Wearable Health Monitoring

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9 papers in the last four weeks, up 80% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 75

May 4, 2026eess.SP

Memory-Efficient EDA Denoising via Knowledge Distillation for Wearable IoT Under Severe Motion Artifacts and Underwater Conditions

Electrodermal activity (EDA) is widely used in wearable Internet of Medical Things (IoMT) systems for continuous health monitoring, including autonomic assessment. However, EDA signals are highly vulnerable to motion artifacts and environmental noise, limiting reliable deployment in harsh operating conditions such as underwater. This study proposes a robust, deployable EDA denoising framework that generalizes across multiple measurement locations and harsh environments. The framework integrates a hybrid CNN-Transformer teacher model with a lightweight depth-wise separable CNN student model via a knowledge distillation (KD) strategy. To further improve robustness, a realistic data augmentation scheme is introduced to simulate diverse motion artifacts and environmental distortions. The KD-based student model significantly reduces model size (7.87 MB to 0.51 MB) and computational cost (105.1M to 11.61M FLOPs) while maintaining denoising performance (MAE: 0.144, SNR improvement: 12.08 dB) using the public dataset validation. In real-world underwater conditions (UMAC dataset) testing, the proposed method substantially improves skin conductance response reconstruction, reducing mean absolute error from 2.809 to 0.215. Furthermore, on independent testing using the CNS-OT dataset, the denoised signals enhanced downstream CNS-OT prediction performance, achieving the highest AUROC (0.806) compared to prior denoising methods. The proposed method also improved the early prediction rate (sensitivity) from 0.550 to 0.767, enabling CNS-OT prediction up to a median of 6.9 minutes before symptom onset. These results demonstrate that the proposed framework not only improves EDA signal quality but also enhances clinically relevant prediction performance while remaining suitable for deployment in resource-constrained wearable Internet of Things systems operating in harsh environments.

Yongbin Lee, Andrew Peitzsch, Youngsun Kong +4

Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA

Efficient Neural Network Inference Wearable Health Monitoring

Apr 28, 2026cs.AR

At the Edge of the Heart: ULP FPGA-Based CNN for On-Device Cardiac Feature Extraction in Smart Health Sensors for Astronauts

The convergence of accelerating human spaceflight ambitions and critical terrestrial health monitoring demands is driving unprecedented requirements for reliable, real-time feature extraction on extremely resource-constrained wearable health sensors. We present an ultra-low-power (ULP) Field-Programmable Gate Array (FPGA) based solution for real-time Seismocardiography (SCG) feature classification using Convolutional Neural Networks (CNNs). Our approach combines quantization-aware training with a systolic-array accelerator to enable efficient integer-only inference on the Lattice iCE40UP5K FPGA, which offers an ideal platform for battery-powered deployments -- particularly in space environments -- thanks to its power efficiency and radiation resilience. The implementation achieves a validation accuracy of 98% while consuming only 8.55 mW, completing inference in 95.5 ms with minimal hardware resources (2,861 LUTs and 7 DSP blocks). These results demonstrate that fully on-device SCG-based cardiac feature extraction is feasible on resource-constrained hardware, enabling energy-efficient, autonomous health monitoring for astronauts in long-duration space missions.

Kazi Mohammad Abidur Rahman, Davis Rakhshan, Philipp Lütke +2

Smart Sensors Group, Hamburg University of Technology, Hamburg, Germany · Distributed Systems, Kiel University, Kiel, Germany · Networked Cyber-Physical Systems, Hamburg University of Technology, Hamburg, Germany

Time Series Classification Wearable Health Monitoring FPGA-Based Neural Network Acceleration On-Device Inference

Apr 26, 2026cs.LG

Cardiac Stability Theory: An Axiomatically Grounded Framework for Continuous Cardiac Health Monitoring via Smartphone Photoplethysmography

We present Cardiac Stability Theory (CST), an axiomatically grounded framework formally defining cardiovascular health as a stability margin around a cardiac dynamical attractor. From four axioms we derive the Cardiac Stability Index (CSI), a composite scalar in [0,1] integrating the largest Lyapunov exponent, recurrence determinism, and signal entropy via time-delay embedding. The ECG-based model (CSISurrogateV2, CNN-Transformer) achieves

R^2=0.8788

, MAE

=0.0234

on PTB-XL (21,799 recordings). We extend CSI to smartphone PPG via Complementary Domain Transfer (CDT): CSISurrogateV2 generates pseudo-labels for the BUT PPG dataset (48 recordings, 12 subjects), training TinyCSINet (122,849 parameters), achieving MAE

