Non-contact physiological signal monitoring system based on Doppler radar

Chen, Long; Yang, Xining; Wu, Jianfeng; Fan, Lingyan

doi:10.1587/elex.14.20161178

Cited by 6 publications

(3 citation statements)

References 8 publications

(6 reference statements)

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“…After classifying all signals according to the expert evaluation mechanism, it is necessary to conduct filtering processing and discrete Fourier transform (DFT) for each group of data. A zero-phase indefinite impulse response (IIR) filter is used for the filtering algorithm, which can completely eliminate the signal phase distortion and improve the real-time performance of detection at the cost of increasing the appropriate computation [42]. After the filtering is completed, DFT processing of the signal will continue to obtain the spectrum diagram.…”

Section: Sample Librarymentioning

confidence: 99%

Driver Fatigue Detection via Differential Evolution Extreme Learning Machine Technique

et al. 2020

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Fatigue driving (FD) is one of the main causes of traffic accidents. Traditionally, machine learning technologies such as back propagation neural network (BPNN) and support vector machine (SVM) are popularly used for fatigue driving detection. However, the BPNN exhibits slow convergence speed and many adjustable parameters, while it is difficult to train large-scale samples in the SVM. In this paper, we develop extreme learning machine (ELM)-based FD detection method to avoid the above disadvantages. Further, since the randomness of the weight and biases between the input layer and the hidden layer of the ELM will influence its generalization performance, we further apply a differential evolution ELM (DE-ELM) method to the analysis of the driver’s respiration and heartbeat signals, which can effectively judge the driver fatigue state. Moreover, not only will the Doppler radar and smart bracelet be used to obtain the driver respiration and heartbeat signals, but also the sample database required for the experiment will be established through extensive signal collections. Experimental results show that the DE-ELM has a better performance on driver’s fatigue level detection than the traditional ELM and SVM.

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Section: Sample Librarymentioning

confidence: 99%

Driver Fatigue Detection via Differential Evolution Extreme Learning Machine Technique

et al. 2020

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“…Vital signs detection has always been a hot spot in real-time monitoring, such as medical supervision, assisted driving, and so on [1,2,3,4]. The most important vital signs include respiration and heartbeat signals for organisms [5,6]. The technology of vital signs detection includes contact vital signs detection and non-contact vital signs detection.…”

Section: Introductionmentioning

confidence: 99%

A Doppler radar vital sign detection system using concurrent dual-band hybrid down conversion architecture

Liu

Yang

2020

IEICE Electron. Express

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In this paper, a concurrent dual-band hybrid down conversion architecture based on continuous-wave (CW) Doppler radar is demonstrated. The proposed system operates at 2.05-/1.64-GHz simultaneously. The dual-band can solve the null detection point problem generated at quarter-wavelength distance between the target and antenna. The detection results from different channels can be mutually verified to improve the system accuracy. The vital sign can be detected by the radar through the wooden board at higher transmit power. For satisfying the requirements of dual-band RF transmitters, and a two-stage dual-band power amplifier is designed. Experimental results have demonstrated the feasibility of the system.

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“…The trend of healthcare development is to capture one's body physiological information and provide long-term monitoring, which can rely on a non-contact and real-time vital-sign radar to detect the movement of chest with reciprocal characteristic [1,2,3]. The Doppler radars [4,5] with continuous wave (CW) or frequency modulated continuous-wave (FMCW) are widely used in long distance detection, vital-sign sensing, and through-wall detection [6,7,8] due to its excellent tracking ability and high sensitivity. However, most of the CW-based radar exhibit high power consumption and complex circuit in comparison to the pulse-based radar.…”

Section: Introductionmentioning

confidence: 99%

An impulse radio duty-cycled radar with ultra-wideband VCO using frequency hopping technique

Chang

et al. 2019

IEICE Electron. Express

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This paper presents a pulse-based radar with ultra-wideband voltage controlled oscillator for vital-sign application. A series of digitalassisted duty-cycled probing pulses with frequency hopping technique is proposed to mitigate unwanted narrowband interferences, thereby increasing overall system signal-to-noise ratio and then enabling high resolution detection. The RF generator is utilized by an on-chip VCO with the characteristic of wide-band response by digitally switching loaded capacitor array for frequency hopping. The conventional ultra-wideband channel is further divided into several sub-channels for vital-sign sensing. An impulse radio Doppler radar is implemented and integrated with the digital-assisted oscillator to detect physiological information.

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Non-contact physiological signal monitoring system based on Doppler radar

Cited by 6 publications

References 8 publications

Driver Fatigue Detection via Differential Evolution Extreme Learning Machine Technique

Driver Fatigue Detection via Differential Evolution Extreme Learning Machine Technique

A Doppler radar vital sign detection system using concurrent dual-band hybrid down conversion architecture

An impulse radio duty-cycled radar with ultra-wideband VCO using frequency hopping technique

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