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.
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.
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arXiv Machine Learning
Jul 3
A Wearable Device Dataset for Mental Health Assessment Using Laser Doppler Flowmetry and Fluorescence Spectroscopy Sensors
arXiv:2502. 00973v2 Announce Type: replace Abstract: Mental health problems such as stress, anxiety, and depression affect millions of people worldwide.
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 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
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 17
Talking to Your Data: Exploring Embodied Conversation as an Interface for Personal Health Reflection
arXiv:2606. 17767v1 Announce Type: cross Abstract: Personal health data from wearables are typically presented through dashboards of charts and summary statistics, requiring users to actively interpret patterns and implications.
arXiv AI
Jul 21
OpenMHC: Accelerating the Science of Wearable Foundation Models
arXiv:2607. 16235v1 Announce Type: cross Abstract: Mobile and wearable devices offer an unprecedented opportunity for continuous, passive health monitoring and active health coaching.
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
Jun 2
A Foundation Model for Wearable Movement Data in Mental Health Research
arXiv:2411. 15240v5 Announce Type: replace-cross Abstract: Wearable movement data is collected by nearly all commercially available smartwatches and is a valuable resource for mental health research, reflecting fine-grained temporal behavioral trends.
arXiv Machine Learning
Jun 2
Longitudinal Multimodal Sensing of Physical Activity and Well-Being in Older Adults
arXiv:2606. 00345v1 Announce Type: new Abstract: Wearable and mobile sensing technologies enable continuous monitoring of human behavior and health in real-world settings.
arXiv Machine Learning
Sep 14
Affective Agent: On-Device Personalized Intervention Reasoning for Wearable Systems
The paper introduces Affective Agent, a three‑layer reference architecture designed for on‑device personalized intervention reasoning in wearable systems. It integrates a compact sub‑billion‑parameter language model with physiological data, context, and user history to determine when and how to intervene, all without cloud support or per‑user retraining. The architecture’s perception, personalization, and reasoning layers adapt through host‑managed structured memory evolution, and evaluation on simulated indoor environmental quality scenarios shows that memory‑driven personalization and two‑pass reasoning enhance intervention decisions.
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.