Abstract:

To address the issue that the existing methods for detecting dynamic changes in Leg Length Discrepancy （LLD） under non-clinical environments fail to exploit the spatiotemporal features of LLD gait fully， a novel method based on a graph spatiotemporal learning model was proposed to detect LLD dynamic changes. In the method， the detection of LLD dynamic changes was considered as a gait graph pattern classification problem， and a spatiotemporal gait graph classification model integrating spatial graph convolution and Long Short-Term Memory （LSTM） network was constructed， so that the superior spatiotemporal graph topology learning ability was used to explore significantly different features hidden in LLD gait patterns， which improved generalization ability of the gait classification model effectively and detect LLD dynamic changes accurately. Skeletal data simulating different degrees of LLD were collected from 26 healthy subjects using Azure Kinect to build LLD gait patterns， which were used to evaluate the effectiveness of the proposed method. Experimental results show that the constructed model can explore significantly different features hidden in LLD gait graph patterns effectively with a classification accuracy of 99.62%， and its classification generalization performance is improved by 5.83 percentage points compared to that of the model combining Convolutional Neural Network （CNN） with LSTM， providing a novel technical solution for detecting LLD dynamic changes in outdoor environments accurately.

Key words: Graph Convolutional Network (GNN), Leg Length Discrepancy (LLD), gait classification, Long Short-Term Memory （LSTM） network, Azure Kinect

摘要：

针对现有的检测非临床环境下肢不等长（LLD）动态变化方法不能充分挖掘LLD步态时空特征的问题，提出一种基于图时空学习模型的LLD动态变化检测方法。该方法将检测LLD动态变化视为步态图模式分类问题，并构建空间图卷积和长短时记忆（LSTM）网络相融合的时空步态图分类模型，从而充分利用优异的时空图拓扑学习性能挖掘隐藏于LLD步态模式的显著差异性特征，进而有效提升步态分类模型的泛化性，并准确检测LLD动态变化。使用Azure Kinect采集26名健康受试者模拟不同程度LLD的骨架数据，从而构建LLD步态模式以评价所提方法的有效性。实验结果表明，所提方法可有效挖掘隐藏于LLD步态图模式的显著差异性特征，分类准确率达到99.62%，它的分类泛化性能相较于卷积神经网络（CNN）结合LSTM的模型提升了5.83个百分点，为准确检测户外LLD动态变化提供了一个新的技术解决方案。

关键词: 图卷积网络, 下肢不等长, 步态分类, 长短时记忆网络, Azure Kinect