This paper introduces heart sound detection by radar systems, which enables touch-free and continuous monitoring of heart sounds. The proposed measurement principle entails two enhancements in modern vital sign monitoring. First, common touch-based auscultation with a phonocardiograph can be simplified by using biomedical radar systems. Second, detecting heart sounds offers a further feasibility in radar-based heartbeat monitoring. To analyse the performance of the proposed measurement principle, 9930 seconds of eleven persons-under-tests’ vital signs were acquired and stored in a database using multiple, synchronised sensors: a continuous wave radar system, a phonocardiograph (PCG), an electrocardiograph (ECG), and a temperature-based respiration sensor. A hidden semi-Markov model is utilised to detect the heart sounds in the phonocardiograph and radar data and additionally, an advanced template matching (ATM) algorithm is used for state-of-the-art radar-based heartbeat detection. The feasibility of the proposed measurement principle is shown by a morphology analysis between the data acquired by radar and PCG for the dominant heart sounds S1 and S2: The correlation is 82.97 ± 11.15% for 5274 used occurrences of S1 and 80.72 ± 12.16% for 5277 used occurrences of S2. The performance of the proposed detection method is evaluated by comparing the F-scores for radar and PCG-based heart sound detection with ECG as reference: Achieving an F1 value of 92.22 ± 2.07%, the radar system approximates the score of 94.15 ± 1.61% for the PCG. The accuracy regarding the detection timing of heartbeat occurrences is analysed by means of the root-mean-square error: In comparison to the ATM algorithm (144.9 ms) and the PCG-based variant (59.4 ms), the proposed method has the lowest error value (44.2 ms). Based on these results, utilising the detected heart sounds considerably improves radar-based heartbeat monitoring, while the achieved performance is also competitive to phonocardiography.

This paper presents a millimeter wave radar in the 24-GHz ISM band for detection, tracking, and classification of human targets. Linear frequency modulation of the transmit signal and two receive antennas enable distance and angle measurements, respectively. Multiple consecutive frequency chirps are used for target velocity calculation. Hardware as well as firmware concepts of the proposed system are described in detail. Various algorithms for human detection and tracking are investigated and combined to a new signal processing routine optimized for compactness and low-power to run on a microcontroller. Additionally, a novel Doppler-compensated angle-ofarrival estimation method as well as a one-class support vector machine for human classification are proposed to further enhance the human detection and tracking performance. The achieved performances of the designed hardware and the implemented algorithm are verified in extensive measurements. The distance and angle errors of the realized radar sensor are at most 25 cm along a measurement range of 18m and 10° for a two-sided angle sweep of 65°, respectively. The achieved range resolution is 0.9 m. Dedicated verifications of the most important signal processing routines are presented to verify their functionality and experiments with several human targets illustrate the performance and limits of the overall tracking algorithm. It is shown that range, velocity, and angle of up to five humans are correctly detected and tracked. The presented one-class classifier successfully distinguishes human targets from other quasi-static targets like trees and shadowing effects of human subjects on walls.

Radar systems enable a contactless, non-invasive method to simultaneously monitor the respiration and heart rate of a human target. To enhance the stability and accuracy of the detected heart rate, this paper introduces a Kalman filter-based tracking of the heartbeat signal. After providing a first rough estimation, the bandwidth of the applied band-pass filter is successively narrowed down and the filter limits are steadily updated with respect to the current heart rate of the target. Measurement segments with random body movements are automatically identified and consequentially ignored for the Kalman filter update. The functionality of the proposed algorithm is proven by various verification measurements using a 60-GHz frequency-modulated continuous-wave radar system. The determined heart rates are compared to the output of a commercial chest belt. Measurement data with common breathing as well as during breath-holding were recorded for fourteen different subjects.

Microwave technology plays a more important role in modern industrial sensing applications. Pushed by the significant progress in monolithic microwave integrated circuit technology over the past decades, complex sensing systems operating in the microwave and even millimeter-wave range are available for reasonable costs combined with exquisite performance. In the context of industrial sensing, this stimulates new approaches for metrology based on microwave technology. An old measurement principle nearly forgotten over the years has recently gained more and more attention in both academia and industry: the six-port interferometer. This paper reviews the basic concept, investigates promising applications in remote, as well as contact-based sensing and compares the system with state-of-the-art metrology. The significant advantages will be discussed just as the limitations of the six-port architecture. Particular attention will be paid to impairment effects and non-ideal behavior, as well as compensation and linearization concepts. It will be shown that in application fields, like remote distance sensing, precise alignment measurements, as well as interferometrically-evaluated mechanical strain analysis, the six-port architecture delivers extraordinary measurement results combined with high measurement data update rates for reasonable system costs. This makes the six-port architecture a promising candidate for industrial metrology.

Radar systems allow for contactless measurements of vital signs such as heart sounds, the pulse signal, and respiration. This approach is able to tackle crucial disadvantages of state-of-the-art monitoring devices such as the need for permanent wiring and skin contact. Potential applications include the employment in a hospital environment but also in home care or passenger vehicles. This dataset consists of synchronised data which are acquired using a Six-Port-based radar system operating at 24 GHz, a digital stethoscope, an ECG, and a respiration sensor. 11 test subjects were measured in different defined scenarios and at several measurement positions such as at the carotid, the back, and several frontal positions on the thorax. Overall, around 223 minutes of data were acquired at scenarios such as breath-holding, post-exercise measurements, and while speaking. The presented dataset contains reference-labeled ECG signals and can therefore easily be used to either test algorithms for monitoring the heart rate, but also to gain insights about characteristic effects of radar-based vital sign monitoring.

Although a lot of research has been done into radar based heartbeat detection for the last years, it is still unknown which physiological effects truly underlie these measurement signals. This paper investigates the cardiovascular system as well as the effects due to the antenna characteristics and establishes a connection to the variety of heartbeat signals that can be found in comparable publications. For the first time, the cause of the diverging signal shapes is researched revealing the locally specific pulse wave curves named sphygmograms. Three different types are investigated: the carotid, the venous and the ventricle sphygmogram. Verification of the measured signal shapes is ensured by a laser sensor. Afterward, the influence of the filtering on the sphygmograms is researched as well as the influence of the antenna propagation on the radar signal. Finally, all novel insights are combined to substantiate the variety of published heartbeat signal curves.