Wearable Health Monitoring

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The field studies how sensors such as PPG, ECG, accelerometers, and continuous glucose monitors can track health during everyday life, supporting tasks like sleep staging, blood-pressure estimation, activity recognition, and stress monitoring. Common approaches combine signal denoising and feature extraction with temporal deep learning, multimodal fusion, personalization, and increasingly large self-supervised foundation models. A major practical focus is making these systems reliable on low-power devices and robust to motion, missing data, and differences across people, devices, and body sites. Strong benchmark results remain in tension with the need for participant-disjoint and external validation, fair performance across subgroups, and evidence that predictions are clinically useful in real-world care.

Latest papers

109 papers11 papers in the last four weeks, level with the four weeks before. 0.1% of all new papers.

Unlocking the Specialization Capability of PPG Foundation Models for Wearable Signals through Structured Pruning and Adaptive Fine-Tuning
Oct 24, 2026Huaming Du, Zhixian Xie, Gaoyang Zhen +6Time-Series Foundation Models Structured Pruning
Southwestern University of Finance and Economics · Jizhi Technology Co., Ltd. · Fuzhou University +2
An Agent Architecture with Tools and Memory for Selective Correction of Wrist PPG Heart Rate Estimates
Oct 22, 2026Xu Wang, Shuhan Yi, Zhen ChengHeart Rate Estimation Wearable Health Monitoring
School of Engineering, The Hong Kong University of Science and Technology, Hong Kong 999077, China · School of Biomedical Engineering, Tsinghua University, Beijing 100084, China
Context-Augmented, Session-Calibrated Blood Pressure Tracking from Photoplethysmography
Oct 21, 2026Farouk Ganiyu Adewumi, Timothy OladunniCuffless Blood Pressure Estimation Wearable Health Monitoring
Department of Computer Science, Morgan State University, Baltimore, MD 21251, USA
PULSE: Personalized Opportunistic Learning for Wearable Devices
Oct 20, 2026Mattia Giovanni Campana, Dariush Salami, Hüseyin Yiğitler +1Personalized Learning Decentralized Learning
IIT-CNR · Aalto University
TimeCap: A Scalable Open-Source Pipeline for Captioning Wearable Health Time-Series Data
Oct 15, 2026Max Rosenblattl, Milana Gurbanova, Karl Aurel Deck +16Time Series Language Modeling Wearable Health Monitoring
Stanford University, USA · Aionic Labs, Switzerland · Agentic Systems Lab, ETH Zurich, Switzerland +7
GlucoPRISM: Protocol-Supervised Pretraining for Cross-Cohort Glucose Transfer
Oct 8, 2026Adinath Madhavrao Dukre, Abdullah Mohammad, Yifan Lu +3Continuous Glucose Monitoring Cross-Domain Transfer Learning
Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE · Vitalverse, Abu Dhabi, UAE · Google, USA
Teaching PPG How not Who: Fixed-Effects Distillation from ECG
Oct 7, 2026Zhongli Wu, Zhuangzhi Gao, Yuankai Wang +3Cross-Modal Knowledge Distillation Photoplethysmography
Liverpool Centre for Cardiovascular Science, University of Liverpool, UK · Shanghai Artificial Intelligence Laboratory, Shanghai, China · Liverpool Heart and Chest Hospital, Liverpool, UK +1
BeatFlow-ECG: Rectified Flow for ECG Reconstruction from Indirect Wearable Signals
Oct 6, 2026Mohamed Kamel, Sahar Selim, Walaa Medhat +1Electrocardiography Wearable Health Monitoring
Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA · School of Information Technology and Computer Science, Nile University, Giza, Egypt
Deep Learning for Sleep Heart Rate Estimation from Accelerometers: Toward Population-Scale Cardiac Insight Without Optical Sensors
Oct 5, 2026Tanbin Islam Rohan, Pranjol Sen Gupta, Tanusree Debi +1Transformer Wearable Sensing
Department of Information Technology Kennesaw State University Kennesaw, GA, USA · School of Computing University of Georgia Athens, GA, USA
Non-invasive Seizure Detection Using Wearable Wrist-worn Accelerometry and Deep Learning
Oct 5, 2026Nilushika Udayangani Hewa Dehigahawattage, Kishor Nandakishor, Marimuthu PalaniswamiWearable Sensing Wearable Health Monitoring
dept. of Electrical and Electronic Engineering University of Melbourne Victoria, Australia
ExpertoRhythm: Morphology-Aware Learning for Waveform Reconstruction and Cuffless Blood Pressure Estimation from Single-Channel PPG
Sep 28, 2026Amir Arjomand, Kenneth B. Kent, Georgiy KrylovPhotoplethysmography Wearable Health Monitoring
University of New Brunswick, Fredericton, Canada
Wearable ECG Quality Assessment: A Deep Learning and Ambulatory Context-Awareness Approach
Sep 24, 2026Xiaopeng Mao, Marike Weisbjerg, Sadasivan PuthusserypadyElectrocardiography Wearable Health Monitoring
Health Technology department Technical University of Denmark Kongens Lyngby, Denmark
TinyCardioUNet: IMU-to-ECG Translation with Graph-Encoded Inter-Axis Dependencies and Tensor Decomposition-Based Parameter Reduction
Sep 24, 2026Seungwoo Han, Ingon Chanpornpakdi, Motoi Noda +3Electrocardiography U-Net
Department of Electrical Engineering and Computer Science, Tokyo University of Agriculture and Technology, Japan
Edge AI on Constrained Devices for Binary Sleep-Wake Classification in Dynamic Environments
Sep 24, 2026Stefan Reitmann, Lena OdenTime Series Classification Wearable Sensing
Faculty of Computer Science, Chemnitz University of Technology, Chemnitz, Germany · Faculty of Computer Science, University of Hagen, Hagen, Germany
