Improved denoising method for through-wall vital sign detection using UWB impulse radar

Ghaffar

et al. 2020

Sensors

Non-invasive remote health monitoring plays a vital role in epidemiological situations such as SARS outbreak (2003), MERS (2015) and the recently ongoing outbreak of COVID-19 because it is extremely risky to get close to the patient due to the spread of contagious infections. Non-invasive monitoring is also extremely necessary in situations where it is difficult to use complicated wired connections, such as ECG monitoring for infants, burn victims or during rescue missions when people are buried during building collapses/earthquakes. Due to the unique characteristics such as higher penetration capabilities, extremely precise ranging, low power requirement, low cost, simple hardware and robustness to multipath interferences, Impulse Radio Ultra Wideband (IR-UWB) technology is appropriate for non-invasive medical applications. IR-UWB sensors detect the macro as well as micro movement inside the human body due to its fine range resolution. The two vital signs, i.e., respiration rate and heart rate, can be measured by IR-UWB radar by measuring the change in the magnitude of signal due to displacement caused by human lungs, heart during respiration and heart beating. This paper reviews recent advances in IR-UWB radar sensor design for healthcare, such as vital signs measurements of a stationary human, vitals of a non-stationary human, vital signs of people in a vehicle, through the wall vitals measurement, neonate's health monitoring, fall detection, sleep monitoring and medical imaging. Although we have covered many topics related to health monitoring using IR-UWB, this paper is mainly focused on signal processing techniques for measurement of vital signs, i.e., respiration and heart rate monitoring.

Section: Previous Work Related To Vital Signs Extraction From Radar Datamentioning

confidence: 99%

An Overview of Signal Processing Techniques for Remote Health Monitoring Using Impulse Radio UWB Transceiver

Ghaffar

et al. 2020

Sensors

“…is the responses from the living person with amplitude a v and time delay τ v (t) , which can be expressed as [33]…”

Section: Life Modelmentioning

confidence: 99%

“…The frequency estimates are 0.32 Hz (400 cm), 0.26 Hz (700 cm), 0.29 Hz (1000 cm), and 0.26 Hz (1200 cm). The methods including AM [36], CFAR [37], and FOC [33] are used as references to compare with the presented algorithm. All results show the excellent capability of improving SNR, clutter and harmonic suppression.…”

Section: Performance Indoorsmentioning

confidence: 99%

See 1 more Smart Citation

Detection of Breathing Rates in Through-wall UWB Radar Utilizing JTFA

2019

KSII TIIS

Through-wall ultra-wide band (UWB) radar has been considered as one of the preferred and non-contact technologies for the targets detection owing to the better time resolution and stronger penetration. The high time resolution is a result of a larger of bandwidth of the employed UWB pulses from the radar system, which is a useful tool to separate multiple targets in complex environment. The article emphasised on human subject localization and detection. Human subject usually can be detected via extracting the weak respiratory signals of human subjects remotely. Meanwhile, the range between the detection object and radar is also acquired from the 2D range-frequency matrix. However, it is a challenging task to extract human respiratory signals owing to the low signal to clutter ratio. To improve the feasibility of human respiratory signals detection, a new method is developed via analysing the standard deviation based kurtosis of the collected pulses, which are modulated by human respiratory movements in slow time. The range between radar and the detection target is estimated using joint time-frequency analysis (JTFA) of the analysed characteristics, which provides a novel preliminary signature for life detection. The breathing rates are obtained using the proposed accumulation method in time and frequency domain, respectively. The proposed method is validated and proved numerically and experimentally.

“…The fast Fourier transform (FFT)-based Hilbert transform is used in analyzing the time-frequency characteristic of the respiratory movements [24,25]. Considering the additive white Gaussian noise (AWGN), a maximum likelihood period estimator with lower complexity is proposed to acquire the period of human respiratory motions [29].…”

Section: Introductionmentioning

confidence: 99%

`Through-wall human being detection using UWB impulse radar

Liang

Zhang

et al. 2018

J Wireless Com Network

Self Cite

Ultra-wideband (UWB) impulse radar plays an important role in contactless vital sign (VS) detection. The VS can be extracted remotely by acquiring the oscillations in the human chest. Unfortunately, it is usually challenging to identify VS due to the low signal-to-noise ratio (SNR) only based on the traditional fast Fourier transform (FFT) especially in complicated conditions. To extract VS accurately, this paper presents a new scheme by analyzing the skewness characteristic of the received UWB impulses, which are modulated by life activities. The distance from the human subject to the radar antenna can be calculated by performing the discrete short-time Fourier transform (DSFT) on skewness. The frequency of human respiratory movement can be estimated based on the developed ensemble empirical mode decomposition (EEMD)-based accumulation technique by canceling out the harmonics effectively. The performance of the developed detection method is tested with several experiments carried out in different environments.