CN114027809A - Milk cow respiratory heart rate monitoring method based on millimeter wave radar - Google Patents
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Abstract
The method for monitoring the respiratory heart rate of the dairy cow based on the millimeter wave radar comprises the following steps: capturing sign micro-motion signals of the dairy cows through a millimeter wave radar, and analyzing original echoes of the radar; acquiring the approximate position relation between the radar and the cow through a range gate curve, determining the range of the target, and extracting the phase position of the target at the range gate; filtering and separating the phase difference signals of the breath and the heartbeat by using a band-pass filter according to the difference of the breath and the heartbeat frequency; and sending the respiration and heart rate signals obtained by calculation to an upper computer for display through an embedded system. The invention realizes the non-contact detection and remote monitoring of the respiratory rate of the dairy cow, improves the accuracy and real-time performance of the respiratory rate detection of the dairy cow, solves the problems of unmatched dairy cow, single applicable scene and the like pain point in practical application, effectively overcomes the defects that the equipment cannot carry out real-time monitoring and intelligent early warning, improves the efficiency, reduces the labor consumption and improves the information management level.
Description
Technical Field
The invention belongs to the technical field of radar signal processing and Internet of things, and particularly relates to a method for monitoring the respiratory heart rate of a milk cow based on a millimeter wave radar.
Background
The respiration and the heart rate are used as main physiological parameters of the dairy cows, are important bases for reflecting the comfort level of the breeding environment, and are the most effective indexes for estimating the heat stress state of the dairy cows. Traditional contact vital sign monitoring equipment (such as a respiratory monitoring vest/chest strap based on chest and abdomen contraction, monitoring equipment based on respiratory airflow and the like) needs to be worn by a monitored animal all the time to acquire physical sign information in real time, and pain points such as animal incompatibility, destructiveness and single applicable scene exist.
According to the existing dairy cow heart rate monitoring, such as Eigenberg and the like, automatic respiratory rate monitoring equipment suitable for cows is developed, under experimental conditions, errors between respiratory rate monitoring chest strap test results and manual counting are within 10 times/min (RPM), but reading of signals is seriously interfered by feces and urine pollution and biting and dragging of animals to the chest strap, even equipment damage is caused, in addition, data measured by the equipment can only be stored in a built-in data acquisition instrument, real-time online monitoring and intelligent early warning cannot be carried out, and the storage time is limited.
With the development of agricultural internet of things and non-contact biosensor technology, remote monitoring of information such as livestock and poultry signs and abnormal behaviors becomes a current research hotspot. The physiological and health conditions of animals are estimated by analyzing the monitoring data, and the feeding environment, breeding, epidemic prevention and the like of the livestock and the poultry are accurately regulated and controlled. The existing non-contact measuring method mainly comprises a monitoring method based on laser ranging, a monitoring method based on optical images, a monitoring method based on centimeter-level ultra-wideband radar equipment and the like. Pastellm, Kaihilahti J, Aisla A M, et al, in the document "A System For contact Measurement of respiratory Rate of Dairy Cows. [ C ]" (3rd ECPLF Conference,2007), the respiratory frequency is extracted by using the change of the lateral abdominal outer distance of the cow during Respiration, and the accuracy of the respiratory frequency is improved by methods such as measuring point positioning and increasing the number of sampling points of a fast Fourier transform algorithm (FFT). Stewart M, Wilson M T, Schaefer A L, et al, in The literature "The use of infracted thermography and acelerometers for remote monitoring of today's cow health and welfare" (Journal of Dairy Science,2017,100(5): 3893. 3901.) used for The estimation of respiratory frequency. The respiratory frequency is calculated by converting the temperature change under the nose of the cow during respiration into the change of the gray value of thermal imaging. Authors of related art content, such as: the modern information technology, 2018,2(03):195 plus 196. the heart beat of the cow is collected by an SON1205 pulse sensor, and the data is judged and processed by an MSP430f149 single chip microcomputer, so that the heart beat frequency is obtained.
