Topological Data Analysis for Robust Gait Biometrics Based on Wearable Sensors

The convergence of hardware, materials, sensors, and artificial intelligence brought a new class of consumer portable devices with advanced computing and connectivity capabilities, serving diverse aspects such as health monitoring, fitness tracking, and entertainment. These devices handle sensitive personal information and necessitate adequate security protection. Inertial measurement units (IMU) commonly integrated into smart wearables can be utilized to derive gait-based personal signatures. Consequently, ongoing research is focused on gait-based security, aiming to develop robust recognition methods that can adapt to various real-world conditions in terms of on-body IMU location and orientation, terrain type, and sensor signal utilization. The present study addresses these challenges through phase-space analysis and topological data analysis. The proposed method has undergone validation using both a custom Gait-TNDA dataset and a public Gait-MIUS dataset, achieving mean accuracies surpassing 96%. Furthermore, its performance was compared with established deep learning methods. The results affirm that topological descriptors effectively capture gait dynamics, suggesting potential applications in the next generation of consumer wearables.