发表机构
University of Illinois Urbana-Champaign; PediaMed AI; Shenzhen Children’s Hospital; Hong Kong Polytechnic University; The Hong Kong University of Science and Technology (Guangzhou)（伊利诺伊大学厄巴纳-香槟分校; PediaMed AI; 深圳市儿童医院; 香港理工大学; 香港科技大学（广州））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对儿童步态分析的临床需求，本文构建含110名受试者的1100余条高帧率视频数据集，指出现有先进模型难以解析其临床细节，提出统一端到端框架并建立自动化儿童步态评估基线。
AI 中文摘要
我们引入了人类动作识别的一个新问题领域：从标准RGB视频对儿童步态行为进行细粒度分析，专门针对3-17岁儿童的步行模式。这类行为在脑瘫、偏瘫等多种关键发育和神经肌肉疾病的诊断与治疗中具有重要作用。尽管其临床价值显著，但当前基于3D传感器的步态分析系统成本高昂、具有侵入性，且对年幼受试者而言往往不实用。为解决这一问题，我们引入了一个新数据集，包含来自110名受试者的1100多个高帧率（60 FPS）视频序列，附带同步的匿名姿态序列。在每个会话中，儿童完成一项5秒的“绕行”任务，从多个视角捕捉步态周期。关键在于，我们证明当前最先进的方法，包括步态基础模型和多模态大语言模型（MLLMs），无法有效解析这些临床细节。我们确定了分析这些不稳定且微妙运动模式的关键技术挑战，并描述了一个用于解码儿童步态基本成分的统一端到端框架。通过全面的实验结果，我们证明该数据集有潜力推动新的研究问题，并为自动化儿童步态评估建立严格的基线。
英文摘要
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.
Journal refECCV 2026
