Heartbeat detection using a Doppler radar sensor based on the scaling function of wavelet transform

Choi, Cheol-Ho; Park, Jae‐Hyun; Lee, Ha‐Neul; Yang, Jong‐Ryul

doi:10.1002/mop.31823

Cited by 17 publications

(13 citation statements)

References 4 publications

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“…However, because of their broadband nature, they are power consuming and require complex architectures and high speed analog-to-digital converters (ADC). Thanks to their lower power consumption, continuous wave (CW) radars [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ] have been proposed as a valid alternative to impulse radars. This family of sensors can accurately measure the chest movements connected with the heart and the respiratory activity, but they fail to provide any information about the patient’s distance.…”

Section: Introductionmentioning

confidence: 99%

An FMCW Radar for Localization and Vital Signs Measurement for Different Chest Orientations

Sacco

Piuzzi

Pittella

et al. 2020

Sensors

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This work tests the ability of a frequency-modulated continuous wave (FMCW) radar to measure the respiratory rate and the heartbeat of a subject in challenging indoor scenarios. To simulate a realistic configuration for ambient assisted living (AAL) applications, in which the thorax orientation towards the antenna is typically unknown, four different scenarios were considered. Measurements were performed on five volunteers positioned with the chest, left, back, and right side facing the antenna, respectively. The 5.8 GHz radar and the antennas used for the measurements were suitably designed for the considered application. To obtain a low cost and compact system, series-fed arrays were preferred over other antenna topologies. The geometry of the patches was opportunely shaped to reduce the side lobe level (SLL) and increase the bandwidth, thus ensuring good system performances. In all scenarios, the vital signs extracted from the radar signal were compared with the ones collected by a photoplethysmograph and a respiratory belt, used as references. A statistical analysis of the measured data on the different subjects and orientations was performed, showing that the radar was able to measure with high accuracy both the respiratory rate and the heartbeat in all considered configurations.

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Section: Introductionmentioning

confidence: 99%

An FMCW Radar for Localization and Vital Signs Measurement for Different Chest Orientations

Sacco

Piuzzi

Pittella

et al. 2020

Sensors

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“…It is not easy to implement a radar front-end with a high SNR by using a low-noise and high-gain design methodology, and a complex radar architecture including calibration circuits and a calibration process could be needed to improve the SNR [ 10 ]. The accuracy of vital-signs detection can be also improved by using signal-processing techniques such as autocorrelation, the wavelet transform, and cross-correlation [ 11 , 12 , 13 , 14 ]. The autocorrelation method improves the detection accuracy for the periodic signal by converging signals to the most representative period frequency but has limitations for accurately monitoring heartbeat signals that change over time and evaluating their variability [ 11 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

“…The accuracy of vital-signs detection can be also improved by using signal-processing techniques such as autocorrelation, the wavelet transform, and cross-correlation [ 11 , 12 , 13 , 14 ]. The autocorrelation method improves the detection accuracy for the periodic signal by converging signals to the most representative period frequency but has limitations for accurately monitoring heartbeat signals that change over time and evaluating their variability [ 11 , 14 ]. The wavelet transform is useful for increasing the accuracy by effectively extracting peaks of the heartbeat signal, but it is difficult to develop a generalized wavelet function that can improve the accuracy while being independent of the measurement environment, such as the characteristics of the subject and the clutter in the surroundings [ 12 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

“…The autocorrelation method improves the detection accuracy for the periodic signal by converging signals to the most representative period frequency but has limitations for accurately monitoring heartbeat signals that change over time and evaluating their variability [ 11 , 14 ]. The wavelet transform is useful for increasing the accuracy by effectively extracting peaks of the heartbeat signal, but it is difficult to develop a generalized wavelet function that can improve the accuracy while being independent of the measurement environment, such as the characteristics of the subject and the clutter in the surroundings [ 12 , 14 ]. The cross-correlation method was proposed for increasing the detection accuracy by exploiting the similarity between vital signs independently measured at multiple frequencies by using a dual-band antenna and commercialized measurement equipment [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

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Vital-Signs Detector Based on Frequency-Shift Keying Radar

Sim¹,

Park²,

Yang³

2020

Sensors

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A frequency-shift keying (FSK) radar in the 2.45-GHz band is proposed for highly accurate vital-signs detection. The measurement accuracy of the proposed detector for the heartbeat is increased by using the cross-correlation between the phase differences of signals at two frequencies used by the FSK radar, which alternately transmits and receives the signals with different frequencies. Two frequencies—2.45 and 2.5 GHz—are effectively discriminated by using the envelope detection with the frequency control signal of the signal generator in the output waveform of the FSK radar. The phase difference between transmitted and received signals at each frequency is determined after calibrating the I / Q imbalance and direct-current offset using a data-based imbalance compensation algorithm, the Gram–Schmidt procedure, and the Pratt method. The absolute-distance measurement results for a human being show that the vital signs obtained at each frequency using the proposed FSK radar have a cross-correlation. The heartbeat detection results for the proposed FSK radar at a distance of < 2.4 m indicate a reduction in the error rate and an increase in the signal-to-noise ratio compared with those obtained using a single operating frequency.

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“…Therefore, the research and analysis of higher accuracy and better computer vision detection algorithm appears. It is extremely important and meaningful [3], [15]- [17].…”

Section: Introductionmentioning

confidence: 99%

Subpixel Computer Vision Detection Based on Wavelet Transform

2020

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Computer vision detection technology is one of the most popular topics in the field of computer vision. With the continuous improvement of the relevant algorithm and the performance-price ratio of the corresponding imaging equipment, the corresponding computer vision detection algorithm is also constantly upgraded and deepened. Computer vision detection technology is mainly used in transportation, public security, national defense and military fields, but the pixel accuracy of traditional computer vision detection technology has been unable to meet today's accuracy requirements. In this paper, firstly, the quantum denoising algorithm based on dual-tree and dual-density wavelet transform is used to realize the combination of quantum image coding expression and wavelet transform, and finally achieve a more detailed and accurate description of the image and realize the noise reduction of the image. In order to further realize sub-pixel image processing, cubic spline interpolation edge detection algorithm will be added to wavelet transform, which mainly calculates the zeros of the second-order function corresponding to the cubic spline function on both sides of the image edge points, so as to realize sub-pixel location of the image edge points. Finally, by comparing with the traditional pixel accuracy detection algorithms, it can be found that the proposed subpixel computer vision detection algorithm based on wavelet transform has good robustness, and its computing time is relatively faster, so it will have better adaptability in practical applications. INDEX TERMS Wavelet transform, vision detection algorithm, quantum denoising algorithm, subpixel vision detection algorithm.

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Heartbeat detection using a Doppler radar sensor based on the scaling function of wavelet transform

Cited by 17 publications

References 4 publications

An FMCW Radar for Localization and Vital Signs Measurement for Different Chest Orientations

An FMCW Radar for Localization and Vital Signs Measurement for Different Chest Orientations

Vital-Signs Detector Based on Frequency-Shift Keying Radar

Subpixel Computer Vision Detection Based on Wavelet Transform

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