=0.0557

,

ρ=0.660

on the held-out test set (

n=1065

windows) at

{<}30

ms mobile latency. CDT is validated on BIDMC, Welltory, and RWS-PPG. Paired validation on 5,035 BIDMC windows yields

r=0.454

(

ρ=0.485

,

p<10^{-295}

), confirming correlated cardiac stability across modalities. CSI is negatively correlated with age (slope

= -0.000225

CSI/year, PTB-XL), discriminates atrial fibrillation from normal sinus rhythm (AUROC

=0.89

), and is robust under Perturbation Invariance Training (max AUC drop 1.65%). We derive HeartSpan, a longitudinal stability metric relative to population age norms, enabling continuous non-invasive cardiac monitoring from commodity smartphones for longevity tracking and cardiac risk stratification.

Timothy Oladunni, Farouk Ganiyu Adewumi

Department of Computer Science, Morgan State University, Baltimore, MD 21251, USA

Dynamical Systems Photoplethysmography Wearable Health Monitoring

Apr 21, 2026cs.AI

Enhancing Construction Worker Safety in Extreme Heat: A Machine Learning Approach Utilizing Wearable Technology for Predictive Health Analytics

Construction workers are highly vulnerable to heat stress, yet tools that translate real-time physiological data into actionable safety intelligence remain scarce. This study addresses this gap by developing and evaluating deep learning models, specifically a baseline Long Short-Term Memory (LSTM) network and an attention-based LSTM, to predict heat stress among 19 workers in Saudi Arabia. Using Garmin Vivosmart 5 smartwatches to monitor metrics such as heart rate, HRV, and oxygen saturation, the attention-based model outperformed the baseline, achieving 95.40% testing accuracy and significantly reducing false positives and negatives. With precision, recall, and F1 scores of 0.982, this approach not only improves predictive performance but also offers interpretable results suitable for integration into IoT-enabled safety systems and BIM dashboards, advancing proactive, informatics-driven safety management in the construction industry.

Syed Sajid Ullah, Amir Khan

Long Short-Term Memory Networks Wearable Health Monitoring

Apr 21, 2026eess.SP

A Hybrid Windkessel-Neural Approach for Improved Noninvasive Blood Pressure Monitoring

Owing to the recent advancements in wearable devices for health care, the importance of BP estimation without cuffs increases. Cuff technologies are inappropriate for continuous BP measurement due to their inconvenient usage, invasive character, necessity of calibration, large size, and inability to perform long-term monitoring. Normally, the algorithm used for cuffless BP prediction employs machine learning models that operate according to the data-driven approach. However, although they show high numerical accuracy, ML models do not provide any interpretability, resulting in poor physiological validity and clinical applicability. We propose a combination of Windkessel and ML models that incorporates the physical aspects into the latter one. It is performed by reformulating Windkessel into a form that will allow employing ML models. The result is a system of ODEs which can be used in the neural network. Thus, the inclusion of physical constraints improves the data-driven approach by making models consistent with physics, understandable, and robust. For illustration, we apply the described technique using a publicly available MIMIC-II database that we obtain from the UCI Machine Learning Repository.

Vaibhav Gollapalli, Aniruth Ananthanarayanan

Department of Physics University of North Texas Denton, United States of America

Physics-Informed ML Wearable Health Monitoring Cuffless Blood Pressure Estimation

Apr 18, 2026cs.AI

If Only My CGM Could Speak: A Privacy-Preserving Agent for Question Answering over Continuous Glucose Data

Continuous glucose monitors (CGMs) used in diabetes care collect rich personal health data that could improve day-to-day self-management. However, current patient platforms only offer static summaries which do not support inquisitive user queries. Large language models (LLMs) could enable free-form inquiries about continuous glucose data, but deploying them over sensitive health records raises privacy and accuracy concerns. In this paper, we present CGM-Agent, a privacy-preserving framework for question answering over personal glucose data. In our design, the LLM serves purely as a reasoning engine that selects analytical functions. All computation occurs locally, and personal health data never leaves the user's device. For evaluation, we construct a benchmark of 4,180 questions combining parameterized question templates with real user queries and ground truth derived from deterministic program execution. Evaluating 6 leading LLMs, we find that top models achieve 94% value accuracy on synthetic queries and 88% on ambiguous real-world queries. Errors stem primarily from intent and temporal ambiguity rather than computational failures. Additionally, lightweight models achieve competitive performance in our agent design, suggesting opportunities for low-cost deployment. We release our code and benchmark to support future work on trustworthy health agents.

