video: Using video from a standard camera, the AI tool automatically tracks a child’s movements as they imitate an instructor performing simple motions. It then compares the movements of the instructor and the child and generates an objective score, eliminating hours of manual review while requiring none of the expensive equipment used in traditional motion analysis.
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Credit: Video by René Vidal and Kaleab Kinfu at Penn Engineering, in partnership with Dr. Stewart Mostofsky at Kennedy Krieger Institute
Baltimore, MD. July 29, 2026—Researchers at Kennedy Krieger Institute and Penn Engineering announcing the development of an advanced artificial intelligence system that measures a child’s ability to imitate simple body movements using ordinary video recordings. The Computerized Assessment of Motor Imitation, or CAMI-2DNet, brings researchers one step closer to a scalable, objective method for evaluating imitation which is an important behavioral marker associated with autism.
Children with autism often imitate movements differently than their neurotypical peers, making motor imitation an important marker during developmental evaluations.
“Motor imitation is a critical building block for social development,” said Dr. Stewart H. Mostofsky, director of the Center for Neurodevelopmental and Imaging Research at Kennedy Krieger Institute and a study co-author. “Our long-term goal is to better understand individual differences in autism and help inform interventions that address those differences.”
Traditionally, measuring motor imitation has required specialized motion-capture systems or painstaking manual analysis by trained experts, limiting where and how often these assessments can be performed.
“By replacing specialized hardware and manual analysis with intelligent computer vision, we can help make objective behavioral assessments more accessible to clinicians and families,” said René Vidal, Rachleff University professor, director of the Innovation in Data Engineering and Science Initiative, and senior author of the study.
CAMI-2DNet is not designed to diagnose autism on its own or replace the expertise of clinicians. Instead, it provides an objective measurement that can complement existing evaluations, helping clinicians make more informed decisions and allowing researchers to track changes over time.
“Our hope is that tools like CAMI-2DNet can reduce barriers to timely, high-quality assessments,” said lead author and Ph.D. candidate Kaleab Kinfu.
The study on CAMI-2DNet was published in IEEE Transactions on Biomedical Engineering.
Journal
IEEE Transactions on Biomedical Engineering
DOI
10.1109/TBME.2025.3637089
Method of Research
Data/statistical analysis
Subject of Research
People
Article Title
Computerized Assessment of Motor Imitation for Distinguishing Autism in Video (CAMI-2DNet)
Article Publication Date
1-Jul-2026
Media Contact
Abigail Lefin
Kennedy Krieger Institute
Cell: 7173851475
Journal
IEEE Transactions on Biomedical Engineering
Funder
U.S. National Science Foundation,
NIH/National Institutes of Health,
Simons Foundation,
The Eagles Foundation
DOI
10.1109/TBME.2025.3637089
Journal
IEEE Transactions on Biomedical Engineering
DOI
10.1109/TBME.2025.3637089
Method of Research
Data/statistical analysis
Subject of Research
People
Article Title
Computerized Assessment of Motor Imitation for Distinguishing Autism in Video (CAMI-2DNet)
Article Publication Date
1-Jul-2026
Tags
/Applied sciences and engineering
/Health and medicine/Diseases and disorders/Developmental disabilities/Autism
Tags: accessible autism detection toolsAI motion analysis for autismAI-assisted autism evaluationautism screening technologyautomated child movement trackingcost-effective autism screening solutionsinnovative autism screening methodsKennedy Krieger Institute autism researchmachine learning in autism diagnosisobjective autism diagnostic scoringPenn Engineering AI developmentvideo-based autism assessment
