Computerized assessment of motor imitation for distinguishing autism in video (CAMI-2DNet)

Kinfu, KA, Pacheco, C, Sperry, AD, Crocetti, D, Tunçgenç, B ORCID: https://orcid.org/0000-0002-1135-1004, Mostofsky, SH and Vidal, R, 2025. Computerized assessment of motor imitation for distinguishing autism in video (CAMI-2DNet). IEEE Transactions on Biomedical Engineering. ISSN 0018-9294

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Abstract

Motor imitation impairments are commonly reported in individuals with autism spectrum conditions (ASCs), suggesting that motor imitation could be used as a phenotype for addressing autism heterogeneity. Traditional methods for assessing motor imitation are subjective and labor-intensive, and require extensive human training. Modern Computerized Assessment of Motor Imitation (CAMI) methods, such as CAMI-3D for motion capture data and CAMI-2D for video data, are less subjective. However, they rely on labor-intensive data normalization and cleaning techniques, and human annotations for algorithm training. To address these challenges, we propose CAMI-2DNet, a scalable and interpretable deep learning-based approach to motor imitation assessment in video data, which eliminates the need for ad hoc normalization, cleaning and annotation. CAMI-2DNet uses an encoder-decoder architecture to map a video to a motion representation that is disentangled from nuisance factors such as body shape and camera views. To learn a disentangled representation, we employ synthetic data generated by motion retargeting of virtual characters through the reshuffling of motion, body shape, and camera views, as well as real participant data. To automatically assess how well an individual imitates an actor, we compute a similarity score between their motion encodings, and use it to discriminate individuals with ASCs from neurotypical (NT) individuals. Our comparative analysis demonstrates that CAMI-2DNet has a strong correlation with human scores while outperforming CAMI-2D in discriminating ASC vs NT children. Moreover, CAMI-2DNet performs comparably to CAMI-3D while offering greater practicality by operating directly on video data and without the need for ad hoc normalization and human annotations.

Item Type:	Journal article
Publication Title:	IEEE Transactions on Biomedical Engineering
Creators:	Kinfu, K.A., Pacheco, C., Sperry, A.D., Crocetti, D., Tunçgenç, B., Mostofsky, S.H. and Vidal, R.
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Date:	26 November 2025
ISSN:	0018-9294
Identifiers:	Number Type 10.1109/TBME.2025.3637089 DOI 2594669 Other
Rights:	© 2025 IEEE. This article has been accepted for publication in IEEE Transactions on Biomedical Engineering. This is the author's version which has not been fully edited and content may change prior to final publication. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Divisions:	Schools > School of Social Sciences
Record created by:	Melissa Cornwell
Date Added:	30 Mar 2026 08:54
Last Modified:	30 Mar 2026 08:54
URI:	https://irep.ntu.ac.uk/id/eprint/55483

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