CN113854990A - Heartbeat detection method and device - Google Patents

Heartbeat detection method and device Download PDF

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Publication number
CN113854990A
CN113854990A CN202111258333.0A CN202111258333A CN113854990A CN 113854990 A CN113854990 A CN 113854990A CN 202111258333 A CN202111258333 A CN 202111258333A CN 113854990 A CN113854990 A CN 113854990A
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peak
heartbeat
frequency
target
valley
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Inventor
郭世盛
张佳舒
韩海力
梁菁菁
慕安臻
欧渝
杨晓波
步雨晴
殷豪杰
崔国龙
孔令讲
刘宏
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Qingdao Hisense Hitachi Air Conditioning System Co Ltd
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Qingdao Hisense Hitachi Air Conditioning System Co Ltd
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/0507—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves  using microwaves or terahertz waves
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7253—Details of waveform analysis characterised by using transforms
    • A61B5/7257—Details of waveform analysis characterised by using transforms using Fourier transforms

Abstract

The application provides a heartbeat detection method and a heartbeat detection device, relates to the technical field of radar detection, and is used for improving the accuracy of heartbeat detection. The method comprises the following steps: transmitting electromagnetic waves to a target space and acquiring echo signals; processing the echo signal to obtain a heartbeat time domain signal of the target human body; determining the number of effective peak-valley pairs in the heartbeat time domain signal according to the heartbeat time domain signal, and determining a frequency selection range according to the number of the effective peak-valley pairs; carrying out fast Fourier transformation on the heartbeat time domain signal to obtain a heartbeat frequency domain signal; finding a target spectrum peak of the heartbeat frequency domain signal in the frequency selection range; and determining the heartbeat frequency of the target human body according to the frequency corresponding to the peak value of the target spectrum peak.

Description

Heartbeat detection method and device
Technical Field
The present application relates to the field of radar detection technologies, and in particular, to a heartbeat detection method and apparatus.
Background
In the field of medical health, the real-time detection of the heartbeat frequency is beneficial to medical staff to master the state of a patient in time, and is also convenient for ordinary users to know the self health condition in time. Therefore, the correct measurement of the heartbeat frequency has important practical significance and wide application prospect.
The traditional contact measurement method often causes physical or psychological discomfort to a tested person, influences the accuracy of measurement results, and is inconvenient for continuous detection in a home environment or an intensive care environment. Therefore, the heartbeat feature detection method based on the radar has wider and wider application because the heartbeat feature detection method does not need to be directly contacted with the user.
Because the signal energy of the heartbeat signal is weak, the micro-motion displacement of the body surface is small, and therefore the heartbeat signal is easily influenced by interference signals of other micro-motions of the body surface and harmonic waves of the respiration signal. Therefore, the heartbeat detection method based on the radar directly utilizes the Fourier transform algorithm to detect the heartbeat, so that effective signals are easily submerged in noise, and the accuracy of the finally determined heartbeat frequency is influenced.
Disclosure of Invention
The embodiment of the application provides a heartbeat detection method and device, which are used for improving the accuracy of heartbeat detection so as to meet the use requirements of users.
In a first aspect, a heartbeat detection method is provided, which is applied to a radar device, and includes: transmitting electromagnetic waves to a target space and acquiring echo signals; processing the echo signal to obtain a heartbeat time domain signal of the target human body; determining the number of effective peak-valley pairs in the heartbeat time domain signal according to the heartbeat time domain signal, and determining a frequency selection range according to the number of the effective peak-valley pairs; the effective peak-valley pair is a peak-valley pair of which the difference value between a peak value and a valley value is greater than or equal to a peak-valley difference threshold value, and the peak-valley pair consists of adjacent peak values and valley values in the heartbeat time domain signal; carrying out fast Fourier transformation on the heartbeat time domain signal to obtain a heartbeat frequency domain signal; finding a target spectrum peak of the heartbeat frequency domain signal in the frequency selection range; and determining the heartbeat frequency of the target human body according to the frequency corresponding to the peak value of the target spectrum peak.
Based on the technical scheme, the number of the effective peak-valley pairs in the heartbeat time domain signal can reflect the rough estimation of the heartbeat frequency, so that the frequency selection range of the heartbeat frequency can be determined according to the number of the effective peak-valley pairs, and the upper limit and the lower limit of the heartbeat frequency can be reasonably limited by the frequency selection range of the heartbeat frequency. Due to the interference of noise, the heartbeat time domain signal is subjected to fast fourier transform to obtain a heartbeat frequency domain signal, and generally, the heartbeat frequency domain signal has a plurality of frequency spectrum peaks. In contrast, according to the technical scheme provided by the embodiment of the application, the target spectrum peak of the heartbeat frequency domain signal is found in the frequency selection range, and the spectrum peaks which are not in the frequency selection range are reasonably eliminated, so that the effective signal (namely the target spectrum peak) is prevented from being submerged in noise, namely the signal-to-noise ratio is improved. Because the target spectrum peak capable of reflecting the heartbeat frequency of the target human body can be accurately determined, the heartbeat frequency of the target human body determined according to the frequency corresponding to the peak value of the target spectrum peak has higher accuracy.
In a second aspect, a heartbeat detection method is provided, which is applied to a radar device, and includes: transmitting electromagnetic waves to a target space, and acquiring echo signals of P channels, wherein P is an integer greater than 1; processing the echo signals of the P channels to obtain heartbeat time domain signals of the P channels of the target human body; for each channel in the P channels, determining the number of effective peak-valley pairs of the channel according to the heartbeat time domain signal of the channel, and determining a frequency selection range according to the number of the effective peak-valley pairs; carrying out fast Fourier transformation on the heartbeat time domain signal of the channel to obtain a heartbeat frequency domain signal of the channel; finding a target spectrum peak of a heartbeat frequency domain signal of the channel in the frequency selection range of the channel; taking the frequency corresponding to the peak point value of the target frequency spectrum peak of the heartbeat frequency domain signal of the channel as the heartbeat frequency estimation value of the channel; the effective peak-valley pair is a peak-valley pair of which the difference value between a peak value and a valley value is greater than or equal to a peak-valley difference threshold value, and the peak-valley pair consists of adjacent peak values and valley values in the heartbeat time domain signal; and determining the heartbeat frequency of the target human body according to the heartbeat frequency estimation values of the P channels.
Based on the technical scheme, for each channel in the P channels, the frequency corresponding to the peak value of the target frequency spectrum peak in the heartbeat frequency domain signal of the channel is used as the heartbeat frequency estimation value of the channel, and then the heartbeat frequency of the target human body is determined according to the heartbeat frequency estimation values of the P channels. Thus, compared with a single receiver channel, the final heartbeat frequency is determined by comprehensively considering the heartbeat frequency estimated values of the P channels, and the accuracy is higher.
In a third aspect, there is provided a radar apparatus comprising: the radar module is used for transmitting electromagnetic waves to a target space and acquiring echo signals; the signal processing module is used for processing the echo signal to obtain a heartbeat time domain signal of the target human body; determining the number of effective peak-valley pairs in the heartbeat time domain signal according to the heartbeat time domain signal, and determining a frequency selection range according to the number of the effective peak-valley pairs; the effective peak-valley pair is a peak-valley pair of which the difference value between a peak value and a valley value is greater than or equal to a peak-valley difference threshold value, and the peak-valley pair consists of adjacent peak values and valley values in the heartbeat time domain signal; carrying out fast Fourier transformation on the heartbeat time domain signal to obtain a heartbeat frequency domain signal; finding a target spectrum peak of the heartbeat frequency domain signal in the frequency selection range; and the heartbeat detection module is used for determining the heartbeat frequency of the target human body according to the frequency corresponding to the peak value of the target spectrum peak.
In a fourth aspect, there is provided a radar apparatus comprising: the radar module is used for transmitting electromagnetic waves to a target space and acquiring echo signals of P channels, wherein P is an integer larger than 1; the signal processing module is used for processing the echo signals of the P channels to obtain heartbeat time domain signals of the P channels of the target human body; for each channel in the P channels, determining the number of effective peak-valley pairs of the channel according to the heartbeat time domain signal of the channel, and determining a frequency selection range according to the number of the effective peak-valley pairs; carrying out fast Fourier transformation on the heartbeat time domain signal of the channel to obtain a heartbeat frequency domain signal of the channel; finding a target spectrum peak of a heartbeat frequency domain signal of the channel in the frequency selection range of the channel; taking the frequency corresponding to the peak point value of the target frequency spectrum peak of the heartbeat frequency domain signal of the channel as the heartbeat frequency estimation value of the channel; the effective peak-valley pair is a peak-valley pair of which the difference value between a peak value and a valley value is greater than or equal to a peak-valley difference threshold value, and the peak-valley pair consists of adjacent peak values and valley values in the heartbeat time domain signal; and the heartbeat detection module is used for determining the heartbeat frequency of the target human body according to the heartbeat frequency estimation values of the P channels.
