Home-based fetal physiological marker testing methods, devices, and fetal monitors

Abstract

This application provides a method, device, and fetal monitor for home-based fetal physiological indicator detection. The method includes: identifying the pregnant woman's abdominal position when the pregnant woman is detected to be in a preset area; transmitting a radar detection signal to the abdominal position and receiving the echo signal returned from the abdominal position; processing the echo signal to determine the position of the fetus inside the pregnant woman's body and calculating the physiological indicators of the fetus. This method allows for the detection of the fetal physiological indicators at home, and because it uses a non-contact, autonomous measurement method, it is simple to operate and user-friendly.

Description

Technical Field

[0001] This application relates to the field of home device technology, and in particular to a method, device and fetal monitor for home fetal physiological index detection. Background Technology

[0002] Currently, pregnant women need to have regular prenatal checkups at the hospital to monitor the physiological indicators and condition of the fetus. However, the frequency of hospital checkups is limited, and problems such as fetal hypoxia may not be detected in time. Therefore, a home fetal monitor is needed to conveniently monitor the fetus's physiological indicators in daily life, allowing for timely detection of problems. While some users have considered bringing hospital-grade monitoring equipment home, such as medical fetal heart monitors with multiple electrodes, these devices often require proper operation by medical professionals, making them unsuitable for users without such expertise. Summary of the Invention

[0003] In view of this, embodiments of this application provide a method, device, fetal monitor, and readable storage medium for home-based fetal physiological indicator detection.

[0004] In a first aspect, embodiments of this application provide a method for home-based fetal physiological indicator detection, including:

[0005] If a pregnant woman is detected to be in a preset area, the location of the pregnant woman's abdomen is identified;

[0006] The radar transmits a radar detection signal to the abdominal location and receives the echo signal returned from the abdominal location.

[0007] The echo signal is processed to determine the location of the fetus in the pregnant woman's body and to calculate the physiological indicators of the fetus.

[0008] In some embodiments, identifying the location of the pregnant woman's abdomen includes:

[0009] The radar performs a full scan of the preset area to obtain spatial point cloud data of the pregnant woman's body surface.

[0010] The current position and posture information of the pregnant woman are obtained by using a trained deep neural network based on the spatial point cloud data.

[0011] Based on the pregnant woman's current position and posture information, the position of the pregnant woman's abdomen is determined.

[0012] In some embodiments, the step of transmitting a radar detection signal toward the abdominal location via radar includes, prior to:

[0013] Select the first point cloud data of the abdominal position from the spatial point cloud data, and calculate the first momentum of the first point cloud data;

[0014] When the first momentum is less than a preset body movement amplitude threshold, the step of transmitting a radar detection signal to the abdominal position via radar is executed.

[0015] When the first momentum is greater than or equal to the preset body motion amplitude threshold, the process returns to the step of performing a full scan of the preset area using the radar.

[0016] In some embodiments, the step of performing a full scan of the preset area using the radar to obtain spatial point cloud data of the pregnant woman's body surface includes:

[0017] The preset region is divided into sub-regions to obtain several sub-regions;

[0018] The radar is used to scan each of the sub-regions sequentially to obtain the reflected signal returned by each of the sub-regions;

[0019] Spatial point cloud data of the pregnant woman's body surface is obtained based on the reflection signal of each of the sub-regions.

[0020] In some embodiments, the step of processing the echo signal to determine the location of the fetus in the pregnant woman's body and calculating the physiological indicators of the fetus includes:

[0021] Based on the echo signal returned from the abdominal location, second point cloud data of the abdominal location is obtained; wherein, the point cloud density of the first point cloud data is less than the point cloud density of the second point cloud data;

[0022] A body surface model of the pregnant woman is constructed using the first point cloud data and the second point cloud data to determine the location of the fetus inside the pregnant woman's body;

[0023] The second momentum of the second point cloud data is calculated, and when the second momentum is less than a preset body movement amplitude threshold, the echo signal returned by the fetal position in the pregnant woman's body is analyzed to obtain the physiological indicators of the fetus in the pregnant woman's body.

[0024] In some embodiments, the step of analyzing the echo signal returned from the fetal position within the pregnant woman's body further includes:

[0025] The second momentum is used to correct the overall signal of the echo signal returned from the fetal position in the pregnant woman's body, resulting in a corrected echo signal, which is used for the signal analysis.

