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Physiological sensing is essential for mental health monitoring, but models often degrade over time due to user behavior changes, sensor noise, and contextual variation. We propose CoRA (Continual and Regularized Adaptation), a lightweight continual learning framework that monitors latent feature drift using class-wise KL divergence and selectively retrains a downstream classifier with Elastic Weight Consolidation (EWC) to prevent forgetting. CoRA operates on top of a pretrained encoder, enabling efficient adaptation without storing raw past samples. In stress detection experiments on LifeSnaps, DAPPER, and WESAD, CoRA improves F1-score by up to 10.4% while reducing retraining overhead by over 40%, demonstrating a robust, personalized solution for real-world physiological monitoring.

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CoRA: Continual Learning for Multimodal Sensing with a Case Study in Mental Health

  • 会议论文

  • 发表日期：

    2026年

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  • Tarannum Ara,
  • Bivas Mitra

摘要

Physiological sensing is essential for mental health monitoring, but models often degrade over time due to user behavior changes, sensor noise, and contextual variation. We propose CoRA (Continual and Regularized Adaptation), a lightweight continual learning framework that monitors latent feature drift using class-wise KL divergence and selectively retrains a downstream classifier with Elastic Weight Consolidation (EWC) to prevent forgetting. CoRA operates on top of a pretrained encoder, enabling efficient adaptation without storing raw past samples. In stress detection experiments on LifeSnaps, DAPPER, and WESAD, CoRA improves F1-score by up to 10.4% while reducing retraining overhead by over 40%, demonstrating a robust, personalized solution for real-world physiological monitoring.