Gait Data Augmentation using Physics-Based Biomechanical Simulation

TL;DR

This paper introduces a physics-based gait data augmentation method using OpenSIM to generate biomechanically plausible walking sequences, improving gait classification and identification accuracy.

Contribution

The paper presents a novel biomechanical simulation framework for gait data augmentation, addressing limitations of standard methods and enhancing model performance.

Findings

Improved gait classifier performance with augmented data.

Achieved 96.11% accuracy in gait-based person identification.

Validated on WBDS and CASIA-B datasets.

Abstract

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.