Ultra-wideband biological radar vital sign monitoring method, equipment and medium

Abstract

The invention discloses an ultra wide band biological radar vital sign monitoring method and device and a medium, and relates to the technical field of driver vital sign monitoring, and the method comprises the following steps: using an ultra wide band biological radar to transmit a short pulse signal to monitor a driver; filtering the monitoring signal to obtain a physical sign signal; extracting signal features of the sign signals, and performing grouping based on different signal features to obtain different feature groups; based on different feature groups, analyzing the relationship between the signal features and the vital signs; monitoring the vital signs of the driver based on the relationship between the signal features and the vital signs; the method is used for solving the problem that the driving safety of a driver is affected to a certain extent due to the fact that the vital signs of the driver are monitored through a multi-purpose contact type sensing device in an existing driver vital sign monitoring technology.

Description

Technical Field

[0001] The present invention relates to the technical field of driver vital sign monitoring, and in particular to an ultra-wideband bio-radar vital sign monitoring method, equipment and medium. Background Art

[0002] Driver vital signs monitoring technology refers to the technology of monitoring the driver's vital signs during driving through various monitoring technologies. This type of technology needs to ensure that the monitoring device will not affect the driver's driving process, while being able to protect the driver's driving safety. When abnormalities are detected in the driver's vital signs, the intelligent driving system can be used to send the driver to a safe place to enhance driving safety.

[0003] Existing driver vital signs monitoring technology usually monitors the monitored object in real time through contact monitoring instruments, but there are many scenarios in life that are not suitable for contact monitoring methods, such as patients with large-area burns and patients with highly infectious diseases. During driving, if you want to monitor the driver's vital signs, a non-contact monitoring method is also required. If a contact monitoring method is used, it may affect the driver's driving comfort and increase the driving risk to a certain extent. For example, in a patent application with publication number CN114469026A, a driver vital signs monitoring method and system are disclosed. This solution is to monitor the driver's vital signs through contact sensing equipment, and contact sensing equipment may cause discomfort to the driver, increasing driving risks. The existing driver vital signs monitoring technology also has the problem of using multiple contact sensing equipment to monitor the driver's vital signs, resulting in a certain impact on the driver's driving safety. Summary of the invention

[0004] The present invention aims to solve one of the technical problems in the prior art to at least a certain extent, by installing an ultra-wideband bioradar on the door of a car, using the ultra-wideband bioradar to transmit a short pulse signal to monitor the driver, obtaining a monitoring signal by receiving a reflected signal, filtering the monitoring signal to obtain a vital sign signal, and then extracting the signal characteristics of the vital sign signal, analyzing the deviation parameter between the frequency characteristic value and the actual breathing frequency based on the characteristic grouping and the actual breathing frequency obtained by monitoring, and then correcting the frequency characteristic value of the characteristic grouping based on the deviation parameter to obtain a calibration frequency value, and then analyzing the relationship between the signal characteristics and the heart rate based on the calibration frequency value and the actual heart rate, analyzing the relationship between the signal characteristics and the heart rate based on different characteristic groupings, analyzing the relationship between the signal characteristics and the vital signs based on different characteristic groupings, and finally monitoring the driver's vital signs based on the relationship between the signal characteristics and the vital signs, so as to solve the problem that the existing driver vital sign monitoring technology still uses a multi-contact sensing device to monitor the driver's vital signs, resulting in a certain impact on the driver's driving safety.

[0005] To achieve the above objectives, in a first aspect, the present application provides an ultra-wideband bioradar vital signs monitoring method, comprising the following steps:

[0006] An ultra-wideband bio-radar is installed on the door of the car, and a short pulse signal is emitted by the ultra-wideband bio-radar to monitor the driver, and a monitoring signal is obtained by receiving the reflected signal;

[0007] Filtering the monitoring signal to obtain a vital sign signal;

[0008] Extract signal features of vital sign signals, group them based on different signal features, and obtain different feature groups;

[0009] Analyze the relationship between signal features and vital signs based on different feature groups;

[0010] The driver's vital signs are monitored based on the relationship between signal characteristics and vital signs.

