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The instantaneous vital sign rates, which are related to physiological dynamics, are important indicators of human health condition. This paper presents a noncontact way to measure the human instantaneous vital signs using digital-intermediate frequency (IF) Doppler radar. The synchrosqueezing transform-based algorithm has been proposed to get a co...
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Abstract The separation of overlapping components is a well-known and difficult problem in multicomponent signals analysis and it is shared by applications dealing with radar, biosonar, seismic, and audio signals. In order to estimate the instantaneous frequencies of a multicomponent signal, it is necessary to disentangle signal modes in a proper d...
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... The attacker can obtain the Doppler shift feature from the frequencydomain signals by applying the Fourier transform on the received signals. Prior works mainly leverage the Doppler shift for activity/gesture recognition and respiration/heart rate estimation using RF signals [25,36,64,69,82,105,106,129,138,171]. ...
Human-centered wireless sensing (HCWS) aims to understand the fine-grained environment and activities of a human using the diverse wireless signals around him/her. While the sensed information about a human can be used for many good purposes such as enhancing life quality, an adversary can also abuse it to steal private information about the human (e.g., location and person's identity). However, the literature lacks a systematic understanding of the privacy vulnerabilities of wireless sensing and the defenses against them, resulting in the privacy-compromising HCWS design. In this work, we aim to bridge this gap to achieve the vision of secure human-centered wireless sensing. First, we propose a signal processing pipeline to identify private information leakage and further understand the benefits and tradeoffs of wireless sensing-based inference attacks and defenses. Based on this framework, we present the taxonomy of existing inference attacks and defenses. As a result, we can identify the open challenges and gaps in achieving privacy-preserving human-centered wireless sensing in the era of machine learning and further propose directions for future research in this field.
... However, traditional heart rate detection techniques such as electrocardiogram (ECG) are mostly based on human bioelectrical signal measurement, which requires electrodes and other sensors to be attached to the skin surface. For critically ill patients, especially those who are bedridden for a long time, their skin moisture content is not high and their human body impedance is low due to symptoms such as muscle atrophy and malnutrition, resulting in inaccurate heart rate signal measurement [2]. Moreover, long-term wear can cause adverse reactions such as allergies and discomfort. ...
Ballistocardiogram (BCG) is a non-invasive physiological signal detection method that can be used for non-contact detection of resting heart rate (RHR) and has been widely used in human health monitoring. However, the BCG signal is vulnerable to noise, making it challenging to accurately measure heart rate. In this paper, we propose a noise reduction model for the BCG signal based on Gramian Angular Field (GAF) and an improved Denoising Autoencoder (DAE), referred to as the GDAE model, to accurately detect heart rate in noise-contaminated signals. First, the Gramian Angular Field transform is used to convert the one-dimensional BCG signal into two-dimensional image information, highlighting the difference between the signal centroid information and noise information; after that, the transformed image is denoised by the Denoising Autoencoder to obtain a denoised BCG signal. After noise reduction, the heart rate of the subject is calculated using the adaptive template matching method. The test proves that under the strong noise interference, the proposed method improves the recall by 6.87% and the accuracy by 6.02% compared with the traditional method, indicating a better detection effect. Furthermore, the comparison test shows that the GDAE model has a significant noise reduction effect on the BCG signal, which improves the practicality of the BCG method for heart rate detection.
... To obtain better usability, it is necessary to realize remote monitoring of HRV using non-contact sensors [12]. The technology based on continuous wave (CW) Doppler radar is one of the most promising methods for non-contact measurement of HRV [13][14][15][16][17][18][19][20]. With the non-contact and noninvasive characteristics, the CW Doppler radar can detect micro-motions caused by human physiological movements due to respiration and heartbeat through the phase modulation effect without contacting patients' body [21]. ...
Real-time monitoring of heart rate (HR), i.e., extraction of heart rate variability (HRV), plays an important role in diagnosis and prevention of cardiovascular diseases. Compared with traditional contact monitoring devices, the use of continuous wave (CW) Doppler radar to monitor HRV does not require contact and is not sensitive to light and temperature, which makes it more and more popular. To monitor the HRV based on CW Doppler radar, the time window must be shortened to less than 5 s, which will lead to the spectrum leakage and degrade the measurement accuracy of HRV. To solve this problem, a custom CW Doppler radar has been developed in an integrated fashion on a single PCB, whose transmitting frequency and power of the radar are 24 GHz and 3 dBm, respectively. Furthermore, four frequency interpolation algorithms are introduced to compare their extraction accuracy. Experiments are performed on three subjects, and results show that the Quinn algorithm can obtain best HRV extraction results compared with other algorithms. Specially, the average HRV extraction error is 3.61% using the Quinn algorithm.
