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Photoplethysmography (PPG)
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Photoplethysmography (PPG) is a non-invasive light-based method that has been used since the 1930s for monitoring cardiovascular activity.
논문
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CAST-Phys: Contactless Affective States Through Physiological signals Database
Non-Contact Health Monitoring During Daily Personal Care Routines
Physiological-Model-Based Neural Network for Heart Rate Estimation during Daily Physical Activities
Foundation Model Hidden Representations for Heart Rate Estimation from Auscultation
Time series saliency maps: explaining models across multiple domains
Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks
Contactless pulse rate assessment: Results and insights for application in driving simulator
Generalised Label-free Artefact Cleaning for Real-time Medical Pulsatile Time Series
PsyCounAssist: A Full-Cycle AI-Powered Psychological Counseling Assistant System
Auto-FEDUS: Autoregressive Generative Modeling of Doppler Ultrasound Signals from Fetal Electrocardiograms
RF-BayesPhysNet: A Bayesian rPPG Uncertainty Estimation Method for Complex Scenarios
Reliable Physiological Monitoring on the Wrist Using Generative Deep Learning to Address Poor Skin-Sensor Contact
Exploring Reliable PPG Authentication on Smartwatches in Daily Scenarios
Recovering Pulse Waves from Video Using Deep Unrolling and Deep Equilibrium Models
TAU: Modeling Temporal Consistency Through Temporal Attentive U-Net for PPG Peak Detection
GPT-PPG: A GPT-based Foundation Model for Photoplethysmography Signals
Generalization of Video-Based Heart Rate Estimation Methods To Low Illumination and Elevated Heart Rates
Adaptive Extensive Cancellation Algorithm and Harmonic Enhanced Heart Rate Estimation based on MMWave Radar
Machine learning for triage of strokes with large vessel occlusion using photoplethysmography biomarkers
Weighted Combination and Singular Spectrum Analysis Based Remote Photoplethysmography Pulse Extraction in Low-light Environments
Machine-learning for photoplethysmography analysis: Benchmarking feature, image, and signal-based approaches
Generalizable deep learning for photoplethysmography-based blood pressure estimation -- A Benchmarking Study
CardiacMamba: A Multimodal RGB-RF Fusion Framework with State Space Models for Remote Physiological Measurement
An LLM-Powered Agent for Physiological Data Analysis: A Case Study on PPG-based Heart Rate Estimation
Artificial Intelligence-derived Vascular Age from Photoplethysmography: A Novel Digital Biomarker for Cardiovascular Health
Joint Attention Mechanism Learning to Facilitate Opto-physiological Monitoring during Physical Activity
Continuous Cardiac Arrest Prediction in ICU using PPG Foundation Model
CodePhys: Robust Video-based Remote Physiological Measurement through Latent Codebook Querying
Finetuning and Quantization of EEG-Based Foundational BioSignal Models on ECG and PPG Data for Blood Pressure Estimation
Pulse-PPG: An Open-Source Field-Trained PPG Foundation Model for Wearable Applications Across Lab and Field Settings
Robust Heart Rate Detection via Multi-Site Photoplethysmography
WildPPG: A Real-World PPG Dataset of Long Continuous Recordings
Tri-Spectral PPG: Robust Reflective Photoplethysmography by Fusing Multiple Wavelengths for Cardiac Monitoring
Mamba-based Deep Learning Approaches for Sleep Staging on a Wireless Multimodal Wearable System without Electroencephalography
EnhancePPG: Improving PPG-based Heart Rate Estimation with Self-Supervision and Augmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.