Yanjun Cui, Ali Emami, Temiloluwa Prioleau +1

Dartmouth College · Emory University

Privacy-Preserving Language Models Healthcare LLM Agent Evaluation Biomedical QA Wearable Health Monitoring

Apr 16, 2026cs.AI

Personalized and Context-Aware Transformer Models for Predicting Post-Intervention Physiological Responses from Wearable Sensor Data

Consumer wearables enable continuous measurement of physiological data related to stress and recovery, but turning these streams into actionable, personalized stress-management recommendations remains a challenge. In practice, users often do not know how a given intervention, defined as an activity intended to reduce stress, will affect heart rate (HR), heart rate variability (HRV), or inter-beat intervals (BBI) over the next 15 to 120 minutes. We present a framework that predicts post-intervention trajectories and the direction of change for these physiological indicators across time windows. Our methodology combines a Transformer model for multi-horizon trajectories of percent change relative to a pre-intervention baseline, direction-of-change calls (positive, negative, or neutral) at each horizon, and an empirical study using wearable sensor data overlaid with user-tagged events and interventions. This proof of concept shows that personalized post-intervention prediction is feasible. We encourage future integration into stress-management tools for personalized intervention recommendations tailored to each person's day following further validation in larger studies and, where applicable, appropriate regulatory review.

Esther Brown, Victoria Dean, Finale Doshi-Velez

Harvard University, Cambridge, MA · Princeton University, Princeton, NJ

Multivariate Time Series Forecasting Wearable Health Monitoring

Apr 16, 2026cs.AI

An AI Co-Data-Scientist for Prioritizing Candidate Biomarkers from Wearable Sensor Data

Wearable devices generate continuous physiological and behavioral data, but converting these signals into clinically reviewable biomarker hypotheses remains labor-intensive. We introduce CoDaS, an AI co-data-scientist that integrates multi-agent hypothesis generation, deterministic statistical analysis, adversarial validation and literature-grounded interpretation under human oversight. Across three wearable cohorts comprising 9,279 participant-observations, CoDaS prioritized candidate associations for mental-health and metabolic endpoints after internal checks for replication, stability, robustness and leakage. The system identified related circadian-instability signals associated with depression, including sleep-duration variability in DWB (

ρ

= 0.252,

p

< 0.001) and sleep-onset variability in GLOBEM (

ρ

= 0.126,

p

< 0.001), and derived a wearable cardiovascular-fitness index associated with insulin resistance (steps/resting heart rate;

ρ

= -0.374,

p

< 0.001). Adding these features to demographic models produced modest gains (

ΔR^2

= 0.040 for depression, 0.021 for insulin resistance). In a 12-clinician review totaling approximately 25 active hours, clinician validity judgments aligned with CoDaS confidence tiers (

ρ

= 0.67,

p

= 0.005), whereas added clinical value and confidence to act were rated lower. CoDaS supports traceable, hypothesis-generating prioritization of wearable candidate biomarkers.

Yubin Kim, Salman Rahman, Samuel Schmidgall +33

Google Research · Massachusetts Institute of Technology · Google DeepMind +7

AI Agents for Scientific Discovery Scientific Discovery Human-in-the-Loop AI Wearable Health Monitoring

Jan 8, 2026cs.LG

GlyRAG: Context-Aware Retrieval-Augmented Framework for Blood Glucose Forecasting

Accurate blood glucose forecasting using continuous glucose monitoring (CGM) data can support the early prediction of dysglycemic risk. However, current neural-network-based forecasting models treat CGM data as a purely numerical sequence without integrating the contextual information contained in CGM signal morphology. Recently, large language models (LLMs) have shown promise for time-series forecasting, yet their role as agentic context extractors in diabetes care remains largely unexplored. In this study, we bridge glucose forecasting and LLM-based contextualization by developing GlyRAG, a context-aware, retrieval-augmented forecasting framework that uses an LLM as a contextualization agent to summarize glucose morphology directly from a timed CGM window. The generated CGM-only narrative is embedded and fused with patch-based glucose representations, while a retrieval module incorporates similar historical training episodes through cross-attention. We evaluate GlyRAG on the OhioT1DM and AZT1D datasets for 5-, 30-, and 60-minute forecasting horizons. Compared with strong CGM-only baselines, GPT-4 GlyRAG significantly improves long-horizon root mean square error (RMSE) over PatchTST on both datasets. For example, RMSE decreases from 13.8 to 10.6 at 30 minutes and from 23.1 to 20.2 at 60 minutes on OhioT1DM. LLaMA 3.1 produces smaller but significant long-horizon gains, suggesting that the contextualization pipeline is not limited to GPT-4. Clinical error-grid analyses further show that approximately 85 percent of predictions fall within the clinically acceptable Clarke Error Grid Zones A and B. These results suggest that CGM-derived linguistic context and case-based retrieval can improve long-horizon glucose forecasting without requiring additional sensing modalities.

