ORCID
- Kush Gupta: 0009-0008-9930-6435
- Amir Aly: 0000-0001-5169-0679
- Emmanuel Ifeachor: 0000-0001-8362-6292
Abstract
Diagnosing Autism Spectrum Disorder (ASD) remains challenging, as it often relies on subjective evaluations and traditional methods using fMRI data. This paper proposes an innovative multi-modal framework that leverages spatiotemporal graph transformers to assess ASD severity using skeletal and optical flow data from the MMASD dataset. Our approach captures movement synchronization between children with ASD and therapists during play therapy interventions. The framework integrates a spatial encoder, a temporal transformer, and an I3D network for comprehensive motion analysis. Through this multi-modal approach, we aim to deliver reliable ASD severity scores, enhancing diagnostic accuracy and offering a scalable, robust alternative to traditional techniques.
DOI Link
DOI
10.5220/0013236900003911
Publication Date
2025-01-01
Publication Title
Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies: - Volume 2: HEALTHINF
ISBN
978-989-758-731-3
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
First Page
686
Last Page
693
Recommended Citation
Gupta, K., Aly, A., & Ifeachor, E. (2025) 'Multi-Modal Framework for Autism Severity Assessment Using Spatio-Temporal Graph Transformers', Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies: - Volume 2: HEALTHINF, , pp. 686-693. Available at: 10.5220/0013236900003911
