Structural Property Guided Gait Parameter Estimation Using Footstep-Induced Floor Vibrations

This paper presents an approach that estimates occupants temporal gait parameters (e.g. step time, stride time, and initial loading response time) through structural floor vibrations while adapting to varying structural conditions. Continuous monitoring of these temporal gait parameters is a key component for elderly fall risk assessment and diagnosing injuries and gait abnormalities in home healthcare. A primary research challenge with using floor vibration sensing is that real-world deployments are often conducted in existing structures where little is known about the underlying structural properties. As such, prior works utilizing floor vibrations combine data-driven approaches with key heuristic observations to achieve accurate results, but often fail to adapt to varying structural conditions without re-training. To overcome this challenge, we present our approach, which extracts dynamic properties of the structure from footstep-induced vibrations and uses those properties to update gait parameter estimation models. By passively learning the dynamic properties of the structure, our approach enables gait parameter estimation that is robust to structural changes. We evaluate our approach by conducting real-world walking experiments on a test bed at Carnegie Mellon University.