CN112244794A - Vital sign detection method and device based on periodic characteristics and storage medium - Google Patents
Vital sign detection method and device based on periodic characteristics and storage medium Download PDFInfo
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Abstract
The invention discloses a method, a device and a storage medium for detecting vital signs based on periodic characteristics, and the method for detecting the vital signs based on the periodic characteristics comprises the following steps: sending a radar signal to a to-be-detected area, and receiving a radar echo signal returned by the to-be-detected area; performing signal preprocessing on the radar echo signal, and determining the state of a human body target in a region to be detected; if the human body target exists in the region to be detected, extracting the vital sign data with the periodic characteristics from the radar echo signal of the region to be detected based on the periodic characteristics of the vital sign data; and carrying out data processing on the vital sign data with the periodic characteristics to obtain a vital sign detection result with the periodic characteristics. The method, the device and the storage medium for detecting the vital sign based on the periodic characteristics disclosed by the embodiment of the invention improve the detection precision of the vital sign data.
Description
Technical Field
The embodiment of the invention provides a signal processing technology, and particularly relates to a method and a device for detecting vital signs based on periodic characteristics and a storage medium.
Background
With the continuous development of society, people pay more and more attention to personal health, and the health condition of individuals is mainly reflected by vital sign information such as respiration, heart rate, body temperature, blood pressure, pulse and the like. The vital signs of the periodic changes such as the respiration and the heart rate can most directly reflect the physiological condition of the human body, so how to accurately detect the vital signs of the periodic changes such as the respiration and the heart rate becomes the most important thing in the monitoring of the health condition of the human body.
At present, the detection means for the periodically changing vital signs such as respiration and heart rate include a contact type detection method and a non-contact type detection method, wherein the contact type detection method needs a detected person to wear a detection device, but on one hand, wearing the detection device for a long time brings discomfort to the detected person, on the other hand, a professional detection device is expensive and needs to be operated by a professional, and the detection precision of the non-professional detection device is not high. The non-contact detection method is easily interfered by the outside because the non-contact detection method does not directly contact the detected human body, thereby also influencing the measurement precision.
In summary, the existing methods for detecting periodically changing vital signs such as respiration and heart rate of a human body have certain defects, and are not suitable for detecting the human body for a long time.
Disclosure of Invention
The invention provides a method, a device and a storage medium for detecting vital signs based on periodic characteristics, which improve the detection precision of vital sign data.
In a first aspect, an embodiment of the present invention provides a method for detecting a vital sign based on a periodic feature, including:
sending a radar signal to a to-be-detected area, and receiving a radar echo signal returned by the to-be-detected area;
performing signal preprocessing on the radar echo signal, and determining the state of a human body target in a region to be detected;
if the human body target exists in the region to be detected, extracting the vital sign data with the periodic characteristics from the radar echo signal of the region to be detected based on the periodic characteristics of the vital sign data;
and carrying out data processing on the vital sign data with the periodic characteristics to obtain a vital sign detection result with the periodic characteristics.
In a possible implementation manner of the first aspect, signal preprocessing is performed on a radar echo signal to determine a state of a human target in an area to be detected, and the method includes:
performing clutter suppression on the radar echo signal to obtain a radar echo signal subjected to clutter suppression;
detecting a human body target according to the radar echo signal after clutter suppression, and determining whether the human body target exists in the region to be detected;
and detecting the human body state of the determined human body target, and verifying the state of the human body target in the region to be detected.
In a possible implementation manner of the first aspect, performing clutter suppression on a radar echo signal includes:
clutter suppression is performed on the radar echo signal using the following formula,
c(m，n)＝α·c(m，n-1)+(1-α)R′(m，n)
R(m，n)＝R′(m，n)-c(m，n)
wherein R '(m, n) ═ R't(m，n)+R′u(m，n)+R′ω(m, n), R '(m, n) represents a radar echo signal, R't(m, n) denotes a target radar echo component, R'u(m, n) denotes a clutter radar echo component and R'ωThe (M, N) represents a receiver thermal noise radar echo component, M is 0, 1, …, M-1 represents a slow time dimension sampling number, N is 0, 1, …, N-1 represents a fast time dimension sampling number, R (M, N) represents a radar echo signal after clutter suppression, c (M, N) represents background clutter, and 1 ≧ α > 0 is an update factor for controlling the degree of influence of the radar echo R' (M, N) on the background clutter c (M, N).