[0026] In some embodiments, the home-based fetal physiological indicator detection method further includes:

[0027] When the second momentum is greater than or equal to the preset body movement amplitude threshold, the process returns to the step of identifying the pregnant woman's abdominal position.

[0028] Secondly, embodiments of this application provide a home-based fetal physiological indicator detection device, comprising:

[0029] The identification module is used to identify the location of the pregnant woman's abdomen when the pregnant woman is detected to be in a preset area;

[0030] The detection module is used to transmit radar detection signals to the abdominal position via radar and receive echo signals returned from the abdominal position.

[0031] The processing module is used to process the echo signal to determine the position of the fetus in the pregnant woman's body and calculate the physiological indicators of the fetus in the pregnant woman's body.

[0032] Thirdly, embodiments of this application provide a fetal monitor, which includes a radar, a processor, and a memory. The radar is used to transmit and receive radar signals, the memory stores a computer program, and the processor is used to execute the computer program to implement the above-described home fetal physiological indicator detection method.

[0033] Fourthly, embodiments of this application provide a readable storage medium storing a computer program, which, when executed on a processor, implements the above-described home-based fetal physiological indicator detection method.

[0034] The embodiments of this application have the following beneficial effects:

[0035] This application's home-based fetal physiological indicator monitoring method detects when the pregnant woman has entered a preset measurement area. It uses radar to identify the pregnant woman's abdominal position, transmits a radar detection signal towards the abdominal position, and receives the echo signal returned from the abdominal position. Then, it processes the echo signal to determine the fetal position within the pregnant woman and calculates the fetal physiological indicators. This method uses a non-contact, autonomous measurement approach. For users, it eliminates the need for contact monitoring devices that require electrodes to be attached to specific locations on the pregnant woman's body. It is simple to operate and allows for monitoring of the fetal physiological status at home, effectively solving the problem of pregnant women being unable to go to the hospital for prenatal checkups in some situations. Attached Figure Description

[0036] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 A first flowchart of a home-based fetal physiological index detection method according to an embodiment of this application is shown;

[0038] Figure 2 A second flowchart of the home-based fetal physiological index detection method according to an embodiment of this application is shown;

[0039] Figure 3 A third flowchart of the home-based fetal physiological index detection method according to an embodiment of this application is shown;

[0040] Figure 4 The fourth flowchart of the home-based fetal physiological index detection method according to an embodiment of this application is shown;

[0041] Figure 5 A schematic diagram of the structure of a home-based fetal physiological indicator detection device according to an embodiment of this application is shown. Detailed Implementation

[0042] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0043] The components of the embodiments of this application described and illustrated in the accompanying drawings can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0044] In the following, the terms “comprising,” “having,” and their cognates, which may be used in various embodiments of this application, are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as excluding, firstly, the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more features, numbers, steps, operations, elements, components, or combinations thereof.

[0045] Furthermore, the terms "first," "second," and "third" are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0046] Unless otherwise specified, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of this application pertain. Terms (such as those defined in commonly used dictionaries) shall be interpreted as having the same meaning as in their contextual meaning in the relevant technical field and shall not be construed as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of this application.

[0047] Example 1

[0048] Please refer to Figure 1 This embodiment proposes a home-based method for detecting fetal physiological indicators, which can be used as a home device for pregnant women to monitor fetal physiological indicators and the pregnant woman's own condition at home. The following description focuses on fetal physiological indicators and explains this home-based method for detecting fetal physiological indicators.

[0049] Exemplary, such as Figure 1 As shown, the home-based fetal physiological indicator detection method includes:

[0050] Step S110: If the pregnant woman is detected to be in a preset area, identify the location of the pregnant woman's abdomen.

[0051] The aforementioned preset area refers to the area that the radar can detect when conducting physiological indicator testing. For example, if a pregnant woman needs to lie in bed for fetal testing, the preset area can be the area where the bed is located; or, if a pregnant woman is sitting on a sofa or at a desk, the preset area can be the area where an object such as a sofa or desk is located.

[0052] In one implementation, point cloud data obtained from radar can be used in conjunction with a neural network model to identify the location of the pregnant woman's abdomen. For example... Figure 2 As shown, step S110 includes the following sub-steps:

[0053] Sub-step S210 involves using radar to perform a full scan of a preset area to obtain spatial point cloud data of the pregnant woman's body surface.

