Non-Contact Detection of Vital Signs Based on Improved Adaptive EEMD Algorithm (July 2022)

The use of radar technology for non-contact measurement of vital parameters is increasingly being examined in scientific studies. Based on a systematic literature search in the PubMed, German National Library, Austrian Library Network (Union Catalog), Swiss National Library and Common Library Network databases, the accuracy of heart rate and/or respiratory rate measurements by means of radar technology was analyzed. In 37% of the included studies on the measurement of the respiratory rate and in 48% of those on the measurement of the heart rate, the maximum deviation was 5%. For a tolerated deviation of 10%, the corresponding percentages were 85% and 87%, respectively. However, the quantitative comparability of the results available in the current literature is very limited due to a variety of variables. The elimination of the problem of confounding variables and the continuation of the tendency to focus on the algorithm applied will continue to constitute a central topic of radar-based vital parameter measurement. Promising fields of application of research can be found in particular in areas that require non-contact measurements. This includes infection events, emergency medicine, disaster situations and major catastrophic incidents.

Section: Resultsmentioning

confidence: 99%

Systematic Literature Review Regarding Heart Rate and Respiratory Rate Measurement by Means of Radar Technology

Liebetruth,

Kehe,

Steinritz

et al. 2024

“…This can be achieved by standing in front of the radar sensor or lying down on a bed equipped with radar technology on the ceiling. In the majority of works, the individual in question assumes an anterior position to the radar without any angular orientation [ 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. The ability to monitor vital signs from various angular positions presents a significant advantage in the context of smart home applications.…”

Section: Introductionmentioning

confidence: 99%

“…As mentioned, in most papers, it is assumed that the living subject is in front of the radar (without any angular position). In the work conducted by authors in [ 10 , 11 , 12 ], commercial FMCW radars were employed to extract physiological indicators from varying distances of up to 2.5 m, without angular orientation and with offline processing. In the study outlined in [ 14 , 15 ], measurements were conducted to monitor vital signs of an individual who lay on a bed in front of the radar.…”

Section: Introductionmentioning

confidence: 99%

“…Also, a time-frequency analysis technique, specifically continuous wavelet transform (

), is suggested to extract vital signs frequencies. In comparison to other techniques such as adaptive decomposition algorithms [ 10 , 12 ], Fast Fourier transform (FFT) [ 9 , 14 ], and independent component analysis (ICA) [ 15 , 24 ], this technique allows for capturing events of different durations and frequencies in the radar data. Unlike FFT, which analyzes frequency under the assumption that the signal is stationary,

can identify and localize transient features and temporal variations in non-stationary signals where frequency components fluctuate over time.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Respiration and Heart Rate Monitoring in Smart Homes: An Angular-Free Approach with an FMCW Radar

Mehrjouseresht,

Hail,

Karsmakers

et al. 2024

This paper proposes a new approach for wide angle monitoring of vital signs in smart home applications. The person is tracked using an indoor radar. Upon detecting the person to be static, the radar automatically focuses its beam on that location, and subsequently breathing and heart rates are extracted from the reflected signals using continuous wavelet transform (CWT) analysis. In this way, leveraging the radar’s on-chip processor enables real-time monitoring of vital signs across varying angles. In our experiment, we employ a commercial multi-input multi-output (MIMO) millimeter-wave FMCW radar to monitor vital signs within a range of 1.15 to 2.3 m and an angular span of −44.8 to +44.8 deg. In the Bland–Altman plot, the measured results indicate the average difference of −1.5 and 0.06 beats per minute (BPM) relative to the reference for heart rate and breathing rate, respectively.

“…Smart shirts and intelligent garments are on the rise and are increasingly being used in medical diagnostics and therapy control as well [ 16 , 17 , 18 , 19 ]. While mobile solutions in cardiovascular monitoring, e.g., heart rate monitoring, have been successfully used for some time, respiratory monitoring systems are mostly limited to monitoring respiratory rate [ 20 , 21 , 22 , 23 , 24 , 25 , 26 ], which can be measured with sufficient accuracy for clinical purposes.…”

Section: Introductionmentioning

confidence: 99%

Characterisation and Quantification of Upper Body Surface Motions for Tidal Volume Determination in Lung-Healthy Individuals

Laufer

Höflinger

Docherty

et al. 2023

Measurement of accurate tidal volumes based on respiration-induced surface movements of the upper body would be valuable in clinical and sports monitoring applications, but most current methods lack the precision, ease of use, or cost effectiveness required for wide-scale uptake. In this paper, the theoretical ability of different sensors, such as inertial measurement units, strain gauges, or circumference measurement devices to determine tidal volumes were investigated, scrutinised and evaluated. Sixteen subjects performed different breathing patterns of different tidal volumes, while using a motion capture system to record surface motions and a spirometer as a reference to obtain tidal volumes. Subsequently, the motion-capture data were used to determine upper-body circumferences, tilt angles, distance changes, movements and accelerations—such data could potentially be measured using optical encoders, inertial measurement units, or strain gauges. From these parameters, the measurement range and correlation with the volume signal of the spirometer were determined. The highest correlations were found between the spirometer volume and upper body circumferences; surface deflection was also well correlated, while accelerations carried minor respiratory information. The ranges of thorax motion parameters measurable with common sensors and the values and correlations to respiratory volume are presented. This article thus provides a novel tool for sensor selection for a smart shirt analysis of respiration.