In a possible implementation manner of the first aspect, the detecting a human target according to a radar echo signal after clutter suppression, and determining whether a human target exists in an area to be detected includes:
superposing the radar echo signals R (M, n) with the clutter of M windows long after being suppressed in a slow time dimension;
carrying out continuous N times of target detection on the data with the fast time dimension by using a constant false alarm algorithm;
and adding the detection results of N times, and if the result is greater than a preset threshold T, determining that the human body target exists in the area to be detected.
In a possible implementation manner of the first aspect, performing human state detection on the determined human target, and verifying the state of the human target in the region to be detected includes:
and (3) performing fast Fourier transform on the radar echo signal R (m, n) after clutter suppression with the window length of K seconds to obtain a frequency domain signal X (m, f), wherein K is greater than the minimum period of the human body vital sign signal:
determining the energy value Z in the frequency range lower than the normal human body vital sign signal according to the frequency domain signal X (m, f)Low(m) energy value Z in frequency range of normal human vital sign signalMid(m) an energy value Z higher than the frequency range of normal human vital sign signalsHigh(m) and total energy Zsum(m)；
And when the proportion of the energy value in the frequency range of the normal human body vital sign signal to the total energy is higher than the proportion of other energy values to the total energy, determining the state of the human body target in the region to be detected.
In a possible implementation manner of the first aspect, extracting, based on a periodic feature of the vital sign data, the vital sign data having the periodic feature from a radar echo signal of the area to be detected includes:
filtering radar echo signals of a to-be-detected area according to the frequency of to-be-detected vital sign data to obtain a plurality of to-be-detected vital sign signals of different distance units;
calculating respective autocorrelation functions of the vital sign signals to be detected of different distance units;
calculating the sum of the minimum value of the autocorrelation function and the absolute value of the second large peak value for each distance unit of each vital sign signal to be detected;
and taking the vital sign signal data of the distance unit with the maximum sum of the absolute values of the vital sign signals to be detected as the vital sign data with the strongest periodic characteristic.
In a possible implementation manner of the first aspect, the processing the vital sign data with the periodic characteristic to obtain the vital sign detection result with the periodic characteristic includes:
if the vital sign data with the periodic characteristics are respiratory data, filtering the vital sign data with the periodic characteristics to obtain an autocorrelation function;
fourier transform is carried out on the autocorrelation function, and a peak value after the transform is obtained;
keeping values at two sides of the peak value, setting other data as 0, and obtaining a processed frequency domain signal;
and performing inverse Fourier transform on the processed frequency domain signal, and obtaining the respiratory frequency of the human body target according to the phase slope.
In a possible implementation manner of the first aspect, the processing the vital sign data with the periodic characteristic to obtain the vital sign detection result with the periodic characteristic includes:
if the vital sign data with the periodic characteristics are heart rate data, performing autocorrelation processing on the vital sign data with the periodic characteristics and removing noise;
fourier transform is carried out on the autocorrelation function, and a plurality of transformed peak values are obtained;
and judging the frequency corresponding to each peak value, and if the frequency corresponding to the peak value is not the higher harmonic of the respiratory frequency, obtaining the heart rate of the human target.
In a second aspect, an embodiment of the present invention provides a vital sign detection apparatus based on periodic characteristics, including:
the radar signal detection module is used for sending radar signals to the area to be detected and receiving radar echo signals returned by the area to be detected;
the human body target detection module is used for preprocessing the radar echo signal and determining the state of a human body target in a region to be detected;
the vital sign data extraction module is used for extracting the vital sign data with periodic characteristics from the radar echo signal of the area to be detected based on the periodic characteristics of the vital sign data if the human body target exists in the area to be detected;
and the vital sign data detection module is used for carrying out data processing on the vital sign data with the periodic characteristics to obtain a vital sign detection result with the periodic characteristics.
In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the periodic feature-based vital sign detection according to any one of the implementations of the first aspect.
The method, the device and the storage medium for detecting vital signs based on the periodic characteristics, which are provided by the embodiment of the invention, firstly send radar signals to a region to be detected, receive radar echo signals returned by the region to be detected, then perform signal preprocessing on the radar echo signals, determine the state of human targets in the region to be detected, if the human targets exist in the region to be detected, perform the extraction of the vital sign data with the periodic characteristics on the radar echo signals of the region to be detected based on the periodic characteristics of the vital sign data, and finally perform data processing on the vital sign data with the periodic characteristics to obtain the vital sign detection result with the periodic characteristics, because the radar signals are used for detection, the non-contact vital sign detection is realized, and the vital sign data are extracted based on the periodic characteristics of the vital sign data, the detection precision of the vital sign data is improved.
