Vital sign parameter monitoring method and device, electronic equipment and storage medium

Abstract

The invention provides a vital sign parameter monitoring method and device, electronic equipment and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring first echo data for a monitoring object; determining whether the monitored object moves based on the first echo data; and monitoring vital sign parameters of the monitored object based on target echo data in the first echo data, wherein the target echo data is echo data when the monitored object does not move. The first echo data comprises echo data monitored when the monitored object generates body movement and echo data monitored when the monitored object does not generate body movement, and the echo data monitored when the monitored object generates body movement can interfere with determination of vital sign parameters, so that the echo data when the monitored object does not generate body movement is used as target echo data, and the vital sign parameters are determined according to the target echo data. Therefore, the vital sign parameters of the monitored object are monitored based on the target echo data, the error influence is reduced, and the accuracy of the monitored vital sign parameters can be improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, device, electronic equipment and storage medium for monitoring vital sign parameters. Background Art

[0002] As people pay more and more attention to the impact of sleep quality on physical health, there is a need to monitor the vital signs parameters of the human body during sleep so as to evaluate the physical health status using the monitored vital signs parameters.

[0003] At present, non-contact monitoring technology is increasingly being used to monitor the vital signs of the human body during sleep. Non-contact monitoring technology is a technology that uses monitoring equipment that does not directly contact the human body to monitor the human body. Non-contact monitoring technology can avoid direct contact between the monitoring equipment and the human body, and will not cause discomfort to the human body and affect the quality of sleep.

[0004] In the related art, when non-contact monitoring equipment is used to monitor the vital signs of human body, the echo signal is obtained by using radar, the distance cell with the strongest amplitude in the echo signal is used to estimate the vital sign parameters, and smoothing filtering is used to alleviate the phase distortion caused by human body movement. However, this method has a large error, and the accuracy of the determined vital sign parameters is low. Summary of the invention

[0005] The present invention provides a method, device, electronic device and storage medium for monitoring vital sign parameters, which are used to solve the defect of low accuracy of vital sign parameters obtained in the prior art and improve the accuracy of vital sign parameters.

[0006] The present invention provides a method for monitoring vital sign parameters, comprising:

[0007] Acquire first echo data for the monitored object;

[0008] determining whether the monitored object has body movement based on the first echo data;

[0009] The vital sign parameters of the monitored object are monitored based on the target echo data in the first echo data, wherein the target echo data is the echo data when the monitored object does not move.

[0010] According to the method for monitoring vital sign parameters provided by the present invention, the first echo data includes first echo sub-data corresponding to different parts of the monitored object;

[0011] The determining whether the monitored object has body movement based on the first echo data includes:

[0012] determining a first amplitude variance based on each of the first echo sub-data;

[0013] determining a correlation coefficient between two adjacent frames of first echo data based on first echo sub-data in two adjacent frames of first echo data;

[0014] Whether the monitored object has body movement is determined based on the first amplitude variance and the correlation coefficient.

[0015] According to the method for monitoring vital sign parameters provided by the present invention, determining whether the monitored object has body movement based on the first amplitude variance and the correlation coefficient includes:

[0016] When the first amplitude variance is less than or equal to a first preset amplitude variance, and the correlation coefficient is greater than or equal to a first preset correlation coefficient, determining that the monitored object has no body movement;

[0017] When the first amplitude variance is less than or equal to the first preset amplitude variance, and the correlation coefficient is less than the first preset correlation coefficient, it is determined that the monitored object has no body movement;

[0018] When the first amplitude variance is greater than the first preset amplitude variance and the correlation coefficient is greater than or equal to the first preset correlation coefficient, determining that the monitored object has no body movement;

[0019] When the first amplitude variance is greater than the first preset amplitude variance and the correlation coefficient is less than the first preset correlation coefficient, it is determined that the monitored object has body movement.

[0020] According to the method for monitoring vital sign parameters provided by the present invention, the first echo data includes first echo sub-data corresponding to different parts of the monitored object;

[0021] The determining whether the monitored object has body movement based on the first echo data includes:

[0022] determining a first amplitude variance based on each of the first echo sub-data, and determining that the monitored object has no body movement when the first amplitude variance is less than a second preset amplitude variance;

[0023] or,

[0024] A correlation coefficient between two adjacent frames of first echo data is determined based on the first echo sub-data in the two adjacent frames of first echo data, and when the correlation coefficient is greater than or equal to a second preset correlation coefficient, it is determined that the monitored object has no body movement.

[0025] According to the method for monitoring vital sign parameters provided by the present invention, the monitoring of the vital sign parameters of the monitored object based on the target echo data in the first echo data includes:

[0026] Determining target echo sub-data corresponding to the chest area of ​​the monitored object based on the target echo data;

[0027] Determine phase change data and amplitude change data based on the target echo sub-data;

[0028] Fusing the phase change data and the amplitude change data to obtain fused data;

[0029] The vital sign parameters of the monitored object are monitored based on the fused data.

[0030] According to the method for monitoring vital sign parameters provided by the present invention, the phase change data and the amplitude change data are fused to obtain fused data, including:

[0031] adjusting a weighting factor corresponding to the amplitude change data based on the first amplitude variance;

[0032] Based on the phase change data, the amplitude change data, the weighting factor corresponding to the phase change data and the adjusted weighting factor corresponding to the amplitude change data, the phase change data and the amplitude change data are fused to obtain fused data.

[0033] According to the method for monitoring vital sign parameters provided by the present invention, before acquiring the first echo data for the monitored object, the method further includes:

[0034] Acquire at least two frames of second echo data, each frame of the second echo data includes second echo sub-data corresponding to different positions of a target monitoring area; the target monitoring area includes an area where the monitored object is located;

[0035] Determine a second amplitude variance based on each second echo sub-data corresponding to each frame of second echo data;

[0036] When the second amplitude variance is greater than or equal to the third preset amplitude variance, it is determined that the monitored object is in bed; and the first echo data is data corresponding to when the monitored object is in bed.

[0037] The present invention also provides a monitoring device for vital sign parameters, comprising:

[0038] An acquisition unit, used for acquiring first echo data of a monitored object;

[0039] a determination unit, configured to determine whether the monitored object has body movement based on the first echo data;

[0040] A monitoring unit is used to monitor the vital sign parameters of the monitored object based on target echo data in the first echo data, wherein the target echo data is echo data when the monitored object is not moving.

[0041] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for monitoring vital sign parameters as described above is implemented.

[0042] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for monitoring vital sign parameters as described in any one of the above is implemented.

[0043] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method for monitoring vital sign parameters as described above is implemented.

[0044] The present invention provides a method, device, electronic device and storage medium for monitoring vital sign parameters. The method obtains first echo data for a monitored object, determines whether the monitored object has body movement based on the first echo data, can use the first echo data to judge the body movement of the monitored object, and monitors the vital sign parameters of the monitored object based on the target echo data in the first echo data. The first echo data includes the echo data monitored when the monitored object has body movement and the echo data monitored when no body movement occurs. Among them, the echo data monitored when body movement occurs will interfere with the monitoring of vital sign parameters. Therefore, the echo data when the monitored object has no body movement is used as the target echo data. Monitoring the vital sign parameters of the monitored object based on the target echo data can eliminate the above interference, thereby reducing the influence of errors during monitoring, so as to improve the accuracy of the monitored vital sign parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 is a flow chart of a method for monitoring vital sign parameters provided by an embodiment of the present invention;

[0047] Figure 2 This is one of the flow charts of the method for monitoring vital sign parameters provided by an embodiment of the present invention;

[0048] Figure 3 This is the second flowchart of the method for monitoring vital sign parameters provided by the embodiment of the present invention;

[0049] Figure 4 is a schematic diagram of the sleep state transition of a monitored object provided by an embodiment of the present invention;

[0050] Figure 5 is a schematic diagram of the structure of a monitoring device for vital sign parameters provided by an embodiment of the present invention;

[0051] Figure 6 It is a schematic diagram of the physical structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] It should be noted that the serial numbers assigned to the objects described in the present invention, such as "first", "second", etc., are only used to distinguish the objects described and do not have any order or technical meaning.

