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. 2021 Mar;6(3):321-328.
doi: 10.1016/j.bpsc.2020.09.001. Epub 2020 Sep 10.

Computerized Assessment of Motor Imitation as a Scalable Method for Distinguishing Children With Autism

Bahar Tunçgenç  1 , Carolina Pacheco  2 , Rebecca Rochowiak  3 , Rosemary Nicholas  4 , Sundararaman Rengarajan  5 , Erin Zou  3 , Brice Messenger  3 , René Vidal  2 , Stewart H Mostofsky  6
Affiliations

Computerized Assessment of Motor Imitation as a Scalable Method for Distinguishing Children With Autism

Bahar Tunçgenç et al. Biol Psychiatry Cogn Neurosci Neuroimaging. 2021 Mar.

Abstract

Background: Imitation deficits are prevalent in autism spectrum conditions (ASCs) and are associated with core autistic traits. Imitating others' actions is central to the development of social skills in typically developing populations, as it facilitates social learning and bond formation. We present a Computerized Assessment of Motor Imitation (CAMI) using a brief (1-min), highly engaging video game task.

Methods: Using Kinect Xbox motion tracking technology, we recorded 48 children (27 with ASCs, 21 typically developing) as they imitated a model's dance movements. We implemented an algorithm based on metric learning and dynamic time warping 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 the more traditional human observation coding (HOC) method across repeated trials and two different movement sequences.

Results: Results revealed poorer imitation in children with ASCs than in typically developing children (ps < .005), with poorer imitation being associated with increased core autism symptoms. While strong correlations between the CAMI and HOC methods (rs = .69-.87) confirmed the CAMI's construct validity, CAMI scores classified the children into diagnostic groups better than the HOC scores (accuracyCAMI = 87.2%, accuracyHOC = 74.4%). Finally, by comparing repeated movement trials, we demonstrated high test-retest reliability of CAMI (rs = .73-.86).

Conclusions: Findings support the 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.

Keywords: Autism; Imitation; Intervention; Machine learning; Motor learning; Social behavior.

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Conflict of interest statement

Financial Disclosures

The authors report no biomedical financial interests or potential conflicts of interest.

Figures

Figure 1.
Figure 1.. Comparisons between the CAMI and HOC methods using motion data of four imitation trials from a sample of 48 children (27 ASC, 21 TD).
(a) Correlations between the CAMI scores and HOC scores in three trials, showing strong correspondence between the two methods. An r = 1 indicates perfect positive association, r = 0 indicates no association and r = −1 indicates perfect negative association (ps< .0001). (b) 3D plots of the CAMI scores and HOC scores in which Trial 1a, Trial 1b, and Trial 2a scores correspond to the respective axes. Each marker represents one subject, and the reported accuracy (Acc) corresponds to average classification accuracy in 3-fold cross-validation of a linear SVM classifier (best possible Acc is 100%, meaning all participants categorised to diagnostic groups accurately). (c) Receiving Operating Characteristic (ROC) curves: true positive rate versus false positive rate as classification threshold is varied. The Area Under the Curve (AUC) indicates the diagnostic ability of the method (left panel for CAMI, right panel for HOC) in each of the three trials (best possible AUC is 1, meaning zero false positives and 100% true positives). (d) ROC curve (left panel) and CAMI scores (right panel) of Trial 2b only. Since this trial does not have any HOC scores, its CAMI scores are computed based on parameters learnt from the other three trials, complying with the splits used for 3-fold cross-validation. The AUC of the ROC curve (0.937) and SVM accuracy value (84.6%) demonstrate the diagnostic classification ability of CAMI scores with this single trial.
Figure 2.
Figure 2.
Imitation performance per diagnostic group (blue = ASC, grey = TD) per trial according to CAMI scores (left) and HOC scores (right) with box plots embedded within violin plots. In the box plots, horizontal lines indicate medians, boxes indicate data within the 25th to 75th percentiles, and whiskers indicate data within the 5th to 95th percentiles.
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