https://github.com/AIDC-rPPG/RF-Net
공식 코드RF-BayesPhysNet: A Bayesian rPPG Uncertainty Estimation Method for Complex Scenarios
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Photoplethysmography (PPG) is a non-invasive light-based method that has been used since the 1930s for monitoring cardiovascular activity.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
RF-BayesPhysNet: A Bayesian rPPG Uncertainty Estimation Method for Complex Scenarios
Heart Rate Estimation from Face Videos for Student Assessment: Experiments on edBB
Motion Matters: Neural Motion Transfer for Better Camera Physiological Measurement
BayesBeat: Reliable Atrial Fibrillation Detection from Noisy Photoplethysmography Data
Stress Detection from Photoplethysmography in a Virtual Reality Environment
SiamQuality: A ConvNet-Based Foundation Model for Imperfect Physiological Signals
Region-Disentangled Diffusion Model for High-Fidelity PPG-to-ECG Translation
Q-PPG: Energy-Efficient PPG-based Heart Rate Monitoring on Wearable Devices
GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing
KID-PPG: Knowledge Informed Deep Learning for Extracting Heart Rate from a Smartwatch
Time series saliency maps: explaining models across multiple domains
Time series saliency maps: explaining models across multiple domains
KID-PPG: Knowledge Informed Deep Learning for Extracting Heart Rate from a Smartwatch
Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning
Robust Heart Rate Detection via Multi-Site Photoplethysmography
BeliefPPG: Uncertainty-aware Heart Rate Estimation from PPG signals via Belief Propagation
Nightbeat: Heart Rate Estimation From a Wrist-Worn Accelerometer During Sleep
Meta-rPPG: Remote Heart Rate Estimation Using a Transductive Meta-Learner
Interpretable Pre-Trained Transformers for Heart Time-Series Data
ApSense: Data-driven Algorithm in PPG-based Sleep Apnea Sensing
GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing
Efficient Deep Learning-based Estimation of the Vital Signs on Smartphones
Efficient Deep Learning-based Estimation of the Vital Signs on Smartphones
Non-Contact Health Monitoring During Daily Personal Care Routines
ALPHA: AnomaLous Physiological Health Assessment Using Large Language Models
Demo: RhythmEdge: Enabling Contactless Heart Rate Estimation on the Edge
Hypertension Detection From High-Dimensional Representation of Photoplethysmogram Signals
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.