[0011] Furthermore, an ultra-wideband bio-radar is installed on the door of the car, and the ultra-wideband bio-radar is used to transmit a short pulse signal to monitor the driver, and the monitoring signal is obtained by receiving the reflected signal, which includes the following sub-steps:

[0012] Mark the point of the steering wheel closest to the driver as the observation direction point;

[0013] Consider the car door as a plane, named the vertical plane, take the observation direction point as the endpoint, draw a straight line perpendicular to the vertical plane, named the observation direction straight line;

[0014] Mark the perpendicular point between the observation direction straight line and the vertical plane as the installation perpendicular point;

[0015] Mark the plane passing through the junction of the door and the window and parallel to the ground as the horizontal plane;

[0016] With the installation vertical point as the endpoint, draw a straight line perpendicular to the horizontal plane, and mark the vertical point between it and the horizontal plane as the radar installation point;

[0017] Installation of ultra-wideband bio-radar at radar installation sites;

[0018] Ultra-wideband bio-radar is used to transmit short pulse signals to monitor the driver, and the monitoring signal is obtained by receiving the reflected signal.

[0019] Furthermore, the filtering process for the monitoring signal adopts Butterworth filtering technology and MTI harmonic suppression technology, and the vital sign signal is obtained after the monitoring signal is processed.

[0020] Furthermore, extracting the signal features of the vital sign signal and grouping them based on different signal features to obtain different feature groups includes the following sub-steps:

[0021] A first number of volunteers are selected, and the first number of volunteers are continuously monitored to obtain a plurality of vital sign signals, wherein the vital sign signals are presented in the form of waves, and the duration of each vital sign signal is the first acquisition duration; at the same time, the actual respiratory rate and the actual heart rate of the volunteers are monitored by a monitoring instrument, wherein the vital sign signals correspond to the actual respiratory rate and the actual heart rate, and each vital sign signal corresponds to a plurality of actual respiratory rates and actual heart rates;

[0022] Obtain the frequency of the vital sign signal and mark it as the frequency characteristic value;

[0023] Get the amplitude of the peak and trough, and mark them as signal peak and signal trough respectively;

[0024] The signal peaks and valleys are numbered from left to right, respectively represented by the symbols F n and G n Indicates, where n is a positive integer and n is the sequence number of F and G;

[0025] Calculate F n -G n , mark the calculated result as the fluctuation characteristic value, through the symbol Q n express;

[0026] The frequency characteristic value and the fluctuation characteristic value together constitute the signal feature;

[0027] Signal features are grouped according to frequency feature values ​​to obtain several feature groups.

[0028] Furthermore, based on different feature groups, analyzing the relationship between signal features and vital signs includes the following sub-steps:

[0029] Based on the feature grouping and the actual respiratory frequency obtained by monitoring, the deviation parameter between the frequency feature value and the actual respiratory frequency is analyzed;

[0030] The frequency characteristic values ​​of the feature groups are corrected based on the deviation parameters to obtain the calibrated frequency values, and then the relationship between the signal characteristics and the heart rate is analyzed based on the calibrated frequency values ​​and the actual heart rate.

[0031] Further, based on the feature grouping and the actual respiratory frequency obtained by monitoring, analyzing the deviation parameter between the frequency feature value and the actual respiratory frequency includes the following sub-steps:

[0032] The feature groups are sorted and numbered in ascending order of frequency feature values, and the symbol P is used to represent the feature groups. mIndicated by, where m is a positive integer and m is the serial number of P, the feature group includes several actual respiratory frequencies, which are numbered and represented by the symbol B(m,i), where i is a positive integer and (m,i) is the serial number of B, and B(m,i) represents P m The actual respiratory rate in

[0033] The deviation parameter calculation formula is used to calculate P m and B i Calculate and obtain the deviation parameter;

[0034] The deviation parameter calculation formula is configured as follows: Among them, DP is the deviation parameter, P m Corresponding feature group P m The frequency characteristic value of , max(m) is the maximum value of m, and max(i) is the maximum value of i.

[0035] Further, based on the deviation parameter, the frequency characteristic value of the characteristic group is corrected to obtain a calibration frequency value, and then based on the calibration frequency value and the actual heart rate, the relationship between the signal characteristic and the heart rate is analyzed, including the following sub-steps:

[0036] Multiply the frequency characteristic value of each feature group by DP to obtain the calibration frequency value, and use the symbol W m Indicates that, where m and P m The m in corresponds to;

[0037] Calculate P m Medium fluctuation characteristic value Q n The average value, denoted as U m , where m and P m The m in corresponds to;

[0038] Calculate W m ×U m , mark the calculation result as the characteristic reference value;

[0039] Each feature group includes a number of actual heart rates, and a plane rectangular coordinate system is established with the feature reference value as the X-axis and the actual heart rate as the Y-axis, which is named as a respiratory heart rate influence distribution map, and the actual heart rates in the feature group are entered into the respiratory heart rate influence distribution map according to the calibration frequency value;

[0040] The respiratory heart rate influence distribution diagram is processed by discrete regression, and the signal characteristics are calculated by fitting, which is named as the characteristic heart rate relationship function.