[0054] People are paying more and more attention to their health status, especially the health status of the elderly, patients or other groups is receiving more and more attention from the society. Therefore, the monitoring of the user's sleep status and vital sign parameters during sleep is of great research significance.

[0055] Among them, vital sign parameters include heart rate, respiratory rate, body temperature, blood pressure, pulse and other parameters that can reflect signs of life. Monitoring of vital sign parameters refers to measuring and observing the vital signs of the monitored object to obtain basic parameter information of the physiological state of the monitored object and provide an evaluation basis for the health status of the monitored object. At present, most of the equipment for monitoring vital sign parameters uses contact monitoring equipment. For people who need care, wearing these contact monitoring devices will cause great discomfort to the monitored objects, which may cause sleep disorders and affect sleep quality. Therefore, non-contact sleep status and vital sign monitoring technology has important research value.

[0056] The non-contact monitoring technology is a technology that uses non-contact monitoring equipment for monitoring. The non-contact monitoring equipment may be, for example, an image monitoring equipment, an ultrasonic monitoring equipment, or an electromagnetic wave monitoring equipment, among which the electromagnetic wave monitoring equipment includes various types of radar equipment.

[0057] In the related art, when radar equipment is used to implement non-contact vital sign parameter monitoring, the focus is on how to use all received echo signals to obtain the vital sign parameters of the monitored object. In the actual sleep monitoring application scenario, for many reasons, the monitored object often leaves the bed; and even in the process of sleeping in bed, the monitored object will inevitably have some body movements such as turning over, moving or stretching. In the process of the monitored object's body movement, the distance cell with the strongest amplitude in the echo data is not necessarily the distance cell corresponding to the chest part of the monitored object, but may be the distance cell corresponding to the body movement part such as the arm or leg. Therefore, these body movement behaviors will affect the echo data and the analysis results of the echo data, resulting in a large error in the monitoring of vital sign parameters. Based on the above problems, the inventor found that in the method of using radar to monitor vital sign parameters in the related art, when deriving the vital sign parameters, whether the monitored object has body movement is not used as a pre-judgment condition, nor is the echo data of the monitored object when the body movement is removed from the monitored echo data, and the vital sign parameters are derived based on the echo data when the monitored object does not have body movement. Since this method does not eliminate the interference of echo data when the monitored object moves on the monitoring result, the vital sign parameters finally obtained will have large errors and low accuracy.

[0058] Based on the above conception, the method for monitoring vital signs parameters provided in an embodiment of the present invention, after obtaining the first echo data for the monitored object, will determine whether the monitored object has body movement based on the first echo data, and monitor the vital signs parameters of the monitored object based on the echo data when the monitored object has no body movement in the first echo data. This can effectively avoid the influence of errors caused by the echo data when the monitored object has body movement, and can improve the accuracy of the monitored vital signs parameters.

[0059] The method for monitoring vital sign parameters provided in the embodiment of the present invention can be applied to various application scenarios where vital sign parameter monitoring is required. For example, vital sign parameter monitoring can be implemented for the elderly or patients in a home environment, a nursing home, or a hospital. Figures 1 to 4 A method for monitoring vital sign parameters provided by an embodiment of the present invention is described.

[0060] Figure 1It is a flow chart of the method for monitoring vital signs parameters provided by an embodiment of the present invention. The executor of the method can be an electronic device such as a radar, a mobile phone, a computer or a server, or a specially designed intelligent device, or a vital signs parameter monitoring device set in the electronic device or the intelligent device. The vital signs parameter monitoring device can be implemented by software, hardware or a combination of the two.

[0061] For example, millimeter wave radar is a radar that works in the millimeter wave band. Due to its high detection accuracy, it can usually be used as a non-contact vital signs monitoring device. When using millimeter wave radar to monitor the monitored object, it can effectively solve the problem of contact discomfort existing in contact devices, and can also effectively detect the body movement of the monitored object. The following will use millimeter wave radar as an example to describe the monitoring method of vital signs parameters. Figure 1 As shown, the method for monitoring vital sign parameters includes steps 110 to 130.

[0062] Step 110: Acquire first echo data for the monitored object.

[0063] For example, the monitored object may be a human body or other animal body that needs to monitor vital sign parameters. The monitored object may be scanned using electromagnetic waves emitted by a millimeter wave radar, and a first echo signal reflected from the monitored object may be received. The first echo data may be data obtained after signal analysis processing of the received first echo signal.

[0064] For example, the millimeter-wave radar is installed on the side above the head of the bed where the monitored object is located, and it works in SISO (Single Input Single Output) mode. A frequency modulation signal (chirp signal) is sent in each frame. The chirp signal is reflected by the monitored object to form a first echo signal. The millimeter-wave radar performs digital-to-analog conversion on the received first echo signal to obtain the ADC (Analog to Digital Converter) data corresponding to the first echo signal. The ADC data is the first echo data. Among them, the SISO mode is a radar working mode. In this working mode, the radar system only needs a single transmitting antenna and a single receiving antenna to operate.

[0065] Step 120: Determine whether the monitored object has body movement based on the first echo data.

[0066] For example, body movement can be the limb movement of the head, trunk or limbs of the monitored object during sleep, such as turning over, stretching or body movement. When the monitored object moves, the spatial position change of the moving body part will cause the reflected first echo signal to change in the characteristics of the signal amplitude, frequency or phase, etc. By using the acquired first echo data for data analysis, the change of the characteristics of the echo signal amplitude, frequency or phase, etc. can be detected. Based on the characteristic change detection result of the first echo data, it can be determined whether the monitored object has body movement.

[0067] Step 130: monitoring the vital sign parameters of the monitored object based on the target echo data in the first echo data, wherein the target echo data is the echo data when the monitored object is not moving.

[0068] For example, the vital sign parameters of the monitored object include at least one of the heart rate and the respiratory rate. The target echo data is the echo data of the monitored object when there is no body movement. Based on the target echo data, the information in the target echo data that can reflect the regular vibration of the body parts of the monitored object is analyzed and counted, so that the vital sign parameters of the monitored object can be monitored and the vital sign parameters of the monitored object can be obtained.

[0069] The embodiment of the present invention provides a method for monitoring vital sign parameters, the method acquires the first echo data for the monitored object, determines whether the monitored object has body movement based on the first echo data, can use the first echo data to judge the body movement of the monitored object, and monitors the vital sign parameters of the monitored object based on the target echo data in the first echo data. The first echo data includes the echo data monitored when the monitored object has body movement and the echo data monitored when no body movement occurs. Among them, the echo data monitored when body movement occurs will interfere with the monitoring of vital sign parameters. Therefore, the echo data when the monitored object has no body movement is used as the target echo data. Monitoring the vital sign parameters of the monitored object based on the target echo data can eliminate the above interference, thereby reducing the influence of errors during monitoring, so as to improve the accuracy of the monitored vital sign parameters.

