Computational Measurement of Motor Imitation and Imitative Learning Differences in Autism Spectrum Disorder

Abstract

Motor imitation is a critical developmental skill area that has been strongly and specifically linked to autism spectrum disorder (ASD). However, methodological variability across studies has precluded a clear understanding of the extent and impact of imitation differences in ASD, underscoring a need for more automated, granular measurement approaches that offer greater precision and consistency. In this paper, we investigate the utility of a novel motor imitation measurement approach for accurately differentiating between youth with ASD and typically developing (TD) youth. Findings indicate that youth with ASD imitate body movements significantly differently from TD youth upon repeated administration of a brief, simple task, and that a classifier based on body coordination features derived from this task can differentiate between autistic and TD youth with 82% accuracy. Our method illustrates that group differences are driven not only by interpersonal coordination with the imitated video stimulus, but also by intrapersonal coordination. Comparison of 2D and 3D tracking shows that both approaches achieve the same classification accuracy of 82%, which is highly promising with regard to scalability for larger samples and a range of non-laboratory settings. This work adds to a rapidly growing literature highlighting the promise of computational behavior analysis for detecting and characterizing motor differences in ASD and identifying potential motor biomarkers.