2023/07/16 by Mritula Chandrasekaran, Chandrasekaran, Mritula, J. Francik +3 · 1 citation
Computer Science · Engineering · Medicine · #Computer Vision and Pattern Recognition (cs.CV) #Diabetic Foot Ulcer Assessment and Management #FOS: Computer and information sciences #Gait Recognition and Analysis #Human Pose and Action Recognition
paper · pdf · doi:10.48550/arxiv.2307.08092
openalex publication_date 2023/07/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.