Non-contact Person Identification by Piezoelectric-Based Gait Vibration Sensing

Abstract

Floor vibrations caused by walking person (hereinafter, gait vibrations) have recently been explored as a way to determine their locations and identities, and the technologies for measuring such gaits may enable low-cost elderly monitoring services and crime prevention systems. In this paper, we report on the development of a system that can accurately capture both high- and low-level signal gait vibrations using a piezoelectric sensor, and then propose a new system that can identify a walking person based on a small number of footsteps. Our proposed system uses two key approaches to accurately obtain such gait vibrations. The first uses a combination of a source follower circuit in parallel and a piezoelectric sensor. The second involves widening the dynamic range by the use of a dual power supply drive. We then show how we can increase the accuracy of our system by combining multiple footsteps rather than using a single footstep, and thus achieve a more robust system regardless of the distance between the sensor and the target. In experiments comparing five different machine learning (ML) models conducted with six test participants to evaluate our system, person identification results obtained using only a single footstep showed accuracy levels up to 70.8% of average F-measure when using the Light Gradient Boosting Machine (LightGBM) classifier, while for other methods, the average F-measures were 63.1%, 75.9%, and 87.1% in cases of using the first, first and second, and from first to third footsteps from each back-and-forth walk test, respectively.