Recent studies showed that Photoplethysmography (PPG) sensors embedded in wearable devices can estimate heart rate (HR) with high accuracy. However, despite of prior research efforts, applying PPG sensor based HR estimation to embedded devices still faces challenges due to the energy-intensive high-frequency PPG sampling and the resource-intensive machine-learning models. In this work, we aim to explore HR estimation techniques that are more suitable for lower-power and resource-constrained embedded devices. More specifically, we seek to design techniques that could provide high-accuracy HR estimation with low-frequency PPG sampling, small model size, and fast inference time. First, we show that by combining signal processing and ML, it is possible to reduce the PPG sampling frequency from 125 Hz to only 25 Hz while providing higher HR estimation accuracy. This combination also helps to reduce the ML model feature size, leading to smaller models. Additionally, we present a comprehensive analysis on different ML models and feature sizes to compare their accuracy, model size, and inference time. The models explored include Decision Tree (DT), Random Forest (RF), K-nearest neighbor (KNN), Support vector machines (SVM), and Multi-layer perceptron (MLP). Experiments were conducted using both a widely-utilized dataset and our self-collected dataset. The experimental results show that our method by combining signal processing and ML had only 5% error for HR estimation using low-frequency PPG data. Moreover, our analysis showed that DT models with 10 to 20 input features usually have good accuracy, while are several magnitude smaller in model sizes and faster in inference time.
TransfoRhythm: A Transformer Architecture Conductive to Blood Pressure Estimation via Solo PPG Signal Capturing
Faculty of Computer Science（计算机科学学院）；Concordia Institute for Information Systems Engineering（康考迪亚信息系统工程学院）；University of New Brunswick（新不伦瑞克大学）；Concordia University（康考迪亚大学）
PPGnet: Deep Network for Device Independent Heart Rate Estimation from Photoplethysmogram
Healthcare Technology Innovation Centre (HTIC)（医疗技术创新中心）；Indian Institute of Technology Madras（印度理工学院马德拉斯分校）；Department of Electrical Engineering, Indian Institute of Technology Madras（印度理工学院马德拉斯分校电气工程系）
Beyond Single-Channel: Multichannel Signal Imaging for PPG-to-ECG Reconstruction with Vision Transformers
Department of Computer Science, University of Toronto（多伦多大学计算机科学系）；Data Science Research Center, Duke Kunshan University（杜克昆山大学数据科学研究中心）；Data Science Lab, Fields Institute for Research in Mathematical Sciences（数学科学研究院数据科学实验室）；Department of Mathematics and Statistics, York University（约克大学数学与统计学系）
本文提出融合原始 PPG、一阶及二阶差分与曲线下面积的四通道信号图像表示，并以 ViT 建模心搏间和心搏内依赖，显著提升 PPG 到 ECG 的重建精度。
Cuffless Blood Pressure Estimation from Six Wearable Sensor Modalities in Multi-Motion-State Scenarios
Faculty of Frontier Sciences, Harbin Institute of Technology, Shenzhen（前沿科学学院，哈尔滨工业大学深圳校区）；University of Melbourne（墨尔本大学）；University of New South Wales（新南威尔士大学）
Self-Supervised Autoencoder Network for Robust Heart Rate Extraction from Noisy Photoplethysmogram: Applying Blind Source Separation to Biosignal Analysis
Mellowing Factory Co. Ltd.（梅洛灵工厂有限公司）；Chungnam National University Hospital（全南国立大学医院）；Department of Radiology, Seoul National University College of Medicine（首尔国立大学医学院放射科）
Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach
Infant Research Centre（婴儿研究中心）；School of Computing（计算机学院）；Dublin City University（都柏林城市大学）；Department of Electrical Engineering（电气工程系）；KU Leuven（鲁汶大学）；Cork University Maternity Hospital（科克大学妇产医院）；Insight SFI Research Centre（Insight SFI研究中心）
Reliable Physiological Monitoring on the Wrist Using Generative Deep Learning to Address Poor Skin-Sensor Contact
Singapore Management University（新加坡管理大学）；The Chinese University of Hong Kong（香港中文大学）；University of Cambridge（剑桥大学）；Eindhoven University of Technology（埃因霍温理工大学）
SpO$_2$ Predictor-Guided Stage-Wise Time-Frequency Reconstruction of Low-Quality Dual-Wavelength PPG for Oxygen Saturation Estimation
Department of Computer Science, University of California, Davis, Davis, CA, U.S.A.（加州大学戴维斯分校计算机科学系）；Department of Electrical and Computer Engineering, University of California, Davis, Davis, CA, U.S.A.（加州大学戴维斯分校电气与计算机工程系）
Cross-View Attention Fusion Net: A Prior-Guided Dual-View Representation Learning for Cardiac Output Estimation from Short-Term PPG Signals
Department of Biomedical Signals and Systems（生物医学信号与系统系）；Department of Cardiovascular and Respiratory Physiology（心血管与呼吸生理学系）；Department of Intensive Care（重症医学系）
EvoMorph: Counterfactual Explanations for Continuous Time-Series Extrinsic Regression Applied to Photoplethysmography
Centre for Digital Health Interventions, ETH Zurich（数字健康干预中心，苏黎世联邦理工学院）；Centre for Digital Health Interventions, University of St.Gallen（数字健康干预中心，圣加伦大学）
RRWaveNet: A Compact End-to-End Multi-Scale Residual CNN for Robust PPG Respiratory Rate Estimation
Chulalongkorn University（朱拉隆功大学）；Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；University of Wisconsin Madison（威斯康星大学麦迪逊分校）；Suankularb Wittayalai School（诗纳卡宁威洛大学附属苏安古拉学校）；Vidyasirimedhi Institute of Science & Technology (VISTEC)（维迪亚西瑞梅迪科学技术学院）
BayesBeat: Reliable Atrial Fibrillation Detection from Noisy Photoplethysmography Data
Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；Pennsylvania State University（宾夕法尼亚州立大学）；United International University（联合国际大学）；University of Rochester（罗切斯特大学）；United Arab Emirates University（阿联酋大学）
