Vibration Source Separation for Multiple People Gait Monitoring Using Footstep-Induced Floor Vibrations

Abstract

In this paper we present a source separation approach which enables occupant gait monitoring for multiple concurrent walkers using footstep-induced floor vibration sensing. Occupant gait monitoring is a critical component of human-centric smart home applications like building security, gait health monitoring, and energy management. Current approaches such as vision sensing, wearables, and pressure sensors are limited in real-world deployments due to requirements of line-of-sight, the need for users to wear/carry a device at all times, and dense sensor deployment. Floor vibration sensing overcomes these limitations due to passive, sparse, and non-intrusive sensing, but prior works are limited to “single walker” scenarios. Real-world gait monitoring, however, typically involves persons walking in groups of two or more people (i.e., “multiple walker” scenarios). To this end, we introduce a frequency domain-based signal separation approach that enables gait monitoring in “multiple walker” scenarios by isolating the response from each concurrent walker. The insight is that the overall vibration response is the sum of each individual response signal at an unknown scale (amplitude) and time shift. We validate our approach with real-world walking experiments with two people walking side-by-side and correctly identify the order of the walkers with 88% accuracy (1.8X improvement over a baseline approach).