[0041] Furthermore, monitoring the driver's vital signs based on the relationship between the signal characteristics and the vital signs includes the following sub-steps:

[0042] Monitor the driver's vital sign signals in real time, extract the frequency characteristic value and the fluctuation characteristic value of the vital sign signals, and mark them as the frequency real-time characteristic value and the fluctuation real-time characteristic value respectively;

[0043] The real-time characteristic value of the frequency is multiplied by DP to obtain the real-time calibration frequency, and the real-time calibration frequency is multiplied by the real-time characteristic value of the fluctuation to obtain the characteristic real-time reference value;

[0044] Substituting the characteristic real-time reference value into the characteristic heart rate relationship function, and marking the calculation result as the reference heart rate;

[0045] The real-time characteristic value of the frequency is used as the real-time breathing frequency of the driver, and the reference heart rate is used as the real-time heart rate of the driver and uploaded to the monitoring platform.

[0046] In a second aspect, the present application provides an electronic device, including a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in the above method are performed.

[0047] In a third aspect, the present application provides a storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in the above method are performed.

[0048] Beneficial effects of the present invention: The present invention installs an ultra-wideband bio-radar on the door of a car and uses the ultra-wideband bio-radar to transmit short pulse signals to monitor the driver. The advantage is that installing the ultra-wideband bio-radar on the door can not only avoid blocking the driver's line of sight, but also more clearly extract the fluctuations of the driver's chest, thereby improving the accuracy and effectiveness of the driver's vital signs monitoring;

[0049] The present invention obtains a monitoring signal by receiving a reflected signal, and then performs filtering processing on the monitoring signal to obtain a vital sign signal, and then extracts the signal feature of the vital sign signal, analyzes the deviation parameter between the frequency feature value and the actual breathing frequency based on the feature grouping and the actual breathing frequency obtained by monitoring, and then corrects the frequency feature value of the feature grouping based on the deviation parameter to obtain a calibrated frequency value. The advantage is that the driver needs to control the steering wheel with both hands during driving, and the movement of both hands will affect the fluctuation of the driver's chest. Therefore, the data obtained by direct monitoring is inaccurate. It is necessary to analyze and eliminate the influence of the hand movement on the fluctuation of the driver's chest through experiments to obtain a more accurate breathing frequency of the driver, thereby improving the accuracy and rationality of the driver's vital sign monitoring.

[0050] The present invention analyzes the relationship between signal features and heart rate based on the calibration frequency value and the actual heart rate, analyzes the relationship between signal features and vital signs based on different feature groups, and finally monitors the driver's vital signs based on the relationship between signal features and vital signs. The advantage is that the human heart rate is related to the respiratory rate and the degree of chest fluctuation. Generally, the higher the human respiratory rate and the greater the degree of chest fluctuation, the higher the heart rate. By experimentally analyzing the relationship between the driver's heart rate and the above, the driver's heart rate can be monitored through a contactless monitoring method, thereby improving the accuracy and effectiveness of the driver's vital signs monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a flow chart of the steps of the method of the present invention;

[0052] Figure 2 A simulated image of the first perspective of the driver in the driving position of the present invention;

[0053] Figure 3 This is a schematic diagram of the present invention in which the vehicle door is regarded as a plane;

[0054] Figure 4 It is a schematic diagram of the installation vertical point, the observation direction straight line and the junction of the door and the window of the present invention;

[0055] Figure 5 A schematic diagram of a radar installation point of the present invention;

[0056] Figure 6 This is a schematic diagram of the ultra-wideband bio-radar of the present invention after installation;

[0057] Figure 7 is the physical sign signal of the present invention;

[0058] Figure 8 A schematic diagram of numbering of signal peak values ​​and signal valley values ​​of the present invention;

[0059] Fig. 9 A schematic diagram of grouping signal features according to frequency feature values ​​of the present invention;

[0060] Fig.10 This is the respiratory and heart rate impact distribution diagram of the present invention. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0062] Example 1, please refer to Figure 1 As shown, the present application provides an ultra-wideband bio-radar vital signs monitoring method, comprising the following steps:

[0063] Step S1, installing an ultra-wideband bio-radar on the door of a car, using the ultra-wideband bio-radar to emit a short pulse signal to monitor the driver, and obtaining a monitoring signal by receiving a reflected signal; Step S1 includes the following sub-steps:

[0064] See also Figure 2 As shown, in step S101, the point of the steering wheel closest to the driver is marked as the observation direction point;