[0054] It is worth noting that in this embodiment, sparse point cloud (i.e., coarse point cloud with low density) will be used to obtain the position and posture information of the pregnant woman during the full scan. Once the position of the pregnant woman's abdomen is accurately located, the point cloud density will be increased, that is, fine point cloud will be used to measure the relevant status of the fetus in the abdomen.

[0055] In one implementation, such as Figure 3As shown, the above sub-step S210 includes sub-steps S211 to S213, as follows:

[0056] Sub-step S211: Divide the preset region into sub-regions to obtain several sub-regions.

[0057] For example, if the preset area is the area where the pregnant woman is lying on the bed, it can be divided into several sub-areas at equal distances from the head to the foot of the bed, so that when the radar transmits a signal to a single sub-area, it can detect the location of the human body in the current sub-area.

[0058] In addition to the bed mentioned above, the preset area can also be the measurement area of ​​the sofa facing the pregnant woman. This measurement area can also be divided into multiple sub-areas for separate human body detection.

[0059] In sub-step S212, the radar is used to scan each sub-region sequentially to obtain the reflected signal returned by each sub-region.

[0060] For example, taking the scanning of one sub-region as an example, a signal of a certain frequency can be emitted by multiple antennas in the radar. Since the pregnant woman is located in the preset area, the emitted signal will be reflected back when it encounters the pregnant woman's body surface. At this time, the corresponding reflected signal can be received by multiple antennas.

[0061] Sub-step S213: Based on the reflection signal of each sub-region, obtain the spatial point cloud data of the pregnant woman's body surface.

[0062] For example, the time difference and angle of arrival between each reflected signal and the transmitted signal can be calculated, thereby obtaining information such as the distance and phase angle of each reflection point from the radar. Then, the spatial position information and radial velocity information of these reflection points in three-dimensional space can be calculated, and the spatial position and radial velocity information of these reflection points constitute the spatial point cloud data of the pregnant woman's body surface.

[0063] Sub-step S220: Based on the spatial point cloud data, the current position and posture information of the pregnant woman are obtained using a trained deep neural network.

[0064] The aforementioned deep neural network can be pre-trained and is mainly used to recognize human postures, including but not limited to recognizing different postures such as standing, sitting, and lying down. It is worth noting that, since this embodiment is mainly used to identify whether a pregnant woman is in a static state such as sitting or lying down, the deep neural network here can be trained and validated based on a certain number of point cloud sample data containing different pregnant women postures. The specific process of network training will not be described in detail here.

[0065] Sub-step S230: Determine the position of the pregnant woman's abdomen based on her current position and posture information.

[0066] As an example, the obtained spatial point cloud data is input into a pre-trained deep network, which can predict and output the approximate position and posture of the human body. The position can be understood as a specific location within a preset area, while the posture can refer to whether the pregnant woman is sitting or lying down within that area. Furthermore, based on the output posture and the body's structural features, the location of the abdomen can be determined through methods such as feature comparison. In addition, the locations of other body parts can be determined according to actual needs, such as the location of the pregnant woman's heart, so that the pregnant woman's physiological characteristics such as heartbeat and respiration can be detected.

[0067] It is worth noting that, in determining the position of the pregnant woman's abdomen, this home-based fetal physiological indicator monitoring method also includes assessing the pregnant woman's body movements. Furthermore, the method also includes:

[0068] Sub-step S240: Select the first point cloud data of the abdominal position from the above spatial point cloud data, and calculate the first momentum of the first point cloud data.

[0069] The first momentum is used to reflect the degree of body movement of the pregnant woman, and can be calculated, for example, based on the degree of positional deviation of all point cloud data per unit time. It can be understood that the "first" in the first point cloud data or first momentum is simply to distinguish it from the second point cloud data and second momentum obtained during targeted measurements mentioned later.

[0070] As an example, after selecting coarse point cloud data of the abdominal area, its corresponding momentum can be calculated. Then, it can be determined whether this momentum is less than a preset threshold for body movement amplitude. If it is less, it indicates that the pregnant woman is essentially at rest, and the next targeted measurement operation can proceed. Conversely, if it is greater than or equal to, a full scan needs to be performed again, and the pregnant woman can be prompted to remain still.

[0071] Sub-step S250: When the first momentum is less than the preset body motion amplitude threshold, the enhanced radar detection signal is transmitted to the abdominal position via radar, i.e., step S120.

[0072] In sub-step S260, if the first momentum is greater than or equal to the preset body movement amplitude threshold, the process returns to the above sub-step S210, that is, the preset area is re-scanned by radar to redetermine the current position of the pregnant woman's abdomen.

[0073] For step S110 above, after obtaining the position of the pregnant woman's abdomen in a static state, the position of the abdomen can be further measured in a targeted manner. By eliminating the interference of other objects or limb movements away from the torso on the measurement results, the accuracy and reliability of the measurement results can be better guaranteed.

[0074] Step S120: The radar transmits a radar detection signal to the abdominal position and receives the echo signal returned from the abdominal position.

[0075] As an example, when conducting targeted measurements, multiple-input multiple-output (MIMO) beamforming technology can be used to enhance the scanning of the abdominal area, thereby increasing the point cloud density, radar angular resolution, and signal-to-noise ratio of the returned signal. For instance, M channels can be used to transmit mutually orthogonal signals, with the multiple waveforms remaining independent in space. After being scattered by the pregnant woman's body surface, these signals are received by N receiving elements. For each element, M matched filters are used to match the echoes, thus obtaining echo data from M×N channels. It is understood that the radar detection signal described above can be a millimeter-wave band signal, or a signal from other frequency bands; this is not limited to these specific frequencies.

[0076] Step S130: Perform signal processing on the echo signal to determine the position of the fetus in the pregnant woman's body and calculate the physiological indicators of the fetus in the pregnant woman's body.

[0077] The aforementioned fetal physiological indicators may include, but are not limited to, information such as fetal heartbeat and heart rate. In this embodiment, after obtaining the echo signal from the abdominal region, the position of the fetus within the body will be further determined, and then the signal from that position will be further processed to obtain physiological indicators such as fetal heart rate.

[0078] In one implementation, such as Figure 3 As shown, step S130 includes the following sub-steps:

[0079] Sub-step S310 involves obtaining second point cloud data for the abdominal location based on the echo signal returned from the abdominal location. The point cloud density of the second point cloud data will be greater than that of the first point cloud data. This is because a multi-transmission, multi-reception enhanced scanning method is used during targeted measurements.

[0080] Sub-step S320: Construct a surface model of the pregnant woman using the first point cloud data and the second point cloud data to determine the location of the fetus inside the body.

[0081] It is understandable that a relatively coarse surface model of the pregnant woman can be constructed using the first point cloud data. Then, by combining it with the second point cloud data, which has a higher point cloud density, more details of the surface model can be obtained, resulting in a more refined surface model, from which the position of the fetus can be determined.

[0082] Sub-step S330: Calculate the second momentum of the second point cloud data and detect whether the second momentum is less than the preset body motion amplitude threshold.

[0083] In sub-step S340, if the second momentum is less than the preset body movement amplitude threshold, the echo signal returned from the fetal position inside the pregnant woman's body is analyzed to obtain the physiological indicators of the fetus inside the pregnant woman's body.

[0084] The second momentum is used to reflect the degree of movement of the pregnant woman's abdomen. Exemplarily, the signal indicating the fetal position is returned is only further analyzed when the second momentum is less than a preset threshold. Otherwise, sub-step S350 is optionally executed.

[0085] In sub-step S350, if the second momentum is greater than or equal to the preset body motion amplitude threshold, then return to step S110.

[0086] Among them, the fetal physiological indicators, such as the fetal heartbeat, can be obtained by analyzing the echo signal returned from the fetal position in the pregnant woman's body. Specifically, this includes: bandpass filtering the echo signal returned from the fetal position in the pregnant woman's body according to a preset fetal heartbeat frequency; and then performing frequency domain analysis on the filtered signal to obtain the fetal heartbeat information.

[0087] In one embodiment, taking frequency modulated continuous wave (FMCW) radar waves as an example, the obtained echo signal is as follows:

[0088]

[0089] make

[0090] Among them, f b φ represents the frequency difference between the transmitted frequency and the reflected frequency. b The phase difference between the transmitted signal and the echo signal is represented by λ; B is the bandwidth of the transmitted signal, T is the sweep period; c is the speed of light, λ is the wavelength, and R is the distance from the target to the radar.

