Communication Dans Un Congrès Année : 2025

Learning longitudinal stress dynamics from irregular self-reports via time embeddings

Résumé

The widespread adoption of mobile and wearable sensing technologies has enabled continuous and personalized monitoring of affect, mood disorders, and stress. When combined with ecological selfreport questionnaires, these systems offer a powerful opportunity to explore longitudinal modeling of human behaviors. However, challenges arise from missing data and the irregular timing of selfreports, which make challenging the prediction of human states and behaviors. In this study, we investigate the use of time embeddings to capture time dependencies within sequences of Ecological Momentary Assessments (EMA). We introduce a novel time embedding method, Ema2Vec, designed to effectively handle irregularly spaced self-reports, and evaluate it on a new task of longitudinal stress prediction. Our method outperforms standard stress prediction baselines that rely on fixed-size daily windows, as well as models trained directly on longitudinal sequences without time-aware representations. These findings emphasize the importance of incorporating time embeddings when modeling irregularly sampled longitudinal data.

Fichier principal
Vignette du fichier
ACII_ArXiv_format.pdf (410.48 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Licence

Dates et versions

hal-05237700 , version 1 (03-09-2025)

Licence

Identifiants

  • HAL Id : hal-05237700 , version 1

Citer

Louis Simon, Mohamed Chetouani. Learning longitudinal stress dynamics from irregular self-reports via time embeddings. 2025 13th International Conference on Affective Computing and Intelligent Interaction (ACII), Oct 2025, Canberra, Australia. ⟨hal-05237700⟩
95 Consultations
136 Téléchargements

Partager

  • More