Transferable Evidence Reconstruction for Longitudinal Glucose Representations
Sep 23, 2026Tian Zhou, Bingqing Peng, Linxiao Yang +6Representation Learning Self-Supervised Time Series Representation Learning
Ant Group · Independent Researcher
A Task-Oriented Multi-Agent Framework for Complex Wearable Health Analysis
Sep 21, 2026Kunpeng YangMulti-Agent LLM Systems LLM Agent Orchestration
Renmin University of China
Smart Insole Human Activity Recognition for Continuous Monitoring in Elderly Care
Sep 16, 2026Edwin Rios, Antony Garcia, Fengpei Yuan +1Human Activity Recognition Wearable Sensing
Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester, MA, USA · Facultad de Ingenier´ıa El´ectrica Universidad Tecnol´ogica de Panam´a Panama City, Panama · Department of Robotics Engineering Worcester Polytechnic Institute Worcester, MA, USA
SOTER: A Generative Time-Series Foundation Model for Wearable Human Physiological Signals
Sep 15, 2026Fangke Chen, Sirry Chen, Wei Chen +1Irregular Time-Series Modeling Time Series Imputation
School of Integrated Circuits, Zhejiang University Hangzhou, China · Shanghai Innovation Institute Shanghai, China · School of Data Science, Fudan University Shanghai, China +1
SIFPBPNet: A Dual-Path Network for Wearable and Cuffless Blood Pressure Estimation via Individualized Steady-state Representation
Sep 11, 2026Shuailong Tang, Xiaoyu Li, Donglin Xie +4Photoplethysmography Wearable Health Monitoring
Shenzhen Technology University, Shenzhen, China · OPPO Health Lab, Shenzhen, China · Peking University, Beijing, China +1
HealthLoopQA: A Context-Aware Question Answering Benchmark for Interpreting Wearable Monitoring Data in Diabetes Care
Sep 7, 2026Yuchen Niu, Yanan Ma, Srinivasan Nandakumar +7Clinical Language Model Evaluation Wearable Health Monitoring
Nanyang Technological University · Mayo Clinic · The University of Manchester +2
WearableQA: A Benchmark for Health Reasoning over Real-World Wearable Data
Sep 4, 2026Ji Soo Lee, Xilun Chen, Pierce Chuang +5Medical QA Wearable Health Monitoring
Meta · KAIST · Korea University
Physiological Information Reliability: Cross-Layer Adaptive Resource Allocation for Cardiovascular Sensing
Aug 31, 2026Navaneeth Krishnan Kamalakannan, Janakiraman Kamalakannan, Harinisri VelmuruganWireless Resource Allocation Wearable Health Monitoring
Department of Electronics and Communication Engineering, Saveetha School of Engineering · Government Kilpauk Medical College and Hospital · Department of Physics and Nanotechnology, SRM Institute of Science and Technology, Chennai, India
Learning Human Health and Diseases from 24-hour Wrist Movement
Aug 30, 2026Yong Wang, Dylan McGagh, Katya Broomberg +21Wearable Health Monitoring Clinical Risk Prediction
Department of Psychiatry, University of Oxford, Oxford, UK. · Pioneer Centre for SMARTbiomed, Oxford, UK. · Big Data Institute, University of Oxford, Oxford, UK. +16
RECAST: Recent & Context-Aware Sampling for Test-Time Adaptation in Streaming Biosignals
Aug 28, 2026Yong-Yeon Jo, Junho Song, Joon-myoung KwonAdaptive Sampling Continual Test-Time Adaptation
Medical AI Co., Ltd. Seoul, Republic of Korea
Spectral Features Dominate BCG Respiratory-Event Detection: A Large-Scale Patient-Independent Comparison of Feature Groups in Sleep Apnea Patients
Aug 28, 2026Israel Campero Jurado, Zoe Bousraou, Lara Benning +5Feature Selection Wearable Health Monitoring
Department of Engineering and Technology Institute Groningen, Faculty of Science and Engineering, University of Groningen, The Netherlands · Department of Pulmonology, University Hospital Zürich, Switzerland · Sensory-Motor Systems Lab, ETH Zürich, Switzerland
CardioFusion-AI: Robust ECG--PPG Fusion for Multimodal Physiological Monitoring Under Signal Degradation
Aug 26, 2026Navaneetha Krishnan Kamalakannan, Janakiraman KamalakannanMultimodal Sensor Fusion Wearable Health Monitoring
Department of Electronics and Communication Engineering, Saveetha School of Engineering · Government Kilpauk Medical College and Hospital
DeMMO: Longitudinal and Cross-Disease Modelling of Digital Mobility Outcomes via Multi-Task Learning
Aug 25, 2026Menghui Zhou, Zhipeng Yuan, Vitaveska Lanfranchi +1Clinical Outcome Prediction Multi-Task Learning
School of Computer Science, University of Sheffield, Sheffield, United Kingdom
Physics-Constrained Deep Learning Model for Contactless Blood Pressure Monitoring from Triaxial Bodyseismography
Aug 24, 2026Yuanyuan Zhang, Yida Zhang, Jiahui Li +6Physics-Informed ML Wearable Health Monitoring
School of Electrical and Computer Engineering, University of Georgia, Athens, 30603, United States · School of Computing, University of Georgia, Athens, 30603, United States · Department of Civil and Environmental Engineering, Stanford University, Stanford, CA, 94305, United States +1
A Multidimensional Data-Driven Hybrid Transformer Framework for Non-invasive Continuous Blood Pressure Prediction
Aug 24, 2026Yuexin Ma, Jingqi Hou, Yuxuan Kang +1Wearable Health Monitoring Time Series Transformers
College of Computer Science, Beijing University of Technology, Beijing, China
Multimodal Injury Risk and Performance Prediction in Tennis Using Weighted Ensemble Learning
Aug 21, 2026Weihao Qu, Dongyang Wang, Ling Zheng +3Multimodal Sensor Fusion Sports Outcome Prediction
Department of Computer Science and Software Engineering Monmouth University, West Long Branch, NJ 07764, USA