... Besides, the SSWT inherits the localization and sparsity properties of the TFR and is robust to a variety of disturbances in the signal. Recently, the SSWT has been generalized from several prospectives [13], [14], [15], [16], and attracted numerous applications in signal processing [17], algorithmic trading [18], sleep stage assessment [19], art investigation [20], inverse synthetic aperture radar imaging [21], seismic wave analysis [22], mechanical vibration [23], physiological dynamics detection [24], chaotic secure communication [25], voltage fluctuations assessment [26], system identification [27], power system forced oscillation analysis [28], fault diagnosis [29], lameness detection [30], and voice jitter estimation [31]. However, both the SSWT [12] and its variants [13], [14], [15], [16] are based on the linear TFR and require time-frequency separation for signal components, which may prevent it from being applied in some practical scenarios. ...
... Other potential applications of the SSFRWT may be found in seismic wave analysis [22], mechanical vibration [23], physiological dynamics detection [24], system identification [27], fault diagnosis [29], and voice jitter estimation [31]. ...
... which alongside (30) leads to 24) where I n R |ξ| n |ψ (ξ)|dξ. Let Γ 2 K k=1 (I 1 |φ k (t)| + ...
The synchrosqueezed wavelet transform (SSWT) has been proven to be a powerful time-frequency analysis tool. However, this transform is unable to deal with signals with fast varying instantaneous frequencies. The objective of this paper is to overcome this deficiency using the fractional wavelet transform (FRWT), which is a generalization of the conventional wavelet transform. We first propose a synchrosqueezed FRWT (SSFRWT), which shares many properties of its SSWT counterpart while offering attractive new features. Then, we present a theoretical analysis of the SSFRWT, including the derivation of its basic properties. Moreover, we show that the discrete form of the SSFRWT admits efficient numerical implementation akin to that of the SSWT. Finally, the theoretical derivations are validated via simulations.
... The received signal around 2.5 GHz was analyzed using the cyclostationary technique [7] so that cardiac and breathing signals are extracted. The same vital signal detection is analyzed at the lower frequency of 1 GHz, using custom-made circuits for RF reception, and an Agilent RF generator operating as a transmitter (TX); the data analysis was later performed under Matlab [8]. Detection of people and respective vital signals in through-wall, non-invasive scenarios can be performed using UWB (Ultra-Wide Band) waveforms in the microwave range [9] and even Infrared (IR) [10], requiring more complex hardware and data processing to separate the signals from the clutter. ...
Software-defined radios (SDRs) have been applied to several applications, taking advantage of their inherent versatility. It is reported a low-cost radar intended to detect human activity, at the frequency of 4.1 GHz, with an SDR on the receiving branch and a programmable RF synthesizer operating as a continuous-wave transmitter. Amplitude variations on the received signal indicate human activity, its operation is tested in indoor and outdoor scenarios. The interface to the SDR is performed using the open-source tool GNU Radio. It was possible to detect human movement at a maximum distance of 9 meters, in an open area. The system is versatile in terms of power and frequency, totally controlled by software in a transparent and straightforward way.
... The attacker can obtain the Doppler shift feature from the frequencydomain signals by applying the Fourier transform on the received signals. Prior works mainly leverage the Doppler shift for activity/gesture recognition and respiration/heart rate estimation using RF signals [33,48,76,82,93,118,119,142,155,189] and acoustic signals [51,68,112,173,184]. ...
Human-centered wireless sensing aims to understand the fine-grained environment and activities of a human using the diverse wireless signals around her. The wireless sensing community has demonstrated the superiority of such techniques in many applications such as smart homes, human-computer interactions, and smart cities. Like many other technologies, wireless sensing is also a double-edged sword. While the sensed information about a human can be used for many good purposes such as enhancing life quality, an adversary can also abuse it to steal private information about the human (e.g., location, living habits, and behavioral biometric characteristics). However, the literature lacks a systematic understanding of the privacy vulnerabilities of wireless sensing and the defenses against them. In this work, we aim to bridge this gap. First, we propose a framework to systematize wireless sensing-based inference attacks. Our framework consists of three key steps: deploying a sniffing device, sniffing wireless signals, and inferring private information. Our framework can be used to guide the design of new inference attacks since different attacks can instantiate these three steps differently. Second, we propose a defense-in-depth framework to systematize defenses against such inference attacks. The prevention component of our framework aims to prevent inference attacks via obfuscating the wireless signals around a human, while the detection component aims to detect and respond to attacks. Third, based on our attack and defense frameworks, we identify gaps in the existing literature and discuss future research directions.
... Moreover, for CW radars using an in-phase and a quadrature channel, the phase and amplitude imbalance between these channels results in a reduction of the signal quality. As described in recent studies, flicker noise and the imbalance between the in-phase and quadrature channel can be mitigated by using a digital intermediate frequency (IF) architecture for the radar system [20,21]. That architecture is also used in this study in order to increase the quality of the measured micro-Doppler signatures. ...