[0065] See also Figure 3 to Figure 4 As shown, in step S102, the door is regarded as a plane named as a vertical plane, and a straight line is drawn perpendicular to the vertical plane with the observation direction point as the endpoint, and is named as the observation direction straight line; the selected door is the door at the driver's seat, the vertical plane is a plane perpendicular to the horizontal plane, and is parallel to the central axis of the vehicle, the central axis of the vehicle is a line connecting the middle point of the front end of the vehicle to the middle point of the rear end of the vehicle, the two middle points are at the same horizontal height, and the spatial position of the vertical plane is located inside the vehicle; the above definition of the vertical plane is to be able to find an installation point of the ultra-wideband bio-radar inside the door;

[0066] Step S103, marking the perpendicular point between the observation direction straight line and the vertical plane as the installation perpendicular point;

[0067] Step S104, marking a plane passing through the junction of the door and the window and parallel to the ground as a horizontal plane;

[0068] See also Figure 5 to Figure 6 As shown, in step S105, a straight line is drawn perpendicular to the horizontal plane with the installation vertical point as the endpoint, and the vertical point between the straight line and the horizontal plane is marked as the radar installation point;

[0069] Step S106, installing an ultra-wideband bio-radar at the radar installation point;

[0070] Step S107, using an ultra-wideband bio-radar to transmit a short pulse signal to monitor the driver, and obtaining a monitoring signal by receiving a reflected signal;

[0071] In the specific implementation, the position of the observation direction point is as follows: Figure 2 As shown, Figure 2 This is a simulated image of the driver's first-person perspective in the driving position. The observation direction point is the point on the steering wheel closest to the driver, which can ensure that the driver's chest will not cross the observation direction point. The door is regarded as a plane. Figure 3 As shown, the installation vertical point is Figure 2 The corresponding position in Figure 4 As shown, Figure 4 The intersection of the door and the window is also indicated in the figure. By drawing and finding the vertical point, the radar installation point is obtained. Figure 5 As shown in the figure, after the ultra-wideband bio-radar is installed, Figure 6 As shown, an ultra-wideband bio-radar is used to transmit a short pulse signal to monitor the driver, and a monitoring signal is obtained by receiving the reflected signal.

[0072] See also Figure 7 As shown, step S2, filtering the monitoring signal to obtain a vital sign signal; the Butterworth filtering technology and the MTI harmonic suppression technology are used to filter the monitoring signal, and the vital sign signal is obtained after the monitoring signal is processed;

[0073] In the specific implementation, the existing technology is used for filtering the monitoring signal. The Butterworth filtering technology and the MTI harmonic suppression technology can be used to extract the vital signs of the chest movement of the monitored object in the monitoring signal, such as Figure 7 shown.

[0074] Step S3, extracting the signal features of the vital sign signal, and grouping based on different signal features to obtain different feature groups; Step S3 includes the following sub-steps:

[0075] Step S301, selecting a first number of volunteers, continuously monitoring the first number of volunteers, and obtaining a plurality of vital sign signals, wherein the vital sign signals are presented in the form of waves, and the duration of each vital sign signal is a first acquisition duration; at the same time, the actual respiratory rate and actual heart rate of the volunteers are monitored by a monitoring instrument, wherein the vital sign signals correspond to the actual respiratory rate and the actual heart rate, and each vital sign signal corresponds to a plurality of actual respiratory rates and actual heart rates;

[0076] In the specific implementation, the first number is set to 100. 100 volunteers are selected to collect signal characteristics of drivers under different driving conditions, which are used as basic data for subsequent judgment and analysis. When conducting the experiment, the volunteers are allowed to sit in a driving simulator, and the drivers are allowed to respond to all driving scenes in a game-like manner. During this period, their vital signs are collected. The first collection time is set to 30 seconds. Within 30 seconds, the vital signs can be formed by the multiple ups and downs of the driver's chest. There is no fixed value for the first number and the first collection time. Usually, the larger the first number, the better. The maximum first collection time is not more than 60 seconds, and the minimum is not less than 30 seconds. During the test, the actual breathing frequency and actual heart rate of the volunteers are collected through contact breathing sensors and heart rate sensors. Usually, the default collection frequency of breathing sensors and heart rate sensors is 2 seconds, that is, data is collected every two seconds. Therefore, 15 actual breathing frequencies and actual heart rates will be collected within 30 seconds. These 15 actual breathing frequencies and actual heart rates correspond to the collected vital signs signals, that is, one vital sign signal corresponds to a group of actual breathing frequencies and actual heart rates.

