, N. Hogan, IEEE Trans. Biomed. Eng. 44, 1024–1028 (1997)CrossRefGoogle Scholar
  14. 14.
    D.A. Gabriel, J.R. Basfor, K.-N. An, IEEE Eng. Med. Biol. Mag. 20, 90–96 (2001)CrossRefGoogle Scholar
  15. 15.
    S. Arjunan, K. Wheeler, H. Shimada, D. Kumar, in Biosignals and Biorobotics Conference (BRC), ISSNIP, pp. 1–4 (2013)Google Scholar
  16. 16.
    G. Naik, D. Kumar, S. Arjunan, in Conference of the IEEE, pp. 364–367 (2009)Google Scholar
  17. 17.
    E. Park, S.G. Meek, IEEE Trans. Biomed. 42, 1048–1052 (1995)CrossRefGoogle Scholar
  18. 18.
    R. Sun, R. Song, K.-Y. Tong, IEEE Trans. Neural Syst. Rehabil. Eng. (2013). doi: 10.1109/TNSRE.2013.2290017
  19. 19.
    J.G.A. Cashaback, T. Cluff, J.R. Potvin, J. Electromyogr. Kinesiol. 23, 78–83 (2013)CrossRefGoogle Scholar
  20. 20.
    D. Osipova, J. Ahverinen, O. Jensen, A. Yilikoski, E. Pekkonen, Neuroimage 27, 835–841 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Civil Engineering, Energy, Environment and Materials (DICEAM)University Mediterranean of Reggio CalabriaReggio CalabriaItaly

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