Acoustic gait analysis using support vector machines
Publication Date
January 2018
Document Type
Contribution to a Book
Publication Title
Proceedings of the 4th International Conference on Information Systems Security and Privacy
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.
Recommended Citation
Jasper Huang, Fabio Troia, and Mark Stamp. "Acoustic gait analysis using support vector machines" Proceedings of the 4th International Conference on Information Systems Security and Privacy (2018).
