Classification of Knee Abnormality Using sEMG Signals with Boosting Ensemble Approaches


Altıntaş A., Yılmaz D.

Journal of Computer Science , vol.2021, pp.48-52, 2021 (Refereed Journals of Other Institutions)

  • Publication Type: Article / Article
  • Volume: 2021
  • Publication Date: 2021
  • Title of Journal : Journal of Computer Science
  • Page Numbers: pp.48-52

Abstract

Knee problems, although increasing in the elderly, are one of the most important orthopedic problems that occur at any age and reduce the person's standard of living by making it difficult to move. In recent years, increasing in the use of surface Electromyography (sEMG) signals from muscles has highlighted the use of these signals in the detection of movement and movement disorders. In this study, sEMG signals, from patients with different knee abnormalities and healthy individuals, the muscles responsible for the bending (flexion) and stretching/extension (extension) movements of the knee (rectus femoris (RF), biceps femoris (FB), semitendinosus (ST), vastus medialis (VM)), recorded during gait, sitting, and standing were evaluated with some statistical-based features. Unlike the literature, the classification processes were alsoperformed for each muscle and each movement, and therefore the effect of the muscles on the classification performance was examined.