51 skills found · Page 1 of 2
inventec-ai-center / Bp BenchmarkA Benchmark for Machine-Learning based Non-Invasive Blood Pressure Estimation using Photoplethysmogram
Healthcare-Robotics / Bodies At RestCode + Data for CVPR 2020 oral paper "Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic Data."
dedeus10 / BloodPressure PPG MLRepository from final conclusion work of Computer Engineering. Estimation of Systolic and Diastolic Blood Pressure Using PPG and ECG Signals and Machine Learning Algorithms
Wu-2 / BP PredictionBlood pressure estimation using PPG
adilkhan095 / SOC Estimation Of Li Ion Battery Using Kalman FilterThe State of charge (SOC) is an important parameter to find the capacity of state. It is equivalent to the fuel gauge for a battery pack in a battery electric vehicle. There are different general methods to precisely estimate the battery SOC using voltage, current integration and pressure but each has its certain drawbacks. Accurate estimation of SOC is one of the major issues in a Battery Management System. To overcome these shortcomings, a Kalman filter is used which is able to adjust to the battery voltage and coulomb counting in real time. To estimate the SOC in both the batteries, an RC circuit is considered and its parameters are calculated and rewritten in state space form which in turn is converted to discrete time form to estimate SOC.
pedr0sorio / Cuffless BP EstimationProject on blood pressure estimation from ECG and PPG signals.
akrlowicz / Ppg Blood Pressure EstimationPredicting blood pressure from rPPG signal using LSTMs
metinaktas / Acoustic Direction Finding Using Single Acoustic Vector Sensor Under High ReverberationWe propose a novel and robust method for acoustic direction finding, which is solely based on acoustic pressure and pressure gradient measurements from single Acoustic Vector Sensor (AVS). We do not make any stochastic and sparseness assumptions regarding the signal source and the environmental characteristics. Hence, our method can be applied to a wide range of wideband acoustic signals including the speech and noise-like signals in various environments. Our method identifies the “clean” time frequency bins that are not distorted by multipath signals and noise, and estimates the 2D-DOA angles at only those identified bins. Moreover, the identification of the clean bins and the corresponding DOA estimation are performed jointly in one framework in a computationally highly efficient manner. We mathematically and experimentally show that the false detection rate of the proposed method is zero, i.e., none of the time-frequency bins with multiple sources are wrongly labeled as single-source, when the source directions do not coincide. Therefore, our method is significantly more reliable and robust compared to the competing state-of-the-art methods that perform the time-frequency bin selection and the DOA estimation separately. The proposed method, for performed simulations, estimates the source direction with high accuracy (less than 1 degree error) even under significantly high reverberation conditions.
Nikitha-ramasetti / BP Estimation PPGBlood pressure (SBP, DBP, and HR) estimation from Photoplethysmography (PPG) signals using Deep Neural Networks
Brophy-E / T2TGANThis repository is for our work submitted to ArXiv and under review at EMBC2021 titled "Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach".
RCHI-Lab / BodyMAPOfficial implementation of "BodyMAP - Jointly Predicting Body Mesh and 3D Applied Pressure Map for People in Bed", CVPR 2024
DiTEC-project / Gnn Pressure EstimationDiTEC research
gntjr2-ops / Adaptive PTT BloodPressure EstimationNo description available
AI4HealthUOL / Ppg Ood GeneralizationGeneralizable Deep Learning for Photoplethysmography-Based Blood Pressure Estimation – A Benchmarking Study
enesbasbug / Blood Pressure Estimation With Webcam Using Deep LearningDeep Learning-Based Blood Pressure Estimation Using Contactless Webcam PPG Measurements
ZhongyueZhang785 / Real Time Noninvasive Continuous Blood Pressure Estimation Using Machine Learning心血管疾病已成为全球范围内致人死亡的头号病因。为了能有效预防心血管疾病，血压的连续测量尤为重要。目前，连续血压测量分为无创测量和有创测量两种方式。有创测量虽然能达到较高的精度，但是操作复杂且存在感染风险。无创测量主要基于脉搏波。随着机器学习的发展，愈来愈多的人使用脉搏波特征参数法。该方法主要存在两点问题。其一，手动提取特征对波形的要求较高，特征选取受研究者先验知识影响，极有可能提取到非相关特征。其二，血压波形中包含的丰富生理信息未能被充分挖掘。大多数研究的预测目标为收缩压、舒张压等单一血压值，较少的研究关注血压整体波形的预测。 针对上述问题，本文创新性地将原本用于二维图像处理的U-Net模型引入一维血压预测中，提出了一种基于U-Net的PPG-ABP转换模型。该方法无需手动提取特征，仅使用光电血管容积脉搏波（PPG）信号便可预测出连续血压波形。相较于脉搏波特征法，本文方法在信号获取和处理上更为便捷，在结果输出上包含更丰富的血压波形信息。本文平均血压预测结果满足美国医疗仪器促进协会（AAMI）标准。在英国高血压协会（BHS） 标准下，舒张压与平均血压可达到等级B。此外，本模型针对高血压与正常人群的血压分类也能取得较好的效果。Cardiovascular disease has become the significant cause of death. To prevent such disease effectively, continuous measurement of blood pressure is important. Nowadays, there are two ways of blood pressure measurement: noninvasive measurement and invasive measurement. Although invasive measurement can achieve high precision, it is complex to operate and has infection risk. The noninvasive measurement uses pulse waves. With the development of machine learning, many studies make handcrafted features from pulse waves to predict blood pressure. There are two problems with this method. Firstly, feature extraction requires a high standard for waveform, which is not easily achieved in reality. Besides, feature selection is influenced by prior knowledge of researchers. It is very likely to extract non-related features. Secondly, the abundant physiological information of the blood pressure waveform is not extracted fully. Specifically, most of the research aims to predict systolic pressure (SBP) and diastolic pressure (DBP). Indeed, less research focuses on the prediction of the overall waveform of blood pressure. Given the above problems, the thesis introduces the U-Net model, originally used in two-dimensional image processing, into one-dimensional blood pressure prediction. A model based on U-Net was proposed, directly converting photoplethysmogram (PPG) to arterial blood pressure (ABP). The method does not need to extract the features manually. The continuous blood pressure waveform can be predicted only by using the PPG signal. In term of signal acquisition and processing, this method is more convenient. What’s more, it contains more information of blood pressure waveform in the output. The results of the mean arterial pressure (MAP) prediction meet the AAMI standard. DBP and MAP can reach level B under the BHS standards. In addition, the model can also achieve ideal results in the classification of hypertension and normal people.
Zest86 / ACL PITNCode of TMC2025 paper 《PITN: Physics-Informed Temporal Networks for Cuffless Blood Pressure Estimation》
sanvsquezsz / PPG Based BP AssessmentThis data set contains PPG recordings from 56 subjects who were not hospitalized during data collection. This dataset is intended to support the development of approaches for blood pressure estimation by analyzing PPG signals.
MSA-Rafi / Domain Knowledge Integrated CNN XLSTM XAtt Multi Stream Feature Fusion Cuffless BP Estimation PPGDeep learning framework for accurate blood pressure (BP) estimation from PPG signals. Features include signal selection & enhancement, dual-path temporal/image-based feature extraction, M-SCAN attention, MSFN fusion, and D-QuEST loss with domain knowledge integration. Extensive diversity analysis ensures robustness of the work.
mohofar / NICE KLMS QkLMSAn implementation to "Transfer Learning in Adaptive Filters: The Nearest Instance Centroid-Estimation Kernel Least-Mean-Square Algorithm" with additional work on blood pressure prediction.
