In the scientific report “AV-FOS: A Transformer-Based Audio-Visual Multi-modal Interaction Style Recognition for Children with Autism Based on the Family Observation Schedule (FOS-II),” researchers from GW Engineering’s Department of Biomedical Engineering, Associate Professor Chung Hyuk Park and doctoral student Zhenhao Zhao, contributed to research on AI-driven Interaction Style recognition using multimodal data to enhance autism assessment and clinical accessibility.
Here is an excerpt from the study abstract: “In this study, we propose a deep-learning based algorithm with audio-visual multimodal-data clinically coded with the FOS-R-III, named AV-FOS model. Our proposed AV-FOS model leverages transformer-based structure and self-supervised learning to intelligently recognize Interaction Styles (IS) in the FOS-R-III scale from subjects' video recordings. This enables the automatic generation of the FOS-R-III measures with clinically acceptable accuracy.”
Read the full study in the IEEE Journal of Biomedical and Health Informatics.