[0077] Step S302, obtaining the frequency of the vital sign signal, marked as a frequency characteristic value;

[0078] In the specific implementation, the frequency characteristic value obtained is 20 times / min. The frequency characteristic value is the number of peaks in the vital sign signal. The peak represents the moment when the chest cavity of the volunteer inhales and swells to the maximum and is ready to exhale. Therefore, the number of peaks can represent the number of times the volunteer inhales. Each inhalation represents the completion of a breath in sequence. Figure 7 There are 10 peaks in total, but Figure 7 There is only 30s of data, which can be converted to minutes as 20 times / min.

[0079] Step S303, obtaining the amplitudes of the peak and the trough, which are marked as signal peak value and signal trough value respectively;

[0080] See also Figure 8 As shown, in step S304, the signal peaks and signal valleys are numbered from left to right, respectively by symbols F n and G n Indicates, where n is a positive integer and n is the serial number of F and G. Usually, the number of selected peaks and troughs is equal, so a unified subscript is used;

[0081] Step S305, calculate F n -G n , mark the calculated result as the fluctuation characteristic value, through the symbol Q n express;

[0082] Step S306, the frequency characteristic value and the fluctuation characteristic value together constitute the signal feature;

[0083] See also Fig. 9 As shown, step S307, grouping the signal features according to the frequency feature values ​​to obtain a number of feature groups;

[0084] In the specific implementation, F n and G n The signal peak and signal valley values ​​corresponding to the numbers are as follows Figure 8 As shown, F 1 and G 1 For example, get F 1 is 0.009, G 1 is -0.0038, and the fluctuation characteristic value Q is calculated 1 =F 1 -G 1 =0.0128, and similarly for Q 2 To Q 10 Calculation is performed to obtain 10 fluctuation characteristic values, which together with the frequency characteristic value constitute the signal characteristics. According to the different frequency characteristic values, the signal characteristics are grouped. Each characteristic group corresponds to a frequency characteristic value, and the characteristic group includes the fluctuation characteristic values ​​of several signal characteristics. The process of grouping signal characteristics according to the frequency characteristic value is as follows: Fig. 9 As shown, Fig. 9 The left side represents different vital signs signals obtained by monitoring, and the right side represents the feature grouping based on the frequency feature value, and the feature grouping also includes the actual breathing frequency and actual heart rate. Fig. 9 Not marked in Fig. 9 For example, the frequency characteristic value corresponding to the first feature group is "frequency characteristic value 1", and all signal features with the frequency characteristic value of "frequency characteristic value 1" are entered into the first feature group, and "fluctuation characteristic value 11" and "fluctuation characteristic value 12" belong to the first feature group, and "fluctuation characteristic value 11" and "fluctuation characteristic value 12" respectively correspond to a group of actual breathing frequency and actual heart rate, so their actual breathing frequency and actual heart rate also belong to the first feature group.

[0085] Step S4, based on different feature groups, analyzing the relationship between signal features and vital signs; Step S4 includes the following sub-steps:

[0086] Step S401, analyzing the deviation parameter between the frequency characteristic value and the actual respiratory frequency based on the characteristic grouping and the actual respiratory frequency obtained by monitoring;

[0087] Step S401 includes the following sub-steps:

[0088] Step S401.1, sort and number the feature groups in ascending order of frequency feature values, using the symbol P m Indicated by, where m is a positive integer and m is the serial number of P, the feature group includes several actual respiratory frequencies, which are numbered and represented by the symbol B(m,i), where i is a positive integer and (m,i) is the serial number of B, and B(m,i) represents P m The actual respiratory rate in

[0089] Step S401.2, calculate P using the deviation parameter calculation formula m and B i Calculate and obtain the deviation parameter;

[0090] The deviation parameter calculation formula is configured as Among them, DP is the deviation parameter, P m Corresponding feature group P m The frequency characteristic value of , max(m) is the maximum value of m, and max(i) is the maximum value of i;

[0091] In the specific implementation, there are 10 feature groups in this embodiment, so the feature groups are sorted and numbered in the order of frequency feature values ​​from small to large to obtain P 1 To P 10 , 1≤m≤10, where P 1 To P 10 The frequency characteristic values ​​are 16 times / min, 17 times / min, 18 times / min, 19 times / min, 20 times / min, 21 times / min, 22 times / min, 23 times / min, 24 times / min and 25 times / min, respectively. 1 For example, in this embodiment, the feature group P 1 There are 315 actual respiratory rates in total, which are numbered from B(1,1) to B(1,315). Due to the large amount of data, the specific values ​​of the data in the deviation parameter calculation formula are no longer listed in this embodiment, and only the final calculation result is given. The deviation parameter DP obtained by calculation in this embodiment is 0.81, and the calculation result is rounded to two decimal places;