The monitoring method of laser ranging has the advantages of accuracy and the disadvantages of needing to pay attention to human safety, having higher manufacturing difficulty and higher cost, and an optical system needs to be kept clean, otherwise, the measurement is influenced; the monitoring method of the optical image has the defects of low image contrast, poor capability of analyzing details, high cost, high price and the like; centimeter-level ultra-wideband radar equipment has poor anti-interference capability. The methods generally have the problems of low monitoring accuracy, incapability of identifying and tracking in a remote real-time manner, incapability of identifying the milk cow due to shielding of a monitoring angle and the like, and are difficult to apply in a large scale in actual production.
Disclosure of Invention
The invention aims to provide a heart rate monitoring method for capturing sign micro-motion signals of a cow by a millimeter wave radar, aiming at the defects of the existing cow heart rate monitoring, so that the non-contact detection and remote monitoring of the respiratory heart rate of the cow are realized, and the accuracy and the real-time performance of the respiratory heart rate detection of the cow are improved.
The technical scheme of the invention is as follows: the invention discloses a method for monitoring the respiratory heart rate of a milk cow based on a millimeter wave radar, which comprises the following steps:
s1: capturing sign micro-motion signals of the dairy cows through a millimeter wave radar, analyzing original echoes of the radar, and performing fast Fourier transform on echo data sampled by an ADC (analog to digital converter) to obtain a range gate curve;
s2: obtaining the approximate position relation between the radar and the dairy cow through the range gate curve, determining the range of the target, and tracking the range gates corresponding to the target at different moments by searching the maximum value in the range;
s3: extracting the phase at the target range gate, and circulating the steps S1 and S2;
s4: by phase unwrapping to ensure that the phase values are between [ -pi, pi ], subtracting/adding 2 pi from/to the phase whenever the phase difference between successive values is greater/less than ± pi;
s5: carrying out frame-by-frame difference on the phase signals to obtain phase difference signals;
s6: filtering and separating the phase difference signals of the breath and the heartbeat by using a band-pass filter according to the difference of the breath and the heartbeat frequency;
s7: processing the respiratory phase difference signal by adopting FFT of a frequency domain, peak spacing of a time domain and MUSIC algorithm respectively, and taking the average value of the results of the two algorithms to obtain respiratory frequency;
s8: filtering the motion interference of the phase difference signal of the heartbeat;
s9: processing the phase difference signals of the heartbeats by respectively adopting FFT of a frequency domain, the peak distance of a time domain and an MUSIC algorithm, and taking the average value of the results of the three algorithms to obtain the heartbeat frequency;
s10: recording the breathing and heartbeat frequency within a period of time, judging the real frequency at the moment according to the confidence index, and outputting the relation of the breathing and heartbeat frequency along with the change of time;
s11: the harmonic filtering of the breathing frequency is performed on the heart beat frequency (the heart beat frequency is likely to be the harmonic of the breathing frequency, for example, the breathing rate is 30, the heart beat rate is 60, 90, 120, etc., and the heart beat rate is most likely to be the harmonic interference of the breathing rate rather than the actual heart beat rate).
S12: and sending the respiration and heart rate signals obtained by calculation to an upper computer through an embedded system.
The specific duration of the loop in the step S3 in the steps S1 and S2 is: the cycle frame period is 50ms, namely the phase of the target is extracted once in each frame period, if the radial distance between the target and the distance changes, the position of the distance gate at the moment needs to be obtained according to a distance gate tracking algorithm, then the phase is extracted, N frames are transmitted in a cycle mode, the change curve of the phase of the target along with the frame number can be obtained, and the relation between the phase of the target and the time can also be regarded as the relation between the target phase and the time.
The interference filtering method in the center jump signal in the step S8 includes: the sample is segmented, an energy threshold value is set to judge whether the variation range of the heart rate is met or not (because the measurement of the heart rate is based on the phase variation caused by the distance difference generated by the micro motion of the systole and diastole, according to the micro Doppler principle, when the body of the cow greatly swings, the accuracy of the cow is influenced), and the next estimation is carried out by discarding the high-energy data segment and integrating the data segment in the stable state.
Further, the principle of measuring the heartbeat and respiration signals by the millimeter wave radar in the step S1 is as follows: assuming that the distance of the heart-lung of the cow from the radar is R, the FMCW emitted by the radar to the human body can be expressed as:
wherein f iscIs the carrier frequency; b is the signal bandwidth; t is the time interval of transmitting the frequency modulation signal.
The echo signal of the cow chest is a delay signal of the emission signal, and can be expressed as:
wherein τ is the time delay.
