Cognitive Human Gait Analysis for Neuro-Physically Challenged Patients by Bat Optimization Algorithm

Abstract

Autism spectrum disorder and cerebral palsy are called developmental disorders that affect the brain development, communication, and behaviour of a child or an adult. Individuals with Cerebral palsy can also display symptoms of autism. Both conditions have varying degrees of severity, which can make it difficult to form a clear diagnosis. This research paper proposes the model-free green environment for the prediction of the above-mentioned disorders by doing gait analysis only with the camera. The new intelligent algorithm CAGLearner (cognitive analysis for gait) works on the standards of graphical extreme machines. CAGLearner uses the new powerful algorithm called bat optimized ELM for classification, which is then related with the prevailing machine learning algorithms such as artificial neural networks (ANN), support vector machines (SVM), and random forest (RF) algorithms in which the accuracy, sensitivity, and response time were analyzed. In terms of prediction time and precision, the model provided in this paper also yields more benefits.