[0092] Step S402, correcting the frequency characteristic value of the characteristic group based on the deviation parameter to obtain a calibration frequency value, and then analyzing the relationship between the signal characteristic and the heart rate based on the calibration frequency value and the actual heart rate;

[0093] Step S402 includes the following sub-steps:

[0094] Step S402.1, multiply the frequency characteristic value of each feature group by DP to obtain the calibration frequency value, and use the symbol W mIndicates that, where m and P m The m in corresponds to;

[0095] Step S402.2, calculate P m Medium fluctuation characteristic value Q n The average value, denoted as U m , where m and P m The m in corresponds to;

[0096] In the specific implementation, P 1 To P 10 The frequency characteristic values ​​are 16 times / min, 17 times / min, 18 times / min, 19 times / min, 20 times / min, 21 times / min, 22 times / min, 23 times / min, 24 times / min and 25 times / min, respectively. By calculating W 1 To W 10 The results are 13 times / min, 14 times / min, 15 times / min, 15 times / min, 16 times / min, 17 times / min, 18 times / min, 19 times / min, 19 times / min and 20 times / min, and the results are integers. 1 For example, in this embodiment, P 1 The fluctuation characteristic value Q 1 To Q 10 They are 0.0128, 0.0309, 0.0358, 0.0336, 0.0248, 0.0087, 0.0125, 0.0249, 0.0229 and 0.0132 respectively. The calculated value is U 1 =0.0220, the result is rounded to four decimal places;

[0097] Step S402.3, calculate W m ×U m , mark the calculation result as the characteristic reference value;

[0098] See also Fig.10 As shown, in step S402.4, each feature group includes a number of actual heart rates, and a plane rectangular coordinate system is established with the feature reference value as the X-axis and the actual heart rate as the Y-axis, which is named as the respiratory heart rate influence distribution map, and the actual heart rates in the feature group are entered into the respiratory heart rate influence distribution map according to the calibration frequency value;

[0099] Step S402.5, performing discrete regression processing on the respiratory heart rate influence distribution diagram, calculating the signal characteristics by fitting, and naming it as characteristic heart rate relationship function;

[0100] In the specific implementation, W 1 13 times / min, U 1is 0.0220, and the calculated characteristic reference value is 0.2860. The calculated result is rounded to four decimal places. 1 For example, P 1 The characteristic reference value is 0.2860, assuming that P 1 There are 4 actual heart rates, namely 63 beats / min, 65 beats / min, 65 beats / min and 67 beats / min, and the corresponding 4 coordinate points in the respiratory heart rate influence distribution map are (0.286, 63), (0.286, 65), (0.286, 65) and (0.286, 67). Similarly, the respiratory influence distribution map is constructed as follows: Fig.10 As shown, the characteristic heart rate relationship function obtained by linear fitting in discrete regression is Y=68.366×X+45.618, where Y is the actual heart rate and X is the characteristic reference value.

[0101] Step S5, monitoring the vital signs of the driver based on the relationship between the signal characteristics and the vital signs; Step S5 includes the following sub-steps:

[0102] Step S501, monitoring the driver's vital sign signal in real time, extracting the frequency characteristic value and the fluctuation characteristic value of the vital sign signal, and marking them as the frequency real-time characteristic value and the fluctuation real-time characteristic value respectively;

[0103] Step S502, multiplying the frequency real-time characteristic value by DP to obtain a real-time calibration frequency, and multiplying the real-time calibration frequency by the fluctuation real-time characteristic value to obtain a characteristic real-time reference value;

[0104] Step S503, substituting the characteristic real-time reference value into the characteristic heart rate relationship function, and marking the calculation result as the reference heart rate;

[0105] Step S504, taking the frequency real-time characteristic value as the driver's real-time breathing frequency, taking the reference heart rate as the driver's real-time heart rate and uploading them to the monitoring platform;

[0106] In the specific implementation, the frequency characteristic value of the driver monitored is 16 times / min, and the fluctuation characteristic value is 0.0224. The real-time characteristic value of the frequency is multiplied by DP, and the real-time calibration frequency is 16 times / min×0.81=13 times / min. The calculation result is retained as an integer. The real-time reference value of the characteristic is calculated to be 13×0.0224=0.2912. X=0.2912 is substituted into Y=68.366×X+45.618, and the reference heart rate is calculated to be 66 times / min. The calculation result is retained as an integer. The real-time breathing frequency of the driver is 13 times / min and the real-time heart rate is 66 times / min through contactless monitoring. The driver's physical health status can be effectively evaluated by monitoring the driver's breathing frequency and heart rate. Integrating it into the intelligent driving system can remind the driver when there is an abnormality in his physical health and stop the vehicle to the roadside in time when the driver operates abnormally.

