Retrieval from out-of-domain foundation models enables personalization of a lightweight transformer for stress detection, yielding +3.92% accuracy and +4.76% F1 gains on WESAD without user labels.
Marie Harkin, and Sebastian Pannasch
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
A VR study found that dynamic feedback based on completion time, straightness or peak speed improved average pointing performance but produced small or opposite effects for some users.
citing papers explorer
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Retrieval-Augmented Personalization with Foundation Models for Wearable Stress Detection
Retrieval from out-of-domain foundation models enables personalization of a lightweight transformer for stress detection, yielding +3.92% accuracy and +4.76% F1 gains on WESAD without user labels.
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Game Changers: Designing and Measuring Dynamic Feedback To Help Users Self-Regulate in a VR Pointing Game
A VR study found that dynamic feedback based on completion time, straightness or peak speed improved average pointing performance but produced small or opposite effects for some users.