arXiv AI
Aug 5
From Wearable Data to Personalized and Actionable Health Insights
arXiv:2608. 03251v1 Announce Type: cross Abstract: Commercial wearable devices continuously capture rich physiological data (e.
Beyond Latent Personalization: Integrating HRV-Conditioned Personalization via LLM for Interpretable Stress Detection
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.
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Hugging Face Trending Papers
Aug 4
From Wearable Data to Personalized and Actionable Health Insights
Commercial wearable devices continuously capture rich physiological data (e. g.
arXiv Machine Learning
Sep 22
Interpretable Stress Detection from ECG Signals Using Motif-Based Anomaly Analysis
arXiv:2609.22179v1 Announce Type: cross Abstract: Stress detection using physiological signals has gained significant attention due to its impact on both physical and mental health. While existing ap...
arXiv Machine Learning
Jun 25
Retrieval-Augmented Personalization with Foundation Models for Wearable Stress Detection
arXiv:2606. 24985v1 Announce Type: new Abstract: Personalization in wearable-based stress detection remains challenging due to substantial inter-individual variability in physiological and behavioral responses.
arXiv Machine Learning
Sep 14
Explainable Prediction from Mobile Sensing Data through LLM-guided Concept Integration
The paper introduces a Concept-Integrated Transformer (CIT) that uses a pretrained large language model to generate concept abnormality targets with confidence weights, eliminating the need for manual concept annotation. CIT is applied to mobile sensing data from two longitudinal datasets, achieving the highest F1 score on the AFFECT dataset (0.756) and tying for the highest on a PHQ-9 dataset (0.765). The model’s learned concept scores reveal interpretable behavioral and physiological patterns, such as differences in sleep quantity and quality between high and low negative affect groups.
arXiv AI
Jun 2
Towards a General Intelligence and Interface for Wearable Health Data
arXiv:2605. 22759v2 Announce Type: replace Abstract: While ubiquitous wearable sensors capture a wealth of behavioral and physiological information, effectively transforming these signals into personalized health insights is challenging.
arXiv AI
Jun 19
VitalAgent: A Tool-Augmented Agent for Reactive and Proactive Physiological Monitoring over Wearable Health Data
arXiv:2605. 29483v2 Announce Type: replace Abstract: Wearable devices enable continuous monitoring of physiological signals such as ECG and PPG, but existing mHealth systems are largely limited to task-specific prediction pipelines or reactive question answering over static summaries.
arXiv AI
Aug 18
Take it Personally: The Limits of General SSL Representations for Real-Life PPG Emotion Detection
arXiv:2608. 14675v1 Announce Type: cross Abstract: While Self-Supervised Learning (SSL) effectively extracts general representations from noisy, unconstrained physiological signals such as photoplethysmography (PPG), its suitability for highly subjective tasks remains unproven.
arXiv AI
Jun 30
HEARTS: Benchmarking LLM Reasoning on Health Time Series
arXiv:2603. 06638v3 Announce Type: replace-cross Abstract: The rise of large language models (LLMs) has shifted time series analysis from narrow analytics to general-purpose reasoning.
arXiv Computation and Language
Aug 28
BALMS: Benchmarking Agentic LLMs for Longitudinal Mental Health Sensing
BALMS is a benchmark for evaluating large language model (LLM) agents that analyze longitudinal wearable data to predict mental‑health wellbeing scores and generate evidence‑grounded rationales. It covers three real‑world datasets, two task families (score prediction and rationale generation), and tests five LLM backbones across open‑ and closed‑source paradigms. The study finds that zero‑shot agents rarely beat a simple mean baseline, and while chain‑of‑thought prompting helps reasoning, it does not ensure temporal grounding or numerical accuracy.
arXiv Computation and Language
Sep 7
WearableQA: A Benchmark for Health Reasoning over Real-World Wearable Data
WearableQA is a new benchmark that tests AI systems on health reasoning using real-world wearable data from 200 users, each with up to 500 days of daily measurements. It contains 4,084 ten‑option multiple‑choice questions derived from wearable time series, blood biomarkers, and demographics, and is organized into 16 question types that distinguish data‑driven computation from physiological interpretation and single‑signal from cross‑signal reasoning. Evaluation of 14 large language models shows wide performance gaps, indicating that the benchmark remains challenging and useful for diagnosing model capabilities.
arXiv AI
Aug 18
Does the Heart Show Your Pain? Tackling the X-ITE Pain Challenge with Self-Supervised ECG Representation Learning
arXiv:2608. 14662v1 Announce Type: cross Abstract: Accurate recognition of pain using physiological signals remains a challenging problem due to pain's subjective nature and high inter-individual variability.