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arXiv Machine Learning By Wei-Hsiang Ma, Ya-Tse Wu, Hsing-Hang Chou, Chi-Chun Lee

Beyond Latent Personalization: Integrating HRV-Conditioned Personalization via LLM for Interpretable Stress Detection

Read the original on arXiv Machine Learning →

The paper introduces an LLM‑enhanced framework for personalized stress detection that combines heart rate variability (HRV) features with personal attributes via semantic interpretation. It reports a 29.9% relative MCC improvement over the best baseline on the TILES‑2018 dataset and generates human‑readable descriptions of individual physiological responses. The approach demonstrates that large language models can provide both higher accuracy and interpretability in stress detection.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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