In a fifth aspect, there is provided a radar apparatus comprising: one or more processors; one or more memories; wherein the one or more memories are configured to store computer program code comprising computer instructions which, when executed by the one or more processors, cause the radar apparatus to perform any of the heartbeat detection methods provided by the first or second aspects above.
A sixth aspect provides a computer-readable storage medium having stored therein instructions which, when run on a computer, cause the computer to perform any one of the heartbeat detection methods provided in the first or second aspects above.
In a seventh aspect, a computer program product is provided, which is directly loadable into a memory and contains software codes, and which, when loaded and executed by a computer, is capable of implementing any of the heartbeat detection methods as provided in the first or second aspect.
For the beneficial effects of the third aspect to the seventh aspect in the present application, reference may be made to beneficial effect analysis of the first aspect or the second aspect, and details are not repeated here.
Drawings
Fig. 1 is a schematic view of an application scenario of a radar apparatus according to an embodiment of the present application;
fig. 2 is a schematic diagram of a heartbeat detection system according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure;
fig. 4 is a flowchart of a heartbeat detection method according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a time domain waveform of a frequency modulated continuous wave;
FIG. 6 is a schematic diagram of a frequency domain waveform of a frequency modulated continuous wave;
fig. 7 is a schematic waveform diagram of a frequency modulated continuous wave transmitted by a radar apparatus according to an embodiment of the present application;
fig. 8 is a waveform diagram of a heartbeat time domain signal according to an embodiment of the present disclosure;
fig. 9 is a waveform diagram of a heartbeat frequency domain signal according to an embodiment of the present application;
fig. 10 is a waveform diagram of a filtered frequency domain heartbeat signal according to an embodiment of the present application;
fig. 11 is a flowchart of another heartbeat detection method provided in the embodiment of the present application;
fig. 12 is a flowchart of another heartbeat detection method provided in the embodiment of the present application;
fig. 13 is a schematic diagram illustrating a deskew principle of an echo signal according to an embodiment of the present application;
fig. 14 is a schematic diagram of an echo signal of a target human body according to an embodiment of the present application;
fig. 15 is a flowchart of another heartbeat detection method provided in the embodiment of the present application;
fig. 16 is a histogram of peak-to-valley difference values of a peak-to-valley pair in a heartbeat time-domain signal according to an embodiment of the present application;
fig. 17 is a flowchart of another heartbeat detection method provided in the embodiment of the present application;
FIG. 18 is a histogram with a kernel smoothing probability density function fit provided by an embodiment of the present application;
FIG. 19 is a schematic diagram of a probability density function of a heartbeat time domain signal according to an embodiment of the present application;
fig. 20 is a flowchart of another heartbeat detection method provided in the embodiment of the present application;
fig. 21 is a waveform diagram of another heartbeat frequency domain signal provided in the embodiment of the present application;
fig. 22 is a waveform diagram of another filtered frequency domain heartbeat signal according to an embodiment of the present application;
FIG. 23 is another histogram with a core smoothing probability density function fit provided by an embodiment of the present application;
fig. 24 is a schematic diagram of a probability density function of another heartbeat time-domain signal according to an embodiment of the present application;
fig. 25 is a flowchart of another heartbeat detection method provided in the embodiment of the present application;
fig. 26 is a flowchart of another heartbeat detection method provided in the embodiment of the present application;
fig. 27 is a flowchart of another heartbeat detection method provided in the embodiment of the present application;
fig. 28 is a schematic structural diagram of a radar apparatus according to an embodiment of the present application;
fig. 29 is a schematic structural diagram of another radar apparatus according to an embodiment of the present application.
Detailed Description
A heartbeat detection method and device provided by the present application will be described in detail below with reference to the accompanying drawings.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone.
The terms "first" and "second" and the like in the description and drawings of the present application are used for distinguishing different objects or for distinguishing different processes for the same object, and are not used for describing a specific order of the objects.
Furthermore, the terms "including" and "having," and any variations thereof, as referred to in the description of the present application, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described herein as "exemplary" or "e.g.," is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts related in a concrete fashion.
In the description of the present application, the meaning of "a plurality" means two or more unless otherwise specified.
As described in the background art, currently, the accuracy of a heartbeat detection method based on radar is low, and the user requirements cannot be met.
Based on this, an embodiment of the present application provides a heartbeat detection method, which specifically includes: transmitting electromagnetic waves to a target space and acquiring echo signals; processing the echo signal to obtain a heartbeat time domain signal of the target human body; determining the number of effective peak-valley pairs in the heartbeat time domain signal according to the heartbeat time domain signal, and determining a frequency selection range according to the number of the effective peak-valley pairs; carrying out fast Fourier transformation on the heartbeat time domain signal to obtain a heartbeat frequency domain signal; finding a target spectrum peak of the heartbeat frequency domain signal in the frequency selection range; and determining the heartbeat frequency of the target human body according to the frequency corresponding to the peak value of the target spectrum peak.
According to the technical scheme provided by the embodiment of the application, the number of the effective peak-valley pairs in the heartbeat time domain signal can reflect the rough estimation of the heartbeat frequency, so that the frequency selection range of the heartbeat frequency can be determined according to the number of the effective peak-valley pairs, and the upper limit and the lower limit of the heartbeat frequency can be reasonably limited by the frequency selection range of the heartbeat frequency. Due to the interference of noise, the heartbeat frequency domain signal obtained by fast fourier transforming the heartbeat time domain signal generally has a plurality of frequency spectrum peaks. In contrast, according to the technical scheme provided by the embodiment of the application, the target spectrum peak of the heartbeat frequency domain signal is found in the frequency selection range, and the spectrum peaks which are not in the frequency selection range are reasonably eliminated, so that the effective signal (namely the target spectrum peak) is prevented from being submerged in noise, namely the signal-to-noise ratio is improved. Because the target spectrum peak capable of reflecting the heartbeat frequency of the target human body can be accurately determined, the heartbeat frequency of the target human body determined according to the frequency corresponding to the peak value of the target spectrum peak has higher accuracy.
In the embodiment of the present application, the radar apparatus is an electronic apparatus that performs target detection using electromagnetic waves, for example: millimeter wave radar, microwave radar, ultra wide band radar, and the like. In some embodiments of the application, a millimeter wave radar with strong anti-interference capability, strong resolving capability and high measurement precision is adopted.
Wherein, the millimeter wave is an electromagnetic wave with a frequency of 30 to 300GHz (with a wavelength of 1 to 10 mm). The millimeter wave has extremely wide bandwidth, so that the problem of frequency domain resource shortage can be relieved; the millimeter wave has narrow beam, and the details of the target object can be observed more clearly. So, some embodiments of this application adopt the millimeter wave to carry out heartbeat detection, effectual interference killing feature, resolving power and the measurement accuracy who has promoted radar equipment.
Illustratively, a radar device may be comprised of a radar transmitter, a radar receiver, and an antenna.
A radar transmitter is a radio device that provides a high-power radio frequency signal for radar equipment, and can generate a high-power radio frequency signal, i.e., an electromagnetic wave, whose carrier is modulated. The transmitter can be classified into a continuous wave transmitter and a pulse transmitter according to a modulation scheme. The transmitter consists of a primary radio frequency oscillator and a pulse modulator.
The radar receiver is a device for frequency conversion, filtering, amplification and demodulation in radar equipment. The weak high frequency signals received by the antenna are selected from accompanying noise and interference through proper filtering, and are amplified and detected for target detection, display or other radar signal processing.
An antenna is a device used in radar equipment to transmit or receive electromagnetic waves and determine the detection direction thereof. When in emission, the energy is intensively radiated to the direction needing to be irradiated; during reception, echoes of the probe direction are received and the azimuth and/or elevation of the target is resolved.
The principle of the radar device for measuring the distance is that the radar device can obtain the distance of the target object by measuring the time difference between the transmission of the electromagnetic wave and the reception of the electromagnetic wave.