Drawings
Fig. 1 is a flowchart of a method for detecting a vital sign based on a periodic characteristic according to an embodiment of the present invention;
fig. 2 is a flowchart of a vital sign data extraction algorithm in the vital sign detection method based on periodic features according to the embodiment of the present application;
fig. 3 is a flow chart of respiratory rate detection in a method for vital sign detection based on periodic characteristics according to an embodiment of the present application;
fig. 4 is a flow chart of heart rate detection in a method for detecting vital signs based on periodic characteristics according to an embodiment of the present application;
FIG. 5 is a graph comparing the breathing rate of a vital sign detection method based on periodic characteristics with the breathing rate of a polysomnography according to an embodiment of the present disclosure;
FIG. 6 is a comparison graph of heart rate of a vital sign detection method based on periodicity characteristics and heart rate of a polysomnography provided by an embodiment of the present application;
fig. 7 is a schematic structural diagram of a vital sign detection apparatus based on a periodic feature according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
At present, the detection of the physiological characteristics of the periodic variation is mainly to detect the respiration and the heart rate, and the following instruments and methods are mainly used for the detection:
1) polysomnography monitor: medical grade Polysomnography (PSG) sleep monitoring technology is the gold standard of the industry, but its price is expensive, needs the tester to wear the respiratory belt when detecting breathing, need paste the electrode piece when detecting the heart rate, all needs the professional to operate, is not suitable for ordinary masses, and is not suitable for being directed at large tracts of land burn patient and the infectious diseases patient such as having dermatosis, and simultaneously, PSG sleep monitoring produces the constraint easily in the test process and feels, can't carry out the monitoring of long-time breathing heart rate.
2) A sleeping mattress: the sleep mattress mainly converts pressure changes caused by the heartbeat of a human body, chest changes during respiration, body movement and the like into charge changes through a built-in pressure sensor, and then extracts respiration and heart rate after amplification and filtering. The detection range of the sleep mattress is limited, so that a tester is required to lie on a bed and cannot have overlarge body position change; the result is easily influenced by external slight vibration, and the measurement precision is not high; effective detection cannot be performed in a microgravity environment.
3) Intelligent bracelet/wrist-watch: the intelligent bracelet/watch generally adopts a photoplethysmography, and the heart rate of a human body is detected through green light emitted by an LED. Firstly, the most fatal shortcoming of intelligence bracelet/wrist-watch is unable detection breathing, and secondly, received signal receives external environment, and body motion etc. produces great error, detects the precision and hangs down, and finally, bracelet/wrist-watch belongs to contact equipment, wears to produce easily for a long time and restricts the sense, and wears the tension too loosely and can produce different influences to the result.
4) A camera sensor: the camera sensor mainly adopts an imaging type photoplethysmography technology to detect respiration and heart rate. Although the camera is a non-contact detection device, the following disadvantages still exist: at first the camera reveals individual privacy easily, and secondly, the camera receives the highlight to disturb easily and leads to the result inaccurate, and secondly, the data volume that the camera needs real-time processing is huge, needs good hardware support, and finally, the camera penetrability is low, receives thick clothes, shelters from the thing influence such as quilt.
5) Blood oxygen heart rate detector: the oximeter mainly detects the change of the heart rate of the human body through the photoelectric sensor, cannot detect the breathing change of the target, secondly needs the tester to wear, cannot measure for a long time, and finally, the wearing mode of the oximeter requires rigorously, and different wearing modes can produce errors.
In summary, the conventional methods for detecting physiological characteristics of periodic changes such as heart rate and respiration all have certain problems.
Fig. 1 is a flowchart of a vital sign detection method based on a periodic feature according to an embodiment of the present invention, and as shown in fig. 1, the vital sign detection method based on the periodic feature according to the embodiment includes:
step S101, sending radar signals to an area to be detected and receiving radar echo signals returned by the area to be detected.
The vital sign detection method based on the periodic characteristics provided by the embodiment is used for detecting the vital signs of the human body which periodically change, wherein the vital signs of the human body which periodically change comprise respiratory rate, heart rate and the like. In order to accurately detect the periodically changing vital signs of the human body and not to bring discomfort to the detected human body, the embodiment adopts a non-contact detection method. The traditional non-contact detection method is easily interfered by external factors, so that a detection result has larger errors. In order to eliminate the error, in the present embodiment, a radar signal is first used as the detection signal.
In consideration of the fact that continuous vital sign data can be obtained only by continuously detecting a human body for a long time when the human body is subjected to vital sign detection, so that the change condition of the vital sign of the human body is mastered, and generally, the human body can be kept at a relatively fixed position for a long time only in a sleep state. Therefore, the vital sign detection method based on the periodic characteristics provided by the embodiment is suitable for detecting the vital signs of the human body in a sleep state or detecting the vital signs of the human body which is relatively static.
First, radar signals need to be sent to an area to be detected, which is an area where human bodies can be relatively fixed, such as an area on a bed. The frequency of the radar signal sent to the area to be detected is determined according to actual requirements as long as a stable echo signal can be obtained. After the radar signal is sent to the area to be detected, the radar signal reaches the area to be detected and can generate an echo, so that the radar echo signal can be detected at the position where the radar signal is sent. Different objects in the area to be detected, such as a bed surface and a human body, will generate different radar echo signals due to the different distances from the position where the radar echo signal is received. And because the breathing of the human body, the state of periodic change such as heartbeat of the human body can also lead to the posture of the human body to change slightly, will make the posture of the human body produce the periodic change too, then through analyzing different radar echo signals that the human body returns, can realize the detection to the life sign of the periodic change of the human body.
And S102, preprocessing the radar echo signal, and determining the state of the human body target in the area to be detected.
The radar signal is continuously sent, so that the radar echo signal can be continuously received, after the radar echo signal is obtained, because the radar echo signal comprises echo signals of all targets in the area to be detected, the radar echo signal also comprises a plurality of static clutter signals besides the signals of the human body targets, the radar echo signal needs to be processed, the interference of other clutter signals is eliminated, and the state of the human body target in the area to be detected is determined. Because the change cycle of the vital signs of the human body periodic change is positioned in a fixed range, the state of the human body target in the area to be detected can be determined according to the frequency change range of the radar echo signal after the clutter signal is eliminated.
Specifically, signal preprocessing is carried out on radar echo signals, and the state of a human body target in an area to be detected is determined, wherein the method comprises the following steps:
1. and performing clutter suppression on the radar echo signal to obtain the radar echo signal subjected to clutter suppression. In the radar echo signal, besides the human target, the radar echo signal also contains a plurality of static clutter, such as beds, wall surfaces, metal pendants and the like. The energy of the clutter is very large and exceeds the energy of the human body echo, so that the false alarm probability is increased, and the heart rate detection of respiration is not facilitated, therefore, the signal interference can be reduced through clutter suppression algorithms such as moving target display, moving target detection, band-pass filtering and moving average.
Further, the radar return signal may be clutter suppressed using the following formula,
c(m，n)＝α·c(m，n-1)+(1-α)R′(m，n)
R(m，n)＝R′(m，n)-c(m，n)
wherein R '(m, n) ═ R't(m，n)+R′u(m，n)+R′ω(m, n), R' (m, n) representsRadar echo signal, R't(m, n) denotes a target radar echo component, R'u(m, n) denotes a clutter radar echo component and R'ωThe radar echo signal processing method comprises the steps that (M, N) represents a receiver thermal noise radar echo component, M is 0, 1, …, M-1 represents a sampling sequence number in a slow time dimension (pulse dimension), N is 0, 1, …, N-1 represents a sampling sequence number in a fast time dimension (or distance dimension), R (M, N) represents a radar echo signal after clutter suppression, c (M, N) represents background clutter, and 1 ≧ alpha > 0 is an updating factor and is used for controlling the influence degree of the radar echo R' (M, N) on the background clutter c (M, N). When alpha is set to 1, the formula represents a clutter suppression method of a secondary canceller; when alpha is larger, c (m, n) is updated slower, and the characteristics of human body small and micro motion are more easily embodied. Alpha can be set according to actual use requirements.
2. And detecting the human body target according to the radar echo signal after clutter suppression, and determining whether the human body target exists in the area to be detected. Common human target detection techniques include unit average constant false alarm processing, logarithmic unit average constant false alarm processing, and the like. The embodiment of the invention takes the unit average constant false alarm rate processing as an example for explanation, and the main steps of target detection are as follows: superposing the radar echo signals R (M, n) with the clutter of M windows long after being suppressed in a slow time dimension; carrying out continuous N times of target detection on the data with the fast time dimension by using a constant false alarm algorithm; and adding the detection results of N times, and if the result is greater than a preset threshold T, determining that the human body target exists in the area to be detected so as to reduce the false alarm. And in each detection in the N detections, if the detection threshold is exceeded, the result is 1, and otherwise, the result is 0.
3. And detecting the human body state of the determined human body target, and verifying the state of the human body target in the region to be detected. Specifically, a specific method for detecting the human body state of the determined human body target may be: and (3) performing fast Fourier transform on the radar echo signal R (m, n) after clutter suppression with the window length of K seconds to obtain a frequency domain signal X (m, f), wherein K is greater than the minimum period of the human body vital sign signal:
determining the energy value Z in the frequency range lower than the normal human body vital sign signal according to the frequency domain signal X (m, f)Low(m) energy value Z in frequency range of normal human vital sign signalMid(m) an energy value Z higher than the frequency range of normal human vital sign signalsHigh(m) and total energy Zsum(m); and when the proportion of the energy value in the frequency range of the normal human body vital sign signal to the total energy is higher than the proportion of other energy values to the total energy, determining that a human body target exists in the region to be detected. Wherein, taking human body vital sign signals as breath and heart rate as an example, because the normal breath frequency and heart rate of the human body are 0.1Hz-2.5Hz, the energy value Z in three ranges of the frequency lower than the human body vital sign by 0-0.1Hz, the frequency in the normal range of 0.1Hz-2.5Hz and the frequency higher than the normal range of 2.5Hz-8.5Hz is calculatedLow(m)、ZMid(m)、ZHigh(m) and total energy Zsum(m) of the reaction mixture. Calculating each fast time dimension ZHigh(m)、ZMid(m)、ZLow(m) total energy Zsum(m) if ZHigh(m) the highest percentage of occupancy indicates that there is a moving object or target for the range cell; zMid(m) if the occupation ratio is the highest, the distance unit is indicated to have a vital sign event, namely the human target state of the region to be detected is determined; zLow(m) occupies the highest proportion, indicating that the range bin has no human target.
And S103, if the human body target exists in the region to be detected, extracting the vital sign data with the periodic characteristics from the radar echo signal of the region to be detected based on the periodic characteristics of the vital sign data.
And if the human body target exists in the region to be detected after the radar echo signal is preprocessed, further extracting the vital sign data with periodic characteristics. Specifically, since vital sign data with periodic characteristics, such as breathing or heartbeat, may cause subtle changes in human body morphology, and different vital signs have different periods, the radar echo signal after signal preprocessing may be analyzed based on the periodic characteristics of the vital sign data, and the vital sign data with periodic characteristics may be extracted therefrom. According to the periodic characteristics of different vital sign data, a plurality of vital sign data which accord with the periodic characteristics of the different vital sign data can be extracted.
Specifically, after it is determined that a human target exists in the region to be detected, a specific distance unit of the vital sign data needs to be further determined, that is, radar echo signals of each position in the region to be detected are further detected respectively, and the vital sign data with periodic characteristics are extracted from data of the optimal position.
In an embodiment, based on the periodic characteristics of the vital sign data, the method for extracting the vital sign data with the periodic characteristics from the radar echo signal of the area to be detected includes the following steps:
filtering radar echo signals of a to-be-detected area according to the frequency of to-be-detected vital sign data to obtain at least one to-be-detected vital sign signal; calculating an autocorrelation function of the vital sign signals to be detected in a plurality of different distance units; for each autocorrelation function, calculating the sum of the minimum value of the autocorrelation function and the absolute value of the second largest peak; and taking the vital sign signal data of the distance unit with the maximum sum of the absolute values of the vital sign signals to be detected as the vital sign data with the strongest periodic characteristic. The above process is a process of adaptive vital sign data extraction.
As shown in fig. 2, fig. 2 is a flow chart of a vital sign data extraction algorithm in the vital sign detection method based on the periodic characteristics provided in the embodiment of the present application, taking the vital signs to be detected as respiration and heart rate, and taking the respiration and heart rate in different frequency ranges, taking 20s radar data of the region to be detected (i.e., the vital sign region) as an example, first obtaining the respiration data through a low-pass filter with a cutoff frequency of 1Hz, obtaining the heart rate data through a band-pass filter with a cutoff frequency of 0.85Hz to 2.5Hz, and then respectively performing the following processing: calculating an autocorrelation function in each vital sign region; finding a minimum value and a second large peak value of the autocorrelation function; calculating the sum of the minimum value of each distance unit and the absolute value of the second large peak value; the data of the distance unit with the largest sum of absolute values is considered as respiration and heart rate data.
And step S104, performing data processing on the vital sign data with the periodic characteristics to obtain a vital sign detection result with the periodic characteristics.
Only after the radar echo signal is preprocessed, the vital sign data extracted according to the periodic characteristics cannot truly reflect the vital signs of the human body in the region to be detected, and in order to obtain an accurate vital sign detection result, the vital sign data with the periodic characteristics also needs to be subjected to data processing, so that the vital sign detection result with the periodic characteristics can be obtained. The data processing includes, but is not limited to, filtering, time-frequency transforming, and removing unnecessary signals from the vital sign data. In summary, the purpose of data processing on the vital sign data with periodic characteristics is to obtain accurate vital sign detection results with periodic characteristics.
The vital sign data with periodic characteristics mainly comprise respiration and heart rate, which are further detailed below with respiration and heart rate detection as examples, respectively.
When the vital sign data is respiratory data, the respiratory Frequency is mainly detected, and after the vital sign data with periodic characteristics is extracted, the respiratory Frequency is detected, and a Regression model estimation method, a multiple signal classification estimation method, a fourier analysis method, a Time-Frequency Phase Regression (FTPR), and the like may be adopted, which takes FTPR as an example in this embodiment.
As shown in fig. 3, fig. 3 is a flow chart of respiratory rate detection in the method for detecting vital signs based on periodic characteristics according to the embodiment of the present application, where if the vital sign data with periodic characteristics is respiratory data, the vital sign data with periodic characteristics is filtered to obtain an autocorrelation function; fourier transform is carried out on the autocorrelation function, and a peak value after the transform is obtained; keeping values at two sides of the peak value, setting other data as 0, and obtaining a processed frequency domain signal; and performing inverse Fourier transform on the processed frequency domain signal, and obtaining the respiratory frequency of the human body target according to the phase slope.
When the vital sign data is heart rate data, after a heart beat signal is extracted, obtaining a heart rate through a designed algorithm, as shown in fig. 4, fig. 4 is a heart rate detection flow chart in the vital sign detection method based on the periodic characteristics provided by the embodiment of the present application, specifically, if the vital sign data with the periodic characteristics is heart rate data, performing autocorrelation processing on the vital sign data with the periodic characteristics and removing noise to improve periodicity; fourier transform is carried out on the autocorrelation function, and a plurality of transformed peak values are obtained; and judging the frequency corresponding to each peak value, and if the frequency corresponding to the peak value is not the higher harmonic of the respiratory frequency, obtaining the heart rate of the human target. Specifically, the frequency corresponding to each peak value is judged, and if the frequency corresponding to the current peak value is not the harmonic of 2, 3, 4, 5 times of the respiratory frequency, the frequency corresponding to the current peak value is considered as the heart rate; and if the frequency corresponding to the current peak value is the higher harmonic of the respiratory frequency, detecting the next peak value until all possible peak values are detected, and if no proper heart rate is found at the moment, keeping the result of the last heart rate estimation.
The method for detecting vital signs based on periodic characteristics provided by this embodiment first sends a radar signal to an area to be detected, receives a radar echo signal returned from the area to be detected, then, the radar echo signals are subjected to signal preprocessing to determine the state of human body targets in the region to be detected, if the human body targets exist in the region to be detected, the periodic characteristics of the vital sign data are used as the basis, extracting the vital sign data with periodic characteristics from the radar echo signal of the area to be detected, finally processing the vital sign data with the periodic characteristics to obtain the vital sign detection result with the periodic characteristics, due to the fact that the radar signals are used for detection, non-contact vital sign detection is achieved, the vital sign data are extracted based on the periodic characteristics of the vital sign data, and the detection precision of the vital sign data is improved.
Taking an experiment for carrying out a specific test according to the method for detecting vital signs based on periodic characteristics provided by the embodiment of the application as an example, the effect of detecting vital signs based on periodic characteristics provided by the embodiment of the application is verified. The environment of the enrollment for this experiment was: the radar is about 1 meter away from a tester and is just opposite to the position of the chest of a human body, meanwhile, the tester wears a respiration detection belt and a heart rate detection sensor of a BIOPAC contact respiration measurement sensor, the radar and the contact sensor start to record data at the same time, the radar adopts an X4M03 module produced by Novalda, and the specific parameters of the radar are shown in Table 1.
TABLE 1 radar basic parameter table
In order to verify the effectiveness of the proposed human respiration estimation algorithm based on the periodic characteristics, a contact respiration measuring instrument of model MP36, manufactured by biapac corporation, was used as a measurement standard instrument, which is a respiration measuring instrument conforming to medical standards.
