Deep Learning-Based Detection of Motor Biomarkers for Autism from Children's Natural Home Video Recordings
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Date
2026-05
Authors
Fırat, Yelda
Kılıçaslan, Yılmaz
Sarıkaya, Hasan Ali
Yılmaz, Murat Kaan
Journal Title
Journal ISSN
Volume Title
Publisher
GRAZ UNIV TECHNOLGOY, INST INFORMATION SYSTEMS COMPUTER MEDIA-IICM
Abstract
Autism Spectrum Disorder is a neurodevelopmental disorder with onset in early childhood and its diagnosis often requires clinical processes based on long, subjective observations. Although early diagnosis and intervention can significantly improve developmental outcomes, existing methods are limited in terms of scalability and objectivity. The aim of this study is to develop a hybrid deep learning model that detects Autism Spectrum Disorder with high accuracy by analyzing motor behaviors from videos of children recorded in their natural home environment. In this study, joint coordinates were extracted using the MediaPipe Pose model and spatial, temporal, frequency and coordination-based features were calculated from these data. The features were processed with a hybrid architecture integrating CNN, BiLSTM and attention mechanism. CNN captured spatial patterns, BiLSTM learned the dynamics over time, and the attention mechanism focused on critical movement segments. The model achieves over 97% accuracy on closed datasets and over 83% on public videos such as YouTube and TikTok. These results show that the method performs robustly under both controlled and real-world conditions. The study provides a scalable, objective and clinically applicable screening tool that overcomes the problems of artificial environments and limited data.
Description
Science Citation Index Expanded (SCI-EXPANDED)
Keywords
Autism Spectrum Disorder , Motor Biomarkers , BiLSTM , CNN , Attention
Citation
Fırat, Y., Kılıçaslan, Y., Sarıkaya, H. A. & Yılmaz, M. K. (2026). Deep Learning-based Detection of Motor Biomarkers for Autism from Children's Video Recordings. Journal of Universal Computer Science, 32(4), 519–554. https://doi.org/10.3897/jucs.161202
