Your Footstep: The Key to Digital Identity

First Place

Gait recognition, the art of identifying individuals by their unique walking styles, is at the forefront of modern biometrics. It has important applications in healthcare surveillance, access control, and fall detection. Imagine a world where the way you walk becomes your digital signature, granting access to secure locations or verifying your identity. My research at UNB’s Health Technologies Lab is pioneering this field by developing advanced algorithms that analyze pressure patterns underfoot to authenticate individuals. We are compiling a groundbreaking collection of foot-pressure data from over 150 diverse individuals, encompassing various ages, genders, and ethnicities, obtained through our state-of-the-art Stepscan flooring. This comprehensive dataset, which exceeds all previously released foot-pressure datasets in the gait recognition literature, will push the boundaries of gait recognition research. The ultimate aim is to revolutionize user authentication by refining deep learning techniques tailored specifically for pressure-based gait recognition. Additionally, the proposed algorithm not only enhances gait recognition performance but also has potential applications in fall detection and early mobility disease diagnosis systems, offering a reliable means of community-based health monitoring.