Investigating effects of force and pressure centre signals on stabilogram analysis

Creative Commons License

Cetin E., BİLGİN S.

IET SCIENCE MEASUREMENT & TECHNOLOGY, cilt.13, sa.9, ss.1305-1310, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 13 Sayı: 9
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1049/iet-smt.2019.0078
  • Dergi Adı: IET SCIENCE MEASUREMENT & TECHNOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1305-1310
  • Anahtar Kelimeler: support vector machines, biomechanics, signal classification, medical signal processing, pressure centre signals, anterior-posterior plane, medial-lateral plane, mean standard deviation values, force signals, COP signals, force effects, stabilogram analysis, centre of pressure, force change signals, time-dependent norms, scatter plots, standard deviation values, c classification algorithms, support vector machine algorithm, GROUND REACTION FORCE, POSTURAL CONTROL, HUMAN BALANCE, CLASSIFICATION, GAIT, VARIABILITY
  • Akdeniz Üniversitesi Adresli: Evet

Özet

In this study, the centre of pressure (COPx) and force change (F-x) in the anterior-posterior (AP) plane, the centre of pressure (COPy) and force change (F-y) signals in medial-lateral (ML) plane were compared. In order to make comparisons in this context, the time-dependent norms of the COPx-COPy, and F-x-F-y signals (F-B, COPB) of 163 subjects were calculated separately. Subsequently, the mean standard deviation values (F-STD, COPSTD) of these values generated by time-based were obtained. The scatter plots in the AP plane $\lpar F_{x_{{\rm STD}}}\comma \; \, {\rm CO}{\rm P}_{x_{{\rm STD}}}\rpar $(FxSTD,COPxSTD) and the ML plane $\lpar F_{y_{{\rm STD}}}\comma \; \, {\rm CO}{\rm P}_{y_{{\rm STD}}}\rpar $(FySTD,COPySTD) are presented in different categories, which depend on the standard deviation values, young-elderly of the measurement group, the firm-soft of the ground and open-close of the eyes. Finally, the separation successes of $F_{x_{{\rm STD}}} - F_{y_{{\rm STD}}}$FxSTD-FySTD and ${\rm CO}{\rm P}_{x_{{\rm STD}}} - {\rm CO}{\rm P}_{y_{{\rm STD}}}$COPxSTD-COPySTD were calculated by means of basic classification algorithms. According to the results obtained, it is observed that the force signals generally provide more successful results than COP signals. In the study, the highest accuracy was performed with force signals and the support vector machine algorithm separated the young-aged group with an accuracy of 81.67%.