... Furthermore, the amplitude and the phase of the in-phase and quadrature baseband signals can have a significant imbalance relative to each other. These problems can be mitigated significantly by performing the downconversion of the IF signals in the digital domain [21,34]. In order to achieve a sufficiently high sensitivity for the insect measurements in this study, the IF signal is down-converted in the digital domain, as will be explained hereafter. ...
Remote sensing techniques in the microwave frequency range have been successfully used in the context of bird, bat and insect measurements. This article breaks new ground in the analysis of freely flying insects by using a continuous-wave (CW) radar system in W-band, i.e., higher mm-wave frequencies, by measuring and analyzing the micro-Doppler signature of their wing beat motion. In addition to numerical and experimental methods, the investigation also includes the development of a new signal processing method using a cepstrogram approach in order to automatically determine the wing beat frequency. In this study, mosquitoes (culex pipiens) and bees (apis mellifera) are considered as model insects throughout the measurement campaign. It was found that 50 independent micro-Doppler measurements of mosquitoes and bees can be clearly distinguished from each other. Moreover, the proposed radar signal model accurately matches the experimental measurements for both species.
... However, these detection methods are essentially contact measurement methods that require electrodes or sensors to contact the person being measured. These methods are inconvenient for epidemiological testing or monitoring the vital signs of isolated persons [4]- [6]. Therefore, during the COVID-19 epidemic, research into radar-based non-contact vital sign monitoring systems is critical. ...
Research on radar-based non-contact vital sign monitoring systems is critical during the COVID-19 epidemic. The accuracy of remote vital sign measurements has increased with the advancement of radar technology and various algorithms. Most studies require subjects to remain stationary, such as standing, sitting in a chair, or lying on a bed, and various measurement algorithms have been proposed. However, maintaining a stationary state as a prerequisite for measurement limits the development and application prospects of radar-based vital sign monitoring systems. Therefore, this paper presents a novel method for monitoring the vital signs of moving targets using a millimeter-wave frequency-modulated continuous-wave (FMCW) radar. The experimental results showed that regardless of whether the subjects walked at 1 m/s or with the left side of their body facing the radar, the accuracy of the heart rate measurement remained high. In the fixed-route experiments, the root mean squared error (RMSE) for heart rate estimation was 4.09 bpm, with an accuracy of 95.88%.
... One of the non-contact technologies that does not need a good lighting for object detection is radar. There are three types of radar: Continuous Wave (CW) radar [11], [12], Ultra-Wide Band (UWB) radar [13], [14] and Frequency-Modulated Continuous Wave (FMCW) radar [15]- [17], which can be used to detect human vital signs [18], [19]. FMCW is able to perform small multi-object detection with a reasonable low transmit power [20]. ...
A respiratory disorder that attacks COVID-19 patients requires intensive supervision of medical practitioners during the isolation period. A non-contact monitoring device will be a suitable solution for reducing the spread risk of the virus while monitoring the COVID-19 patient. This study uses Frequency-Modulated Continuous Wave (FMCW) radar and Machine Learning (ML) to obtain respiratory information and analyze respiratory signals, respectively. Multiple subjects in a room can be detected simultaneously by calculating the Angle of Arrival (AoA) of the received signal and utilizing the Multiple Input Multiple Output (MIMO) of FMCW radar. Fast Fourier Transform (FFT) and some signal processing are implemented to obtain a breathing waveform. ML helps the system to analyze the respiratory signals automatically. This paper also compares the performance of several ML algorithms such as Multinomial Logistic Regression (MLR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), CatBoosting (CB) Classifier, Multilayer Perceptron (MLP), and three proposed stacked ensemble models, namely Stacked Ensemble Classifier (SEC), Boosting Tree-based Stacked Classifier (BTSC), and Neural Stacked Ensemble Model (NSEM) to obtain the best ML model. The results show that the NSEM algorithm achieves the best performance with 97.1% accuracy. In the real-time implementation, the system could simultaneously detect several objects with different breathing characteristics and classify the respiratory signals into five different classes.
... They have used the maximum likelihood estimator to predict the respiration rate through various channels. Authors in Ref. [12] introduced another non-invasive method to monitor human vitals using infrared doppler radar. They have used the instantaneous vital signs using intermediate frequency to predict the health in different postures. ...
Health monitoring has been a growing area of interest in recent times. It provides many beforehand benefits including early detection of potentially harmful illnesses. Till now, it has been achieved primarily by using devices such as wearable bands to monitor breathing rate and heart rate. Wearing and carrying devices make the person uncomfortable. In this paper, we introduce a deep learning based device-free way to monitor breathing rate and heart rate in different human positions to provide insight into whether the person is healthy. A convolutional neural network (CNN) model along with Gaussian naive bayes (GaussianNB) and support vector machine (SVM) is introduced and the device-free monitoring is implemented using WiFi signals. An individual in proximity causes a change in the channel state information (CSI) and the distortion is measured to provide corresponding vitals. The vitals give an insight into the health of the individual. The experimental results are quite encouraging with the evidence of superiority of SVM with CNN.