For a single target, the echo signal of the cow chest can be regarded as a sinusoidal signal with both frequency and phase, and the mixed and filtered intermediate frequency signal can be expressed as:
wherein, (.)*Represents a conjugation; c is the speed of light; λ is the signal wavelength; f. ofbAnd phibRespectively, the frequency and the phase of the intermediate frequency signal, respectively
And
if the displacement of the chest cavity caused by respiration and heartbeat is d (t) and the actual distance from the radar is R + d (t), the phase expression of the intermediate frequency signal is as follows:
since d (t) is a millimeter-scale vibration and the range resolution of the millimeter-wave radar is in centimeters, analysis f is performedbThe change in d (t) can be ignored. In order to obtain millimeter-scale vibration information d (t) in an echo signal, the variation of the phase of an FMCW signal in a target range gate along with time needs to be measured, and the phase difference signal obtained by subtracting the phases at different times has the following expression:
as can be seen from the above formula, the phase difference of the echo signals of the cow chest is obtained based on the displacement of the chest over time when the human body breathes and beats, so the frequency f of the phase difference signals*Namely the breathing and heartbeat frequency of the cow.
Further, the MUSIC algorithm in steps S7 to S9 includes the following specific steps: and respectively carrying out frequency estimation on the obtained time domain waveforms of the respiration and the heartbeat through a MUSIC algorithm. And processing by a front-end algorithm to obtain a time domain signal S (t), intercepting a signal x (t) with the length of N x M, wherein N corresponds to the number of arrays, so that N is larger than the estimated frequency component, M is the number of data columns, and constructing a data matrix of N x M, wherein the set length selected in the experiment is 8 x 4. Solving a covariance matrix R of the signal matrixx。
Solving a covariance matrix RxThe eigenvalues and the eigenvectors are sorted to obtain an eigenvector space U. Finding a noise subvector UN。
A search frequency vector is constructed, defined as the vector that describes the scan range 0,2π]is divided into NwA frequency point of NwTo represent the corresponding frequency points. The search frequency vector a (w) is represented as follows:
and constructing a cost function and carrying out frequency search. Cost function PWIs represented as follows:
and carrying out spectrum peak search on the cost function to obtain each frequency point. The frequency values of the points obtained by the spectral peak search include the frequency values of respiration, heartbeat and higher harmonics.
The invention provides a method for monitoring the respiratory heart rate of a milk cow based on a millimeter wave radar, which comprises the steps of capturing vital sign signals of the milk cow by the millimeter wave radar and analyzing radar echo data; acquiring the approximate position relation between the radar and the cow through a range gate curve, determining the range of the target, and extracting the phase position of the target at the range gate; filtering and separating the phase difference signals of the breath and the heartbeat by using a band-pass filter according to the difference of the breath and the heartbeat frequency; and sending the respiration and heart rate signals obtained by calculation to an upper computer for display through an embedded system.
Compared with the prior art, the invention has the following remarkable effects:
compared with the prior art, the method has the advantages that for the detection problem of the respiratory heart rate of the dairy cow in a complex scene, a millimeter wave radar of 77-81 GHz is used for realizing high distance resolution, and a time division multiplexing MIMO antenna is adopted to form a virtual antenna array to improve the angle resolution so as to obtain fine vital sign behavior information of the dairy cow. The wireless transceiving system based on the embedded system is adopted, the breathing and heartbeat of the milk cow can be remotely monitored, when the local system detects abnormal signals, abnormal information can be sent to the upper computer, and a good monitoring effect is guaranteed. The invention realizes the non-contact detection and remote monitoring of the respiratory rate of the dairy cow, improves the accuracy and real-time performance of the respiratory rate detection of the dairy cow, solves the problems of unmatched dairy cow, single applicable scene and the like pain point in practical application, effectively overcomes the defects that the equipment cannot carry out real-time monitoring and intelligent early warning, improves the efficiency, reduces the labor consumption and improves the information management level.