The radar equipment can be applied to a scene of heartbeat detection of a human body. As shown in fig. 1, a radar apparatus transmits an electromagnetic wave to a space in which a target human body is located through a transmitting antenna, receives the electromagnetic wave (i.e., an echo signal) reflected by the target human body through a receiving antenna, and sends the echo signal to a receiver for signal processing. After the echo signals are processed, the radar apparatus may determine the frequency of the heartbeat of the target human body.
As shown in fig. 2, a schematic diagram of a heartbeat detection system provided in an embodiment of the present application is shown. The system may include: radar equipment and electronic equipment. The radar equipment and the electronic equipment can be connected in a wired or wireless mode. For example, the radar device and the electronic device are connected through a wireless local area network.
The electronic equipment is used for sending a control instruction to the radar equipment and receiving a detection result of the radar equipment. For example, the electronic device in the embodiment of the present application may be a mobile phone, a tablet computer, a desktop computer, a laptop computer, a handheld computer, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, a cellular phone, a Personal Digital Assistant (PDA), an Augmented Reality (AR) \ Virtual Reality (VR) device, and the like. The present disclosure does not specifically limit the specific form of the electronic device. The electronic device can interact with a user in one or more modes such as a keyboard, a touch pad, a touch screen, a remote controller, voice interaction or handwriting equipment.
As shown in fig. 3, the electronic device in the embodiment of the present application may be a mobile phone 100. The embodiment will be specifically described below by taking the mobile phone 100 as an example. Handset 100 may include bus 110, processor 120, memory 130, user input module 150, display module 160, communication interface 170, and other similar and/or suitable components.
Bus 110 may be circuitry that interconnects the above-described elements and passes communications (e.g., control messages) between the above-described elements.
The processor 120 may receive commands from the above-described other elements (e.g., the memory 130, the user input module 150, the display module 160, the communication interface 170, etc.) through the bus 110, may interpret the received commands, and may perform calculations or data processing according to the interpreted commands.
Memory 130 may store commands or data received from processor 120 or other elements (e.g., user input module 150, display module 160, communication interface 170, etc.) or commands or data generated by processor 120 or other elements.
The user input module 150 may receive commands or data input from a user via input-output means (e.g., a sensor, a keyboard, a touch screen, etc.) and may transmit the received commands or data to the processor 120 or the memory 130 through the bus 110. The display module 160 may display videos, images, data, and the like to a user.
The display module 160 may display various information (e.g., multimedia data, text data) received from the above elements.
The communication interface 170 may control a short-range communication connection with another electronic device.
It should be understood that the handset 100 as shown in fig. 3 is only one example of the electronic device described above, and that the handset 100 may have more or fewer components than shown in fig. 3, may combine two or more components, or may have a different configuration of components.
The scheme provided by the application is specifically explained in the following with the attached drawings of the specification.
The embodiment of the application provides a heartbeat detection method, which is applied to radar equipment, and as shown in fig. 4, the method comprises the following steps:
s101, transmitting electromagnetic waves to a target space and acquiring echo signals.
In the embodiment of the present application, the target space may be a space in which the radar device is placed, such as a ward, a bedroom, a study room, and the like, which is not limited thereto.
In some embodiments, the electromagnetic waves transmitted by the radar device and the echo signals received by the radar device are collectively referred to as radar signals. The radar signal can be detected by using a Frequency Modulated Continuous Wave (FMCW), which can effectively reduce the probability of intercepted interference. Fig. 5 is a schematic diagram of the FMCW time domain waveform, and the characteristic of the signal frequency increasing linearly with time can be seen from fig. 5. FIG. 6 is a schematic diagram of the FMCW frequency domain waveform, and from FIG. 6 it can be seen that the FMCW signal frequency domain waveform is a straight line, where TcIs the pulse width of the chirp signal, B is the bandwidth of the radar signal waveform, and the initial frequency is fc。
In some embodiments, the radar device transmits FMCW signals to the target space for heartbeat detection. As shown in fig. 7, for a pulsed FMCW wave transmitted by a radar apparatus, the interval between each frame is referred to as a frame period (or pulse interval). Alternatively, the sampling frequency of the radar device may be 20HZ, the Pulse Repetition Interval (PRI) may be 50ms, and the duration of the Chirp signal (Chirp signal) may be 50 us.
In a possible implementation manner, after receiving the detection instruction, the radar device starts a radar function, transmits electromagnetic waves to a target space, and acquires an echo signal. The detection instruction is used for indicating the detection of the heartbeat frequency.
For example, the radar device may be connected to the electronic device by means of a wired connection (e.g., signal line) or a wireless connection (e.g., bluetooth, Wi-Fi). When a user needs to perform heartbeat detection, the detection function is started through the electronic equipment. The electronic device sends a detection instruction to the radar device in response to an operation of turning on the detection function by a user. Accordingly, the radar device receives a detection instruction from the electronic device.
And S102, processing the echo signal to obtain a heartbeat time domain signal of the target human body.
Please refer to fig. 8, which is a waveform diagram of the heartbeat time-domain signal of the target human body, it can be seen from the diagram that the heartbeat time-domain signal of the target human body is a non-stationary signal similar to a sine wave and has a certain frequency fluctuation, and there are a plurality of signals including harmonics and beat signals of the breathing signal. Therefore, to accurately perform the detection of the heartbeat frequency, the radar apparatus continues to perform steps S103 to S107.
S103, determining the number of effective peak-valley pairs in the heartbeat time domain signal according to the heartbeat time domain signal.
Wherein the effective peak-valley pair is a peak-valley pair in which a difference between the peak value and the valley value is greater than or equal to a peak-valley difference threshold. The peak-valley pair consists of adjacent peak and valley values in the heartbeat time domain signal.
It should be understood that the number of effective peak-valley pairs mentioned above specifically refers to the number of effective peak-valley pairs in one detection period. Illustratively, the detection period may be 0.5 min.
As a possible implementation manner, since the heartbeat time-domain signal generally has M peak-valley pairs, for each of the M peak-valley pairs, a difference between a peak value and a valley value of the peak-valley pair may be calculated first, and then it is determined whether the difference is greater than or equal to a peak-valley difference threshold. If the difference between the peak value and the valley value of the peak-valley pair is greater than the peak-valley difference threshold, the peak-valley pair can be determined to be an effective peak-valley pair. Based on this, N effective peak-valley pairs can be screened out from the M peak-valley pairs, and then the number of the effective peak-valley pairs (namely N) can be counted. M, N are positive integers, and M is greater than or equal to N.
Illustratively, as shown in fig. 8, 41 pairs of peaks and valleys exist in the heartbeat time domain signal, and assuming that the peak-valley difference threshold is 0.8mm, 29 pairs of peaks and valleys whose difference between the peak value and the valley value is greater than or equal to the peak-valley difference threshold can be found out, so that 29 effective pairs of peaks and valleys exist in the heartbeat time domain signal shown in fig. 8.
And S104, determining a frequency selection range according to the number of the effective peak-valley pairs.
As a possible implementation manner, the product of the number of effective peak-valley pairs in the detection period and the duration coefficient is used as a rough estimation value of the heartbeat frequency; the frequency selection range is determined based on a rough estimate of the heart beat frequency. The duration coefficient is a ratio of a preset duration to a duration of the detection period, and the preset duration is 60s (or 1 min).
For example, assuming that the number of valid peak-valley pairs detected within 0.5min is 32 pairs, the coarse estimation of the heartbeat frequency is 64 times/minute (bpm), and a range of ± 5bpm around the coarse estimation of the heartbeat frequency, i.e., [59bpm, 69bpm ], can be used as the frequency selection range.
And S105, carrying out fast Fourier change on the heart beat time domain signal to obtain a heart beat frequency domain signal.
The Fast Fourier Transform (FFT) is obtained by improving an algorithm of the discrete fourier transform according to characteristics of the discrete fourier transform, such as odd, even, imaginary, and real.
Illustratively, the FFT may satisfy the following equation (1):
Figure BDA0003324628080000091
and S106, finding a target spectrum peak of the heartbeat frequency domain signal in the frequency selection range.
As a possible implementation manner, according to the heartbeat frequency domain signal, one or more spectrum peaks whose frequencies corresponding to the peak values are located in the frequency selection range are searched; a target spectral peak is then determined from the one or more spectral peaks.
In some embodiments, if only one spectral peak of the heartbeat frequency signal is found within the frequency selection range, the spectral peak is taken as the target spectral peak.
