A new Multiple ANFIS model for classification of hemiplegic gait.

Yardimci A., ASILKAN O.

Studies in health technology and informatics, cilt.205, ss.88-92, 2014 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 205
  • Basım Tarihi: 2014
  • Doi Numarası: 10.3233/978-1-61499-432-9-88
  • Dergi Adı: Studies in health technology and informatics
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CINAHL, Compendex, EMBASE, MEDLINE
  • Sayfa Sayıları: ss.88-92
  • Anahtar Kelimeler: Adaptive neuro-fuzzy inference system, Fuzzy logic, Hemiplegic, Gait analysis, BODY MOTION, SIGNALS, WALKING
  • Akdeniz Üniversitesi Adresli: Evet

Özet

Neuro-fuzzy system is a combination of neural network and fuzzy system in such a way that neural network learning algorithms, is used to determine parameters of the fuzzy system. This paper describes the application of multiple adaptive neuro-fuzzy inference system (MANFIS) model which has hybrid learning algorithm for classification of hemiplegic gait acceleration (HGA) signals. Decision making was performed in two stages: feature extraction using the wavelet transforms (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the HGA signals.