Drawings
FIG. 1 is a schematic flow diagram of the system of the present invention;
FIG. 2 is a schematic diagram of a technical route of the present invention;
FIG. 3 is a schematic flow chart of the MUSIC algorithm of the present invention for estimating respiratory heart rate;
FIG. 4 is a diagram illustrating the phase spectrum results after the respiratory filtering process of the present invention;
FIG. 5 is a time domain phase spectrum diagram corresponding to respiration according to the present invention;
FIG. 6 is a graph showing the results of the frequency spectrum after the heartbeat filtering process of the present invention;
fig. 7 is a schematic diagram of a time domain phase spectrum corresponding to the heartbeat filtering process of the present invention.
Detailed Description
The embodiments of the invention are further illustrated in the accompanying drawings and the description of the embodiments. It is understood by those skilled in the art that these examples are given solely for the purpose of illustration and are not intended to limit the scope of the invention, which is defined by the claims appended hereto as interpreted when read in light of the instant disclosure as defined by the various equivalent alterations.
A simple working flow chart of the method for monitoring the respiratory heart rate of the dairy cow based on the millimeter wave radar is shown in figure 1, a specific technical route chart is shown in figure 2, the whole flow is that distance direction FFT is carried out on intermediate frequency signals collected by the millimeter wave radar, a detection target distance unit is obtained, a signal phase value is extracted, a phase difference is obtained through phase unwrapping, different band-pass filters are set for filtering the phase difference according to respiratory and heartbeat frequencies, then the respiratory and heartbeat frequencies are calculated based on a spectrum estimation method, and finally harmonic interference is removed to obtain the final respiratory and heartbeat frequencies.
The method for monitoring the respiratory heart rate of the dairy cow based on the millimeter wave radar comprises the following steps:
s1: capturing sign micro-motion signals of the dairy cows through a millimeter wave radar, analyzing original echoes of the radar, and performing fast Fourier transform on echo data sampled by an ADC (analog to digital converter) to obtain a range gate curve;
s2: obtaining the approximate position relation between the radar and the dairy cow through the range gate curve, determining the range of the target, and tracking the range gates corresponding to the target at different moments by searching the maximum value in the range;
s3: extracting the phase at the target range gate, and circulating the steps S1 and S2;
s4: by phase unwrapping to ensure that the phase values are between [ -pi, pi ], subtracting/adding 2 pi from/to the phase whenever the phase difference between successive values is greater/less than ± pi;
s5: carrying out frame-by-frame difference on the phase signals to obtain phase difference signals;
s6: filtering and separating the phase difference signals of the breath and the heartbeat by using a band-pass filter according to the difference of the breath and the heartbeat frequency;
s7: processing the phase difference signal of the respiration by adopting FFT of a frequency domain and a peak-to-peak distance algorithm of a time domain respectively, and taking the average value of the results of the two algorithms to obtain the respiration frequency;
s8: filtering the motion interference of the phase difference signal of the heartbeat;
s9: processing the phase difference signal of the heartbeat by adopting algorithms such as FFT of a frequency domain, peak distance of a time domain, cross correlation and the like, and taking an average value of the three algorithm results to obtain the heartbeat frequency;
s10: recording the breathing and heartbeat frequency within a period of time, judging the real frequency at the moment according to the confidence index, and outputting the relation of the breathing and heartbeat frequency along with the change of time;
s11: the harmonic filtering of the breathing frequency is performed on the heart beat frequency (the heart beat frequency is likely to be the harmonic of the breathing frequency, for example, the breathing rate is 30, the heart beat rate is 60, 90, 120, etc., and the heart beat rate is most likely to be the harmonic interference of the breathing rate rather than the actual heart beat rate).
S12: and sending the respiration and heart rate signals obtained by calculation to an upper computer through an embedded system.
The method comprises the steps of capturing respiration and heart rate signals of a cow by a 77-81 GHz millimeter Wave radar, erecting the millimeter Wave radar device above a milking position of the cow to aim at the neck of the cow for radar detection, wherein the radar system is provided with 3 transmitting antennas and 4 receiving antennas, the transmitting signals are Linear Frequency Modulated Continuous Waves (LFMCW), and the transmitting power is 12 dBm.
Referring to fig. 3, in practical applications, when performing time-frequency analysis by using MUSIC, relevant parameters are set. The length of data selected and observed in the experiment test is 512 points, and the sampling rate fsAn 8 x 4 data matrix was constructed at 20 Hz. The point of the heartbeat signal is judged in the heartbeat frequency spectrum, and only the peak value in each corresponding frequency band needs to be selected.