In other embodiments, if a plurality of spectral peaks of the heartbeat frequency domain signal are found within the frequency selection range, a spectral peak with a maximum peak value is selected from the plurality of spectral peaks as the target spectral peak.
Illustratively, as shown in fig. 9, the heartbeat frequency domain signal is obtained after the heartbeat time domain signal is subjected to fast fourier transform. As can be seen from fig. 9, there are several more obvious spectral peaks in the frequency spectrum, and it cannot be accurately determined which spectral peak is the finally selectable heartbeat frequency conforming to the real data, so that the target spectral peak whose peak point value falls within the frequency selection range is searched according to the frequency selection range determined in step S104. As shown in fig. 10, the target spectrum peak of the heartbeat frequency domain signal found in the frequency selection range is shown.
And S107, determining the heartbeat frequency of the target human body according to the frequency corresponding to the peak value of the target spectrum peak.
The frequency corresponding to the peak value of the target spectrum peak refers to the frequency corresponding to the peak value of the target spectrum peak.
In some embodiments, the frequency corresponding to the peak value of the target spectrum peak is the heartbeat frequency of the target human body. For example, as shown in fig. 10, the peak value of the target spectrum peak corresponds to a frequency of 82.07bpm, and then the heartbeat frequency of the target human body is 82.07 bpm.
In other embodiments, in the long-time heartbeat detection process, since noise interference may cause that the frequency corresponding to the peak value of the target spectrum peak is different from the actual heartbeat frequency by too much, in order to further reduce the noise interference, the heartbeat frequency of the target human body may be predicted according to the historical heartbeat frequency, and then the heartbeat frequency of the target human body is determined by combining the frequency corresponding to the peak value of the target spectrum peak and the predicted heartbeat frequency. Specifically, as shown in fig. 11, step S107 may be implemented as the following steps:
s1071, obtaining the historical heartbeat frequency of the target human body.
It should be understood that the historical heartbeat frequency of the target human body is the heartbeat frequency of the target human body detected by the radar device at the historical time.
In some embodiments, the historical heartbeat frequency of the target human body may be stored in a radar device; alternatively, the historical heartbeat frequency of the target human body may be stored in the electronic device.
As a possible implementation manner, the radar device sends an acquisition instruction to the electronic device, where the acquisition instruction is used to instruct the electronic device to send the stored historical heartbeat frequency of the target human body to the radar device, so that the radar device acquires the historical heartbeat frequency of the target human body.
As another possible implementation manner, the radar device obtains the historical heartbeat frequency of the target human body by searching the database.
S1072, determining the predicted heartbeat frequency according to the historical heartbeat frequency of the target human body.
As a possible implementation mode, Kalman filtering processing is carried out on the historical heartbeat frequency of the target human body to obtain the predicted heartbeat frequency.
Specifically, the radar device continuously detects a plurality of heartbeat frequencies, and it is assumed that K heartbeat frequencies are obtained:
Figure BDA0003324628080000111
further constructing a target state vector of the (k-1) th heartbeat frequency, wherein the target state vector of the (k-1) th heartbeat frequency can satisfy the following formula (2):
Figure BDA0003324628080000112
wherein Y (k-1) represents the target state vector for the (k-1) th heartbeat frequency.
From kalman filtering, the further prediction equation of the target state may satisfy the following equation (3):
Figure BDA0003324628080000113
wherein the content of the first and second substances,
Figure BDA0003324628080000114
the predicted value of the state at k-1 period k and F is a state transition matrix.
Further derived for kalman filtering, the prediction mean square error matrix may satisfy the following equation (4):
P(k|k-1)＝FP(k-1)FT+Qwformula (4)
Where P (k) is the error correlation matrix for state estimation, QwIs the system noise correlation matrix.
The filter gain matrix may satisfy the following formula (5):
K(k)＝P(k|k-1)HT[HP(k|k-1)HT+Qv]-1formula (5)
Where H is the observation matrix, QwIs the system noise correlation matrix.
The state estimation may satisfy the following equation (6):
Figure BDA0003324628080000115
the mean square error matrix may satisfy the following formula (7):
p (k) ═ I-k (k) H ] P (k | k-1) formula (7)
Obtaining a heart rate result y after the target is smoothed in K periods through the filtering processingk，k＝1，2，……K。ykI.e. the predicted heart beat frequency.
S1073, when the absolute value of the difference between the frequency corresponding to the peak value of the target spectrum peak and the predicted heartbeat frequency is less than or equal to a preset value, taking the frequency corresponding to the peak value of the target spectrum peak as the heartbeat frequency of the target human body.
In this way, when the absolute value of the difference between the frequency corresponding to the peak value of the target spectrum peak and the predicted heartbeat frequency is less than or equal to the preset value, it is indicated that the deviation between the heartbeat frequency detected by the radar device and the actual heartbeat frequency of the target human body is not large, and therefore, the frequency corresponding to the peak value of the target spectrum peak is taken as the heartbeat frequency of the target human body.
S1074, when the absolute value of the difference between the frequency corresponding to the peak value of the target spectrum peak and the predicted heartbeat frequency is larger than a preset value, taking the predicted heartbeat frequency as the heartbeat frequency of the target human body.
Therefore, when the absolute value of the difference between the frequency corresponding to the peak value of the target spectrum peak and the predicted heartbeat frequency is greater than the preset value, it is indicated that the heartbeat frequency jump problem can occur due to the overlarge deviation between the heartbeat frequency detected by the radar device and the actual heartbeat frequency of the target human body, and therefore, the predicted heartbeat frequency is used as the heartbeat frequency of the target human body, and the heartbeat detection result can be more accurate.
It should be understood that the heartbeat frequency determined in step S107 is the heartbeat frequency within one detection period. The radar device may periodically perform the embodiment shown in fig. 4 to detect the heartbeat frequency of the target human body for a long period of time.
Based on the technical scheme provided by the embodiment of the application, the number of the effective peak-valley pairs in the heartbeat time domain signal can reflect the rough estimation of the heartbeat frequency, so that the frequency selection range of the heartbeat frequency can be determined according to the number of the effective peak-valley pairs, and the upper limit and the lower limit of the heartbeat frequency can be reasonably limited by the frequency selection range of the heartbeat frequency. Due to noise interference, the heartbeat frequency domain signal obtained by fast fourier transforming the heartbeat time domain signal generally has a plurality of frequency spectrum peaks. In contrast, according to the technical scheme provided by the embodiment of the application, the target spectrum peak of the heartbeat frequency domain signal is found in the frequency selection range, and the spectrum peaks which are not in the frequency selection range are reasonably eliminated, so that the effective signal (namely the target spectrum peak) is prevented from being submerged in noise, namely the signal-to-noise ratio is improved. Because the target spectrum peak capable of reflecting the heartbeat frequency of the target human body can be accurately determined, the heartbeat frequency of the target human body determined according to the frequency corresponding to the peak value of the target spectrum peak has higher accuracy.
Optionally, as shown in fig. 12, step S102 may be implemented as the following steps:
and S1021, sequentially performing pulse compression and moving target display processing on the echo signal, and determining a target range image.
The target range image refers to a scattering intensity distribution map of scattering points of the target, and provides distribution information of the scattering points of the target along a range direction. The object range image can reflect the shape and structural characteristics of the object.
The pulse compression means that the linear frequency modulation signal echo is subjected to pulse compression and sidelobe suppression, and the wide pulse is compressed into the narrow pulse, so that the output signal has a peak value at a range gate of a target, and meanwhile, the signal-to-noise ratio is improved. The radar equipment transmits a large-time-width and bandwidth signal at a transmitting end so as to improve the speed measurement precision and speed resolution of the signal; at a receiving end, the wide pulse signal is compressed into the narrow pulse signal through pulse compression, and the distance resolution precision and the distance resolution of the radar equipment to the target can be improved.
In some embodiments, the echo signals are first deskewed to obtain range information for the target prior to pulse compression. As shown in fig. 13, which is a schematic diagram of the deskew of the echo signal, it is assumed that the target is at a distance from the radar device R, and the electromagnetic wave emitted by the radar device reflects the echo signal after contacting the target. The distance between the receiver of the radar equipment and the target can cause a signal delay tdAnd can be expressed as 2R/c. The difference signal (IF signal) is obtained by mixing (mixer) the transmitted electromagnetic wave with the received echo signal, the frequency of which is fb，fbThe following formula (8) can be satisfied:
Figure BDA0003324628080000131
wherein, TcThe pulse width of the chirp signal, B the bandwidth of the echo signal waveform, c the light velocity constant, and R the distance between the target and the radar. The IF signal only has TX chirp and RX chirpThe pulses overlap for a period of time.
