  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.
