Noncontact Vital Sign Monitoring With FMCW Radar via Maximum Likelihood Estimation

• Published in: IEEE internet of things journal Vol. 11; no. 23; pp. 38686 - 38703
• Main Authors: Yao, Shaohui, Cong, Jingyu, Li, Du, Deng, Zhenmiao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Cloud computing; Continuous radiation; Damage detection; Error analysis; Frequency-modulated continuous-wave (FMCW) radar; Heart beat; Heart rate; Higher harmonics; Internet of Things; Internet of Things (IoT); Maximum likelihood estimation; maximum likelihood estimation (MLE); Maximum likelihood estimators; Medical services; Monitoring; Newton’s method; Noise monitoring; Parameter estimation; Radar; Radar detection; Remote monitoring; Telemedicine; vital sign monitoring
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2024.3449408

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.