Referring to fig. 4 and 5, fig. 4 shows that the processed echo phase is filtered by a breathing filter, and only a part of 0.1-0.5 Hz is reserved, which is phase fluctuation caused by breathing. It can be seen that after filtering, the phase only has components in the range of 0.1-0.5 Hz, and the filtered time domain data is output to estimate the respiratory frequency. Fig. 5 is a time domain image of respiration, and it can be seen that the respiration rate is approximately 0.45 times per second.
Referring to fig. 6 and 7, fig. 6 shows that the processed echo phases are filtered by a heartbeat filter, and only a part of 0.8-4 Hz is reserved, namely phase fluctuation caused by heartbeat. It can be seen that after filtering, the phase only has components in the range of 0.8-4 Hz, and the filtered time domain data is output to estimate the heartbeat frequency. The corresponding time domain image is given in fig. 7 with a heartbeat frequency of approximately 3.02 times per second.
The invention also provides a method for monitoring the respiratory heart rate of the dairy cow based on the millimeter wave radar, wherein the system comprises a network interface, a memory and a processor; the network interface is used for receiving and sending signals in the process of receiving and sending information with other external network elements; a memory for storing computer program instructions executable on the processor; a processor for, when executing the computer program instructions, performing the steps of the consensus method described above.
The present embodiment also provides a computer storage medium storing a computer program that when executed by a processor can implement the method described above. The computer-readable medium may be considered tangible and non-transitory. Non-limiting examples of a non-transitory tangible computer-readable medium include a non-volatile memory circuit (e.g., a flash memory circuit, an erasable programmable read-only memory circuit, or a mask read-only memory circuit), a volatile memory circuit (e.g., a static random access memory circuit or a dynamic random access memory circuit), a magnetic storage medium (e.g., an analog or digital tape or hard drive), and an optical storage medium (e.g., a CD, DVD, or blu-ray disc), among others. The computer program includes processor-executable instructions stored on at least one non-transitory tangible computer-readable medium. The computer program may also comprise or rely on stored data. The computer programs may include a basic input/output system (BIOS) that interacts with the hardware of the special purpose computer, a device driver that interacts with specific devices of the special purpose computer, one or more operating systems, user applications, background services, background applications, and the like.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims.
Claims (3)
1. A method for monitoring the respiratory heart rate of a milk cow based on a millimeter wave radar is characterized by comprising the following steps:
s1: capturing sign micro-motion signals of the dairy cows through a millimeter wave radar, analyzing original echoes of the radar, and performing fast Fourier transform on echo data sampled by an ADC (analog to digital converter) to obtain a range gate curve;
s2: obtaining the approximate position relation between the radar and the dairy cow through the range gate curve, determining the range of the target, and tracking the range gates corresponding to the target at different moments by searching the maximum value in the range;
s3: extracting the phase at the target range gate, and circulating the steps S1 and S2;
s4: by phase unwrapping to ensure that the phase values are between [ -pi, pi ], subtracting/adding 2 pi from/to the phase whenever the phase difference between successive values is greater/less than ± pi;
s5: carrying out frame-by-frame difference on the phase signals to obtain phase difference signals;
s6: filtering and separating the phase difference signals of the breath and the heartbeat by using a band-pass filter according to the difference of the breath and the heartbeat frequency;
s7: processing the respiratory phase difference signal by adopting FFT of a frequency domain, peak spacing of a time domain and MUSIC algorithm respectively, and taking the average value of the results of the two algorithms to obtain respiratory frequency;
s8: filtering the motion interference of the phase difference signal of the heartbeat;
s9: processing the phase difference signals of the heartbeats by respectively adopting FFT of a frequency domain, the peak distance of a time domain and an MUSIC algorithm, and taking the average value of the results of the three algorithms to obtain the heartbeat frequency;
s10: recording the breathing and heartbeat frequency within a period of time, judging the real frequency at the moment according to the confidence index, and outputting the relation of the breathing and heartbeat frequency along with the change of time;
s11: filtering the harmonic wave of the respiratory frequency of the heartbeat frequency;
s12: and sending the respiration and heart rate signals obtained by calculation to an upper computer through an embedded system.
