To understand and communicate the risk of chronic lower-limb joint diseases associated with aging, it is crucial to investigate the relationship between age and gait dynamics, particularly through angular kinematics. One key challenge is that angular kinematic trajectories are highly interconnected, and the structures of the interconnections vary across different components. Neglecting the interconnections and the variability in the connectivity structures impairs the understanding of age-associated gait coordination. To this end, we develop a novel kinematic connectivity analysis framework, grounded in multiple functional regression, to evaluate lower-limb joint effective age and uncover age-related kinematic features. The proposed approach is built upon the concept of filtration, a widely used tool in network analysis and topological data analysis for multi-resolution exploration. Specifically, we develop a forest-structured covariate grouping framework in which different kinematic trajectories are aggregated hierarchically to capture both (partially) shared and idiosyncratic motion signatures which are strongly associated with aging. We also develop a novel filtrated functional partial least squares approach for model estimation and feature extraction. Compared to existing approaches, our proposed approach demonstrates superior predictive power while providing novel insights into the coordinated evolution of angular kinematics during aging. In addition, the proposed framework is broadly applicable and can be readily extended in other scientific domains.
Categories: stat.ME stat.CO