Poster abstract: Gait health monitoring through footstep-induced floor vibrations

Abstract

Gait health monitoring is critical for condition diagnosis and fall prediction in elderly populations. Existing methods for gait health monitoring (e.g. direct observation and sensing) are not suitable for non-clinical environments due to qualitative assessments or operational limitations. Our method utilizes footstep-induced floor vibration sensing to provide a passive gait health monitoring platform that can be used in non-clinical environments (e.g. home settings) to provide gait health information in a timely manner. We decompose vibration responses to obtain signal peaks that correspond to temporal gait information and leverage foot dominance to learn a signal amplitude-footstep ground reaction force transfer function. Preliminary results show that temporal gait parameters can be estimated with up to 99% accuracy and gait balance symmetry can be estimated with as low as 10.4% error.