Gait recognition via deep learning of the center-of-pressure trajectory

TL;DR

This study demonstrates that deep learning models can accurately identify individuals based on their unique center-of-pressure gait trajectories, with high accuracy even from minimal data, highlighting the potential for gait-based biometric identification.

Contribution

The paper introduces a novel approach using CNNs to identify individuals from center-of-pressure trajectories, achieving near-perfect accuracy with minimal training data.

Findings

CNN achieved 99.9% accuracy on test segments.

Transfer learning enabled 100% accuracy with just two segments per person.

Unique gait pressure trajectories can reliably identify individuals.

Abstract

The fact that every human has a distinctive walking style has prompted a proposal to use gait recognition as an identification criterion. Using end-to-end learning, I investigated whether the center-of-pressure trajectory is sufficiently unique to identify a person with a high certainty. Thirty-six adults walked on a treadmill equipped with a force platform that recorded the positions of the center of pressure. The raw two-dimensional signals were sliced into segments of two gait cycles. A set of 20,250 segments from 30 subjects was used to configure and train convolutional neural networks (CNNs). The best CNN classified a separate set containing 2,250 segments with 99.9% overall accuracy. A second set of 4,500 segments from the six remaining subjects was then used for transfer learning. Several small subsamples of this set were selected randomly and used for fine tuning. Training with two segments per subject was sufficient to achieve 100% accuracy. The results suggest that every person produces a unique trajectory of underfoot pressures and that CNNs can learn the distinctive features of these trajectories. Using transfer learning, a few strides could be sufficient to learn and identify new gaits.