A Skeleton-Based Movement Dataset for Autism Spectrum Disorder (ASD) Collected via an Augmented Reality Game
Received: 28 January 2026 | Revised: 1 March 2026 | Accepted: 18 March 2026 | Online: 2 June 2026
Corresponding author: Iyas Qaddara
Abstract
This study recorded movement data for children with Autism Spectrum Disorder (ASD) and Typically Developing (TD) children on a skeleton-based serious game based on Augmented Reality (AR). The dataset contains full-body skeletal motions in interactive and task-oriented activities, unlike current datasets that use passive observation or detection of a part of the body. The AR environment was characterized as arousing clinically relevant movements of the upper limbs, lower limbs, balance, and body rotation. A Microsoft Kinect v2 sensor was used to capture data, and a series of steps were taken to process them, namely cleaning, segmentation, spatial normalization, and temporal normalization to create fixed-length sequences. The data consist of equal recordings of the ASD and TD groups and can be used in Machine Learning (ML) studies. Random Forest and CNN-LSTM baseline experiments show that the dataset can be used to learn discriminative movement patterns. This dataset can lead to the evolution of movement-based autism analysis and AR-based behavioral data collection.
Keywords:
Autism Spectrum Disorder (ASD), skeleton-based movement analysis, augmented reality serious games, Machine Learning (ML)References
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Copyright (c) 2026 Iyas Qaddara, Mohammad O. Hiari, Talal. S. M. Haimur
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