Yacun Wang | Searcharxiv

arXiv2026

Modeling Physical Activity Change as Smooth Transformations: Temporal and Amplitude Patterns Associated with Physical Function in Older Women

Purpose: To investigate whether longitudinal changes in timing and magnitude of PA are associated with physical function (PF) in older women. Methods: Women from OPACH study with accelerometry at baseline and WHISH study W1 and W2 were included. Minute-level PA counts were averaged and smoothed as diurnal PA curves. Consecutive-visit change was modeled within periods (baseline--W1 and W1--W2) as a Riemannian deformation from earlier to later curves, with two-dimensional initial momenta characterizing timing and magnitude shifts. Multivariate functional principal component analysis (MFPCA) summarized coupled timing-magnitude patterns, and principal component (PC) scores and deformation energy were derived for each participant-period. Linear mixed-effects models related these features to RAND-36 PF, adjusting for baseline PF and covariates. Results: Mean PA deformation in both periods showed downward shifts in PA magnitude and temporal redistribution after 10:00. Top 15 PCs explained at least 90% of variability in both periods. PC1 captured diurnal PA increase/decrease, explaining 22.4% of variability for baseline--W1 and 20.8% for W1--W2. Among participants with complete PF scores and baseline covariates (N=1,157), higher PC1 scores, reflecting relative increase/maintenance of PA across day, were positively associated with PF (P<0.0001). Deformation energy, a metric for overall diurnal pattern change between visits, showed a significant interaction with period for PF (P=0.003), with a larger positive association during W1--W2 than during baseline--W1. Conclusions: In older women, longitudinal changes in diurnal PA accumulation were associated with PF. Riemannian deformation analysis identified clinically interpretable markers of PA pattern change that may capture functional-aging information not represented by conventional PA summaries.

stat.AP↗

arXiv2025

MoCA: Multi-modal Cross-masked Autoencoder for Time Series in Digital Health

Wearable devices enable continuous multi-modal physiological and behavioral monitoring, yet analysis of these data streams faces fundamental challenges including the lack of gold-standard labels and incomplete sensor data. While self-supervised learning approaches have shown promise for addressing these issues, existing multi-modal extensions present opportunities to better leverage the rich temporal and cross-modal correlations inherent in simultaneously recorded wearable sensor data. We propose the Multi-modal Cross-masked Autoencoder (MoCA), a self-supervised learning framework that combines transformer architecture with masked autoencoder (MAE) methodology, using a principled cross-modality masking scheme that explicitly leverages correlation structures between sensor modalities. MoCA demonstrates strong performance boosts across reconstruction and downstream classification tasks on diverse benchmark datasets. We further establish theoretical guarantees by establishing a fundamental connection between multi-modal MAE loss and kernelized canonical correlation analysis through a Reproducing Kernel Hilbert Space framework, providing principled guidance for correlation-aware masking strategy design. Our approach offers a novel solution for leveraging unlabeled multi-modal wearable data while handling missing modalities, with broad applications across digital health domains.

stat.ML↗