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

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Tarannum Ara
Bivas Mitra
Published in
Information Integration and Web Intelligence
Publisher
Springer Nature Switzerland

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Abstract

This chapter introduces CoRA, a continual learning framework designed for personalized stress detection using multimodal sensing. The framework addresses the challenges of non-stationary data and catastrophic forgetting, which are common in real-world stress monitoring. CoRA uses a self-supervised encoder for feature extraction and a lightweight classifier for stress prediction. A key innovation is the use of KL-divergence-based drift detection to monitor distributional shifts and trigger model updates only when necessary. The framework also employs Elastic Weight Consolidation (EWC) regularization to prevent the model from forgetting past knowledge. Experiments on multiple real-world stress datasets demonstrate that CoRA consistently outperforms static and naive baselines, maintaining higher accuracy over time. The chapter provides a detailed overview of the methodology, including encoder pretraining, drift detection, and regularized updates. It also presents the results of evaluations on three public multimodal datasets, highlighting the effectiveness of CoRA in coping with evolving deployment conditions.
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previous chapter BATT2GRAPH: A Hybrid CNN-LSTM and Temporal Graph-Based Approach for Lithium-Ion Battery SOH Prediction and Anomaly Detection
next chapter Peak Pattern Based Similarity Search for High-Dimensional Spectral Data
Title
CoRA: Continual Learning for Multimodal Sensing with a Case Study in Mental Health
Authors
Tarannum Ara
Bivas Mitra
Copyright Year
2026
Book
Information Integration and Web Intelligence

Print ISBN: 978-3-032-11975-9

Electronic ISBN: 978-3-032-11976-6

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-032-11976-6_10
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