[0070] For example, the first echo signals reflected back at different distances by the electromagnetic waves emitted by the radar can reflect information of different parts of the monitored object. Since the first echo data is data obtained after signal processing is performed on the first echo signal, the first echo data includes first echo sub-data corresponding to different parts of the monitored object, so the first echo sub-data can be used to determine whether the monitored object has body movement.

[0071] In one embodiment, the first echo data includes first echo sub-data corresponding to different parts of the monitored object; determining whether the monitored object has body movement based on the first echo data can be achieved in the following manner:

[0072] Determine the first amplitude variance based on each first echo sub-data; determine the correlation coefficient between two adjacent frames of first echo data based on the first echo sub-data in two adjacent frames of first echo data; and determine whether the monitored object moves based on the first amplitude variance and the correlation coefficient.

[0073] Specifically, after the millimeter-wave radar emits electromagnetic waves into space, it can receive echo signals reflected from objects at different distances from the millimeter-wave radar. A plurality of distance cells are set upward from the distance of the millimeter-wave radar, and the echo signal reflected from the object in each distance cell can be regarded as an echo sub-signal. It should be understood that when different parts of the monitored object are in different distance cells, the first echo signal reflected from the monitored object includes the first echo sub-signals reflected from different parts of the monitored object. The first echo sub-signal is subjected to signal analysis processing to obtain the first echo sub-data.

[0074] Specifically, the amplitude variance statistic is calculated based on each first echo sub-data, that is, the first amplitude variance of the first echo data of the monitored object within the distance cell is calculated. Since the first amplitude variance is determined based on a frame of first echo data, it can be understood as a characteristic parameter of the spatial dimension, which can reflect the information of the position change of each part of the monitored object in space.

[0075] For the two consecutive frames of electromagnetic wave signals emitted, the first echo signals corresponding to the two adjacent frames of electromagnetic wave signals can be received. After the two frames of first echo signals are subjected to signal analysis processing, the first echo data corresponding to the two adjacent frames of first echo signals can be obtained. The correlation coefficient between the two adjacent frames of first echo data can be determined by calculating the correlation coefficient based on the first echo sub-data corresponding to different parts of the monitored object in each first echo data. Among them, since the correlation coefficient is determined based on the first echo data of two adjacent frames, it can be understood as a characteristic parameter of the time dimension, which can reflect the information of the position change of each part of the monitored object in a continuous time.

[0076] After the first amplitude variance and the correlation coefficient are determined, analysis and judgment can be performed based on the size of the first amplitude variance and the size of the correlation coefficient, so as to determine whether the monitored object has body movement.

[0077] For example, if the millimeter wave radar is installed above the bedside of the monitored object, the distance cell can be set along the distance direction of the millimeter wave radar. The distance cell can be expressed as m statr ~m end Among them, mend It is related to the length of the bed and the installation position of the millimeter-wave radar. For example, if the length of the bed is 2 meters, the installation height of the millimeter-wave radar is 1.5 meters from the ground, and the distance between the millimeter-wave radar and the end of the bed is 2.5 meters, then m statr Can be set to 0 meters, m end It can be set to 2.5 meters. In the distance upward of the millimeter-wave radar, the echo signal reflected back within the range of 0 meters to 2.5 meters from the millimeter-wave radar includes the first echo signal reflected back from the monitored object. In order to analyze the first echo sub-data corresponding to different parts of the monitored object, multiple distance cells can be set up in the distance upward of the millimeter-wave radar. For example, in the range of 0 meters to 2.5 meters from the millimeter-wave radar, every 0.5 meters is divided into a distance cell, and then 5 distance cells can be divided. When the monitored object is sleeping in the bed, different parts of the monitored object's body will be in different distance cells. Data processing is performed based on the first echo signal reflected back by the monitored object, and the first echo sub-data corresponding to each part of the monitored object can be obtained.

[0078] For a chirp signal frame emitted by the millimeter-wave radar, the received first echo signal is converted into digital-to-analog format to obtain the ADC data corresponding to the first echo signal, which is the first echo data. The ADC data is processed by fast time dimension FFT (Fast Fourier Transform) to obtain distance processing data. If the number of points processed by FFT is m, then the distance processing data M of m×1 dimension can be obtained. R .

[0079] Furthermore, the distance processing data M can be processed according to the following formula (1): R Perform MTI (Moving Target Indication) processing to remove static clutter information and obtain an m×1 dimensional dynamic data matrix M MTI Among them, MTI is a commonly used algorithm in radar signal processing. It is a technology that uses clutter suppression filters to suppress clutter and improves the signal-to-clutter ratio of radar signals to facilitate moving target detection. In practical applications, two-pulse cancellers are usually used to achieve this.

[0080]

[0081] in, It represents the distance processing data after the fast time dimension FFT processing of the i-th frame signal; It represents the distance processing data after the fast time dimension FFT processing of the i-1 frame signal. Represents the data after MTI processing of the i-th frame signal.

[0082] After the above data processing is performed on a single frame signal, an m×1 dimensional dynamic data matrix M can be obtained. MTI , the matrix M MTI Each element in can be understood as the first echo sub-data in the first echo data. The following formula (2) is used to transform the matrix M MTI The first amplitude variance corresponding to the first echo data can be calculated by taking the average value of each element in , and using the following formula (3). The first amplitude variance is the distance cell m occupied by the monitored object. statr ~m end The amplitude variance of .

[0083]

[0084]

[0085] in, Indicates M MTI The i-th element in; μ1 represents M MTI The mean of each element in; D1 represents M MTI Based on this, the first amplitude variance can be determined based on each first echo sub-data.

[0086] For two consecutive chirp signals emitted by the millimeter-wave radar, the first echo signals corresponding to the two adjacent chirp signals can be received, and the ADC data corresponding to each first echo signal can be obtained by performing data processing on each first echo signal. The distance processing data M corresponding to each first echo signal can be obtained by performing fast time dimension FFT processing on each ADC data. R , distance processing data M R Each element in can be understood as the first echo sub-data obtained after the first echo signal is subjected to signal analysis processing. The correlation coefficient between two adjacent frames of first echo data can be calculated using the following formula (4). It should be noted that the distance processing data M is not used when calculating the correlation coefficient. R Performing MTI processing can avoid partial information in the echo signal being suppressed or eliminated during MTI processing, thereby improving the accuracy of the correlation coefficient.

[0087]

[0088] in, Represents the amplitude value of the rth distance cell of the i-1th frame data after FFT processing in the fast time dimension; It represents the amplitude mean of m distance cells of the i-1th frame data after FFT processing in the fast time dimension; Represents the amplitude value of the rth distance cell of the i-th frame data after FFT processing in the fast time dimension; It represents the amplitude mean of m distance cells of the i-th frame data after FFT processing in the fast time dimension; ρ represents the correlation coefficient between the i-1-th frame echo data and the i-th frame echo data.

[0089] After determining the first amplitude variance and the correlation coefficient between two adjacent frames of first echo data, the first amplitude variance and the correlation coefficient between two adjacent frames of first echo data can be judged using the set judgment threshold, and the combined judgment results of the two can determine whether the monitored object has body movement.

[0090] In one embodiment, determining whether the monitored object has body movement based on the first amplitude variance and the correlation coefficient can be specifically implemented in the following manner:

[0091] When the first amplitude variance is less than or equal to the first preset amplitude variance and the correlation coefficient is greater than or equal to the first preset correlation coefficient, it is determined that the monitored object has no body movement;

[0092] When the first amplitude variance is less than or equal to the first preset amplitude variance and the correlation coefficient is less than the first preset correlation coefficient, it is determined that the monitored object has no body movement;

[0093] When the first amplitude variance is greater than the first preset amplitude variance and the correlation coefficient is greater than or equal to the first preset correlation coefficient, it is determined that the monitored object has no body movement;

[0094] When the first amplitude variance is greater than the first preset amplitude variance and the correlation coefficient is less than the first preset correlation coefficient, it is determined that the monitored object has body movement.

[0095] For example, the first preset amplitude variance may be a judgment threshold set for the first amplitude variance; the first preset correlation coefficient may be a judgment threshold set for the correlation coefficient. Both the first preset amplitude variance and the first preset correlation coefficient may be preset according to statistical data or actual experience.

[0096] Millimeter-wave radar is sensitive to micro-movements of the human body. If a person moves, the amplitude of the echo signal from different parts of the body will fluctuate greatly. At the same time, it will cause the amplitude within the distance cell where the monitored object is located to change greatly in continuous time, reducing the correlation coefficient of the distance cell where the monitored object is located between adjacent frames.

[0097] For example, in the actual scenario of monitoring the sleep of the elderly, body movement is a low-probability event. Through a feature parameter of a spatial dimension and a feature parameter of a temporal dimension, body movement behavior can be identified more accurately, thereby improving the accuracy of body movement recognition, and ultimately obtaining more accurate vital sign parameters.

[0098] For example, Table 1 is a classification table for determining whether the monitored object has body movement provided by an embodiment of the present invention. As shown in Table 1, when determining whether the monitored object has body movement, it can be specifically divided into four categories. Set the first preset amplitude variance to D T-move , set the first preset correlation coefficient to ρ T-move Based on the first amplitude variance D1, the correlation coefficient ρ and Table 1, it can be determined whether the monitored object has body movement.

[0099] Table 1

[0100] Situation Classification The first amplitude method Correlation coefficient judgment Whether body movement occurs Case 1 <![CDATA[D1≤D T-move ]]> <![CDATA[ρ≥ρ T-move ]]> No body movement Case 2 <![CDATA[D1≤D T-move ]]> <![CDATA[ρ<ρ T-move ]]> No body movement Case 3 <![CDATA[D1>D T-move ]]> <![CDATA[ρ≥ρ T-move ]]> No body movement Case 4 <![CDATA[D1>D T-move ]]> <![CDATA[ρ<ρ T-move ]]> Body movement

[0101] When the condition 1 is met, the first amplitude variance D1 is less than or equal to the first preset amplitude variance D T-mov , and the correlation coefficient ρ is greater than or equal to the first preset correlation coefficient ρ T-move , which indicates that the amplitude of the first echo signal does not fluctuate significantly, and the amplitude in the distance cell where the monitored object is located does not change significantly in continuous time, and the correlation coefficient between adjacent frames does not decrease to the first preset correlation coefficient ρ T-move Therefore, in this case, it is determined that the monitored object has not moved.

[0102] When the condition 2 is met, the first amplitude variance D1 is less than or equal to the first preset amplitude variance D T-move , and the correlation coefficient ρ is less than the first preset correlation coefficient ρ T-mov , which indicates that the amplitude of the first echo signal has not fluctuated significantly. Although the amplitude in the distance cell where the monitored object is located changes in continuous time, the correlation coefficient between adjacent frames is reduced to the first preset correlation coefficient ρ T-move However, the amplitude change in the distance cell where the monitored object is located during the continuous time is not caused by body movement, but may be caused by other micro-movements in the distance cell, so in this case it is determined that the monitored object has no body movement.

[0103] When the condition 3 is met, the first amplitude variance D1 is greater than the first preset amplitude variance D T-mov , and the correlation coefficient ρ is greater than or equal to the first preset correlation coefficient ρ T-move , which indicates that the amplitude of the first echo signal has fluctuated greatly, but the amplitude in the distance cell where the monitored object is located has not changed in the continuous time, and the correlation coefficient between adjacent frames has not decreased to the first preset correlation coefficient ρ T-mov Next, the large fluctuation in the amplitude of the first echo signal may be caused by the chest vibration of the monitored object during breathing or heartbeat. The change is a periodic regular change, so the correlation coefficient is still large. Therefore, in this case, it is determined that the monitored object has no body movement.

[0104] When the condition 4 is met, the first amplitude variance D1 is greater than the first preset amplitude variance D T-move , and the correlation coefficient ρ is less than the first preset correlation coefficient ρ T-mov , which indicates that the amplitude of the first echo signal has fluctuated greatly, and the amplitude in the distance cell where the monitored object is located has also changed greatly in continuous time, and the correlation coefficient between adjacent frames is reduced to the first preset correlation coefficient ρ T-mov Next, by integrating the characteristic parameter first amplitude variance D1 of the spatial dimension and the characteristic parameter correlation coefficient ρ of the time dimension, it can be determined that the monitored object has body movement in this case.

[0105] In this embodiment, the first amplitude variance is determined based on the first echo sub-data, and the correlation coefficient between two adjacent frames of first echo data is determined based on the first echo sub-data in two adjacent frames of first echo data. By using the first amplitude variance and the correlation coefficient, a comprehensive judgment can be made in two dimensions, namely, the spatial dimension and the temporal dimension, to determine whether the monitored object has body movement. Further, a comprehensive judgment can be made based on the first preset amplitude variance and the first preset correlation coefficient, to more accurately determine whether the monitored object has body movement.

[0106] In order to improve the efficiency of determining whether the monitored object has body movement, the determination may be made based only on the first amplitude variance and the second preset amplitude variance, or based only on the correlation coefficient of two adjacent frames of first echo data and the second preset correlation coefficient.

[0107] In one embodiment, the first echo data includes first echo sub-data corresponding to different parts of the monitored object; determining whether the monitored object has body movement based on the first echo data can be specifically implemented in the following manner:

[0108] A first amplitude variance is determined based on each first echo sub-data, and when the first amplitude variance is less than a second preset amplitude variance, it is determined that no body movement has occurred in the monitored object; or a correlation coefficient between two adjacent frames of first echo data is determined based on the first echo sub-data in the two adjacent frames of first echo data, and when the correlation coefficient is greater than or equal to a second preset correlation coefficient, it is determined that no body movement has occurred in the monitored object.

[0109] For example, the first echo data includes first echo sub-data, and the first echo sub-data may be data obtained after data processing of first echo signals reflected from different parts of the monitored object. Determining the first amplitude variance based on the first echo sub-data is similar to the method in the above embodiment; determining the correlation coefficient between two adjacent frames of first echo data based on the first echo sub-data in two adjacent frames of first echo data is similar to the method in the above embodiment, and will not be repeated in this embodiment.

[0110] The second preset amplitude variance may be a threshold value set for the first amplitude variance for judging that the monitored object has not moved; the second preset correlation coefficient may be a threshold value set for the correlation coefficient for judging that the monitored object has not moved. Both the second preset amplitude variance and the second preset correlation coefficient may be preset based on statistical data or actual experience. Among them, the second preset amplitude variance may be equal to or different from the first preset amplitude variance; the second preset correlation coefficient may be equal to or different from the first preset correlation coefficient.

[0111] The first amplitude variance can reflect the amplitude change of the single frame first echo data. When the first amplitude variance is small, it can be indicated that the amplitude change of the first echo data is small. When the first amplitude variance is smaller than the second preset amplitude variance, it can be understood that the position change of each part of the monitored object's body in space is small, and it can be determined that the monitored object has no body movement.

[0112] The correlation coefficient between two adjacent frames of first echo data can reflect the information of the position change of each part of the monitored object in a continuous time. When the correlation coefficient is large, it can be shown that the similarity between the two adjacent frames of first echo data is high. When the correlation coefficient is greater than or equal to the second preset correlation coefficient, it can be understood that the position change of each part of the detected object is small within the time of two consecutive frames of first echo data, and it can be determined that the monitored object has not moved.

[0113] In this embodiment, whether the monitored object has body movement is determined by comparing the first amplitude variance with the second preset amplitude variance; or whether the monitored object has body movement is determined by comparing the correlation coefficient with the second preset correlation coefficient. When it is necessary to determine whether the monitored object has body movement, any one of the above two determination methods can be used to quickly determine whether the monitored object has body movement, thereby improving the efficiency of body movement determination.

[0114] In actual application scenarios, phase waveform disorder is easily caused by problems such as phase unwrapping. After obtaining the corresponding phase change data using the echo signal, if only the phase change data is used to determine the vital sign parameters, the accuracy of the vital sign parameters will be low. Therefore, in order to improve the accuracy of the determined vital sign parameters, the target echo sub-data corresponding to the chest area of ​​the monitored object can be determined based on the target echo data, and the phase change data and amplitude change data can be determined based on the target echo sub-data, and the data fusion method can be used to obtain more accurate vital sign parameters.

[0115] In one embodiment, monitoring the vital sign parameters of the monitored object based on the target echo data in the first echo data can be specifically implemented in the following manner:

[0116] Based on the target echo data, target echo sub-data corresponding to the chest area of ​​the monitored object are determined; based on the target echo sub-data, phase change data and amplitude change data are determined; the phase change data and the amplitude change data are fused to obtain fused data; and based on the fused data, vital sign parameters of the monitored object are monitored.

[0117] For example, the heartbeat and breathing of the monitored object will cause the chest area of ​​the monitored object to fluctuate significantly. When the monitored object does not move, the fluctuation of the chest area will make the echo signal scattered by the chest area the strongest. Therefore, the vital sign parameters of the monitored object can be determined based on the target echo sub-data reflecting the chest area of ​​the monitored object in the target echo data corresponding to the monitored object when the monitored object does not move, which can improve the accuracy of the vital sign parameters.

[0118] For example, in each distance cell of the millimeter wave radar upward distance, the maximum value search method can be used to lock the distance cell where the chest is located, where the maximum value can be the maximum amplitude of the echo signal, and the distance cell where the chest is located is determined by determining the maximum amplitude of the echo signal. When the monitored object does not move, the target echo sub-data can be obtained by performing signal analysis processing on the first echo sub-signal reflected from the distance cell.

[0119] Phase change data can be data reflecting the phase change of the echo signal determined based on the target echo sub-data, and can be obtained based on the imaginary and real parts of the echo data. Amplitude change data can be data reflecting the amplitude change of the echo signal determined based on the target echo sub-data, and can also be obtained based on the imaginary and real parts of the echo data. Both phase change data and amplitude change data can reflect the vital signs information of the monitored object. Therefore, the two can be fused to obtain fused data, and data analysis and statistics can be performed based on the fused data to achieve accurate monitoring of the vital signs parameters of the monitored object.

[0120] For example, after determining the phase change data and amplitude change data based on the target echo sub-data, the Kalman filtering method can be used to fuse the phase change data and the amplitude change data to obtain fused data, and data analysis and data statistics are performed based on the fused data to accurately calculate the vital sign parameters of the monitored object.

[0121] In this embodiment, the phase change data and the amplitude change data are determined based on the target echo sub-data corresponding to the chest area of ​​the monitored object, thereby avoiding interference of the target echo data corresponding to other body parts in the target echo data on the phase change data and the amplitude change data, so that the accuracy of the phase change data and the amplitude change data is relatively high. In addition, when monitoring the vital sign parameters of the monitored object, the fused data formed by the fusion of the phase change data and the amplitude change data is used, which can reduce the errors that may be caused when only the phase change data is used to monitor the vital sign parameters, and further improve the accuracy of monitoring the vital sign parameters.

[0122] When the phase change data and the amplitude change data are fused, a corresponding weighting factor may be set, and the weighting factor may be adjusted to improve the accuracy of the fused data, thereby further improving the accuracy of the vital sign monitoring parameters.

[0123] In one embodiment, the phase change data and the amplitude change data are fused to obtain fused data, which can be specifically achieved in the following manner:

[0124] The weighting factor corresponding to the amplitude change data is adjusted based on the first amplitude variance; based on the phase change data, the amplitude change data, the weighting factor corresponding to the phase change data and the weighting factor corresponding to the adjusted amplitude change data, the phase change data and the amplitude change data are fused to obtain fused data.

[0125] For example, a weighting factor corresponding to the phase change data and a weighting factor corresponding to the amplitude change data may be set, and the weights of the phase change data and the amplitude change data in the fused data may be adjusted by adjusting the corresponding weighting factors. The weighting factor corresponding to the phase change data and the weighting factor corresponding to the amplitude change data may be preset based on calculated data, empirical values, or statistical data.

[0126] Specifically, by calculating the variance statistics of the first echo data based on each first echo sub-data, the first amplitude variance corresponding to the first echo signal can be determined, and the first amplitude variance can reflect the information of the position change of each part of the monitored object in space. When a local micro-motion occurs in a body part of the monitored object, the amplitude change data will affect the accuracy of the fused data, and the local micro-motion can be reflected from the change of the first amplitude variance. Therefore, the weighting factor corresponding to the amplitude change data can be adjusted based on the first amplitude variance to reduce the adverse effect of the local micro-motion of the monitored object on the fused data and vital sign parameters. Among them, the local micro-motion can be a smaller amplitude movement occurring in a part of the body, such as head rotation, arm movement or leg bending, and the movement amplitude of the local micro-motion is smaller than the movement amplitude of the body movement.

[0127] For example, adjusting the weighting factor corresponding to the amplitude change data based on the first amplitude variance may be to reduce the weighting factor corresponding to the amplitude change data when the first amplitude variance is greater than or equal to the amplitude variance threshold, and to increase the weighting factor corresponding to the amplitude change data when the first amplitude variance is less than the amplitude variance threshold. The amplitude variance threshold is a judgment threshold set for the first amplitude variance for determining whether to adjust the weighting factor corresponding to the amplitude change data, and the judgment threshold may be preset based on calculation data, empirical values, or statistical data.

[0128] For example, the preset amplitude variance threshold is D T-weight After determining the first amplitude variance D1 based on each first echo sub-data, it is determined whether the first amplitude variance D1 is equal to the preset amplitude variance threshold D T-weight The numerical relationship between D1 and D T-weight In the case of T-weight The weighting factor corresponding to the amplitude change data is adjusted by the value of , wherein the adjustment can be performed in at least the following two ways:

[0129] Optionally, calculate D1 and D T-weight Divide the difference by D T-weight The adjustment ratio is obtained, and the weighting factor corresponding to the amplitude change data is multiplied by the adjustment ratio to obtain an adjustment value, and then the weighting factor corresponding to the amplitude change data is subtracted from the adjustment value to obtain the weighting factor corresponding to the adjusted amplitude change data.

[0130] Optionally, D1 and D T-weight The corresponding relationship between the difference range and the adjustment value, wherein the corresponding relationship can be at least two. By calculating D1 and D T-weight The difference is determined, the difference range of the difference is determined, the adjustment value corresponding to the difference range is determined as the target adjustment value, and the weighting factor corresponding to the amplitude change data after adjustment is obtained by subtracting the target adjustment value from the weighting factor corresponding to the amplitude change data.

