This paper focuses on addressing the problem of data scarcity for gait analysis. Standard augmentation methods may produce gait sequences that are not consistent with the biomechanical constraints of human walking. To address this issue, we propose a novel framework for gait data augmentation by using OpenSIM, a physics-based simulator, to synthesize biomechanically plausible walking sequences. The proposed approach is validated by augmenting the WBDS and CASIA-B datasets and then training gait-based classifiers for 3D gender gait classification and 2D gait person identification respectively. Experimental results indicate that our augmentation approach can improve the performance of model-based gait classifiers and deliver state-of-the-art results for gait-based person identification with an accuracy of up to 96.11% on the CASIA-B dataset.

翻译：本文聚焦于解决步态分析中数据稀缺的问题。标准的数据增强方法可能生成不符合人体行走生物力学约束的步态序列。为此，我们提出了一种新颖的步态数据增强框架，通过使用基于物理的仿真器OpenSIM来合成符合生物力学原理的行走序列。所提出的方法通过在WBDS和CASIA-B数据集上进行数据增强，并分别训练基于步态的3D性别分类器和2D行人识别分类器进行验证。实验结果表明，我们的增强方法能够提升基于模型步态分类器的性能，并在CASIA-B数据集上实现高达96.11%的步态行人识别准确率，达到当前最优水平。