Topological Data Analysis for Gait Pattern Classification

The classification of gait patterns is an important challenge in movement analysis, as it supports clinical assessment and decision-making by enabling diagnosis and severity stratification. In this talk, I will discuss the potential of Topological Data Analysis (TDA) for gait pattern classification. Unlike conventional approaches that rely on explicit detection of Gait Events (GEs) to compute Spatiotemporal Gait Parameters (SGPs), TDA characterises the global structure of gait signals directly, capturing relationships and patterns in the data without requiring GEs. This is particularly relevant in real-world settings, where GE detection can be diQicult due to heterogeneity in walking conditions and gait patterns, potentially biasing clinically relevant metrics and, consequently, decision-making.
Within our department, preliminary results have shown that TDA-based features can achieve classification performance comparable to that of SGPs in fall-risk assessment. These findings suggest that topology oQers a competitive alternative for representing gait data, with the potential to better handle variability across subjects and pathological
conditions.
Building on these results, we plan to further extend the TDA framework in two directions. First, we aim to investigate time-aware topological methods to better capture the
temporal structure of gait signals. Second, we will explore Topological Deep Learning (TDL) approaches to reduce reliance on handcrafted design choices and potentially
improve classification performance. By combining the robustness of topology with datadriven representation learning, this work seeks to provide robust tools for the classification of typical and pathological gait patterns.