Cognitive Model for Human Behavior Analysis

Abstract

The number of autistic children and young people is rising rapidly across the world. Children with intellectual disabilities need special attention from trained experts. Educating them on improving their lifestyle is critical through the traditional teaching-learning environment. This study introduces an interactive educational framework that helps children with special needs have an improved and exciting learning process and explores the need to incorporate physical exercise into their everyday lives. Virtual Reality (VR) seeks more attention from autistic students. This research presents a Machine Learning-based Virtual Reality Application (ML-VRA) for Mentally Challenged Children and keeps the Human Behavior Analysis log files. Machine learning can predict the score of brain data ability. The visual short-term memory and visual-spatial memory are further assessed to identify students’ interaction with the VR application. Support Vector Regression prediction algorithm and Baseline Prediction algorithm are used to assess the score prediction for visual short memory and visual-spatial memory. Using an audio technology that allows autistic persons to hear various sounds, the cognitive method VRA instructs autistic children. Further, this study proposes a cognitive model for intellectual task processes and problem-solving using metacognitive architecture. Thus, children can acquire different levels of learning knowledge and skills. The case study performed on these model results with the highest prediction accuracy of 93.65%.