[0091] Specifically, assuming the pregnant woman does not exhibit significant body movement during the measurement, the reflected signal strength is strongest at the location of the pregnant woman's abdomen. At this point, R and t can be calculated. Since R and t are fixed during each data acquisition, f... b It is a constant. Based on this, the vibrations on the pregnant woman's body surface caused by respiration and heart rate will cause φ b The change in φ is therefore determined by reconstructing the echo signal from each acquisition. b This yields the aforementioned echo signal. It should be understood that if the pregnant woman exhibits significant body movement during the testing process, the values ​​of R and t must be recalculated after the woman's movement ceases.

[0092] Furthermore, based on the pre-set fetal heart rate, a corresponding bandpass filter can be set to filter the echo signal to extract the signal containing the fetal heart rate information. Then, by performing Fourier transform (FFT) and power spectral density estimation on the filtered signal in the time and frequency domain, the fetal heart rate can be obtained.

[0093] As an optional approach, for the above sub-step S340, such as Figure 4 As shown, signal analysis was performed on the echo signal returning from the fetal position within the pregnant woman's body, which included the following steps:

[0094] In sub-step S360, the echo signal returned from the fetal position inside the pregnant woman's body is corrected using the second momentum to obtain the corrected echo signal.

[0095] It is understandable that this corrected echo signal will be used to replace the aforementioned echo signal for signal analysis. By correcting the echo signal at an overall level, the influence of the pregnant woman's overall body movement can be eliminated, resulting in more accurate measurement results.

[0096] This embodiment of the home-based fetal physiological indicator detection method first uses coarse point cloud data obtained from radar to roughly locate the pregnant woman's abdomen based on her position and posture. This requires constant monitoring of any excessive body movements by the pregnant woman to ensure measurements are taken only when she is stationary. Then, after determining the pregnant woman's abdomen, fine point cloud data is used to precisely locate the fetus within the abdomen. The signal returned from the fetal position is further processed, including overall horizontal correction to mitigate the influence of the pregnant woman's movements, resulting in more accurate fetal physiological indicators. This method uses a non-contact, autonomous measurement approach, eliminating the need for contact monitoring devices that require electrodes to be attached to specific locations on the pregnant woman's body. It is simple to operate and allows for monitoring of fetal physiological indicators at home, effectively addressing the challenge of pregnant women being unable to visit a hospital for prenatal checkups in certain situations.

[0097] Example 2

[0098] Please refer to Figure 5 Based on the method of Embodiment 1 above, this embodiment proposes a home-based fetal physiological indicator detection device 100. Exemplarily, the home-based fetal physiological indicator detection device 100 includes:

[0099] The identification module 110 is used to identify the abdominal position of the pregnant woman when the pregnant woman is detected to be in a preset area.

[0100] The detection module 120 is used to transmit radar detection signals to the abdominal position via radar and receive echo signals returned from the abdominal position.

[0101] The processing module 130 is used to process the echo signal to determine the position of the fetus in the pregnant woman's body and calculate the physiological indicators of the fetus in the pregnant woman's body.

[0102] It is understood that the apparatus of this embodiment corresponds to the method of embodiment 1 above, and the options in embodiment 1 above are also applicable to this embodiment, so they will not be described again here.

[0103] This application also provides a fetal monitor, exemplary of which includes a radar, a processor, and a memory, wherein the radar is used to transmit and receive radar signals, the memory stores a computer program, and the processor, by running the computer program, enables the terminal device to perform the functions of the various modules in the above-described home fetal physiological indicator detection method or the above-described home fetal physiological indicator detection device.

[0104] For example, in one embodiment, the radar in the fetal monitor can be mounted above the bed via a bracket or other means, or in front of a sofa, or in other different locations. When the device is turned on, the radar can detect whether the pregnant woman is in a preset area. If the pregnant woman is detected to be in the preset area, the system will begin monitoring the physiological indicators of the fetus within the pregnant woman's body. For details, please refer to the method in Embodiment 1 above, which will not be repeated here.

[0105] This application also provides a readable storage medium for storing the computer program used in the above-described fetal monitor.

