• DocumentCode
    1701744
  • Title

    Age related changes in the complexity of surface EMG in biceps: A model based study

  • Author

    Arjunan, Sridhar P. ; Wheeler, Kevin ; Shimada, Hiroki ; Kumar, Dinesh

  • Author_Institution
    Biosignals Lab., RMIT Univ., Melbourne, VIC, Australia
  • fYear
    2013
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    It has been observed that there is a loss of complexity in the human body as we age. This is more prominent in the muscle strength and activity. Surface Electromyogram (sEMG) reflects the strength of muscle contraction. This age related changes in sEMG have been associated with a reduction in the number of muscle fibers and a drop in the ratio of type II muscle fibers. In this study, we have modified our existing EMG model by populating lifelike parameters which is related to the changes in the muscle due to age. In order to verify and identify the reasons for these changes, experiments were conducted on subjects belonging to younger (20-29 years) and older (61-69) age groups. Fractal dimension of sEMG, a measure of complexity was computed for both experimental and simulated sEMG signal. Results show that there was significant change in the fractal dimension of sEMG and this change was observed in both experimental and simulated sEMG. This study has developed a model to observe the changes in the muscle activity as the age progress, which can be useful in analyzing the neuromuscular disorders due to age.
  • Keywords
    electromyography; fractals; medical disorders; medical signal processing; neurophysiology; EMG model; age related changes; biceps; fractal dimension; human body complexity; model based study; muscle activity; muscle contraction strength; muscle fibers; neuromuscular disorders; simulated sEMG signal; surface EMG complexity; surface electromyogram; Biological system modeling; Complexity theory; Computational modeling; Electromyography; Force; Fractals; Muscles;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Biosignals and Biorobotics Conference (BRC), 2013 ISSNIP
  • Conference_Location
    Rio de Janerio
  • ISSN
    2326-7771
  • Print_ISBN
    978-1-4673-3024-4
  • Type

    conf

  • DOI
    10.1109/BRC.2013.6487547
  • Filename
    6487547