The original target range image can be obtained by the pulse compression processing. However, there are typically some static objects in the target space, such as cabinets, beds, walls, etc. Therefore, the original target range image obtained by pulse compression will be disturbed by many static targets.
In some embodiments, since the static object is completely in a static state, and the detected target human body fluctuates slightly, the radar apparatus may adopt a Moving Target Indication (MTI) technology to eliminate the static object. Because clutter components in echo signals received by radar equipment are the same, only moving targets bring phase change due to the change of the distance of the moving targets, therefore, MTI mainly adopts a pulse cancellation method to cancel two front and rear pulses, so that static targets are completely cancelled. The above moving target display processing may satisfy the following formula (9):
RIMTI(t-Tr)＝RI(t-Tr) -RI (t) formula (9)
Wherein RI (t) represents a target range profile without moving target display, RIMTI(t-Tr) Representing a range image of the object displayed past the moving object, RI (T-T)r) Indicating a distance T before the current timerTarget range image of time.
Based on the step S1021, the echo signals are processed by pulse compression and moving target display, so that a target distance image without static target interference is obtained, and the accuracy of human target detection can be effectively improved.
And S1022, detecting the human body target of the target distance image and determining a distance unit of the target human body.
The distance unit of the target human body is used for indicating the relative distance between the target human body and the radar equipment.
In some embodiments, a constant false alarm rate detection method based on ranking statistics may be employed to determine the distance units of the target human body.
Wherein the constant false alarm rate of the Ordered Statistics (OS) ((CFAR) detection method, which is a method in which, in radar detection, the sensitivity of the radar can be automatically adjusted along with the change of external intensity interference, so that the false alarm probability remains unchanged. In target detection, the target threshold is VTWhen the noise level does not exceed the set threshold level and no target is detected, the method is called as 'false alarm', and the probability of false alarm is Pla(ii) a When the noise level exceeds the set threshold level and the target is mistakenly judged to be detected, the false alarm is called, and the false alarm probability is Pfa. Therefore, in order to make the detection probability PdAt maximum, it is necessary to ensure that the false alarm probability is within a certain range.
In some embodiments, reference cell sample data x is acquired from non-coherently accumulated datai(i ═ 1,2, … …, R). Assuming that a noise signal and a clutter signal in a radar receiver obey Gaussian distribution and the envelope of the noise signal and the clutter signal is Rayleigh distribution, after passing through a square-law detector, a reference unit samples xi(i ═ 1,2, … …, R) obeys an exponential distribution, whose Probability Density Function (PDF) may satisfy the following equation (10), and whose Cumulative Distribution Function (CDF) may satisfy the following equation (11):
Figure BDA0003324628080000141
F(x)＝1-e-x/λ′x is more than or equal to 0 formula (11)
Wherein λ' may satisfy the following formula (12):
Figure BDA0003324628080000142
where μ represents the total power level of the clutter signal and the noise signal, λ is the ratio of the average power of the echo signal to the clutter signal and the noise signal, H0Is the assumption that there is no target, H1Is an assumption that the target exists. In a homogeneous clutter background xi(i-1, 2, … …, R) are statistically independent and identically distributed.
In the CFAR detection of the OS, the reference unit samples are sorted from small to large. In a homogeneous clutter background, the PDF from the kth ordered sample of the R ensemble samples may satisfy the following equation (13):
Figure BDA0003324628080000143
the CDF from the kth ordered sample of the R population samples may satisfy the following equation (14):
Figure BDA0003324628080000144
wherein f (x) and F (x) represent the reference unit sample x in the uniform clutter background respectivelyiPDF and CDF of (i ═ 1,2, … …, R).
In the CFAR detection of the OS, a Cell Under Test (CUT) is a reference cell sample to be detected, and the reference cell sample is first sorted according to size, where the sorting can satisfy the following formula (15):
x(1)≤x(2)≤…≤x(R)formula (15)
After the sorting process, take the k-th sorted sample x(k)As an estimate of the clutter signal power level, Z, i.e. Z ═ x(k). Then, as can be seen from equation (13), the PDF of Z in the uniform clutter background may satisfy the following equation (16):
Figure BDA0003324628080000151
in a homogeneous clutter background, the moment-generating function (MGF) of Z may satisfy the following equation (17):
Figure BDA0003324628080000152
where u is a variable of the intalox. When in use
Figure BDA0003324628080000153
And then, the detection probability of the target can be obtained by calculating the moment mother function. Wherein T is defined as the normalization factor.
Therefore, the detection probability of CFAR detection of the OS in the uniform clutter background may satisfy the following formula (18), and the false alarm probability may satisfy the following formula (19):
Figure BDA0003324628080000154
Figure BDA0003324628080000155
as can be seen from equation (16), the statistical average of Z may satisfy the following equation (20):
Figure BDA0003324628080000156
therefore, the Average Decision Threshold (ADT) of CFAR detection of the OS may satisfy the following formula (21):
Figure BDA0003324628080000161
in some embodiments, when the target human body is detected to be present based on the constant false alarm rate of the sequencing statistics, data collection is started, and collected data are accumulated and stored in a designated address of the radar device. Optionally, the radar device detects one or more range units of the target human body through the CFAR, and stores the range unit of each target human body separately, and performs signal processing separately to achieve target separation.
And S1023, according to the distance unit of the target human body, determining the distance unit data of the target human body from the target distance image.
And S1024, sequentially carrying out phase unwrapping, phase difference and band-pass filtering on the distance unit data of the target human body to obtain a heartbeat time domain signal of the target human body.
(1) Phase unwrapping
In some embodiments, after the radar apparatus achieves target separation, the peak of the target range profile is represented by a complex signal, and the real part and the imaginary part of the complex signal form the real phase information of the signal. As shown in fig. 14, (a) in fig. 14 is a radar echo real part signal of the target human body, and (b) in fig. 14 is a radar echo imaginary part signal of the target human body. The signal waveform contains position information of the target human body and phase information of the heartbeat time domain signal.
It should be understood that the phase value of the echo signal can be mathematically derived by an arctan function, but there is a problem with phase wrapping in computer operation. That is, the angles of the arctangent function in the first quadrant and the second quadrant are 0 to pi and the angles in the third quadrant and the fourth quadrant are 0 to-pi in the calculation. If an angle changes from 0 to 2 pi, the actual result is 0-pi, and further-pi-0, jump occurs at the angle pi, the jump amplitude is 2 pi, and the phase winding problem is solved. In radar signal processing, a phase value of each pulse after distance Fourier transform is obtained through an arc tangent function. In order to solve the phase winding problem (i.e., phase unwrapping), the phase unwrapping is performed by an algorithm, and the processed phase data is corrected.
In some embodiments, the algorithm flow for phase solution is as follows: setting the current phase value to
Figure BDA0003324628080000162
The next phase value is
Figure BDA0003324628080000163
When in use
Figure BDA0003324628080000164
When the angle is larger than the preset value, the angle jumps at pi. Then, the analysis was carried out in two cases when
Figure BDA0003324628080000165
When it is, then
Figure BDA0003324628080000166
When in use
Figure BDA0003324628080000167
When it is, then
Figure BDA0003324628080000168
(2) Phase difference
The instantaneous phase change curve after phase unwrapping not only contains the heartbeat time domain signal of a target human body, but also contains a series of harmonic interference, high-frequency noise and the like brought by a radar nonlinear system.
In some embodiments, a phase difference method is used to perform a phase difference operation on the unwrapped phases by subtracting adjacent phase values to eliminate harmonic interference and high frequency noise. Alternatively, the phase difference operation may satisfy the following formula (22):
a (m) ═ a (m) -a (m-1) formula (22)
Where m points to the index of the sequence, and a (m) refers to the amplitude of the corresponding point.
(3) Band pass filtering
The band-pass filtering refers to a filtering method for filtering high-frequency and low-frequency signals and reserving intermediate-frequency signals in the process of signal processing. Filters have many applications in data acquisition and analysis, which change the frequency content of a time signal by reducing or amplifying certain frequencies, through which a band-pass filtering process can be performed. Common digital filters include: IIR filters and FIR filters. The unit impulse response of the IIR filter is infinite, and a feedback loop is arranged in the network. The unit impulse response of the FIR filter is finite in length, with no feedback loop in the network.
