Bimodal Deep Learning Model for Autism Spectrum Disorder Detection Using Gait Analysis

This study presents a deep neural network approach using bimodal convolutional neural networks to detect ASD in children by analyzing gait patterns from video recordings. The method combines non-invasive gait analysis with deep learning to achieve objective, high-accuracy ASD identification.