Multiscale SPD manifold learning for rehabilitation exercise evaluation.

ABSTRACT: Rehabilitation exercise assessment plays a crucial role in patient recovery, particularly for individuals recovering from injuries, surgeries, or illnesses affecting mobility. In this paper, we propose a novel approach for the assessment of skeleton-based data recorded from rehabilitation exercise, where we present the data as points on symmetric positive definite (SPD) manifold. Our method addresses the limitations of traditional Euclidean-based approaches by leveraging the SPD manifold's ability to preserve motion variations and spatial relationships. We propose a novel framework leveraging SPD manifold to preserve the intrinsic geometry of human motion and capture nonlinear variations in complex movements. By embedding motion data into SPD manifolds, we integrate unsupervised K-Nearest

SUBMITTER: Bai Z 

PROVIDER: S-EPMC12606120 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature