CN110974196A - Non-contact respiration and heart rate detection method in motion state - Google Patents
Non-contact respiration and heart rate detection method in motion state Download PDFInfo
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- 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
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Abstract
The invention relates to a non-contact respiration and heart rate detection method in a motion state, which realizes the measurement of respiration and heart rate in the motion state, provides that the motion amplitude in the motion state is far larger than the amplitude of the respiration and heart rate, the envelope can be approximately considered to be caused by motion, adopts a wavelet transformation method to extract the envelope of a CSI signal, which is the sum of walking, respiration and heart rate signals, subtracts the motion signal from a total signal to obtain the respiration and heart rate signals, and adopts a Butterworth band-pass filter to separate the respiration and the heart rate according to the difference of frequency ranges. The invention realizes accurate measurement of respiration and heart rate in motion state.
Description
Technical Field
The invention relates to the field of channel state information processing, in particular to a non-contact respiration and heart rate detection method in a motion state.
Background
According to the data provided by the cardiovascular disease report in China, the prevalence rate of cardiovascular diseases in China is continuously increased. The number of cardiovascular diseases in China currently exceeds 2.9 hundred million. Authoritative data of capital medical university show that the incidence rate of the Chinese lung cancer in 2017 reaches 80 thousands, the number of deaths is nearly 70 thousands, and the number of deaths accounts for one fourth of the number of deaths caused by all cancers. This figure is still growing at 26.9% per year, and it is estimated that by 2025, china will die of lung cancer in 100 million each year. These diseases can be detected and treated early if respiratory and heart rate information can be detected on a person. However, since hospital electrocardiography and breath testing are expensive and many patients are delayed, it is critical to find a quick, inexpensive or even free test device.
In recent years, the detection of non-contact vital signals has attracted much attention. Vital signal detection based on FMCW radar can result in more accurate measurements, but is difficult to generalize due to the high cost. With the popularization of WiFi signals and the low price thereof, the detection of vital signals by utilizing the WiFi signals draws great attention, and the commercial WiFi equipment has wide prospect for detecting the respiration and the heart rate of a human body. However, the current methods are all used for measuring the static state of a person such as sleeping or sitting, and the detection of heart rate and breathing in a motion state is not proposed yet.
Disclosure of Invention
In view of this, the present invention provides a non-contact respiration and heart rate detection method in a motion state, which solves the problem of difficulty in detecting heart rate and respiration in the motion state, and realizes accurate measurement of respiration rate and heart rate in the motion state.
The invention is realized by adopting the following scheme: a non-contact respiration and heart rate detection method in a motion state comprises the following steps:
step S1: selecting the best sub-carrier: setting a transmitting end as an antenna, wherein the receiving end comprises three antennas; a person to be detected moves randomly within the visual distance range of the transmitting end and the receiving end; each antenna of a receiving end can obtain 30 subcarriers, the three receiving antennas receive 90 subcarriers in total, the power spectrum of each subcarrier is calculated, and the subcarrier with the maximum power spectrum peak value is found out to be used as a signal for measuring respiration and heart rate;
step S2: filtering the signals of the respiration and heart rate measurement in the step S1 by a hampel filter to the subcarrier with the maximum power spectrum peak value, and removing singular points; s (t) is the filtered signal, r (t) is the respiration signal, h (t) is the heart rate signal;
s(t)＝q(t)+r(t)+h(t)
s (t) is the filtered signal, r (t) is the respiration signal, h (t) is the heart rate signal; q (t) is a signal of walking;
step S3: decomposing the signal subjected to singular point filtering in the step S2 by using wavelet transform, selecting haar wavelet, and selecting a heuristic SURE domain value selection method for the decomposed signal on the 5 th layer of decomposition to perform threshold processing; wavelet reconstruction using inverse wavelet transform, i.e. wf(a, b) carrying out inverse transformation to obtain a motion signal envelope w (t);
Ψ ((t-b)/a) is the selection of appropriate stretch and translation factors for the wavelet basis functions f (t) is the filtered signal, t is time, where a is the stretch factor and b is the translation factor;
step S4: subtracting the motion signal envelope of the step S3 from the signal of the step S2 after singular point filtering to obtain respiration and heart rate signals;
r(t)+h(t)＝s(t)-w(t)
step S5: performing Butterworth band-pass filtering on the respiration and heart rate signals acquired in the step S4, wherein the pass band frequency is 0.15-0.5Hz, and the respiration signals are filtered out, and the pass band frequency is 0.8-2.5 Hz;
step S6: discrete fourier transform is performed on the two signals obtained in step S5, and the frequencies corresponding to the peak points detected in the frequency spectrum are the respiration rate and the heart rate.
Further, in step S1, the power spectrum of each subcarrier is calculated, and the subcarrier with the largest peak of the power spectrum is found; the power spectrum calculation formula is as follows:
finding the maximum power spectrum peak y ═ max (max (E)i,j))，i＝1,2,3；j＝1,…,30；
E is the signal power, x (t) is the subcarrier signal, t is time, | X (jw) | is the Fourier transform of x (t) signal, y is the best subcarrier found, E is the signal power, t is the time, l X (jw) is the Fourier transform of x (t) signal, E is the best subcarrier foundi,jFor the power of different sub-carriers, i denotes different antennas and j denotes the number of sub-carriers.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a method for extracting the envelope of a CSI signal by adopting a wavelet transform method, wherein the envelope is the sum of walking signals, breathing signals and heart rate signals, the motion signals are subtracted from the total signal to form the breathing and heart rate signals, and a Butterworth band-pass filter is adopted to separate the breathing and the heart rate according to the difference of frequency ranges. Accurate measurement of respiration and heart rate under motion state is realized.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention.
FIG. 2 is a diagram of a test environment according to an embodiment of the present invention.
Fig. 3 is a diagram of filtered CSI magnitudes according to an embodiment of the invention.
Fig. 4 is an extracted motion envelope diagram according to an embodiment of the present invention.
FIG. 5 is a respiratory waveform diagram according to an embodiment of the present invention, wherein FIG. 5(a) is a respiratory time domain waveform diagram and FIG. 5(b) is a respiratory frequency domain waveform diagram.
Fig. 6 is a heart rate waveform diagram according to an embodiment of the invention, in which fig. 6(a) is a heart rate time domain waveform diagram, and fig. 6(b) is a heart rate frequency domain waveform diagram.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As shown in FIG. 2, in the motion state of a human, the motion amplitude is far larger than the amplitude of the respiration and the heart rate, so that the envelope is approximately considered to be caused by the motion, and the respiration rate and the heart rate can be measured after the envelope is filtered. The amplitude of the signals caused by movement is large, the amplitudes of the respiration and heart rate signals are relatively small and almost submerged, the respiration and heart rate signals can be obtained through the embodiment, the embodiment is provided with a signal sending end and a signal receiving end in a wide field, the sending end is an antenna, and the receiving end comprises three antennas; a person to be detected moves randomly within the visual distance range of the transmitting end and the receiving end; the person walks about at will and interferes with the signal, and then the measurement of breathing and heart rate is carried out.
As shown in fig. 1, the present embodiment provides a non-contact respiration and heart rate detection method in a motion state, including the following steps:
step S1: selecting the best sub-carrier: WiFi adopts OFDM modulation technology, a transmitting end is set as one antenna in a specified field, and a receiving end comprises three antennas; the personnel to be detected move freely within the sight distance range of the transmitting end and the receiving end; each antenna of a receiving end can obtain 30 subcarriers, the three receiving antennas receive 90 subcarriers in total, the power spectrum of each subcarrier is calculated, and the subcarrier with the maximum power spectrum peak value is found out to be used as a signal for measuring respiration and heart rate;
step S2: filtering the signals of the respiration and heart rate measurement in the step S1 by a hampel filter to the subcarrier with the maximum power spectrum peak value, and removing singular points;
s(t)＝q(t)+r(t)+h(t)
s (t) is the filtered signal, r (t) is the respiration signal, h (t) is the heart rate signal; q (t) is a signal of walking;
step S3: decomposing the signal subjected to the singular point filtering in the step S2 by using wavelet transform, and selecting haar wavelets; on the 5 th layer of decomposition, selecting a heuristic SURE domain value selection method for the decomposed signals to carry out threshold processing; wavelet reconstruction using inverse wavelet transform, i.e. wf(a, b) carrying out inverse transformation to obtain a motion signal envelope w (t);
Ψ ((t-b)/a) is the selection of appropriate stretch and translation factors for the wavelet basis functions, f (t) is the filtered signal, t is time, where a is the stretch factor and b is the translation factor; as shown in fig. 3 and 4.
Step S4: subtracting the motion signal envelope of the step S3 from the signal of the step S2 after singular point filtering to obtain respiration and heart rate signals;
r(t)+h(t)＝s(t)-w(t)
step S5: the respiration is relatively slow at 9 breaths per minute with a frequency of 0.15Hz, and most rapidly no more than 36 breaths per minute with a frequency of 0.6Hz, and the heart rate is typically between 48 and 150 breaths per minute with a corresponding frequency of 0.8-2.5 Hz. Performing Butterworth band-pass filtering on the respiration and heart rate signals acquired in the step S4, wherein the pass band frequency is 0.15-0.5Hz, and the respiration signals are filtered out, and the pass band frequency is 0.8-2.5 Hz;
step S6: the two signals obtained in step S5 are respectively subjected to discrete fourier transform, and the frequencies corresponding to the peak points detected in the frequency spectrum are the respiration rate and the heart rate, as shown in fig. 5 and 6.
