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.

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Memory-Efficient EDA Denoising via Knowledge Distillation for Wearable IoT Under Severe Motion Artifacts and Underwater Conditions
May 4, 2026Yongbin Lee, Andrew Peitzsch, Youngsun Kong +4Efficient Neural Network Inference Wearable Health Monitoring
Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA
At the Edge of the Heart: ULP FPGA-Based CNN for On-Device Cardiac Feature Extraction in Smart Health Sensors for Astronauts
Apr 28, 2026Kazi Mohammad Abidur Rahman, Davis Rakhshan, Philipp Lütke +2Time Series Classification Wearable Health Monitoring
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
Cardiac Stability Theory: An Axiomatically Grounded Framework for Continuous Cardiac Health Monitoring via Smartphone Photoplethysmography
Apr 26, 2026Timothy Oladunni, Farouk Ganiyu AdewumiDynamical Systems Photoplethysmography
Department of Computer Science, Morgan State University, Baltimore, MD 21251, USA
Enhancing Construction Worker Safety in Extreme Heat: A Machine Learning Approach Utilizing Wearable Technology for Predictive Health Analytics
Apr 21, 2026Syed Sajid Ullah, Amir KhanLong Short-Term Memory Networks Wearable Health Monitoring
A Hybrid Windkessel-Neural Approach for Improved Noninvasive Blood Pressure Monitoring
Apr 21, 2026Vaibhav Gollapalli, Aniruth AnanthanarayananPhysics-Informed ML Wearable Health Monitoring
Department of Physics University of North Texas Denton, United States of America
If Only My CGM Could Speak: A Privacy-Preserving Agent for Question Answering over Continuous Glucose Data
Apr 18, 2026Yanjun Cui, Ali Emami, Temiloluwa Prioleau +1Privacy-Preserving Language Models Healthcare
Dartmouth College · Emory University
Personalized and Context-Aware Transformer Models for Predicting Post-Intervention Physiological Responses from Wearable Sensor Data
Apr 16, 2026Esther Brown, Victoria Dean, Finale Doshi-VelezMultivariate Time Series Forecasting Wearable Health Monitoring
Harvard University, Cambridge, MA · Princeton University, Princeton, NJ
An AI Co-Data-Scientist for Prioritizing Candidate Biomarkers from Wearable Sensor Data
Apr 16, 2026Yubin Kim, Salman Rahman, Samuel Schmidgall +33AI Agents for Scientific Discovery Scientific Discovery
Google Research · Massachusetts Institute of Technology · Google DeepMind +7
GlyRAG: Context-Aware Retrieval-Augmented Framework for Blood Glucose Forecasting
Jan 8, 2026Shovito Barua Soumma, Hassan GhasemzadehTime Series Forecasting Blood Glucose Forecasting
College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA · School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USA
DeepFeature: LLM-Empowered Context-aware Feature Generation for Wearable Biosignals
Dec 9, 2025Kaiwei Liu, Yuting He, Bufang Yang +6Wearable Sensing Wearable Health Monitoring
The Chinese University of Hong Kong, China · The Education University of Hong Kong, China · Hong Kong Applied Science and Technology Research Institute, China +1
Robust Photoplethysmography Signal Denoising via Mamba Networks
Oct 13, 2025I Chiu, Yu-Tung Liu, Kuan-Chen Wang +2Photoplethysmography Wearable Health Monitoring
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
Energy-Efficient Real-Time 4-Stage Sleep Classification at 10-Second Resolution
Aug 6, 2025Zahra Mohammadi, Parnian Fazel, Siamak MohammadiSleep Stage Classification Energy-Efficient ML
School of Electrical and Computer Engineering, University of Tehran.
Glucose-ML: A collection of longitudinal diabetes datasets for development of robust AI solutions
Jul 18, 2025Temiloluwa Prioleau, Baiying Lu, Yanjun CuiForecasting Benchmarks Blood Glucose Forecasting
Department of Computer Science Emory University Atlanta, GA 30322 · Department of Computer Science Dartmouth College Hanover, NH 03755
A Wearable Device Dataset for Mental Health Assessment Using Laser Doppler Flowmetry and Fluorescence Spectroscopy Sensors
Feb 3, 2025Minh Ngoc Nguyen, Khai Le-Duc, Tan-Hanh Pham +7Medical Diagnosis Mental Health
Aston University, Birmingham, UK · University of Toronto, Toronto, Canada · University Health Network, Toronto, Canada +5
Wearable Device-Based Real-Time Monitoring of Physiological Signals: Evaluating Cognitive Load Across Different Tasks
Jun 11, 2024Ling He, Yanxin Chen, Wenqi Wang +2Educational Assessment Wearable Health Monitoring
Faculty of Education, Jiangxi Science and Technology Normal University, Nanchang, China · Decheng Technical School, Zhongshan, China

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

Yongbin Lee, Andrew Peitzsch, Youngsun Kong, Jarod Zizza+3

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

Efficient Neural Network Inference Wearable Health Monitoring

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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.

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