- Accurate Wearable Heart Rate Monitoring During Physical Exercises Using PPG | [Wiener Filter, Phase Vocoder and Viterbi Decoding]
- PPG-based heart rate estimation using Wiener filter, phase vocoder and Viterbi decoding
- Measuring Heart Rate During Physical Exercise by Subspace Decomposition and Kalman Smoothing | [Kalman Filter]
- A time-frequency domain approach of heart rate estimation from photoplethysmographic (PPG) signal | [LMS]
- Cascade and Parallel Combination (CPC) of Adaptive Filters for Estimating Heart Rate During Intensive Physical Exercise from Photoplethysmographic Signal
- SPECMAR: Fast Heart Rate Estimation from PPG Signal using a Modified Spectral Subtraction Scheme with Composite Motion Artifacts Reference Generation
- Heart Rate Tracking using Wrist-Type Photoplethysmographic (PPG) Signals during Physical Exercise with Simultaneous Accelerometry | [Iterative Method with Adaptive Thresholding (IMAT) ]
- Robust Filtering of Time Series with Trends | []
- Learning an Outlier-Robust Kalman Filter | [WRKF/EM/robustKF]
- HeartBEAT: Heart Beat Estimation through Adaptive Tracking | [RLS]
- HEAL-T: An Efficient PPG based Heart-Rate and IBI Estimation During Physical Exercise | [fast-ICA][RLS][BHW filter]
- Online Heart Rate Prediction using Acceleration from a Wrist Worn Wearable | [Active Learning]
- CNNs for Heart Rate Estimation and Human Activity Recognition in Wrist Worn Sensing Applications
- Heart rate tracking in photoplethysmography signals affected by motion artifacts: a review | [Review]
- Heart Rate Estimation From Wrist-Worn Photoplethysmography: A Review | [Review]
- TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise | [SSA]
- PARHELIA: Particle Filter-Based Heart Rate Estimation From Photoplethysmographic Signals During Physical Exercise | [Particle Filter]
- Particle Filtering and Sensor Fusion for Robust Heart Rate Monitoring using Wearable Sensors | [Partilce Filter]
- Multi-Mode Particle Filtering Methods for Heart Rate Estimation From Wearable Photoplethysmography | [Particle Filter]
- Combining Adaptive Filter and Phase Vocoder for Heart Rate Monitoring Using Photoplethysmography During Physical Exercise | [Phase Vocoder]
- Robust real-time PPG-based heart rate monitoring | [Particle Filter | Viterbi]
- Robust Heart Rate Estimation using Wrist-based PPG Signals in the Presence of Intense Physical Activities | [Backward Shortest Path Search(BSPS)]
- A Novel Time-Varying Spectral Filtering Algorithm for Reconstruction of Motion Artifact Corrupted Heart Rate Signals During Intense Physical Activities Using a Wearable Photoplethysmogram Sensor | [Spectral filter algorithm for Motion Artifacts and heart rate reconstruction (SpaMA)]
- Photoplethysmographic Time-Domain Heart Rate Measurement Algorithm for Resource-Constrained Wearable Devices and its Implementation | [AMPD]
- Online Clustering of Processes | [Online Clustering]
- A Bayesian Framework for Robust Kalman Filtering Under Uncertain Noise Statistics | []
- Removal of Motion Artifacts in Photoplethysmograph Sensors during Intensive Exercise for Accurate Heart Rate Calculation Based on Frequency Estimation and Notch Filtering | []
- Finite State Machine Framework for Instantaneous Heart Rate Validation Using Wearable Photoplethysmography During Intensive Exercise | [FSM/CF]
- PREHEAT: Precision heart rate monitoring from intense motion artifact corrupted PPG signals using constrained RLS and wavelets | [cRLS/EEMD/AR/Wavelets]
- Real-Time Robust Heart Rate Estimation From Wrist-Type PPG Signals Using Multiple Reference Adaptive Noise Cancellation | [RLS]
- A Robust Heart Rate Monitoring Scheme Using Photoplethysmographic Signals Corrupted by Intense Motion Artifacts | [EEMD/RLS/Portion Extracting]
- Photoplethysmographic Time-Domain Heart Rate Measurement Algorithm for Resource-Constrained Wearable Devices and its Implementation | [AMPD]
- A Novel Adaptive Spectrum Noise Cancellation Approach for Enhancing Heartbeat Rate Monitoring in a Wearable Device | [spectral overlapped]
- A Robust Motion Artifact Detection Algorithm for Accurate Detection of Heart Rates From Photoplethysmographic Signals Using Time–Frequency Spectral Features | [CDM | MNAs | TifMA | VFCDM | SVM] | VFCDM Video Final | Homepage | [https://biosignal.uconn.edu/#]
- Two-Stage Approach for Detection and Reduction of Motion Artifacts in Photoplethysmographic Data | []
- Heart Rate Tracking Using a Wearable Photoplethysmographic Sensor During Treadmill Exercise | [VFCDM]
- Adaptive Multi-Trace Carving for Robust Frequency Tracking in Forensic Applications |
- An Efficient Algorithm for Automatic Peak Detection in Noisy Periodic and Quasi-Periodic Signals | code | [AMPD]