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Unlocking the Specialization Capability of PPG Foundation Models for Wearable Signals through Structured Pruning and Adaptive Fine-Tuning

Huaming Du, Zhixian Xie, Gaoyang Zhen, Gang Kou+5

Southwestern University of Finance and Economics · Jizhi Technology Co., Ltd. · Fuzhou University+2

Time-Series Foundation Models Structured Pruning Wearable Health Monitoring

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Photoplethysmography (PPG) plays a central role in wearable health monitoring and clinical decision support. However, existing approaches to universal PPG representation learning are predominantly trained on high-quality large-scale datasets obtained from oximeters in hospital settings and often struggle to adapt to sparse and low-quality real-world datasets, thereby limiting their generalization to clinically relevant tasks on prevalent PPG data such as collected from wearable devices. Surprisingly, even after full fine-tuning, PPG foundation models cannot consistently outperform general-purpose time-series models. Therefore, a key question is how to effectively adapt PPG foundation models to target prediction tasks. Through empirical studies on various PPG foundation models, we find that pretrained models often exhibit inherent sparsity and computational redundancy, suggesting that PPG foundation models have learned to activate task-relevant network substructures for different prediction tasks. To preserve this valuable prior knowledge, we propose a structured pruning-and-fine-tuning framework, SPAFT, which regularizes the adaptive fine-tuning process by constraining it to a more relevant and compact parameter space based on parameter importance and structural redundancy. Extensive experiments across four PPG foundation models, five benchmark datasets, and ten downstream tasks demonstrate that SPAFT achieves win rates of 82.8% and 87.5% on regression and classification tasks, respectively. This prune-then-finetune paradigm often enables PPG foundation models to achieve state-of-the-art performance, laying a promising foundation for the real-world clinical deployment of PPG foundation models and the development of personalized PPG foundation models.