解码儿童的步态行为

摘要

我们引入了一个人体动作识别的新问题领域：基于标准RGB视频对儿童步态行为进行细粒度分析。我们特别关注3至17岁儿童的行走模式。此类行为在脑瘫、偏瘫等若干关键发育性和神经肌肉疾病的诊断与治疗过程中自然产生。尽管具有重要的临床价值，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切实际。为解决这一问题，我们构建了一个新数据集，包含来自110名受试者的超过1100个高帧率（60 FPS）视频序列，并配以同步且匿名化处理的人体姿态序列。在每次采集会话中，儿童执行一项5秒的"自由行走"任务，从多个视角捕捉完整步态周期。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）均无法有效解析这些临床细微差别。我们识别了分析这些不规则且微妙的运动模式所面临的关键技术挑战，并描述了一个用于解码儿科步态基本组成部分的统一端到端框架。通过全面的实验结果表明，该数据集具有推动新颖研究问题产生的潜力，并为自动化儿童步态评估建立了严格的基准。

English

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.