Fig. 5 and 6 are the results of comparing the respiration rate and heart rate results estimated by the respiration heart rate algorithm based on the periodicity characteristics with the respiration rate and heart rate of the industry gold standard BIOPAC polysomnography, respectively. Fig. 5 is a comparison graph of a respiratory rate of a vital sign detection method based on a periodic feature and a respiratory rate of a multi-lead sleep monitor provided in the embodiment of the present application, and fig. 6 is a comparison graph of a heart rate of a vital sign detection method based on a periodic feature and a heart rate of a multi-lead sleep monitor provided in the embodiment of the present application. Fig. 5 is a curve 51 of a respiratory rate detected by the vital sign detection method based on the periodicity characteristic provided in the embodiment of the present application, a curve 52 of a respiratory rate detected by the BIOPAC polysomnography, a curve 61 of fig. 6 of a heart rate detected by the vital sign detection method based on the periodicity characteristic provided in the embodiment of the present application, and a curve 52 of a heart rate detected by the BIOPAC polysomnography.
As can be seen from fig. 5 and fig. 6, the vital sign detection method based on the periodicity characteristic provided by the present embodiment is substantially consistent with the respiratory heart rate provided by the polysomnography. The accuracy of the method provided by this embodiment is further illustrated by calculating the Mean Absolute Error (MAE) of the two, and the calculation formula of the Mean Absolute Error is:
in the formula, RiThe result of the ith breath or heart rate detected by the method proposed in this example, BiThe ith breath and heart rate results are given for the contact sensor BIOPAC polysomnography. Through calculation, the average absolute error of the respiratory rate is 0.4515, and the average absolute error of the heart rate is 0.5126, so that the respiratory rate error of the vital sign detection method based on the periodic characteristics and the BIOPAC polysomnography monitor provided by the embodiment is 0.4515 times/min, and the heart rate error is 0.5126 times/min, which proves the accuracy of the result of the vital sign detection method based on the periodic characteristics provided by the embodiment.
Fig. 7 is a schematic structural diagram of the vital sign detection device based on the periodic characteristics according to the embodiment of the present invention, and as shown in fig. 7, the vital sign detection device based on the periodic characteristics according to the embodiment of the present invention includes:
and the radar signal detection module 71 is configured to send a radar signal to the area to be detected, and receive a radar echo signal returned by the area to be detected.
And the human body target detection module 72 is used for performing signal preprocessing on the radar echo signal and determining the state of the human body target in the area to be detected.
And the vital sign data extraction module 73 is configured to, if a human target exists in the region to be detected, extract the vital sign data with the periodic characteristics from the radar echo signal of the region to be detected based on the periodic characteristics of the vital sign data.
And the vital sign data detection module 74 is configured to perform data processing on the vital sign data with the periodic characteristics to obtain a vital sign detection result with the periodic characteristics.
The vital sign detection device based on the periodic characteristics provided in this embodiment is used to implement the technical scheme of the vital sign detection method based on the periodic characteristics shown in fig. 1, and the implementation principle and the technical effect are similar, which are not described herein again.
The present invention also provides a storage medium containing computer executable instructions which, when executed by a computer processor, are operable to perform a method of vital sign detection based on a periodic signature, the method comprising:
sending a radar signal to a to-be-detected area, and receiving a radar echo signal returned by the to-be-detected area; performing signal preprocessing on the radar echo signal to determine whether a human body target exists in the area to be detected; if the human body target exists in the region to be detected, extracting the vital sign data with the periodic characteristics from the radar echo signal of the region to be detected based on the periodic characteristics of the vital sign data; and carrying out data processing on the vital sign data with the periodic characteristics to obtain a vital sign detection result with the periodic characteristics.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments illustrated herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A method for detecting vital signs based on periodic characteristics, comprising:
sending a radar signal to a to-be-detected area, and receiving a radar echo signal returned by the to-be-detected area;
performing signal preprocessing on the radar echo signal to determine the state of the human body target in the area to be detected;
if the human body target exists in the region to be detected, extracting the vital sign data with the periodic characteristics from the radar echo signal of the region to be detected based on the periodic characteristics of the vital sign data;
and carrying out data processing on the vital sign data with the periodic characteristics to obtain a vital sign detection result with the periodic characteristics.
2. The method according to claim 1, wherein the signal preprocessing the radar echo signal to determine the state of the human target in the region to be detected comprises:
performing clutter suppression on the radar echo signal to obtain a radar echo signal subjected to clutter suppression;
detecting a human body target according to the radar echo signal after clutter suppression, and determining whether the human body target exists in the region to be detected;
and detecting the human body state of the determined human body target, and verifying the state of the human body target in the region to be detected.
