Autism detection using facial and motor analysis using machine learning
Amirbay A. Baigabylov N. Mukhanova A. Mukhambetova K. Zaitov E. Burganova R. Khusanova K. Akhmedova F.
October 2025Institute of Advanced Engineering and Science
Bulletin of Electrical Engineering and Informatics
2025#14Issue 53985 - 4000 pp.
This paper proposes a method for detecting autism spectrum disorders (ASD) through the analysis of facial and motor features using machine learning. The aim is to develop an algorithm for automatic ASD diagnosis based on spatiotemporal behavioral patterns. Traditional diagnostic methods rely on subjective expert observations, often delaying intervention. To address this, a hybrid convolutional neural network and long short-term memory (CNN+LSTM) model was employed. Convolutional layers extracted spatial features from video frames, while recurrent layers tracked temporal dynamics. Using MediaPipe face mesh, pose, and hands models, 1,639 parameters were obtained, including facial and pose coordinates, hand landmarks, mouth aspect ratio (MAR), and motion energy. The dataset comprised 100 children, aged 5–9 years (50 with ASD, 50 typically developing (TD)). Stratified cross-validation was applied to ensure subject-independent evaluation. Results showed 90% accuracy on the training set, 85–90% on validation, and an area under the curve (AUC) greater than 0.90, confirming model stability. Data visualization highlighted significant differences in motor activity and emotional expression between groups. The proposed approach demonstrates the potential for robust and objective ASD detection. It can be applied in clinical and educational contexts to improve early diagnosis and timely intervention.
Autism spectrum disorders , Early diagnosis , Facial analysis , Long short-term memory , Mouth aspect ratio , Spatiotemporal patterns
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Department of Information Systems, Faculty of Information Technology, L.N. Gumilyov Eurasian National University, Astana, Kazakhstan
Department of Sociology, Faculty of Social Sciences, L.N. Gumilyov Eurasian National University, Astana, Kazakhstan
Department of Sociology, Faculty of Social Sciences, National University of Uzbekistan named after Mirzo Ulugbek, Tashkent, Uzbekistan
Department of Social Work and Tourism, Esil University, Astana, Kazakhstan
Department of Information Systems
Department of Sociology
Department of Sociology
Department of Social Work and Tourism
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