In the heartbeat detection process, the heartbeat time domain signal can be extracted through band-pass filtering. The typical value of the heartbeat frequency is in the range of 0.9 Hz-2 Hz as proved by investigation.
In some embodiments, frequency components other than the heartbeat frequency, as well as clutter signals, noise signals, and the like, may be filtered out by setting the pass band cutoff frequency of the filter to 0.9Hz, the stop band cutoff frequency to 2Hz, and the signal sampling frequency to 20 Hz.
Based on the step S1024, the unnecessary frequency components, the clutter signals, the noise signals, and the like may be filtered out by band-pass filtering, and the heartbeat time domain signals of the target human body may be filtered out.
Alternatively, as shown in fig. 15, the peak-to-valley difference threshold mentioned in step S103 may be obtained by:
s201, calculating a peak-valley difference value of each peak-valley pair in the heartbeat time domain signal.
Wherein the peak-valley difference is the difference between the peak-valley value and the valley-point value of the peak-valley pair. Alternatively, the peak-to-valley difference value may satisfy the following formula (23):
Di＝Pi-Viformula (23)
Wherein D isiDenotes the peak-to-valley difference, P, of the ith peak-to-valley pairiDenotes the peak value, V, of the ith peak-valley pairiDenotes a valley point value of the ith peak-valley pair, and i denotes a number of the peak-valley pair.
In some embodiments, the peak-to-valley difference is visualized to represent the distribution of the difference. Illustratively, as shown in fig. 16, the peak-to-valley difference D obtained according to the formula (23)iA histogram with a number of blocks of 15 is made. Therefore, the fluctuation condition of the heartbeat time domain signal can be visually seen, and a useful signal can be accurately screened out.
S202, determining a peak-valley difference threshold value according to the peak-valley difference value of each peak-valley pair in the heartbeat time domain signal.
Alternatively, as shown in fig. 17, step S202 may be implemented as the following steps:
s2021, performing kernel density estimation on the peak-valley difference value of each peak-valley pair in the heart jump time domain signal, and determining a probability density function related to the peak-valley difference value.
Wherein the probability density function is used to describe the probability distribution to which the continuous type random variable obeys. The probability density function satisfies the characteristic of integration 1, and as shown in equation (24), the probability density function is integrated and the area is represented as 1.
Figure BDA0003324628080000181
Where K (x) represents a probability density function.
Kernel density estimation, a non-parametric method for estimating probability density functions, uses a smooth peak function to fit observed data points, thereby simulating a true probability distribution curve.
In some embodiments, the kernel density estimation process for the peak-to-valley difference of each peak-to-valley pair in the heart-beat time domain signal may satisfy the following equation (25):
Figure BDA0003324628080000182
wherein f ish(x) Representing the probability density function, K (·) is the kernel function, h is the smoothing parameter, and n is the total number of sample points.
The kernel function can reduce the amount of calculation generated after mapping the input data from a low-dimensional space to a high-dimensional space, and the larger the dimension number mapped to the high-dimensional space is, the better the performance of distinguishing the difference of the input data is. The nature of the kernel function is convolution smoothing to achieve probability density estimation.
In some embodiments, the peak-to-valley difference D of the beat time domain signaliThe kernel density estimation is performed on the basis of the histogram of (a), resulting in a histogram with a kernel smoothing probability density function fit as shown in fig. 18. Meanwhile, according to fig. 18, a probability density function of the heartbeat time domain signal of the target human body shown in fig. 19 can be obtained.
S2022, determining a peak-valley difference threshold value according to the probability density function.
In the embodiment of the present application, a ratio of the number of peak-valley pairs having a peak-valley difference value smaller than or equal to the peak-valley difference threshold to the number of peak-valley pairs existing in the heartbeat time domain signal is equal to a preset ratio. The preset ratio may be determined through expert experience, big data learning, and the like. For example, the preset ratio may be 10%.
Based on the above, the peak-valley difference threshold is used as an unknown number, the integral operation is performed in a numerical interval from 0 to the peak-valley difference threshold of the probability density function, the result of the integral operation is equal to a preset proportion and is used as an objective function, and the peak-valley difference threshold is calculated by solving the objective function.
Based on the technical scheme, the peak-valley difference value of the peak-valley pair is calculated, and the peak-valley difference value data is fitted by utilizing kernel density estimation to obtain a probability density function, so that the peak-valley difference threshold value is determined. Therefore, the distribution characteristics of the data can be researched from the aspect of the peak-valley difference data, and the peak-valley difference threshold can be reasonably determined. It should be appreciated that by properly setting the peak-to-valley difference threshold and screening out the effective peak-to-valley pair based on the peak-to-valley difference threshold, a reasonable frequency selection range can be determined to assist in reducing noise interference.
The above method embodiments have been described primarily in terms of a radar apparatus using one receiver channel for heartbeat detection. The following description will be made in terms of a radar apparatus using a plurality of receiver channels for heartbeat detection.
As shown in fig. 20, an embodiment of the present application provides a heartbeat detection method, including the following steps:
s301, transmitting electromagnetic waves to a target space, and acquiring echo signals of P channels.
Wherein P is an integer greater than 1.
Step S301 is similar to step S101 in the embodiment shown in fig. 4, and the detailed description may refer to the foregoing, which is not repeated herein.
S302, echo signals of the P channels are processed to obtain heartbeat time domain signals of the P channels of the target human body.
And S303, respectively determining the number of effective peak-valley pairs in the heartbeat time domain signal of each channel in the P channels according to the heartbeat time domain signal of the channel.
S304, for each channel in the P channels, determining the frequency selection range of the channel according to the number of effective peak-valley pairs in the heartbeat time domain signal of the channel.
S305, carrying out fast Fourier numbering on the heartbeat time domain signal of each channel in the P channels to obtain the heartbeat frequency domain signal of the channel.
S306, for each channel in the P channels, finding out a target spectrum peak in the heartbeat frequency domain signal of the channel in the frequency selection range of the channel.
For example, taking the radar device to perform the heartbeat detection by using four receiver channels, as shown in fig. 21, the heartbeat frequency domain signal is obtained after the heartbeat time domain signal is subjected to fast fourier transform. As can be seen from fig. 21, the frequency spectrums of the four channels each have several distinct frequency spectrum peaks, and it is impossible to accurately determine which frequency spectrum peak is the final selectable target frequency spectrum peak, so that the target frequency spectrum peak whose peak point value falls within the frequency selection range is searched for according to the frequency selection range determined in step S304. As shown in fig. 22, target spectrum peaks of the heartbeat frequency domain signals found in the frequency selection range are found for the four channels.
The steps S303 to S306 are similar to the steps S103 to S106 in the embodiment shown in fig. 4, and the detailed description may refer to the foregoing, which is not repeated herein.
It should be understood that the peak-to-valley difference threshold may be different for different ones of the P channels, and the peak-to-valley difference threshold for each of the P channels may be determined according to the embodiment shown in fig. 15. In this way, the peak-to-valley difference threshold of each channel is adapted to the receiving condition of each channel to eliminate the interference of the noise of each channel, so that the number of effective peak-to-valley pairs of each channel determined in the subsequent process is accurate.
Illustratively, taking the example of a radar device using four receiver channels for heartbeat detection, the peak-to-valley difference thresholds may be different for different ones of the four channels. As shown in fig. 23, for each of the four channels, kernel density estimation is performed on the peak-to-valley difference values of each peak-to-valley pair in the heart-skip time domain signal, so as to obtain a histogram with kernel smooth probability density function fitting related to the peak-to-valley difference values. Meanwhile, probability density functions of the heartbeat time domain signals of the target human body of the four channels shown in fig. 24 can be obtained according to fig. 23, and peak-to-valley difference thresholds of the four channels are respectively determined according to the probability density functions.
And S307, for each channel in the P channels, taking the frequency corresponding to the peak value of the target spectrum peak in the heartbeat frequency domain signal of the channel as the heartbeat frequency estimation value of the channel.
And S308, determining the heartbeat frequency of the target human body according to the heartbeat frequency estimation values of the P channels.
In some embodiments, an average of the estimated values of the heartbeat frequencies of the P channels is calculated, and the average is directly used as the heartbeat frequency of the target human body.