[0107] Embodiment 2, the present application provides an electronic device, which may include: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus. The memory stores computer-readable instructions, and the processor can call the instructions in the memory. When the computer-readable instructions are executed by the processor, the steps in an ultra-wideband bio-radar vital signs monitoring method are executed to achieve the following functions: using an ultra-wideband bio-radar to transmit a short pulse signal to monitor the driver; filtering the monitoring signal to obtain a vital sign signal; extracting the signal features of the vital sign signal, grouping based on different signal features to obtain different feature groups; based on different feature groups, analyzing the relationship between the signal features and the vital signs; monitoring the driver's vital signs based on the relationship between the signal features and the vital signs.

[0108] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art, and the computer software product is stored in a storage medium, including several instructions to enable 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 application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk.

[0109] Example 3. The present application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute an ultra-wideband bioradar vital signs monitoring method provided by the above methods, the method including: using an ultra-wideband bioradar to transmit a short pulse signal to monitor the driver; filtering the monitoring signal to obtain a vital sign signal; extracting signal features of the vital sign signal, grouping based on different signal features to obtain different feature groups; based on different feature groups, analyzing the relationship between signal features and vital signs; monitoring the driver's vital signs based on the relationship between signal features and vital signs.

[0110] Embodiment 4. The present application also provides a computer-readable storage medium. The present application provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above ultra-wideband bioradar vital signs monitoring method are executed to achieve the following functions: use an ultra-wideband bioradar to transmit a short pulse signal to monitor the driver; filter the monitoring signal to obtain a vital sign signal; extract signal features of the vital sign signal, group it based on different signal features, and obtain different feature groups; based on different feature groups, analyze the relationship between signal features and vital signs; monitor the driver's vital signs based on the relationship between signal features and vital signs.

[0111] Through the description of the above implementation methods, the embodiments of the present invention can be provided as methods, systems or computer program products. Based on such an understanding, the above technical solutions can be essentially or partly contributed to the prior art in the form of software products, which can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and include several instructions for enabling 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.

[0112] In the embodiments provided in the present application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of systems, modules and units can be electrical, mechanical or other forms.

[0113] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application 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 application.

Claims

1. An ultra-wideband bio-radar vital signs monitoring method, characterized in that: The steps include: An ultra-wideband bio-radar is installed on the door of the car, and a short pulse signal is emitted by the ultra-wideband bio-radar to monitor the driver, and a monitoring signal is obtained by receiving the reflected signal; Filter the monitoring signal to obtain a vital sign signal; Extract signal features of vital sign signals, group them based on different signal features, and obtain different feature groups; Analyze the relationship between signal features and vital signs based on different feature groups; The driver's vital signs are monitored based on the relationship between signal characteristics and vital signs.

2. The method for monitoring vital signs using ultra-wideband bioradar according to claim 1, characterized in that: An ultra-wideband bio-radar is installed on the door of a car, and a short pulse signal is emitted by the ultra-wideband bio-radar to monitor the driver. The monitoring signal is obtained by receiving the reflected signal, and the following sub-steps are included: Mark the point of the steering wheel closest to the driver as the observation direction point; Consider the car door as a plane, named the vertical plane, take the observation direction point as the endpoint, draw a straight line perpendicular to the vertical plane, named the observation direction straight line; Mark the perpendicular point between the observation direction straight line and the vertical plane as the installation perpendicular point; Mark the plane passing through the junction of the door and the window and parallel to the ground as the horizontal plane; With the installation vertical point as the endpoint, draw a straight line perpendicular to the horizontal plane, and mark the vertical point between it and the horizontal plane as the radar installation point; Installation of ultra-wideband bio-radar at radar installation sites; Ultra-wideband bio-radar is used to transmit short pulse signals to monitor the driver, and the monitoring signal is obtained by receiving the reflected signal.

3. The method for monitoring vital signs using ultra-wideband bioradar according to claim 2, characterized in that: The filtering process for the monitoring signal adopts Butterworth filtering technology and MTI harmonic suppression technology, and the vital sign signal is obtained after the monitoring signal is processed.