[0131] After determining the weighting factor corresponding to the adjusted amplitude change data, the weighted amplitude change data can be determined based on the weighting factor corresponding to the adjusted amplitude change data and the amplitude change data, and the weighted phase change data can be determined based on the weighting factor corresponding to the phase change data and the phase change data. The weighted phase change data and the weighted amplitude change data are fused using a data fusion method for phase data and amplitude data to obtain fused data.

[0132] In this embodiment, the weighting factor corresponding to the amplitude change data is adjusted based on the first amplitude variance, so that the weighting factor corresponding to the amplitude change data can be corrected by using the amplitude variance statistic of the distance dimension, and the weight of the amplitude change data in the fused data can be effectively regulated, thereby reducing the error caused by the local micro-motion of the monitored object. Furthermore, the fused data is obtained based on the phase change data, the amplitude change data, the weighting factor corresponding to the phase change data, and the weighting factor corresponding to the adjusted amplitude change data, which can improve the accuracy of the fused data and thus improve the accuracy of the monitored vital sign parameters.

[0133] The method provided in the above embodiment can monitor the vital sign parameters of the monitored object based on the target echo data when the monitored object is in bed and does not move. In practical applications, the monitored object may leave the bed, and the echo data obtained after the monitored object leaves the bed will affect the monitoring of the vital sign parameters. Therefore, it is necessary to determine the bed state of the monitored object.

[0134] In one embodiment, before obtaining the first echo data for the monitored object, the method also includes: obtaining at least two frames of second echo data, each frame of second echo data including second echo sub-data corresponding to different positions of a target monitoring area; the target monitoring area includes an area where the monitored object is located; determining a second amplitude variance based on each second echo sub-data corresponding to each frame of second echo data; determining that the monitored object is in bed when the second amplitude variance is greater than or equal to a third preset amplitude variance; the first echo data is data corresponding to when the monitored object is in bed.

[0135] For example, the second echo data may be echo data obtained after signal analysis processing including echo signals reflected back within a spatial range at various positions within the target monitoring area. The target monitoring area may be an area including the monitored object, for example, an area of ​​the bed where the monitored object is located. At least two frames of second echo data are acquired, and the second amplitude variance may be determined based on each second echo sub-data corresponding to each frame of the second echo data. The second echo sub-data may be data corresponding to different positions of the bed in the second echo data. The second amplitude variance may be an amplitude variance value obtained after calculating the amplitude variance statistic of the second echo data.

[0136] For example, a millimeter-wave radar is installed above the head of the bed of the monitored object, and multiple distance cells are set in the range from the head to the foot of the bed. The echo signal received by the millimeter-wave radar in the distance cell is subjected to signal analysis processing to obtain the second echo data. The elements in the second echo data are the second echo sub-data, which can also be understood as the data obtained after the echo signal reflected in different distance cells is subjected to signal analysis processing.

[0137] For example, the second echo data is ADC data obtained after the echo signal is converted into digital-to-analog data. By performing fast time dimension FFT processing on the ADC data, the distance processing data M' can be obtained. R , distance processing data M' R Each element in is the second echo sub-data. Optionally, the distance processing data M' R By performing MTI processing, we can obtain an m×1 dimensional dynamic data matrix M' MTI Collect n consecutive frames of echo signals and obtain the dynamic data matrix M' corresponding to the n consecutive frames of echo signals MTI , cache n dynamic data matrices M' MTI Then, an m×n dimensional dynamic information data matrix M' can be obtained. By accumulating the energy of the second echo sub-data in the distance cell along the distance direction, a 1×n dimensional dynamic information data accumulation matrix M can be obtained. ACCU . Wherein, the energy accumulation can be incoherent accumulation.

[0138] Set a certain observation window length N, which can be longer than a complete breathing cycle, for example, N is 10 seconds. ACCU Perform data statistics analysis and calculate the dynamic information data accumulation matrix M within the observation window ACCU The mean μ1 and variance D2 of each element in , the variance D2 is the second amplitude variance. Specifically, the mean μ1 can be calculated using the following formula (5), and the variance D2 can be calculated using the following formula (6).

[0139]

[0140]

[0141] in, Represents the dynamic information data accumulation matrix M ACCU It should be noted that n represents the n-th frame echo signal within the observation window length N.

[0142] The third preset amplitude variance may be a threshold value for determining the bed state of the monitored object set for the second amplitude variance, and may be preset based on calculation data, experience values, or statistical data.

[0143] When there is no one in the radar monitoring area, the received echo signal is an interference signal and a noise signal, and the mean and variance values ​​of the echo signal are at a low level; when there is someone in the radar observation area, due to the body movement or local micro-movement of the human body, a larger mean value and a larger variance value will be generated. In order to enhance the robustness of the method of the present invention in actual application scenarios and avoid interference from certain strong noises in the echo signal, the variance value can be used as a basis to judge whether the monitored object is in bed, that is, to determine whether the monitored object is in bed based on the second amplitude variance and the third preset amplitude variance.

[0144] The third preset amplitude variance can be used with D T-target The second amplitude variance D2 is greater than or equal to the third preset amplitude variance D T-target When it is determined that the monitored object is in bed, the echo signal reflected from the monitored object is the first echo signal, and the echo data corresponding to the first echo signal is the first echo data, that is, the first echo data is the data corresponding to the monitored object in bed.

[0145] In this embodiment, the second amplitude variance can be determined based on the acquired at least two frames of second echo data and the second echo sub-data corresponding to each frame of second echo data. The second amplitude variance is judged based on the third preset amplitude variance, and it can be accurately determined whether the monitored object is in the bed state. Further, the echo data of the monitored object in the bed state is determined as the first echo data, and the monitored object is monitored based on the target echo data in the first echo data, which can improve the accuracy of vital sign parameter monitoring.

[0146] Figure 2 This is one of the flow charts of the method for monitoring vital sign parameters provided by the embodiment of the present invention, such as Figure 2 As shown, in the SISO mode of the millimeter wave radar, the echo signal is received and the first echo data for the monitored object is obtained. The first echo data can be preprocessed to obtain preprocessed data, wherein the data preprocessing can include performing FFT processing of the ADC data in the fast time dimension to obtain distance processing data; the data preprocessing can also include performing MTI processing on the distance processing data to obtain a dynamic data matrix M MTI .

[0147] Based on the data obtained after preprocessing, the in-bed characteristic parameters are extracted, and whether the monitored object is in bed is monitored based on the in-bed characteristic parameters. When it is determined that the monitored object is in bed, whether the monitored object has body movement is determined based on the first echo data. The body movement characteristic parameters can be extracted based on the first echo data to detect body movement of the monitored object. Among them, the body movement characteristic parameters can include the first amplitude variance D1 and the correlation coefficient ρ between the first echo data of two adjacent frames.

[0148] The weighting factor corresponding to the amplitude variation data is adjusted based on the first amplitude variance, and the weighted phase variation data and the weighted amplitude variation data are fused using the Kalman filter method to obtain fused data. The vital sign parameters of the monitored object are calculated based on the fused data to obtain vital sign parameters with higher accuracy.

[0149] The method for monitoring vital sign parameters provided by the embodiment of the present invention realizes the judgment of whether a person is in bed by using the statistical mean and variance of the MTI data in the time dimension as characteristic parameters; realizes the judgment of whether the person being monitored is moving by using the statistical amplitude variance of the range where the monitored object is located and the correlation coefficient of the adjacent frame data as the spatial dimension characteristic parameters and the time dimension characteristic parameters; finally, the breathing and heartbeat parameters are estimated by using the fusion data based on the phase change data and the amplitude change data in the resting state, so as to realize the monitoring of the vital sign parameters of the monitored object and improve the accuracy of the vital sign parameters.

[0150] Figure 3 This is a flow chart of the second method for monitoring vital sign parameters provided by an embodiment of the present invention. Figure 2 Based on Figure 3 The steps of determining whether the monitored object is in bed, whether there is body movement, and how to perform data fusion are described in detail. Figure 3 As shown, a distance cell is set for the millimeter wave radar, and a dynamic data matrix M of n-frame echo signals based on the distance cell is obtained along the distance direction. MTI Energy accumulation is performed to obtain a 1×n-dimensional dynamic information data accumulation matrix M ACCU According to the obtained human respiratory cycle reference value, the observation window length is set, and the dynamic information data accumulation matrix M is calculated within the observation window length. ACCU Extract the data amplitude statistics, that is, determine the second amplitude variance D2, based on the third preset amplitude D T-target The second amplitude variance D2 is judged, if the second amplitude variance D2 is greater than or equal to the third preset amplitude variance D T-target When the monitored object is in bed, it can be determined that the monitored object is in bed state, otherwise it can be determined that the monitored object is out of bed state. The echo data when the monitored object is in the out of bed state is not used to determine the vital sign parameters.

[0151] After receiving the echo signal, the millimeter wave radar obtains the first echo data for the monitored object, performs distance FFT processing on the first echo data, that is, fast time dimension FFT processing, and obtains the distance processing data M R To remove static clutter information, the distance processing data M can be R Perform MTI processing to obtain an m×1 dimensional dynamic data matrix M MTI. For the matrix M MTI By taking the average value of each element in and calculating the amplitude variance, the first amplitude variance D1 corresponding to the first echo data can be determined.

[0152] When the monitored object is in bed, the first amplitude variance D1 and the correlation coefficient ρ between two adjacent frames of first echo data are determined based on each first echo sub-data in the first echo data. Whether the monitored object moves is determined based on the first amplitude variance D1 and the correlation coefficient ρ between two adjacent frames of first echo data. The first preset amplitude variance D T-move And the first preset correlation coefficient ρ T-move , when D1>D T-move And ρ<ρ T-mov When the monitored object moves, it can be determined that the monitored object has body movement, and the echo data when the monitored object moves is not used to determine the vital sign parameters. The phase change data and the amplitude change data are determined based on the target echo data when the monitored object has no body movement. The weighting factor corresponding to the amplitude change data is adjusted based on the first amplitude variance D1, and the phase change data and the amplitude change data are fused using Kalman filtering based on the phase change data, the amplitude change data, the weighting factor corresponding to the phase change data, and the weighting factor corresponding to the adjusted amplitude change data to obtain fused data, and the vital sign parameters of the monitored object are obtained based on the fused data.

[0153] The monitoring method of vital sign parameters provided by the embodiment of the present invention can solve the scene where the sleep state and vital signs of the elderly need to be continuously monitored and monitored in the home environment, nursing home or hospital. If the monitored object leaves the bed or moves, the non-contact measurement method will produce a large error in the estimation of its vital sign parameters, causing the problem of inaccurate assessment of the health level of the detected object; in addition, the current sleep state monitoring scheme based on millimeter wave radar is mostly focused on the estimation of vital sign parameters when the measured person is lying still in bed, without considering the situation of the detected object leaving the bed or moving. This method can use limited hardware resources and storage resources in the SISO working mode of the radar, adopt the method of extracting the spatiotemporal characteristic parameters of the radar echo data, and combine the Bayesian idea to realize the judgment of whether the measured person is in bed and whether the body moves, and synchronously use the fusion data of phase change data and amplitude change data to estimate the vital sign parameters, which can avoid the problem of inaccurate estimation of vital sign parameters caused by the movement of the monitored object, and use the fusion data of phase change data and amplitude change data to improve the accuracy of vital sign parameter estimation, so that the sleep quality and vital sign status of the monitored object can be evaluated in more detail and systematically. The present invention can be implemented by adopting a simple SISO radar working mode, has low implementation cost, simple algorithm and is easy to implement in engineering.

[0154] Figure 4Schematic diagram of the sleep state transition of the monitoring object provided by the embodiment of the present invention, such as Figure 4 As shown, the sleep state of the monitored object can be mainly divided into "out of bed state", "in bed with body movement state" and "in bed without body movement state". In practical applications, according to the actual behavior of the monitored object, since body movement is inevitable when the monitored object goes in and out of bed, there will only be mutual conversion between "out of bed state" and "in bed with body movement state", and mutual conversion between "in bed with body movement state" and "in bed without body movement state" among the three states. Using the method in the above embodiment, the sleep state of the monitored object can be accurately determined, and after the sleep state is determined, the sleep state can be output to a mobile phone, computer or other electronic device in real time.

[0155] Based on the vital sign parameter monitoring method provided in the above embodiment, the sleep state and vital sign parameters of the monitored object can be determined. Based on the sleep state and vital sign parameters of the monitored object, the number of times the monitored object wakes up at night, the number of body movements at night, the stable state of sleep, and the fluctuations in the respiratory rate and heart rate during the sleep of the monitored object can be counted.

[0156] Optionally, if the monitored subject remains out of bed for a long time, a warning message is recorded and reported, and the alarm message is used to remind the guardian or family members to take corresponding measures to ensure the life safety of the monitored subject, thereby achieving the effect of intelligent management of the monitored subject's sleep process.

[0157] The following is a description of the monitoring device for vital sign parameters provided by the present invention. The monitoring device for vital sign parameters described below and the monitoring method for vital sign parameters described above can correspond to each other.

[0158] Figure 5 is a schematic diagram of the structure of a monitoring device for vital signs parameters provided by an embodiment of the present invention, with reference to Figure 5 As shown, the monitoring device 500 for vital sign parameters includes:

[0159] An acquisition unit 510 is used to acquire first echo data for a monitored object;

[0160] A determination unit 520, configured to determine whether the monitored object has body movement based on the first echo data;

[0161] The monitoring unit 530 is used to monitor the vital sign parameters of the monitored object based on the target echo data in the first echo data, where the target echo data is the echo data when the monitored object is not moving.

[0162] In an exemplary embodiment, the first echo data includes first echo sub-data corresponding to different parts of the monitored object; the determination unit 520 is specifically used to: determine the first amplitude variance based on each first echo sub-data; determine the correlation coefficient between two adjacent frames of first echo data based on the first echo sub-data in the two adjacent frames of first echo data; and determine whether the monitored object has body movement based on the first amplitude variance and the correlation coefficient.

[0163] In an exemplary embodiment, the determination unit 520 is specifically used to: determine that the monitored object has not had any body movement when the first amplitude variance is less than or equal to the first preset amplitude variance and the correlation coefficient is greater than or equal to the first preset correlation coefficient; determine that the monitored object has not had any body movement when the first amplitude variance is less than or equal to the first preset amplitude variance and the correlation coefficient is less than the first preset correlation coefficient; determine that the monitored object has not had any body movement when the first amplitude variance is greater than the first preset amplitude variance and the correlation coefficient is greater than or equal to the first preset correlation coefficient; determine that the monitored object has had body movement when the first amplitude variance is greater than the first preset amplitude variance and the correlation coefficient is less than the first preset correlation coefficient.