- Auto-threshold peak detection in physiological signals | [ATPD]
- Adaptive threshold method for the peak detection of photoplethysmographic waveform | [ATPD]
- An Efficient and Automatic Systolic Peak Detection Algorithm for Photoplethysmographic Signals
- A High Resolution Approach to Estimating Time-Frequency Spectra and Their Amplitudes | [VFCDM]
- A Robust Algorithm for Pitch Tracking (RAPT) |
- A spectral/temporal method for robust fundamental frequency tracking |
- Robust Bayesian Pitch Tracking Based on the Harmonic Model |
- An optimal filter for short photoplethysmogram signals | digital filter
- 【Empirical mode decomposition】
- 【Nonlinear mode decomposition】
- Nonlinear mode decomposition: A noise-robust, adaptive decomposition method
- Linear and synchrosqueezed time-frequency representations revisited: Overview, standards of use, resolution, reconstruction, concentration, and algorithms
- Linear and synchrosqueezed time-frequency representations revisited. Part I: Overview, standards of use, related issues and algorithms
- Linear and synchrosqueezed time-frequency representations revisited. Part II: Resolution, reconstruction and concentration
- On the extraction of instantaneous frequencies from ridges in time-frequency representations of signals
- Detecting the harmonics of oscillations with time-variable frequencies
- Nonlinear mode decomposition: a new algorithm of interest for the biomedical field?
- Remote Photoplethysmography Using Nonlinear Mode Decomposition
- Feasibility Study of Deep Neural Network for Heart Rate Estimation from Wearable Photoplethysmography and Acceleration Signals
- Deep Learning for Heart Rate Estimation From Reflectance Photoplethysmography With Acceleration Power Spectrum and Acceleration Intensity
- An Introduction to Convolutional Neural Networks
- CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization
- What Do We Understand About Convolutional Networks?
- 三次简化一张图：一招理解LSTM/GRU门控机制
- LSTM神经网络输入输出究竟是怎样的？
- LSTM细节分析理解（pytorch版）
- Recurrent Neural Networks Tutorial, Part 1 – Introduction to RNNs
- Recurrent Neural Networks (RNNs) Implementing an RNN from scratch in Python
- The Unreasonable Effectiveness of Recurrent Neural Networks
- Understanding LSTM Networks
- RNN vs GRU vs LSTM
- Illustrated Guide to LSTM’s and GRU’s: A step by step explanation
- Illustrated Guide to Recurrent Neural Networks
- Animated RNN, LSTM and GRU
- Attention Is All You Need
- Attention Mechanism
- Attention? Attention!
- Illustrated Guide to Transformers- Step by Step Explanation
- The Illustrated Transformer
- What is a Transformer?
- Squeeze-and-Excitation Networks
- Squeeze-and-Excitation Networks
- Understanding and visualizing SE-Nets
- Squeeze and Excitation Networks Explained with PyTorch Implementation
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- Batch normalization in 3 levels of understanding
- Why is Batch Normalization useful in Deep Neural Networks?
- Deep Residual Learning for Image Recognition
- MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
- Generative Adversarial Networks
- U-Net: Convolutional Networks for Biomedical Image Segmentation
- US20180110469 - Heart rate path optimizer
- US20160051158A1 - Harmonic template classifier
- US20200359919A1- Motion Artifact Removal by Time Domain Projection
- US20160051157A1 - Frequency domain projection algorithm
- Nonlinear Signal Processing - A Statistical Approach
- Robust Statistics for Signal Processing : code
- An Introduction to Sequential Monte Carlo by Nicolas Chopin
- tsfresh : Time Series Feature extraction based on scalable hypothesis tests
- TSFEL : Time Series Feature Extraction Library
- Merlion : A Machine Learning Library for Time Series
- Human Activity Recognition Using Smartphones Data Set
- WISDM Smartphone and Smartwatch Activity and Biometrics Dataset Data Set
- WISDM: WIreless Sensor Data Mining
- A Public Domain Dataset For Real-life Human Activity Recognition Using Smartphone Sensors
- Activity Recognition from Single Chest-Mounted Accelerometer Data Set
- Human Activity Recognition using Physiological Data from Wearables : https://github.com/DigitalBiomarkerDiscoveryPipeline/Human-Activity-Recognition
- 2017- Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks-269 : code
- 2018-T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling-37 | code
- 2018-Data augmentation using synthetic data for time series classification with deep residual networks-78 | code
- 2019-Deep learning for time series classification:review-1076 | code
- 2020-Time Series Data Augmentation for Neural Networks by Time Warping with a Discriminative Teacher-8 | code
- 2021-An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks-41 | code