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This study introduces a novel dataset and framework for analyzing children's gait behaviors from standard RGB video, targeting the ambulatory patterns of children aged 3-17. The research highlights the limitations of existing state-of-the-art methods, including gait foundation models and Multimodal Large Language Models, in capturing the subtle nuances of pediatric gait relevant for diagnosing developmental disorders. By providing over 1,100 high-frame-rate video sequences and a unified framework for analysis, this work establishes a rigorous baseline for automated assessment of children's gait, paving the way for advancements in clinical applications.
Current models struggle with the complexities of children's gait, revealing a critical gap in automated assessment tools for pediatric neuromuscular disorders.
We introduce a new problem domain for human action recognition: the fine-grained analysis of children's gait behaviors from standard RGB video. We specifically target the ambulatory patterns of children aged 3-17 years. Such behaviors arise naturally in the diagnosis and treatment of several critical developmental and neuromuscular disorders, such as cerebral palsy and hemiplegia. Despite their clinical value, current 3D sensor-based gait analysis systems are expensive, intrusive, and often impractical for young subjects. To address this, we introduce a new dataset comprising over 1,100 high-frame-rate (60 FPS) video sequences from 110 subjects, accompanied by synchronized, anonymized pose sequences. In each session, the child performs a 5-second"walk-around"task, capturing the gait cycle from multiple viewpoints. Crucially, we demonstrate that current state-of-the-art approaches, including gait foundation models and Multimodal Large Language Models (MLLMs), fail to effectively resolve these clinical nuances. We identify the key technical challenges in analyzing these erratic and subtle motor patterns and describe a unified end-to-end framework for decoding fundamental components of pediatric gait. Through comprehensive experimental results, we demonstrate the potential of this dataset to drive novel research questions and establish a rigorous baseline for automated child gait assessment.
