Fine-grained Vital Sign Reconstruction through Machine Learning on Multi-channel Radar Signals

Abstract

Monitoring vital signs such as breathing rate (BR) and heart rate (HR) is crucial for early detection of health issues and supports a wide range of health-related applications. Traditional monitoring methods often involve body-attached medical devices, which can be intrusive and inconvenient for continuous use in daily life. Contactless monitoring using radio frequency (RF) signals has emerged as a promising alternative, but acquiring precise vital sign measurements remains challenging due to the limited sensing resolution of RF devices. In this paper, we design a high-resolution contactless vital sensing system by leveraging advanced beamforming in combination with machine learning (ML) methods. The key idea of our system is to reconstruct fine-grained vital sign measurements from RF signals, achieving low estimation error, comparable to that of dedicated medical devices such as photoplethysmography sensors, respiration monitoring belts. To enhance the reconstruction performance, we integrate an antenna array with double phase shifters to acquire RF data that captures precise chest displacement of human subjects. An encoder-decoder model based on a 1D convolutional neural network is then developed to map the RF signals into vital sign measurements. Extensive evaluations show that our system has low errors of 0.3 beat per minute (BPM) for BR estimation and 2.7 BPM for HR estimation.