Acoustic Gait Analysis using Support Vector Machines

Jasper Huang, Fabio Di Troia, Mark Stamp

2018

Abstract

Gait analysis, defined as the study of human locomotion, can provide valuable information for low-cost analytic and classification applications in security, medical diagnostics, and biomechanics. In comparison to visual-based gait analysis, audio-based gait analysis offers robustness to clothing variations, visibility issues, and angle complications. Current acoustic techniques rely on frequency-based features that are sensitive to changes in footwear and floor surfaces. In this research, we consider an approach to surface-independent acoustic gait analysis based on time differences between consecutive steps. We employ support vector machines (SVMs) for classification. Our approach achieves good classification rates with high discriminative one-vs-all capabilities and we believe that our technique provides a promising avenue for future development.

Paper Citation

in Harvard Style

Huang J., Di Troia F. and Stamp M. (2018). Acoustic Gait Analysis using Support Vector Machines.In Proceedings of the 4th International Conference on Information Systems Security and Privacy - Volume 1: ForSE, ISBN 978-989-758-282-0, pages 545-552. DOI: 10.5220/0006730705450552

in Bibtex Style

@conference{forse18,
author={Jasper Huang and Fabio Di Troia and Mark Stamp},
title={Acoustic Gait Analysis using Support Vector Machines},
booktitle={Proceedings of the 4th International Conference on Information Systems Security and Privacy - Volume 1: ForSE,},
year={2018},
pages={545-552},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006730705450552},
isbn={978-989-758-282-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Information Systems Security and Privacy - Volume 1: ForSE,
TI - Acoustic Gait Analysis using Support Vector Machines
SN - 978-989-758-282-0
AU - Huang J.
AU - Di Troia F.
AU - Stamp M.
PY - 2018
SP - 545
EP - 552
DO - 10.5220/0006730705450552