Riemannian vs. Euclidean Representation of Gait Kinematics: A Comparative Analysis

TL;DR

This paper compares Riemannian and Euclidean methods for analyzing gait kinematics, showing that Riemannian geometry captures non-linear dynamics and efficiency patterns better than traditional linear models.

Contribution

It introduces a Riemannian framework using SPD matrices and Log-Euclidean metrics for gait analysis, revealing non-linear variability patterns across speeds.

Findings

Euclidean metrics show linear variability increase with speed.

Riemannian metrics reveal a non-linear 'inverted-U' pattern.

High-speed gait stabilization suggests optimized motor efficiency.

Abstract

Accurate quantification of complex human movements, such as gait, is essential for clinical diagnosis and rehabilitation but is often limited by traditional linear models rooted in Euclidean geometry. These frameworks frequently fail to capture the intrinsic non-linear dynamics and posture-dependent dependencies of biological systems. To address this, we present a computational framework that maps kinematic data onto a Riemannian manifold of Symmetric Positive Definite (SPD) matrices. Using the Log-Euclidean metric, we transformed raw skeletal pose sequences into geometric feature vectors to quantify gait variability and smoothness across three velocity profiles: slow, medium, and fast. Our comparative analysis reveals a critical divergence between geometric approaches. While Euclidean metrics exhibit a strictly linear increase in variability with speed (Slow < Medium < Fast), implying instability, the proposed Riemannian metrics reveal a non-linear "inverted-U'' pattern with varying speeds. Specifically, we observed a stabilization of variance at high speeds (sprinting), suggesting that the motor system optimizes efficiency by adhering to geodesic trajectories of minimum effort. These findings demonstrate that manifold-based representations offer superior sensitivity to biomechanical efficiency compared to standard linear methods, providing a robust foundation for future diagnostic algorithms and explainable machine learning models in clinical biomechanics.