A Supervised Machine Learning Algorithm for Heart Rate Detection Using Doppler Motion-Sensing Radar


The development of vital sign radar technology has shown to be an effective tool for measuring various physiological processes, such as heartbeat and respiration. In this discipline, there are still various Signal Processing issues, such as overcoming the nonlinearities and harmonics that abound in the power spectrum.

Due to the huge signal amplitude, respiration harmonics distort and overwhelm the detection of heartbeat. The gamma filter, a supervised machine learning technique, provides an effective, calibration-free approach for modeling the time series cardiac signal in the presence of breathing and respiration artifacts.

A 5.8-GHz quadrature Doppler radar provides the measured signal, and a modified ECG signal serves as the ground truth for training the filter. The heartbeat is independent and separate from respiration, according to experimental results, and the method may be applied in real time.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : A Multitask Learning Model for Traffic Flow and Speed Forecasting ABSTRACT: Accurate short-term traffic state forecasting is beneficial to Intelligent Transportation Systems (ITS) research and applications. This
PROJECT TITLE : A Novel Electricity Price Forecasting Approach Based on Dimension Reduction Strategy and Rough Artificial Neural Networks ABSTRACT: In deregulated energy markets, accurate electricity price forecasting (EPF)
PROJECT TITLE : Comparing Different Resampling Methods in Predicting Students Performance Using Machine Learning Techniques ABSTRACT: Predicting students' performance is one of the most valuable and important research areas in
PROJECT TITLE : Convolutional Recurrent Neural Networks for Glucose Prediction ABSTRACT: Blood glucose control is critical for diabetes management. Machine learning techniques are used in current digital therapy approaches for
PROJECT TITLE : Deep Learning for Smartphone-Based Malaria Parasite Detection in Thick Blood Smears ABSTRACT: This study looks into the possibilities of using smartphones to detect malaria parasites in thick blood smears. We've

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry