AXON: Action Characterization Through Cross-Modal Knowledge Distillation for Neurodiverse Individuals

Abstract Understanding the communicative behaviors of non- and minimally-speaking individuals with autism spectrum disorder (ASD) and complex neurodevelopmental disorders (NDDs) remains a critical challenge for both clinical support and machine learning (ML) research. However, developing automated systems for this task is hindered by data scarcity, privacy concerns, heterogeneous and idiosyncratic behaviors, and the significant domain shift from neurotypical to neurodiverse populations. To address these challenges, we first present a novel, large-scale, privacy-preserving action recognition dataset with 2,721 3D skeleton samples capturing in-home interactions of individuals with ASD and complex NDDs. Second, we propose AXON, a novel cross-modal knowledge distillation method that transfers the rich semantic understanding of a pre-trained CLIP model to a graph-based Hyperformer model, outperforming other cross-modal knowledge distillation baselines in action recognition. We further introduce a gradient-based interpretability method to characterize how individuals with ASD and complex NDDs perform communicative actions. Our analysis uncovers both individual- and population-level communicative styles, tendencies, and biases. Our foundational study helps spur the development of more adaptive and personalized augmentative technologies, aiming to foster greater communicative autonomy and understanding for this underserved population.