Short-Range Vital Signs Sensing Based on EEMD and CWT Using IR-UWB Radar

Hu, Xiaoming; Jin, Tian

doi:10.3390/s16122025

Cited by 91 publications

(44 citation statements)

References 38 publications

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“…Clutter effect can be removed by using the singular value decomposition algorithm (SVD) [158]. In [159], the ensemble empirical decomposition (EEMD) and continuous wavelet transform (CWT) were used in a UWB radar to improve the signal-to-noise (SNR) ratio and separate the respiration and heartbeat signals. A low complexity approximation employing the maximum likelihood (ML) estimation of the period of the signal mixed with additive white Gaussian noise (AWGN) was proposed and tested in [160].…”

Section: Algorithms and Signal Processingmentioning

confidence: 99%

Human Vital Signs Detection Methods and Potential Using Radars: A Review

Kebe

Gadhafi

Mohammad

et al. 2020

Sensors

126

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Continuous monitoring of vital signs, such as respiration and heartbeat, plays a crucial role in early detection and even prediction of conditions that may affect the wellbeing of the patient. Sensing vital signs can be categorized into: contact-based techniques and contactless based techniques. Conventional clinical methods of detecting these vital signs require the use of contact sensors, which may not be practical for long duration monitoring and less convenient for repeatable measurements. On the other hand, wireless vital signs detection using radars has the distinct advantage of not requiring the attachment of electrodes to the subject’s body and hence not constraining the movement of the person and eliminating the possibility of skin irritation. In addition, it removes the need for wires and limitation of access to patients, especially for children and the elderly. This paper presents a thorough review on the traditional methods of monitoring cardio-pulmonary rates as well as the potential of replacing these systems with radar-based techniques. The paper also highlights the challenges that radar-based vital signs monitoring methods need to overcome to gain acceptance in the healthcare field. A proof-of-concept of a radar-based vital sign detection system is presented together with promising measurement results.

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Section: Algorithms and Signal Processingmentioning

confidence: 99%

Human Vital Signs Detection Methods and Potential Using Radars: A Review

Kebe

Gadhafi

Mohammad

et al. 2020

Sensors

126

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“…Recently, Hu and Jin investigated the UWB-IR radar in detecting HR and RR, by using ensemble empirical mode decomposition (EEMD) and continuous-wavelet transform (CWT) [ 70 ]. After a clutter removal stage, EEMD was used to decompose the signal into its intrinsic mode functions (IMFs), thus obtaining a noise reduction effect.…”

Section: Related Workmentioning

confidence: 99%

A Radar-Based Smart Sensor for Unobtrusive Elderly Monitoring in Ambient Assisted Living Applications

2017

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Continuous in-home monitoring of older adults living alone aims to improve their quality of life and independence, by detecting early signs of illness and functional decline or emergency conditions. To meet requirements for technology acceptance by seniors (unobtrusiveness, non-intrusiveness, and privacy-preservation), this study presents and discusses a new smart sensor system for the detection of abnormalities during daily activities, based on ultra-wideband radar providing rich, not privacy-sensitive, information useful for sensing both cardiorespiratory and body movements, regardless of ambient lighting conditions and physical obstructions (through-wall sensing). The radar sensing is a very promising technology, enabling the measurement of vital signs and body movements at a distance, and thus meeting both requirements of unobtrusiveness and accuracy. In particular, impulse-radio ultra-wideband radar has attracted considerable attention in recent years thanks to many properties that make it useful for assisted living purposes. The proposed sensing system, evaluated in meaningful assisted living scenarios by involving 30 participants, exhibited the ability to detect vital signs, to discriminate among dangerous situations and activities of daily living, and to accommodate individual physical characteristics and habits. The reported results show that vital signs can be detected also while carrying out daily activities or after a fall event (post-fall phase), with accuracy varying according to the level of movements, reaching up to 95% and 91% in detecting respiration and heart rates, respectively. Similarly, good results were achieved in fall detection by using the micro-motion signature and unsupervised learning, with sensitivity and specificity greater than 97% and 90%, respectively.

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“…To avoid the codomain restriction in arctangent demodulation (AD), an extended differentiate and cross-multiply (DACM) method was proposed in [21]. Existing vital sign detection techniques are not suitable for trapped victims because they consider only some of the issues, such as removing clutter, determining respiration characteristics, and heart rate estimation [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41]. An algorithm for vital sign detection based on radar data was developed in [16].…”

Section: Introductionmentioning

confidence: 99%

“…Further, it was shown that this approach can be used for vital sign detection in other types of noise. Ensemble empirical mode decomposition was used in [38] for heart rate detection.…”

Section: Introductionmentioning

confidence: 99%

Clutter Elimination and Harmonic Suppression of Non-Stationary Life Signs for Long-Range and Through-Wall Human Subject Detection Using Spectral Kurtosis Analysis (SKA)-Based Windowed Fourier Transform (WFT) Method

Yang

Qin

Liang³

et al. 2019

Applied Sciences

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Life sign detection is important in many applications, such as locating disaster victims. This can be difficult in low signal to noise ratio (SNR) and through-wall conditions. This paper considers life sign detection using an impulse ultra-wideband (UWB) bio-radar with an improved sensing algorithm for clutter elimination, harmonic suppression and random-noise de-noising. To improve detection performance, two filters are used to improve SNR of these life signs. The automatic gain method is performed in fast time to improve the respiration signals. The spectral kurtosis analysis (SKA)-based windowed Fourier transform (WFT) method and an accumulator in the frequency domain are used to provide two distance estimates between the radar and human subject. Further, the accumulator can also provide the frequency estimate of the respiration signals. These estimates are used to determine if a human is present in the detection environment. Results are presented which show that the range and respiration frequency can be estimated accurately in low signal to noise and clutter ratio (SNCR) environments. In addition, the performance is better than with other techniques given in the literature.

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Short-Range Vital Signs Sensing Based on EEMD and CWT Using IR-UWB Radar

Cited by 91 publications

References 38 publications

Human Vital Signs Detection Methods and Potential Using Radars: A Review

Human Vital Signs Detection Methods and Potential Using Radars: A Review

A Radar-Based Smart Sensor for Unobtrusive Elderly Monitoring in Ambient Assisted Living Applications

Clutter Elimination and Harmonic Suppression of Non-Stationary Life Signs for Long-Range and Through-Wall Human Subject Detection Using Spectral Kurtosis Analysis (SKA)-Based Windowed Fourier Transform (WFT) Method

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