AttentiveGRUAE: An Attention-Based GRU Autoencoder for Temporal Clustering and Behavioral Characterization of Depression from Wearable Data

Nidhi Soley, Vishal M. Patel, Casey Overby Taylor

Keywords: time-series clustering;wearable sensing;interpretable time-series; outcome-aware representation learning; cross-cohort generalization; ablation study;

TL;DR: AttentiveGRUAE: attention-GRU autoencoder that learns outcome-aware sleep embeddings and GMM subtypes. Beats baselines (AUC 0.74), generalizes (AUC 0.61; Sil 0.63), produces stable clusters (ARI≈0.89), and highlights salient days.

Abstract: In this study, we present AttentiveGRUAE, a novel attention-based gated recurrent unit (GRU) autoencoder designed for temporal clustering and prediction of outcome from longitudinal wearable data. Our model jointly optimizes three objectives: (1) learning a compact latent representation of daily behavioral features via sequence reconstruction, (2) predicting end-of-period depression rate through a binary classification head, and (3) identifying behavioral subtypes through the Gaussian Mixture Model (GMM) based soft clustering of learned embeddings. We evaluate AttentiveGRUAE on longitudinal sleep data from 372 participants (GLOBEM 2018–2019), and it demonstrates superior performance over baseline clustering, domain-aligned self-supervised, and ablated models in both clustering quality (silhouette score = 0.70 vs (0.32–0.70)) and depression classification (AUC = 0.74 vs (0.50–0.67)). Additionally, external validation on cross-year cohorts from 332 participants (GLOBEM 2020–2021) confirms cluster reproducibility (silhouette score = 0.63, AUC = 0.61) and stability. We further perform subtype analysis and visualize temporal attention, which highlights sleep-related differences between clusters and identifies salient time windows that align with changes in sleep regularity, yielding clinically interpretable explanations of risk.

Submission Number: 79