title: A new Multiple ANFIS model for classification of hemiplegic gait.
abstract: 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.
authors.affiliation: Faculty of Medicine, Department of Bioistatistic and Medical Informatics, Akdeniz University Kampus 07059, Antalya Turkey yardimci@akdeniz.edu.tr, oasilkan@gmail.com.
authors.forename: A
authors.initials: A
authors.lastname: Yardimci
authors.affiliation: Faculty of Medicine, Department of Bioistatistic and Medical Informatics, Akdeniz University Kampus 07059, Antalya Turkey yardimci@akdeniz.edu.tr, oasilkan@gmail.com.
authors.forename: O
authors.initials: O
authors.lastname: Asilkan
mesh_terms: D061725:Accelerometry / Q000379:methods*; D056044:Actigraphy / Q000379:methods; D003198:Computer Simulation; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D017143:Fuzzy Logic; D020233:Gait Disorders, Neurologic / Q000145:classification / Q000175:diagnosis* / Q000209:etiology; D006429:Hemiplegia / Q000145:classification / Q000150:complications / Q000175:diagnosis*; D006801:Humans; D015233:Models, Statistical; D009415:Nerve Net; D010363:Pattern Recognition, Automated / Q000379:methods*