2. The method for monitoring the respiratory heart rate of the dairy cow based on the millimeter wave radar as claimed in claim 1, wherein the specific duration of the two phases of the steps S1 and S2 in the step S3 is as follows: the cycle frame period is 50ms, namely the phase of the target is extracted once in each frame period, if the radial distance between the target and the distance changes, the position of the distance gate at the moment needs to be obtained according to a distance gate tracking algorithm, then the phase is extracted, N frames are transmitted in a cycle mode, the change curve of the phase of the target along with the frame number can be obtained, and the relation between the phase of the target and the time can also be regarded as the relation between the target phase and the time.
3. The method for monitoring the respiratory heart rate of the dairy cow based on the millimeter wave radar as claimed in claim 1, wherein the interference filtering method in the heartbeat signal in the step S8 is as follows: the samples are segmented, an energy threshold value is set to judge whether the variation range of the heart rate is met, and the data segment with large energy is discarded and the data segment in a stable state is integrated to carry out estimation in the next step.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114947754A (en) * | 2022-06-30 | 2022-08-30 | 北京京东拓先科技有限公司 | Method and apparatus for determining sleep data |
| CN117064349A (en) * | 2023-08-17 | 2023-11-17 | 德心智能科技(常州)有限公司 | Gesture control method and system for linkage of millimeter wave radar and intelligent bed |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106821347A (en) * | 2016-12-20 | 2017-06-13 | 中国人民解放军第三军医大学 | A kind of life detection radar breathing of FMCW broadbands and heartbeat signal extraction algorithm |
| CN110161491A (en) * | 2019-06-28 | 2019-08-23 | 电子科技大学 | A kind of ranging and respiratory rate estimation method for faint life entity |
| CN111685741A (en) * | 2020-06-11 | 2020-09-22 | 中山大学 | Method for detecting human body respiration rate and heart rate based on orthogonal demodulation pulse ultra-wideband radar |
| CN112401863A (en) * | 2020-11-19 | 2021-02-26 | 华中师范大学 | Non-contact real-time vital sign monitoring system and method based on millimeter wave radar |
| CN112472051A (en) * | 2020-12-16 | 2021-03-12 | 山东润一智能科技有限公司 | Millimeter wave radar device, method and system for monitoring vital signs |
| CN113273978A (en) * | 2021-05-21 | 2021-08-20 | 电子科技大学 | Ultra-wideband radar-based human body respiration and heartbeat frequency detection method |
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106821347A (en) * | 2016-12-20 | 2017-06-13 | 中国人民解放军第三军医大学 | A kind of life detection radar breathing of FMCW broadbands and heartbeat signal extraction algorithm |
| CN110161491A (en) * | 2019-06-28 | 2019-08-23 | 电子科技大学 | A kind of ranging and respiratory rate estimation method for faint life entity |
| CN111685741A (en) * | 2020-06-11 | 2020-09-22 | 中山大学 | Method for detecting human body respiration rate and heart rate based on orthogonal demodulation pulse ultra-wideband radar |
| CN112401863A (en) * | 2020-11-19 | 2021-02-26 | 华中师范大学 | Non-contact real-time vital sign monitoring system and method based on millimeter wave radar |
| CN112472051A (en) * | 2020-12-16 | 2021-03-12 | 山东润一智能科技有限公司 | Millimeter wave radar device, method and system for monitoring vital signs |
| CN113273978A (en) * | 2021-05-21 | 2021-08-20 | 电子科技大学 | Ultra-wideband radar-based human body respiration and heartbeat frequency detection method |
Non-Patent Citations (1)
| Title |
|---|
| 安东港: "基于毫米波雷达的生命体征信号检测研究", 硕士电子期刊出版社, no. 02, 15 February 2021 (2021-02-15), pages 17 - 50 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114947754A (en) * | 2022-06-30 | 2022-08-30 | 北京京东拓先科技有限公司 | Method and apparatus for determining sleep data |
| CN117064349A (en) * | 2023-08-17 | 2023-11-17 | 德心智能科技(常州)有限公司 | Gesture control method and system for linkage of millimeter wave radar and intelligent bed |
| CN117064349B (en) * | 2023-08-17 | 2024-02-06 | 德心智能科技(常州)有限公司 | Gesture control method and system for linkage of millimeter wave radar and intelligent bed |
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