Computerized Assessment of Motor Imitation for Distinguishing Autism in Video (CAMI-2DNet)

2025-01-15

AI Summary

Early identification of Autism Spectrum Condition (ASC) is hindered by manual, time-consuming, and subjective motor imitation assessments. Method: We propose an end-to-end, video-based automatic assessment framework. We design an interpretable, disentangled encoder-decoder network that explicitly separates motor representations from confounding factors—including body morphology and viewing angle—for the first time. Furthermore, we introduce a synthetic-data-driven motion retargeting training paradigm, eliminating reliance on real-world annotated data, motion-capture hardware, or manual preprocessing. Results: Our method significantly outperforms CAMI-2D and matches the performance of CAMI-3D in ASC vs. neurotypical (NT) child classification. It achieves strong correlation with clinical human ratings (r > 0.85). This work establishes a novel paradigm for contactless, low-cost, and highly interpretable motor assessment in ASC.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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, 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 data normalization, cleaning and annotation. CAMI-2DNet uses an encoder-decoder architecture to map a video to a motion encoding 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 data normalization and human annotations.

Problem

• Autism Assessment
• Mimicry Evaluation
• Computational Methods

Innovation

• CAMI-2DNet
• Automated Imitation Learning
• Autism Spectrum Disorder Detection