用图卷积网络分析步态视频，精准识别共济失调及其严重程度。
AtGCN: A Graph Convolutional Network For Ataxic Gait Detection
- 构建时空图卷积网络，捕捉步态关键特征。
- 在149个视频小数据集上实现93.46%检测准确率和0.4169的误差。
- 适合医疗影像分析、智能康复等场景使用。
基于视频的步态分析旨在通过患者行走视频诊断病理，如共济失调。本文提出一种名为AtGCN的图卷积网络，用于从2D视频中检测共济失调步态并评估其严重程度。该任务极具挑战性，因共济失调步态与健康步态差异微小，且现有数据集规模极小，最大仅含149个视频。为应对这一挑战，论文采用特殊时空图卷积以有效提取相关步态特征；针对数据量少的问题，利用动作识别预训练模型进行系统裁剪后，在共济失调数据集上微调得到AtGCN。同时提出一种增强策略，将视频序列切分为多个步态周期，模型在单个步态周期的肢体部位图上运行。评估结果表明，AtGCN在检测与严重程度预测上均优于当前最先进方法，准确率达93.46%，平均绝对误差（MAE）为0.4169，且模型体积仅为前者的1/5.5。
原文摘要 · Abstract (English)
Video-based gait analysis can be defined as the task of diagnosing pathologies, such as ataxia, using videos of patients walking in front of a camera. This paper presents a graph convolution network called AtGCN for detecting ataxic gait and identifying its severity using 2D videos. The problem is especially challenging as the deviation of an ataxic gait from a healthy gait is very subtle. The datasets for ataxic gait detection are also quite small, with the largest dataset having only 149 videos. The paper addresses the first problem using special spatiotemporal graph convolution that successfully captures important gait-related features. To handle the small dataset size, a deep spatiotemporal graph convolution network pre-trained on an action recognition dataset is systematically truncated and then fine-tuned on the ataxia dataset to obtain the AtGCN model. The paper also presents an augmentation strategy that segments a video sequence into multiple gait cycles. The proposed AtGCN model then operates on a graph of body part locations belonging to a single gait cycle. The evaluation results support the strength of the proposed AtGCN model, as it outperforms the state-of-the-art in detection and severity prediction with an accuracy of 93.46% and a MAE of 0.4169, respectively, while being 5.5 times smaller than the state-of-the-art.
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