3. The method of claim 2, wherein the performing clutter suppression on the radar return signal comprises:
the radar echo signal is clutter suppressed using the following formula,
c(m，n)＝α·c(m，n-1)+(1-α)R′(m，n)
R(m，n)＝R′(m，n)-c(m，n)
wherein R '(m, n) ═ R't(m，n)+R′u(m，n)+R′ω(m, n), R '(m, n) represents a radar echo signal, R't(m, n) denotes a target radar echo component, R'u(m, n) denotes a clutter radar echo component and R'ω(M, N) represents a receiver thermal noise radar echo component, M is 0, 1, …, M-1 represents a slow time dimension sampling number, N is 0, 1, …, N-1 represents a fast time dimension sampling number, and R (M, N) represents a radar after clutter suppressionThe echo signal c (m, n) represents the background clutter, and alpha is more than or equal to 1 and more than 0, which is used for controlling the influence degree of the radar echo R' (m, n) on the background clutter c (m, n).
4. The method according to claim 3, wherein the detecting the human target according to the radar echo signal after clutter suppression and determining whether the human target exists in the region to be detected comprises:
superposing the radar echo signals R (M, n) with the clutter of M windows long after being suppressed in a slow time dimension;
carrying out continuous N times of target detection on the data with the fast time dimension by using a constant false alarm algorithm;
and adding the detection results of N times, and if the result is greater than a preset threshold T, determining that the human body target exists in the area to be detected.
5. The method according to claim 4, wherein the human body state detection is performed on the determined human body target, and the verification of the state of the human body target in the region to be detected comprises:
and (3) performing fast Fourier transform on the radar echo signal R (m, n) after clutter suppression with the window length of K seconds to obtain a frequency domain signal X (m, f), wherein K is greater than the minimum period of the human body vital sign signal:
determining the energy value Z in the frequency range lower than the normal human body vital sign signal according to the frequency domain signal X (m, f)Low(m) energy value Z in frequency range of normal human vital sign signalMid(m) an energy value Z higher than the frequency range of normal human vital sign signalsHigh(m) and total energy Zsum(m)；
And when the total energy proportion of the energy values in the normal human body vital sign signal frequency range is higher than that of other energy values, determining the state of the human body target in the region to be detected.
6. The method according to any one of claims 1 to 5, wherein the periodic feature-based vital sign data-based extraction of the periodic feature vital sign data from the radar echo signal of the region to be detected comprises:
filtering the radar echo signal of the to-be-detected area according to the frequency of the to-be-detected vital sign data to obtain a plurality of different distance units to-be-detected vital sign signals;
calculating respective autocorrelation functions of the vital sign signals to be detected of the plurality of distance units;
calculating the sum of the minimum value of the autocorrelation function and the absolute value of the second large peak value for each distance unit of each vital sign signal to be detected;
and taking the vital sign signal data of the distance unit with the maximum sum of the absolute values of the vital sign signals to be detected as the vital sign data with the strongest periodic characteristic.
7. The method according to any one of claims 1 to 5, wherein the performing data processing on the vital sign data with periodic characteristics to obtain the vital sign detection result with periodic characteristics includes:
if the vital sign data with the periodic characteristics are respiratory data, filtering the vital sign data with the periodic characteristics to obtain an autocorrelation function;
fourier transform is carried out on the autocorrelation function, and a peak value after the transform is obtained;
keeping values at two sides of the peak value, setting other data as 0, and obtaining a processed frequency domain signal;
and carrying out inverse Fourier transform on the processed frequency domain signal, and obtaining the respiratory frequency of the human body target according to the phase slope.
8. The method according to any one of claims 1 to 5, wherein the performing data processing on the vital sign data with periodic characteristics to obtain the vital sign detection result with periodic characteristics includes:
if the vital sign data with the periodic characteristics is heart rate data, performing autocorrelation processing on the vital sign data with the periodic characteristics and removing noise;
fourier transform is carried out on the autocorrelation function, and a plurality of transformed peak values are obtained;
and judging the frequency corresponding to each peak value, and if the frequency corresponding to the peak value is not the higher harmonic of the respiratory frequency, obtaining the heart rate of the human target.
9. A vital sign detection device based on periodic features, comprising:
the radar signal detection module is used for sending radar signals to an area to be detected and receiving radar echo signals returned by the area to be detected;
the human body target detection module is used for preprocessing the radar echo signal and determining the state of the human body target in the area to be detected;
the vital sign data extraction module is used for extracting the vital sign data with periodic characteristics from the radar echo signal of the to-be-detected area based on the periodic characteristics of the vital sign data if the to-be-detected area has the human body target;
and the vital sign data detection module is used for carrying out data processing on the vital sign data with the periodic characteristics to obtain a vital sign detection result with the periodic characteristics.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a periodic feature based vital sign detection as claimed in any one of claims 1 to 8.
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