Computerised Assessment of Motor Imitation (CAMI) as a scalable method for distinguishing children with autism

Abstract Background Imitation deficits are prevalent in autism spectrum conditions (ASC) and are associated with core autistic traits. Imitating others’ actions is central to the development of social skills in typically-developing (TD) populations, as it facilitates social learning and bond formation. We present a Computerised Assessment of Motor Imitation (CAMI) using a brief (one-minute), highly-engaging videogame task. Methods Using Kinect Xbox motion tracking technology, we recorded 48 children (27 ASC, 21 TD) as they imitated a model’s dance movements. We implemented an algorithm based on metric learning and dynamic time warping (DTW) that automatically detects and evaluates the important joints and returns a score considering spatial position and timing differences between the child and the model. To establish construct validity and reliability, we compared imitation performance measured by the CAMI method to more traditional human observation coding (HOC) method across repeated trials and two different movement sequences. Results Results revealed poorer imitation in ASC than TD children (ps Conclusions Findings support CAMI as an objective, highly scalable, directly interpretable method for assessing motor imitation differences, providing a promising biomarker for defining biologically meaningful ASC subtypes and guiding intervention.