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.

本文介绍了一种基于 OpenSIM 的新型步态数据增强框架，通过仿真出符合人体行走生物力学约束的步态序列，增强 WBDS 和 CASIA-B 两个数据集，并在 3D 性别步态分类和 2D 步态个人识别中训练步态分类器，实验结果表明我们的增强方法可以提高基于模型的步态分类器的性能，使步态个人识别准确率达到最高 96.11％。