Noncontact Vital Sign Monitoring With FMCW Radar via Maximum Likelihood Estimation | IEEE Journals & Magazine | IEEE Xplore

Noncontact Vital Sign Monitoring With FMCW Radar via Maximum Likelihood Estimation


Abstract:

Traditional vital sign monitoring devices typically involve direct contact with the skin using electrodes, making them unsuitable for daily vital sign monitoring due to t...Show More

Abstract:

Traditional vital sign monitoring devices typically involve direct contact with the skin using electrodes, making them unsuitable for daily vital sign monitoring due to the discomfort and skin damage. Remote detection technology provides an effective solution to these issues. This article introduces an efficient and robust algorithm for estimating vital signs using frequency-modulated continuous-wave (FMCW) radar. The integration of this method with emerging technologies, such as the Internet of Things (IoT), enables long-term and contactless vital sign monitoring, which facilitates a new model of self-management for chronic diseases and their prevention. While the breathing estimation accuracy is typically constrained by noise, heart rate (HR) estimation is primarily hindered by strong interference from the breathing signal and its higher-order harmonics. A maximum likelihood estimator based on the Newton’s method is derived and proposed in this article to accurately assess the breathing and heartbeat frequencies by enhancing the precision of vital sign parameter estimation. The proposed algorithm is validated utilizing a 77 GHz FMCW radar and compared with a reliable reference sensor. Experimental results from eight subjects demonstrate that the proposed method enhances the estimation accuracy, outperforming both conventional spectral estimation and other methods. Specifically, the root mean-square error between the reference sensor measurements and the estimations is lower than 1 beats per minute (bpm) for breathing rates and 1.5 bpm for HRs. Additionally, the Bland-Altman plots demonstrate a high level of agreement between these estimations and the reference measurements.
Published in: IEEE Internet of Things Journal ( Volume: 11, Issue: 23, 01 December 2024)
Page(s): 38686 - 38703
Date of Publication: 26 August 2024

ISSN Information:

Funding Agency:


I. Introduction

As the Internet of Things (IoT) technology grows by leaps and bounds, smart healthcare has become a key research direction in the medical field [1], [2]. Features, such as automation, digitization, and information sharing have made smart hospitals a focal point of attention [3]. Timely assessment of patients’ physiological conditions can prevent further complications and even avoid life-threatening situations, especially in intensive care units (ICUs). Moreover, the increasingly urgent demand for aging health care requires home-based monitoring devices to be designed to further expand the application scenarios and monitoring scope of the IoT technology [4]. The issue of diverse measurement indicators can be tackled by multisensor fusion to achieve real-time monitoring of multiple vital sign parameters, enabling health warnings and disease monitoring [5]. Therefore, continuous monitoring of patients’ vital signs is crucial.

References

References is not available for this document.