[0106] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that, in alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0107] In addition, the functional modules or units in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0108] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0109] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for home-based fetal physiological indicator detection, characterized in that, include: If a pregnant woman is detected to be in a preset area, the location of the pregnant woman's abdomen is identified; The radar transmits a radar detection signal to the abdominal location and receives the echo signal returned from the abdominal location. Signal processing is performed on the echo signal to determine the location of the fetus in the pregnant woman's body and to calculate the physiological indicators of the fetus, including: Based on the echo signal returned from the abdominal position, the second point cloud data of the abdominal position is obtained; the body surface model of the pregnant woman is constructed using the first point cloud data and the second point cloud data to determine the position of the fetus in the pregnant woman's body; wherein, the first point cloud data is the point cloud data of the abdominal position selected from the spatial point cloud data of the pregnant woman's body surface, and the point cloud density of the first point cloud data is less than the point cloud density of the second point cloud data. The second momentum of the second point cloud data is calculated, and when the second momentum is less than a preset body movement amplitude threshold, the echo signal returned by the fetal position in the pregnant woman's body is analyzed to obtain the physiological indicators of the fetus in the pregnant woman's body.

2. The method for home-based fetal physiological indicator detection according to claim 1, characterized in that, The identification of the pregnant woman's abdominal location includes: The radar performs a full scan of the preset area to obtain the spatial point cloud data of the pregnant woman's body surface; The current position and posture information of the pregnant woman are obtained by using a trained deep neural network based on the spatial point cloud data. Based on the pregnant woman's current position and posture information, the position of the pregnant woman's abdomen is determined.

3. The method for home-based fetal physiological indicator detection according to claim 2, characterized in that, The step of transmitting a radar detection signal toward the abdominal location via radar includes: Select the first point cloud data of the abdominal position from the spatial point cloud data, and calculate the first momentum of the first point cloud data; When the first momentum is less than a preset body movement amplitude threshold, the step of transmitting a radar detection signal to the abdominal position via radar is executed. When the first momentum is greater than or equal to the preset body motion amplitude threshold, the process returns to the step of performing a full scan of the preset area using the radar.

4. The method for home-based fetal physiological indicator detection according to claim 3, characterized in that, The step of performing a full scan of the preset area using the radar to obtain spatial point cloud data of the pregnant woman's body surface includes: The preset region is divided into sub-regions to obtain several sub-regions; The radar is used to scan each of the sub-regions sequentially to obtain the reflected signal returned by each of the sub-regions; Spatial point cloud data of the pregnant woman's body surface is obtained based on the reflection signal of each of the sub-regions.

5. The method for home-based fetal physiological indicator detection according to claim 1, characterized in that, The step of analyzing the echo signal returned from the fetal position within the pregnant woman's body also includes: The second momentum is used to correct the overall signal of the echo signal returned from the fetal position in the pregnant woman's body, resulting in a corrected echo signal, which is used for the signal analysis.

6. The method for home-based fetal physiological indicator detection according to claim 1, characterized in that, Also includes: When the second momentum is greater than or equal to the preset body movement amplitude threshold, the step of identifying the pregnant woman's abdominal position is returned.

7. A home-based fetal physiological indicator detection device, characterized in that, include: The identification module is used to identify the location of the pregnant woman's abdomen when the pregnant woman is detected to be in a preset area; The detection module is used to transmit radar detection signals to the abdominal position via radar and receive the echo signals returned from the abdominal position. The processing module is used to perform signal processing on the echo signal to determine the position of the fetus in the pregnant woman's body and calculate the physiological indicators of the fetus in the pregnant woman's body, including: Based on the echo signal returned from the abdominal position, the second point cloud data of the abdominal position is obtained; the body surface model of the pregnant woman is constructed using the first point cloud data and the second point cloud data to determine the position of the fetus in the pregnant woman's body; wherein, the first point cloud data is the point cloud data of the abdominal position selected from the spatial point cloud data of the pregnant woman's body surface, and the point cloud density of the first point cloud data is less than the point cloud density of the second point cloud data. The second momentum of the second point cloud data is calculated, and when the second momentum is less than a preset body movement amplitude threshold, the echo signal returned by the fetal position in the pregnant woman's body is analyzed to obtain the physiological indicators of the fetus in the pregnant woman's body.

8. A fetal monitoring device, characterized in that, The fetal monitor includes a radar, a processor, and a memory, wherein the radar is used to transmit and receive radar signals, the memory stores a computer program, and the processor is used to execute the computer program to implement the home-based fetal physiological indicator detection method according to any one of claims 1-6.

9. A readable storage medium, characterized in that, It stores a computer program, which, when executed on a processor, implements the home-based fetal physiological indicator detection method according to any one of claims 1-6.

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