Decoding Children’s Gait Behavior

We introduce a new problem domain for human action recog-nition: the fine-grained analysis of children’s gait behaviors from stan-dard RGB video. We specifically target the ambulatory patterns of chil-dren aged 3–17 years. Such behaviors arise naturally in the diagnosis andtreatment of several critical developmental and neuromuscular disorders,such as cerebral palsy and hemiplegia. Despite their clinical value, cur-rent 3D sensor-based gait analysis systems are expensive, intrusive, andoften impractical for young subjects. To address this, we introduce anew dataset comprising over 1,100 high-frame-rate (60 FPS) video se-quences from 110 subjects, accompanied by synchronized, anonymizedpose sequences. In each session, the child performs a 5-second "walk-around" task, capturing the gait cycle from multiple viewpoints. Cru-cially, we demonstrate that current state-of-the-art approaches, includ-ing gait foundation models and Multimodal Large Language Models(MLLMs), fail to effectively resolve these clinical nuances. We identifythe key technical challenges in analyzing these erratic and subtle mo-tor patterns and describe a unified end-to-end framework for decodingfundamental components of pediatric gait. Through comprehensive ex-perimental results, we demonstrate the potential of this dataset to drivenovel research questions and establish a rigorous baseline for automatedchild gait assessment. Project page: pediamedai.com/ChildrenGait