Hallway Gait Monitoring Using Novel Radar Signal Processing and Unsupervised Learning

Abstract

We propose a novel corridor or hallway gait monitoring system based on radar signal processing, unsupervised learning, and a subject detection, association and tracking method. This paper proposes an algorithm that could be paired with any type of MIMO FMCW radar to capture human gait in a highly cluttered environment without needing radar antenna alteration. We validate algorithm functionality by capturing spatiotemporal gait values (e.g., speed, step points, step time, step length, and step count) of people walking in a long hallway. We show that our proposed algorithm yields an average absolute error for speed estimation between 0.0040 m/s to 0.0435 m/s. These preliminary results demonstrate the promising potential of our algorithm to accurately monitor gait in hallways, which increases opportunities for its applications in institutional and home environments.