[0164] In an exemplary embodiment, the first echo data includes first echo sub-data corresponding to different parts of the monitored object; the determination unit 520 is specifically used to: determine the first amplitude variance based on each first echo sub-data, and determine that the monitored object has no body movement when the first amplitude variance is less than the second preset amplitude variance; or,

[0165] A correlation coefficient between two adjacent frames of first echo data is determined based on the first echo sub-data in the two adjacent frames of first echo data, and when the correlation coefficient is greater than or equal to a second preset correlation coefficient, it is determined that the monitored object has no body movement.

[0166] In an exemplary embodiment, the monitoring unit 530 is specifically used to: determine target echo sub-data corresponding to the chest area of ​​the monitored object based on the target echo data; determine phase change data and amplitude change data based on the target echo sub-data; fuse the phase change data and the amplitude change data to obtain fused data; and monitor the vital sign parameters of the monitored object based on the fused data.

[0167] In an exemplary embodiment, the monitoring unit 530 is specifically used to: adjust the weighting factor corresponding to the amplitude change data based on the first amplitude variance; fuse the phase change data and the amplitude change data based on the phase change data, the amplitude change data, the weighting factor corresponding to the phase change data and the weighting factor corresponding to the adjusted amplitude change data to obtain fused data.

[0168] In an exemplary embodiment, the acquisition unit 510 is further configured to acquire at least two frames of second echo data, each frame of second echo data including second echo sub-data corresponding to different positions of a target monitoring area; the target monitoring area includes an area where the monitored object is located;

[0169] The determining unit 520 is further configured to determine a second amplitude variance based on each second echo sub-data corresponding to each frame of second echo data;

[0170] The determination unit 520 is further configured to determine that the monitored subject is in bed when the second amplitude variance is greater than or equal to a third preset amplitude variance; the first echo data is data corresponding to when the monitored subject is in bed.

[0171] The device of this embodiment can be used to execute the method of any embodiment in the embodiment of the monitoring method for vital signs parameters. Its specific implementation process and technical effects are similar to those in the embodiment of the monitoring method for vital signs parameters. For details, please refer to the detailed description in the embodiment of the monitoring method for vital signs parameters, which will not be repeated here.

[0172] Figure 6 is a schematic diagram of the physical structure of an electronic device provided by an embodiment of the present invention, such as Figure 6 As shown, the electronic device may include: a processor 610, a communication interface 620, a memory 630 and a communication bus 640, wherein the processor 610, the communication interface 620 and the memory 630 communicate with each other through the communication bus 640. The processor 610 may call the logic instructions in the memory 630 to execute the above-mentioned method for monitoring vital sign parameters, which includes: obtaining first echo data for the monitored object; determining whether the monitored object has body movement based on the first echo data; monitoring the vital sign parameters of the monitored object based on the target echo data in the first echo data, wherein the target echo data is the echo data when the monitored object has no body movement.

[0173] In addition, the logic instructions in the above-mentioned memory 630 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0174] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the above-mentioned method for monitoring vital signs parameters, which method includes: obtaining first echo data for the monitored object; determining whether the monitored object has body movement based on the first echo data; monitoring the vital signs parameters of the monitored object based on target echo data in the first echo data, the target echo data being echo data when the monitored object has no body movement.

[0175] On the other hand, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the method for monitoring vital sign parameters provided by the above-mentioned methods, the method comprising: acquiring first echo data for a monitored object; determining whether the monitored object has body movement based on the first echo data; monitoring the vital sign parameters of the monitored object based on target echo data in the first echo data, the target echo data being echo data when the monitored object has no body movement.

[0176] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0177] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0178] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring vital sign parameters, characterized in that: include: Acquire first echo data for the monitored object; determining whether the monitored object has body movement based on the first echo data; The vital sign parameters of the monitored object are monitored based on the target echo data in the first echo data, wherein the target echo data is the echo data when the monitored object does not move.

2. The method for monitoring vital sign parameters according to claim 1, characterized in that: The first echo data includes first echo sub-data corresponding to different parts of the monitored object; The determining whether the monitored object has body movement based on the first echo data includes: determining a first amplitude variance based on each of the first echo sub-data; determining a correlation coefficient between two adjacent frames of first echo data based on first echo sub-data in two adjacent frames of first echo data; Whether the monitored object has body movement is determined based on the first amplitude variance and the correlation coefficient.

3. The method for monitoring vital sign parameters according to claim 2, characterized in that: The determining whether the monitored object has body movement based on the first amplitude variance and the correlation coefficient includes: When the first amplitude variance is less than or equal to a first preset amplitude variance, and the correlation coefficient is greater than or equal to a first preset correlation coefficient, determining that the monitored object has no body movement; When the first amplitude variance is less than or equal to the first preset amplitude variance, and the correlation coefficient is less than the first preset correlation coefficient, it is determined that the monitored object has no body movement; When the first amplitude variance is greater than the first preset amplitude variance and the correlation coefficient is greater than or equal to the first preset correlation coefficient, determining that the monitored object has no body movement; When the first amplitude variance is greater than the first preset amplitude variance and the correlation coefficient is less than the first preset correlation coefficient, it is determined that the monitored object has body movement.

4. The method for monitoring vital sign parameters according to claim 1, characterized in that: The first echo data includes first echo sub-data corresponding to different parts of the monitored object; The determining whether the monitored object has body movement based on the first echo data includes: determining a first amplitude variance based on each of the first echo sub-data, and determining that the monitored object has no body movement when the first amplitude variance is less than a second preset amplitude variance; or, A correlation coefficient between two adjacent frames of first echo data is determined based on the first echo sub-data in the two adjacent frames of first echo data, and when the correlation coefficient is greater than or equal to a second preset correlation coefficient, it is determined that the monitored object has no body movement.

5. The method for monitoring vital sign parameters according to claim 2 or 3, characterized in that: The monitoring of the vital sign parameters of the monitored object based on the target echo data in the first echo data includes: Determining target echo sub-data corresponding to the chest area of ​​the monitored object based on the target echo data; Determine phase change data and amplitude change data based on the target echo sub-data; Fusing the phase change data and the amplitude change data to obtain fused data; The vital sign parameters of the monitored object are monitored based on the fused data.

6. The method for monitoring vital sign parameters according to claim 5, characterized in that: The fusing the phase change data and the amplitude change data to obtain fused data includes: adjusting a weighting factor corresponding to the amplitude change data based on the first amplitude variance; Based on the phase change data, the amplitude change data, the weighting factor corresponding to the phase change data and the adjusted weighting factor corresponding to the amplitude change data, the phase change data and the amplitude change data are fused to obtain fused data.

7. The method for monitoring vital sign parameters according to any one of claims 1 to 4, characterized in that: Before acquiring the first echo data for the monitored object, the method further includes: Acquire at least two frames of second echo data, each frame of the second echo data includes second echo sub-data corresponding to different positions of a target monitoring area; the target monitoring area includes an area where the monitored object is located; Determine a second amplitude variance based on each second echo sub-data corresponding to each frame of second echo data; When the second amplitude variance is greater than or equal to the third preset amplitude variance, it is determined that the monitored object is in bed; and the first echo data is data corresponding to when the monitored object is in bed.

8. A monitoring device for vital sign parameters, characterized in that: include: An acquisition unit, used for acquiring first echo data of a monitored object; a determination unit, configured to determine whether the monitored object has body movement based on the first echo data; A monitoring unit is used to monitor the vital sign parameters of the monitored object based on target echo data in the first echo data, wherein the target echo data is echo data when the monitored object is not moving.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for monitoring vital sign parameters according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for monitoring vital sign parameters according to any one of claims 1 to 7 is implemented.

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