Early Diagnosis of Autism: A Review of Video-Based Motion Analysis and Deep Learning Techniques

The growing incidence of ASD has made the development of early diagnostic techniques increasingly crucial. The paper provides a systematic review of video-based motion analysis and deep learning (DL) techniques for early diagnosis of ASD. It evaluates related articles published between 2018 and June 2024. We categorize the leading research according to the body parts involved: head pose, hand and arm pose, and trunk pose. And conduct an in-depth analysis of the procedures, feature extraction techniques, and classification models used in these studies. The paper delves into the general processes and methods proposed in the field, offering a detailed analysis. It presents a timeline of the evolution of ASD diagnostic methods and summarizes publicly available datasets, categorized into behavioral and multimodal datasets. The paper also reports the successes and challenges of applying AI in motion analysis for ASD detection. It provides a complete and systematic overview for researchers in the field of ASD.