In other embodiments, since noise interference may cause the difference between the average of the estimated values of the heartbeat frequencies of the P channels and the actual heartbeat frequency to be too large, the heartbeat frequency of the target human body detected by the radar device is inaccurate, and therefore, in order to reduce the noise interference, the heartbeat frequency of the target human body may be predicted according to the historical heartbeat frequency, and the heartbeat frequency of the target human body is determined by combining the average of the estimated values of the heartbeat frequencies of the P channels and the predicted heartbeat frequency. Specifically, as shown in fig. 25, step S308 may be implemented as the following steps:
s3081, obtaining the historical heartbeat frequency of the target human body.
S3082, determining the predicted heartbeat frequency according to the historical heartbeat frequency of the target human body.
In some embodiments, kalman filtering is performed on the historical heartbeat frequency of the target human body to obtain the predicted heartbeat frequency. The processing procedure of the kalman filtering process is similar to step S1072 in the embodiment shown in fig. 11, and for the specific description, reference may be made to the foregoing, and details are not repeated here.
S3083, when the absolute value of the difference value between the average value of the heartbeat frequency estimated values of the P channels and the predicted heartbeat frequency is less than or equal to a preset value, taking the average value of the heartbeat frequency estimated values of the P channels as the heartbeat frequency of the target human body.
In this way, when the absolute value of the difference between the average of the estimated heartbeat frequencies of the P channels and the predicted heartbeat frequency is less than or equal to the preset value, it indicates that the deviation between the heartbeat frequency detected by the radar device and the actual heartbeat frequency of the target human body is not large, and therefore, the average of the estimated heartbeat frequencies of the P channels is used as the heartbeat frequency of the target human body.
S3084, when the absolute value of the difference value between the average value of the estimated heartbeat frequency values of the P channels and the predicted heartbeat frequency is larger than a preset value, taking the predicted heartbeat frequency as the heartbeat frequency of the target human body.
Therefore, when the absolute value of the difference between the average value of the estimated heartbeat frequency values of the P channels and the predicted heartbeat frequency is larger than the preset value, the problem of heartbeat frequency jump can occur due to overlarge deviation between the heartbeat frequency detected by the radar equipment and the actual heartbeat frequency of the target human body, and therefore the predicted heartbeat frequency is used as the heartbeat frequency of the target human body, and the result of heartbeat detection can be more accurate.
Based on the embodiment shown in fig. 20, a technical solution is provided in which the radar device uses P receiver channels to perform heartbeat detection, and on the basis of the embodiment shown in fig. 4, for each of the P channels, a frequency corresponding to a peak value of a target spectrum peak in a heartbeat frequency domain signal of the channel is used as a heartbeat frequency estimation value of the channel, and then the heartbeat frequency of the target human body is determined according to the heartbeat frequency estimation values of the P channels. Thus, compared with a single receiver channel, the final heartbeat frequency is determined by comprehensively considering the heartbeat frequency estimated values of the P channels, and the accuracy is higher.
Optionally, as shown in fig. 26, the step 302 may be implemented as the following steps:
and S3021, sequentially performing pulse compression and moving target display processing on the echo signals of the P channels, and respectively determining target range images of the P channels.
And S3022, detecting the human body target by the target distance images of the P channels, and determining the distance unit of the target human body.
Optionally, as shown in fig. 27, step S3022 may be specifically implemented as the following steps:
s30221, performing incoherent accumulation on the target range images of the P channels to obtain data after incoherent accumulation.
It should be understood that the target range image obtained in step S3021 includes one or more peak values, and the range unit of the target human body may be obtained by peak value detection. Effective measures can be taken to improve the signal-to-noise ratio before peak detection is performed. Common methods for improving the signal-to-noise ratio are coherent accumulation and non-coherent accumulation. Coherent accumulation is to add the signals of adjacent periods directly and accumulate the energy of the signals by using the correlation of the signals in the integration time. Since the noise has no coherence during the integration time, the signal-to-noise ratio can be significantly improved. Incoherent integration is performed by squaring (detecting) the signal modulo and then accumulating. Compared with coherent accumulation, incoherent accumulation removes phase information, only retains amplitude information, and is simpler to process. Thus, in some embodiments of the present application, data is processed in a non-coherent accumulation manner.
In the embodiment of the application, the target range images of the P channels are subjected to incoherent accumulation to obtain data with high signal to noise ratio, so that the range unit of the target human body can be determined more accurately.
S30222, determining a distance unit of the target human body from the data after incoherent accumulation by using a constant false alarm rate detection method based on sequencing statistics.
For a specific description of the constant false alarm rate detection method based on the sorting statistics, reference may be made to the foregoing description, and details are not repeated here.
S3023, for each channel in the P channels, determining distance unit data of the target human body from the target distance image of the channel according to the distance unit of the target human body; and sequentially carrying out phase unwrapping, phase difference and band-pass filtering on the distance unit data of the target human body to obtain a heartbeat time domain signal of the target human body.
The steps S3021 to S3023 are similar to the steps S1021 to S1023 in the embodiment shown in fig. 12, and the detailed description can refer to the foregoing, and are not repeated herein.
It can be seen that the foregoing describes the solution provided by the embodiments of the present application primarily from a methodological perspective. To implement the above functions, it includes hardware structures and/or software modules for performing the respective functions. Those of skill in the art will readily appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is performed as hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiment of the present application, functional modules may be divided according to the above method example, for example, each functional module may be divided corresponding to each function, or two or more functions may be integrated into one processing module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. Optionally, the division of the modules in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation.
As shown in fig. 28, an embodiment of the present application provides a radar device, which is configured to execute the heartbeat detection method provided in the embodiment shown in fig. 4, and can improve accuracy of heartbeat detection. The above radar apparatus 300 includes: a radar module 301, a signal processing module 302 and a heartbeat detection module 303.
The radar module 301 is configured to emit an electromagnetic wave to a target space and acquire an echo signal.
The signal processing module 302 is configured to process the echo signal to obtain a heartbeat time domain signal of the target human body; determining the number of effective peak-valley pairs in the heartbeat time domain signal according to the heartbeat time domain signal, and determining a frequency selection range according to the number of the effective peak-valley pairs; the effective peak-valley pair is a peak-valley pair of which the difference value between a peak value and a valley value is greater than or equal to a peak-valley difference threshold value, and the peak-valley pair consists of adjacent peak values and valley values in the heartbeat time domain signal; carrying out fast Fourier transformation on the heartbeat time domain signal to obtain a heartbeat frequency domain signal; and finding a target spectrum peak of the heartbeat frequency domain signal in the frequency selection range.
And the heartbeat detection module 303 is configured to determine a heartbeat frequency of the target human body according to a frequency corresponding to a peak value of the target spectrum peak.
In some embodiments, the signal processing module 302 is specifically configured to calculate a peak-to-valley difference value of each peak-to-valley pair in the heartbeat time-domain signal, where the peak-to-valley difference value is a difference value between a peak value and a valley value in the peak-to-valley pair; and determining a peak-valley difference threshold value according to the peak-valley difference value of each peak-valley pair in the heartbeat time domain signal.
In some embodiments, the signal processing module 302 is specifically configured to perform kernel density estimation on a peak-to-valley difference value of each peak-to-valley pair in the heartbeat time-domain signal, and determine a probability density function related to the peak-to-valley difference value; and determining a peak-to-valley difference threshold value according to the probability density function.
In some embodiments, the signal processing module 302 is specifically configured to find a plurality of spectrum peaks of the heartbeat frequency domain signal within a frequency selection range; and selecting the spectral peak with the maximum peak point value from the plurality of spectral peaks as the target spectral peak.
In some embodiments, the heartbeat detecting module 303 is specifically configured to obtain a historical heartbeat frequency of the target human body; determining a predicted heartbeat frequency according to the historical heartbeat frequency of the target human body; when the absolute value of the difference value between the frequency corresponding to the peak value of the target spectrum peak and the predicted heartbeat frequency is less than or equal to a preset value, taking the frequency corresponding to the peak value of the target spectrum peak as the heartbeat frequency of the target human body; and when the absolute value of the difference value between the frequency corresponding to the peak value of the target spectrum peak and the predicted heartbeat frequency is greater than a preset value, taking the predicted heartbeat frequency as the heartbeat frequency of the target human body.
In some embodiments, the heartbeat detection module 303 is further configured to perform kalman filtering on the historical heartbeat frequency of the target human body to obtain a predicted heartbeat frequency.
In some embodiments, the signal processing module 302 is specifically configured to perform pulse compression and moving target display processing on the echo signal in sequence, and determine a target range profile; detecting a human body target for the target distance image, and determining a distance unit of the target human body; determining distance unit data of the target human body from the target distance image according to the distance unit of the target human body; and sequentially carrying out phase unwrapping, phase difference and band-pass filtering on the distance unit data of the target human body to obtain a heartbeat time domain signal of the target human body.
