An Efficient Feature Extraction Approach with Improved ANFIS Model for Detection of Dyslexia from Eye Movements

Abstract

There is lakhs and millions of children suffered from the Learning Disability (LD) Dyslexia problem across the world. Based on the characteristics of dyslexia, the detection or identification of dyslexia students becomes one of the main significant issues in now a day. The eye movement signals of each child are recorded and detection methods are applied to recorded signals. So the eye movement’s signal plays majors important role in dyslexia detection. So the analysis of eye movements has become one of the most important problems in Learning Disability. All of the existing work only focuses on identification of children suffering from dyslexia only without measuring eye movement signals results. Up to now still none of the work analyzes eye movements signals based on their word length. The goal of this study was to analysis the eye movement’s signals using word net tool. Then Multivariate autoregressive model (MAR) is proposed for feature extraction. Finally proposed an Improved Adaptive Neuro-Fuzzy Inference System (ANFIS) which combines particle swarm optimization for dyslexia detection. Video Oculo Graphic (VOG) is used to measure the Eye movement’s signals of children through single reading and four non reading tasks. Experimentation confirmed that the proposed ANFIS-PSO model has good detection results than ANFIS and ANN model in terms of parameters like sensitivity, specificity, detection accuracy and p-value.