4. The method for monitoring vital signs using ultra-wideband bioradar according to claim 3, characterized in that: Extracting the signal features of the vital sign signal and grouping them based on different signal features to obtain different feature groups includes the following sub-steps: A first number of volunteers are selected, and the first number of volunteers are continuously monitored to obtain a plurality of vital sign signals, wherein the vital sign signals are presented in the form of waves, and the duration of each vital sign signal is the first acquisition duration; at the same time, the actual respiratory rate and the actual heart rate of the volunteers are monitored by a monitoring instrument, wherein the vital sign signals correspond to the actual respiratory rate and the actual heart rate, and each vital sign signal corresponds to a plurality of actual respiratory rates and actual heart rates; Obtain the frequency of the vital sign signal and mark it as the frequency characteristic value; Get the amplitude of the peak and trough, and mark them as signal peak and signal trough respectively; The signal peaks and valleys are numbered from left to right, respectively represented by the symbols F n and G n Indicates, where n is a positive integer and n is the sequence number of F and G; Calculate F n -G n , mark the calculated result as the fluctuation characteristic value, through the symbol Q n express; The frequency characteristic value and the fluctuation characteristic value together constitute the signal feature; Signal features are grouped according to frequency feature values ​​to obtain several feature groups.

5. The method for monitoring vital signs using ultra-wideband bioradar according to claim 4, characterized in that: Based on different feature groups, analyzing the relationship between signal features and vital signs includes the following sub-steps: Based on the feature grouping and the actual respiratory frequency obtained by monitoring, the deviation parameter between the frequency feature value and the actual respiratory frequency is analyzed; The frequency characteristic values ​​of the feature groups are corrected based on the deviation parameters to obtain the calibrated frequency values, and then the relationship between the signal characteristics and the heart rate is analyzed based on the calibrated frequency values ​​and the actual heart rate.

6. The method for monitoring vital signs using ultra-wideband bioradar according to claim 5, characterized in that: Based on the feature grouping and the actual respiratory frequency obtained by monitoring, analyzing the deviation parameter between the frequency feature value and the actual respiratory frequency includes the following sub-steps: The feature groups are sorted and numbered in ascending order of frequency feature values, and the symbol P is used to represent the feature groups. m Indicated by, where m is a positive integer and m is the serial number of P, the feature group includes several actual respiratory frequencies, which are numbered and represented by the symbol B(m,i), where i is a positive integer and (m,i) is the serial number of B, and B(m,i) represents P m The actual respiratory rate in The deviation parameter calculation formula is used to calculate P m and B i Calculate and obtain the deviation parameter; The deviation parameter calculation formula is configured as follows: Among them, DP is the deviation parameter, P m Corresponding feature group P m The frequency characteristic value of , max(m) is the maximum value of m, and max(i) is the maximum value of i.

7. The method for monitoring vital signs using ultra-wideband bioradar according to claim 6, characterized in that: Correcting the frequency characteristic value of the characteristic grouping based on the deviation parameter to obtain a calibration frequency value, and then analyzing the relationship between the signal characteristic and the heart rate based on the calibration frequency value and the actual heart rate includes the following sub-steps: Multiply the frequency characteristic value of each feature group by DP to obtain the calibration frequency value, and use the symbol W m Indicates that, where m and P m The m in corresponds to; Calculate P m Medium fluctuation characteristic value Q n The average value, denoted as U m , where m and P m The m in corresponds to; Calculate W m ×U m , mark the calculation result as the characteristic reference value; Each feature group includes a number of actual heart rates, and a plane rectangular coordinate system is established with the feature reference value as the X-axis and the actual heart rate as the Y-axis, which is named as a respiratory heart rate influence distribution map, and the actual heart rates in the feature group are entered into the respiratory heart rate influence distribution map according to the calibration frequency value; The respiratory heart rate influence distribution diagram is processed by discrete regression, and the signal characteristics are calculated by fitting, which is named as the characteristic heart rate relationship function.

8. The method for monitoring vital signs using ultra-wideband bio-radar according to claim 7, characterized in that: Monitoring the driver's vital signs based on the relationship between signal characteristics and vital signs includes the following sub-steps: Monitor the driver's vital sign signals in real time, extract the frequency characteristic value and the fluctuation characteristic value of the vital sign signals, and mark them as the frequency real-time characteristic value and the fluctuation real-time characteristic value respectively; The real-time characteristic value of the frequency is multiplied by DP to obtain the real-time calibration frequency, and the real-time calibration frequency is multiplied by the real-time characteristic value of the fluctuation to obtain the characteristic real-time reference value; Substituting the characteristic real-time reference value into the characteristic heart rate relationship function, and marking the calculation result as the reference heart rate; The real-time characteristic value of the frequency is used as the real-time breathing frequency of the driver, and the reference heart rate is used as the real-time heart rate of the driver and uploaded to the monitoring platform.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the steps in the method according to any one of claims 1 to 7 are executed.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 7 are executed.

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