As shown in fig. 29, an embodiment of the present application provides a radar device, which is configured to execute the heartbeat detection method provided in the embodiment shown in fig. 20, and can better improve the accuracy of heartbeat detection. The above-described radar apparatus 400 includes: a radar module 401, a signal processing module 402 and a heartbeat detection module 403.
The radar module 401 is configured to transmit an electromagnetic wave to a target space and acquire echo signals of P channels, where P is an integer greater than 1.
The signal processing module 402 is configured to process the echo signals of the P channels to obtain heartbeat time domain signals of the P channels of the target human body; for each channel in the P channels, determining the number of effective peak-valley pairs of the channel according to the heartbeat time domain signal of the channel, and determining a frequency selection range according to the number of the effective peak-valley pairs; carrying out fast Fourier transformation on the heartbeat time domain signal of the channel to obtain a heartbeat frequency domain signal of the channel; finding a target spectrum peak of a heartbeat frequency domain signal of the channel in the frequency selection range of the channel; taking the frequency corresponding to the peak point value of the target frequency spectrum peak of the heartbeat frequency domain signal of the channel as the heartbeat frequency estimation value of the channel; the effective peak-valley pair is a peak-valley pair in which the difference between the peak value and the valley value is greater than or equal to a peak-valley difference threshold, and the peak-valley pair is composed of adjacent peak value and valley value in the heartbeat time domain signal.
And a heartbeat detection module 403, configured to determine a heartbeat frequency of the target human body according to the heartbeat frequency estimation values of the P channels.
An embodiment of the present application provides a radar apparatus, including: one or more processors; one or more memories. Wherein the one or more memories are configured to store computer program code comprising computer instructions which, when executed by the one or more processors, cause the radar apparatus to perform any of the heartbeat detection methods provided by the embodiments above.
The embodiment of the present application further provides a computer-readable storage medium, where the computer-readable storage medium includes computer-executable instructions, and when the computer-executable instructions are executed on a computer, the computer is caused to execute any one of the heartbeat detection methods provided in the foregoing embodiments.
The embodiment of the present invention further provides a computer program product, where the computer program product includes a computer instruction, and when the computer instruction runs on a computer, the computer can implement any one of the heartbeat detection methods provided in the foregoing embodiments after being executed by the computer.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented using a software program, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer-executable instructions. The processes or functions according to the embodiments of the present application are generated in whole or in part when the computer-executable instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer executable instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer executable instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). Computer-readable storage media can be any available media that can be accessed by a computer or can comprise one or more data storage devices, such as servers, data centers, and the like, that can be integrated with the media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
While the present application has been described in connection with various embodiments, other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed application, from a review of the drawings, the disclosure, and the appended claims. In the claims, the word "Comprising" does not exclude other elements or steps, and the word "a" or "an" does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.
Although the present application has been described in conjunction with specific features and embodiments thereof, it will be evident that various modifications and combinations can be made thereto without departing from the spirit and scope of the application. Accordingly, the specification and figures are merely exemplary of the present application as defined in the appended claims and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
The above is only an embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions within the technical scope of the present disclosure should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (10)

1. A heartbeat detection method, applied to a radar device, the method comprising:
transmitting electromagnetic waves to a target space and acquiring echo signals;
processing the echo signal to obtain a heartbeat time domain signal of a target human body;
determining the number of effective peak-valley pairs in the heartbeat time domain signal according to the heartbeat time domain signal, and determining a frequency selection range according to the number of the effective peak-valley pairs; wherein the effective peak-valley pair is a peak-valley pair in which a difference between a peak value and a valley value is greater than or equal to a peak-valley difference threshold, the peak-valley pair being composed of adjacent peak values and valley values in the heartbeat time-domain signal;
carrying out fast Fourier transformation on the heartbeat time domain signal to obtain a heartbeat frequency domain signal;
finding a target spectrum peak of the heartbeat frequency domain signal in the frequency selection range;
and determining the heartbeat frequency of the target human body according to the frequency corresponding to the peak value of the target spectrum peak.
2. The method according to claim 1, wherein before said determining the number of valid pairs of peaks and valleys in the heartbeat time domain signal according to the heartbeat time domain signal, comprising:
calculating a peak-valley difference value of each peak-valley pair in the heartbeat time domain signal, wherein the peak-valley difference value is a difference value between a peak value and a valley value in the peak-valley pair;
and determining the peak-valley difference threshold according to the peak-valley difference value of each peak-valley pair in the heartbeat time domain signal.
3. The method according to claim 2, wherein determining the peak-to-valley difference threshold value according to the peak-to-valley difference value of each peak-to-valley pair in the heartbeat time-domain signal comprises:
performing kernel density estimation on the peak-valley difference value of each peak-valley pair in the heartbeat time domain signal, and determining a probability density function related to the peak-valley difference value;
and determining the peak-valley difference threshold according to the probability density function.
4. The method according to claim 1, wherein finding the target spectral peak of the heartbeat frequency domain signal within the frequency selection range comprises:
finding a plurality of frequency spectrum peaks of the heartbeat frequency domain signal in the frequency selection range;
and selecting the frequency spectrum peak with the maximum peak point value from the plurality of frequency spectrum peaks as the target frequency spectrum peak.
5. The method according to claim 1, wherein the determining the heartbeat frequency of the target human body according to the frequency corresponding to the peak value of the target spectral peak comprises:
acquiring historical heartbeat frequency of the target human body;
determining a predicted heartbeat frequency according to the historical heartbeat frequency of the target human body;
when the absolute value of the difference value between the frequency corresponding to the peak value of the target spectrum peak and the predicted heartbeat frequency is smaller than or equal to a preset value, taking the frequency corresponding to the peak value of the target spectrum peak as the heartbeat frequency of the target human body;
and when the absolute value of the difference value between the frequency corresponding to the peak value of the target spectrum peak and the predicted heartbeat frequency is greater than a preset value, taking the predicted heartbeat frequency as the heartbeat frequency of the target human body.
6. The method of claim 5, wherein determining a predicted heartbeat frequency from a historical heartbeat frequency of the target human body comprises:
and performing Kalman filtering processing on the historical heartbeat frequency of the target human body to obtain the predicted heartbeat frequency.
7. The method according to any one of claims 1 to 6, wherein the processing the echo signal to obtain a heartbeat time domain signal of the target human body comprises:
sequentially performing pulse compression and moving target display processing on the echo signals to determine a target range profile;
detecting a human body target for the target distance image, and determining a distance unit of a target human body;
determining distance unit data of the target human body from the target distance image according to the distance unit of the target human body;
and sequentially carrying out phase unwrapping, phase difference and band-pass filtering on the distance unit data of the target human body to obtain a heartbeat time domain signal of the target human body.
8. A heartbeat detection method, applied to a radar device, the method comprising:
transmitting electromagnetic waves to a target space, and acquiring echo signals of P channels, wherein P is an integer greater than 1;
processing the echo signals of the P channels to obtain heartbeat time domain signals of the P channels of the target human body;
for each channel in the P channels, determining the number of effective peak-valley pairs of the channel according to the heartbeat time domain signal of the channel, and determining a frequency selection range according to the number of the effective peak-valley pairs; carrying out fast Fourier transformation on the heartbeat time domain signal of the channel to obtain a heartbeat frequency domain signal of the channel; finding a target spectrum peak of a heartbeat frequency domain signal of the channel in the frequency selection range of the channel; taking the frequency corresponding to the peak point value of the target frequency spectrum peak of the heartbeat frequency domain signal of the channel as the heartbeat frequency estimation value of the channel; the effective peak-valley pair is a peak-valley pair of which the difference value between a peak value and a valley value is greater than or equal to a peak-valley difference threshold value, and the peak-valley pair is formed by adjacent peak values and valley values in the heartbeat time domain signal;
and determining the heartbeat frequency of the target human body according to the heartbeat frequency estimation values of the P channels.
9. A radar apparatus, comprising:
one or more processors;
one or more memories;
wherein the one or more memories are to store computer program code comprising computer instructions which, when executed by the one or more processors, cause the radar apparatus to perform the heartbeat detection method of any of claims 1 to 8.
10. A computer-readable storage medium comprising computer-executable instructions that, when executed on a computer, cause the computer to perform the heartbeat detection method of any of claims 1 to 8.
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