Shovito Barua Soumma, Hassan Ghasemzadeh

College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA · School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USA

Time Series Forecasting Blood Glucose Forecasting LLM-Based Time Series Forecasting Retrieval-Augmented Time Series Forecasting Wearable Health Monitoring

Dec 9, 2025cs.AI

DeepFeature: LLM-Empowered Context-aware Feature Generation for Wearable Biosignals

Biosignals collected from wearable devices are widely utilized in healthcare applications. Machine learning models used in these applications often rely on features extracted from biosignals due to their effectiveness, lower data dimensionality, and wide compatibility across various model architectures. However, existing feature extraction methods often lack task-specific contextual knowledge, struggle to identify optimal features in high-dimensional combinatorial feature space, and are prone to automated code generation and execution errors. In this paper, we propose DeepFeature, the first LLM-empowered, context-aware feature generation framework for wearable biosignals. DeepFeature introduces a multi-source feature generation mechanism that integrates the inherent ability of LLMs, expert knowledge and inter-feature interactions. It also employs an iterative feature refinement process that uses feature assessment-based feedback for feature re-selection. Additionally, DeepFeature utilizes a robust multi-layer filtering and verification approach for feature description-to-code translation to ensure that the feature extraction functions run without crashing. Experimental evaluation results show that DeepFeature achieves the highest average AUROC across eight tasks under both sample-level and subject-level settings, outperforming the best baselines by 4.60% and 4.61%, respectively. DeepFeature achieves the most pronounced gains on the PPG-BP tasks, while remaining competitive with the best-performing baselines on Epilepsy, WESAD, and our self-collected SEN dataset.

Kaiwei Liu, Yuting He, Bufang Yang +6

The Chinese University of Hong Kong, China · The Education University of Hong Kong, China · Hong Kong Applied Science and Technology Research Institute, China +1

Wearable Sensing Wearable Health Monitoring Automated Feature Engineering

Oct 13, 2025cs.LG

Robust Photoplethysmography Signal Denoising via Mamba Networks

Photoplethysmography (PPG) is widely used in wearable health monitoring, but its reliability is often degraded by noise and motion artifacts, limiting downstream applications such as heart rate (HR) estimation. This paper presents a deep learning framework for PPG denoising with an emphasis on preserving physiological information. In this framework, we propose DPNet, a Mamba-based denoising backbone designed for effective temporal modeling. To further enhance denoising performance, the framework also incorporates a scale-invariant signal-to-distortion ratio (SI-SDR) loss to promote waveform fidelity and an auxiliary HR predictor (HRP) that provides physiological consistency through HR-based supervision. Experiments on the BIDMC dataset show that our method achieves strong robustness against both synthetic noise and real-world motion artifacts, outperforming conventional filtering and existing neural models. Our method can effectively restore PPG signals while maintaining HR accuracy, highlighting the complementary roles of SI-SDR loss and HR-guided supervision. These results demonstrate the potential of our approach for practical deployment in wearable healthcare systems.

I Chiu, Yu-Tung Liu, Kuan-Chen Wang +2

Taiwan International Graduate Program in Artificial Intelligence of Things, Academia Sinica and National Taiwan University, Taipei, Taiwan · Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan · Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan +1

Photoplethysmography Wearable Health Monitoring

Aug 6, 2025eess.SP

Energy-Efficient Real-Time 4-Stage Sleep Classification at 10-Second Resolution

Sleep stage classification is critical for diagnosing and managing disorders like sleep apnea and insomnia. However, conventional methods like polysomnography are costly and impractical for long-term, home-based monitoring. This study presents an energy-efficient approach for detecting four sleep stages (wake, rapid eye movement (REM), light sleep, deep sleep) using a single-lead electrocardiogram (ECG) signal. We evaluate various machine learning and deep learning models, introducing two windowing strategies: (1) a 5-minute window with 30-second steps for machine learning and (2) a 30-second window with 10-second steps for deep learning, enabling 10-second temporal resolution for real-time predictions. While deep learning models like MobileNet-v1 achieve high accuracy (92%) and F1-score (91%), their energy demands make them unsuitable for wearables. To address this, we design SleepLiteCNN, optimized for ECG-based sleep staging, achieving 89% accuracy and 89% F1-score while minimizing energy use. Applying 8-bit quantization further reduces energy consumption to 5.48 microJ per inference, with 90% accuracy and F1-score. Additionally, field-programmable gate array (FPGA) deployment shows significant reductions in resource usage. This approach provides a practical, energy-efficient solution for continuous ECG-based sleep monitoring in resource-constrained wearable devices.