In this embodiment, the power spectrum of each subcarrier is calculated in step S1, and the subcarrier with the largest peak of the power spectrum is found; the power spectrum calculation formula is as follows:
finding the maximum power spectrum peak y ═ max (max (E)i,j))，i＝1,2,3；j＝1,…,30；
E is the signal power, x (t) is the subcarrier signal, t is time, | X (jw) | is the Fourier transform of x (t) signal, y is the best subcarrier found, E is the signal power, t is the time, l X (jw) is the Fourier transform of x (t) signal, E is the best subcarrier foundi,jFor the power of different sub-carriers, i denotes different antennas and j denotes the number of sub-carriers.
Preferably, in this embodiment, 90 subcarriers are first screened, the subcarrier with the largest power spectrum peak value is found out as a signal for measuring respiration and heart rate, a hampel filter is used to perform singular point filtering on the selected subcarrier, then wavelet transformation is performed on the selected subcarrier to obtain a motion envelope, the envelope is subtracted from the carrier signal to obtain respiration and heart rate signals, and finally the respiration and heart rate are accurately measured by a butterworth band pass filter.
The above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the claims of the present invention should be covered by the present invention.
Claims (2)
1. A non-contact respiration and heart rate detection method in a motion state is characterized in that: the method comprises the following steps:
step S1: selecting the best sub-carrier: setting a transmitting end as an antenna, wherein the receiving end comprises three antennas; a person to be detected moves randomly within the visual distance range of the transmitting end and the receiving end; each antenna of a receiving end can obtain 30 subcarriers, the three receiving antennas receive 90 subcarriers in total, the power spectrum of each subcarrier is calculated, and the subcarrier with the maximum power spectrum peak value is found out to be used as a signal for measuring respiration and heart rate;
step S2: filtering the signals of the respiration and heart rate measurement in the step S1 by a hampel filter to the subcarrier with the maximum power spectrum peak value, and removing singular points;
s(t)＝q(t)+r(t)+h(t)
s (t) is the filtered signal, r (t) is the respiration signal, h (t) is the heart rate signal; q (t) is a signal of walking;
step S3: decomposing the signal subjected to the singular point filtering in the step S2 by using wavelet transform, and selecting haar wavelets; on the decomposed 5 th layer, selecting a heuristic SURE threshold selection method for the decomposed signals to perform threshold processing; wavelet reconstruction using inverse wavelet transform, i.e. wf(a, b) carrying out inverse transformation to obtain a motion signal envelope w (t);
Ψ ((t-b)/a) is the selection of appropriate stretch and translation factors for the wavelet basis functions, f (t) is the filtered signal, t is time, where a is the stretch factor and b is the translation factor;
step S4: subtracting the motion signal envelope of the step S3 from the signal of the step S2 after singular point filtering to obtain respiration and heart rate signals;
r(t)+h(t)＝s(t)-w(t)
step S5: performing Butterworth band-pass filtering on the respiration and heart rate signals acquired in the step S4, wherein the pass band frequency is 0.15-0.5Hz, and the respiration signals are filtered out, and the pass band frequency is 0.8-2.5 Hz;
step S6: discrete fourier transform is performed on the two signals obtained in step S5, and the frequencies corresponding to the peak points detected in the frequency spectrum are the respiration rate and the heart rate.
2. A method of non-contact breath and heart rate detection in motion according to claim 1, wherein: calculating the power spectrum of each subcarrier in the step S1, and finding out the subcarrier with the maximum peak value of the power spectrum; the power spectrum calculation formula is as follows:
finding the maximum power spectrum peak y ═ max (max (E)i,j))，i＝1,2,3；j＝1,…,30；
E is the signal power, x (t) is the subcarrier signal, t is time, | X (jw) | is the Fourier transform of x (t) signal, y is the best subcarrier found, E is the signal power, t is the time, l X (jw) is the Fourier transform of x (t) signal, E is the best subcarrier foundi,jFor the power of different sub-carriers, i denotes different antennas and j denotes the number of sub-carriers.
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| CN111657897A (en) * | 2020-07-02 | 2020-09-15 | 武汉领科新云科技有限公司 | Artificial intelligence algorithm for detecting human body signs |
| CN115040092A (en) * | 2022-06-13 | 2022-09-13 | 天津大学 | Heart rate monitoring method and respiratory event detection method based on channel state information |
| CN115040092B (en) * | 2022-06-13 | 2024-06-14 | 天津大学 | Channel state information-based heart rate monitoring method and respiratory event detection method |
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