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The Bio-Radar is herein presented as a non-contact radar system able to capture vital signs remotely without requiring any physical contact with the subject. In this work, the ability to use the proposed system for emotion recognition is verified by comparing its performance on identifying fear, happiness and a neutral condition, with certified measuring equipment. For this purpose, machine learning algorithms were applied to the respiratory and cardiac signals captured simultaneously by the radar and the referenced contact-based system. Following a multiclass identification strategy, one could conclude that both systems present a comparable performance, where the radar might even outperform under specific conditions. Emotion recognition is possible using a radar system, with an accuracy equal to 99.7% and an F1-score of 99.9%. Thus, we demonstrated that it is perfectly possible to use the Bio-Radar system for this purpose, which is able to be operated remotely, avoiding the subject awareness of being monitored and thus providing more authentic reactions.

Purpose: Ultra-wideband bio-radar (UWB) is a new non-contact technology that can be used to screen for obstructive sleep apnea (OSA). However, little information is available regarding its reliability. This study aimed to evaluate the effectiveness of UWB and to determine if UWB could provide a novel and reliable method for the primary screening of sleep-related breathing disorders., Method: Subjects with suspected OSA from the sleep center of the First Hospital of the China Medical University were assessed over the period of September 2018 to April 2019 for enrollment in the study. Three detection methods were simultaneously used, including the STOP-Bang questionnaire (SBQ), UWB, and standard polysomnography (PSG). The data were analyzed using a fourfold table, receiver operating characteristic curves, Spearman rank correlation coefficients, Bland-Altman plots, and epoch-by-epoch analysis., Result: Of 67 patients, 56 were men, mean age was 43 ± 11 years, mean body mass index was 27.8 ± 4.8 kg/m 2 , and mean SBQ score was 4.8 ± 1.6. The apnea-hypopnea index (AHI) (r = 0.82, p < 0.01) and minimum arterial oxygen saturation (r = 0.80, p < 0.01) of the UWB were positively correlated with those obtained from the PSG. UWB performed better than SBQ, as indicated by the larger area under the curve (0.85 vs. 0.632). The sensitivity and specificity of the UWB-AHI were good (100%, 70%, respectively)., Conclusions: UWB performs well in the screening of OSA and can provide reliable outcomes for the screening of OSA at the primary level., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Noncontact monitoring of human vital signs has been an emerging research topic in recent years. A key approach to this monitoring is the use of the Doppler radar concept which enables real-time vital signs detection, resulting in a new class of radar system known as bio-radar. The antennas are a key component of any bio-radar module and their designs should meet the common requirements of bio-radar applications such as high radiation directivity and mechanical flexibility. This paper presents the design of a four-element antenna array on a flexible liquid crystal polymer (LCP) substrate of 100 μm thickness and ε r of 3.35. The designed antenna array can be used with a 24 GHz bio-radar for vital signs monitoring in a non-contact manner. It features a relatively compact size of 36.5 × 53 mm 2 and measured gain of 5.81 dBi. The two vital signs: breathing rate (BR) and heart rate (HR) of two human subjects are detected with relatively good accuracy using the fabricated antenna array and radio frequency (RF) output power of -3 dBm from a distance of approximately 60 cm. The effect of bending on the antenna performance is also analyzed.

In this article we discuss a novel approach to Bio-Radar, contactless measurement of bio-signals, called Cognitive Bio-Radar. This new approach implements the Bio-Radar in a Software Defined Radio (SDR) platform in order to obtain awareness of the environment where it operates. Due to this, the Cognitive Bio-Radar can adapt to its surroundings in order to have an intelligent usage of the radio frequency spectrum to improve its performance. In order to study the feasibility of such implementation, a SDR based Bio-Radar testbench was developed and evaluated. The prototype is shown to be able to acquire the heartbeat activity and the respiratory effort. The acquired data is compared with the acquisitions from a Biopac research data acquisition system, showing coherent results for both heartbeat and breathing rate.

Doppler-based radar systems have been seen as a promising tool to assess vital signs, since they are capable to monitor the respiratory and cardiac signal remotely, by measuring the chest-wall displacement. However, due to the spectral overlap of these signals, their proper separation is a challenging task. In this paper, we demonstrate the effectiveness of using Discrete Wavelet Transform in the cardiac signal extraction, by comparing this method with other approaches widely used in literature, namely a standalone Band-Pass Filtering, the Ensembled Empirical Mode Decomposition, the Continuous Wavelet Transform and the Wavelet Packet Decomposition. The comparison metrics were defined taking into consideration the heart rate computation accuracy, and also the peak detection consistency to further evaluate the Heart Rate Variability. The efficiency of those methods is also tested considering real application scenarios, characterized by non-controlled monitoring environment conditions and the ability to equally assess the vital signs of different subjects, regardless their physical stature. [ABSTRACT FROM AUTHOR]

Radar systems have been widely explored as a monitoring tool able to assess the subject's vital signs remotely. However, their implementation in real application scenarios is not straightforward. Received signals encompass parasitic reflections that occur in the monitoring environment. Generally, those parasitic components, often treated as a complex DC (CDC) offsets, must be removed in order to correctly extract the bio-signals information. Fitting methods can be used, but their implementation were revealed to be challenging when bio-signals are weak or when these parasitic reflections arise from non-static targets, changing the CDC offset properties over time. In this work, we propose a dynamic digital signal processing algorithm to extract the vital signs from radar systems. This algorithm includes a novel arc fitting method to estimate the CDC offsets on the received signal. The method revealed being robust to weaker signals, presenting a success rate of 95 % , irrespective of the considered monitoring conditions. Furthermore, the proposed algorithm is able to adapt to slow changes in the propagation environment. [ABSTRACT FROM AUTHOR]