Zahra Mohammadi, Parnian Fazel, Siamak Mohammadi

School of Electrical and Computer Engineering, University of Tehran.

Sleep Stage Classification Energy-Efficient ML Time Series Classification Wearable Health Monitoring

Jul 18, 2025cs.AI

Glucose-ML: A collection of longitudinal diabetes datasets for development of robust AI solutions

Artificial intelligence (AI) algorithms are a critical part of state-of-the-art digital health technology for diabetes management. Yet, access to large high-quality datasets is creating barriers that impede development of robust AI solutions. To accelerate development of transparent, reproducible, and robust AI solutions, we present Glucose-ML, a collection of 10 publicly available diabetes datasets, released within the last 7 years (i.e., 2018 - 2025). The Glucose-ML collection comprises over 300,000 days of continuous glucose monitor (CGM) data with a total of 38 million glucose samples collected from 2500+ people across 4 countries. Participants include persons living with type 1 diabetes, type 2 diabetes, prediabetes, and no diabetes. To support researchers and innovators with using this rich collection of diabetes datasets, we present a comparative analysis to guide algorithm developers with data selection. Additionally, we conduct a case study for the task of blood glucose prediction - one of the most common AI tasks within the field. Through this case study, we provide a benchmark for short-term blood glucose prediction across all 10 publicly available diabetes datasets within the Glucose-ML collection. We show that the same algorithm can have significantly different prediction results when developed/evaluated with different datasets. Findings from this study are then used to inform recommendations for developing robust AI solutions within the diabetes or broader health domain. We provide direct links to each longitudinal diabetes dataset and openly provide our code.

Temiloluwa Prioleau, Baiying Lu, Yanjun Cui

Department of Computer Science Emory University Atlanta, GA 30322 · Department of Computer Science Dartmouth College Hanover, NH 03755

Forecasting Benchmarks Blood Glucose Forecasting Clinical Prediction ML Reproducibility Wearable Health Monitoring Cross-Dataset Generalization

Feb 3, 2025cs.LG

A Wearable Device Dataset for Mental Health Assessment Using Laser Doppler Flowmetry and Fluorescence Spectroscopy Sensors

Mental health problems such as stress, anxiety, and depression affect millions of people worldwide. These conditions are usually assessed using questionnaires, which rely on how people describe their own feelings. In this study, we explore whether a wearable device can help measure mental health using physical signals from the body. The device records small changes in blood flow and tissue activity from the fingertip. We collected data from 132 adults across 19 countries and compared these signals with mental health questionnaire results. We found that patterns in blood flow and tissue activity are linked to stress-related symptoms. This approach may help develop new tools for simple, non-invasive mental health monitoring in everyday life. Code and datasets are publicly available: https://github.com/leduckhai/Wearable_LDF-FS

Minh Ngoc Nguyen, Khai Le-Duc, Tan-Hanh Pham +7

Aston University, Birmingham, UK · University of Toronto, Toronto, Canada · University Health Network, Toronto, Canada +5

Medical Diagnosis Mental Health Wearable Sensing Wearable Health Monitoring

Jun 11, 2024cs.HC

Wearable Device-Based Real-Time Monitoring of Physiological Signals: Evaluating Cognitive Load Across Different Tasks

This study employs cutting-edge wearable monitoring technology to conduct high-precision, high-temporal-resolution (1-second interval) cognitive load assessment on electroencephalogram (EEG) data from the FP1 channel and heart rate variability (HRV) data of secondary vocational students. By jointly analyzing these two critical physiological indicators, the research delves into their application value in assessing cognitive load among secondary vocational students and their utility across various tasks. The study designed two experiments to validate the efficacy of the proposed approach: Initially, a random forest classification model, developed using the N-BACK task, enabled the precise decoding of physiological signal characteristics in secondary vocational students under different levels of cognitive load, achieving a classification accuracy of 97%. Subsequently, this classification model was applied in a cross-task experiment involving the National Computer Rank Examination (Level-1), demonstrating the method's significant applicability and cross-task transferability in diverse learning contexts. Conducted with high portability, this research holds substantial theoretical and practical significance for optimizing teaching resource allocation in secondary vocational education, as well as for cognitive load assessment methods and monitoring. Currently, the research findings are undergoing trial implementation in the school.

Ling He, Yanxin Chen, Wenqi Wang +2

Faculty of Education, Jiangxi Science and Technology Normal University, Nanchang, China · Decheng Technical School, Zhongshan, China

Educational Assessment Wearable Health Monitoring