Remote data acquisition system and method for early screening of ankylosing spondylitis

Abstract

The present application discloses a remote data acquisition system and method for early screening of ankylosing spondylitis. The system includes: a basic information acquisition unit, a posture information acquisition unit, a thoracic expansion information acquisition unit, and an information extraction unit. The basic information acquisition unit is used to receive the symptom description information of the user; the posture information acquisition unit is used to acquire the continuous posture information of the user; the thoracic expansion information acquisition unit is used to acquire the thoracic expansion information of the user during the breathing process; the information extraction unit is used to extract the corresponding symptom description data, posture data, and thoracic expansion data of the user based on the symptom description information, the posture information, and the thoracic expansion information respectively, and can accurately obtain the user's state information in multiple dimensions, providing accurate data support for early screening by doctors.

Description

Technical Field

[0001] The present disclosure generally relates to the technical field of remote data processing, and particularly relates to a remote data acquisition system and method for early screening of ankylosing spondylitis. Background Art

[0002] Ankylosing Spondylitis (AS) is a lifelong disability with high disability rate, which can cause disability due to the formation of spinal osteophytes and hip joint destruction. Previous data showed that up to 40% of ankylosing spondylitis patients eventually had complete spinal fusion, and even extreme distortion of the body.

[0003] In the reply to the "Proposal on Strengthening the Prevention and Treatment of Ankylosing Spondylitis" by the National Health Commission in 2020, it was mentioned that "strengthening the prevention and control of ankylosing spondylitis is also an important measure to implement the Outline of the 'Healthy China 2030' Plan and promote the construction of a healthy China". Accurate screening of early-stage patients is an important measure for strengthening prevention and control, and accurately obtaining the lesion information of early-stage patients is the premise for effectively controlling the progression of the disease.

[0004] In the prior art, disease screening is usually carried out only based on the phenomena such as pain and limited movement caused by spinal fusion in users. However, such a single source of features limits the accuracy of the early screening results of ankylosing spondylitis. Moreover, due to the relatively mild degree of the lesion, such as a relatively small change in the user's posture, it requires doctors with relatively rich experience to accurately detect the lesion abnormality. Especially in the medical information collection scenarios mainly implemented by nurses in physical examination institutions and the like, it is difficult to collect effective information about ankylosing spondylitis lesions. Summary of the Invention

[0005] In view of the above-mentioned defects or deficiencies in the prior art, it is desirable to provide a remote data acquisition system and method for early screening of ankylosing spondylitis, which can accurately obtain the user's status information in multiple dimensions and provide accurate data support for doctors' early screening.

[0006] In a first aspect, an embodiment of the present application provides a remote data acquisition system for early screening of ankylosing spondylitis, including: a basic information acquisition unit, a posture information acquisition unit, a thoracic expansion information acquisition unit, and an information extraction unit.

[0007] The basic information acquisition unit is configured to receive the symptom description information of the user.

[0008] The posture information acquisition unit is configured to acquire the continuous posture information of the user.

[0009] The thoracic expansion information acquisition unit is configured to acquire the thoracic expansion information of the user during the breathing process.

[0010] The information extraction unit is used to extract the symptom description data, posture data, and thoracic expansion data corresponding to the user based on the symptom description information, the posture information, and the thoracic expansion information, respectively.

[0011] In some embodiments, the information extraction unit is further configured to:

[0012] Obtain the symptom description information, perform iterative exploration in the symptom description information using an optimized crayfish iterative algorithm, and obtain at least one candidate symptom acquisition strategy;

[0013] Evaluate the at least one candidate symptom acquisition strategy based on a preset fitness function, and select the candidate symptom acquisition strategy with the highest fitness as the target symptom acquisition strategy;

[0014] Extract the symptom description data from the symptom description information based on the target symptom acquisition strategy.

[0015] In some embodiments, the information extraction unit is further configured to:

[0016] Obtain the initial symptom description information for iterative exploration;

[0017] Perform iterative exploration on the initial symptom description information based on the crayfish iterative algorithm to obtain the symptom acquisition strategy corresponding to the initial symptom description;

[0018] When the number of consecutive iterations in which the symptom acquisition strategy does not change reaches a preset number, use the symptom acquisition strategy as the candidate symptom acquisition strategy, and return to obtain new initial symptom description information.

[0019] In some embodiments, the posture information acquisition unit includes a first sensor and a second sensor, and the information extraction unit is further configured to:

[0020] Obtain the first image frame collected by the first sensor and the second image frame collected by the second sensor at the same moment, respectively;

[0021] Perform three-dimensional data fusion on the first image frame and the second image frame, and use a CNN-based target edge detection algorithm to obtain the posture boundary corresponding to this moment;

[0022] Perform posture estimation on the image frame at this moment based on the posture boundary to obtain the posture data.

[0023] In some embodiments, the information extraction unit is further configured to:

[0024] During the posture estimation process, use a joint loss function to optimize the posture and obtain the optimal posture estimation value as the posture data;

[0025] The combined loss function at least includes a smoothing term, a scene-aware contact term, a pose prior term, and a grid-to-point term.

[0026] In some embodiments, the information extraction unit is further configured to:

[0027] Select at least one key region around the thoracic cavity; the key region at least includes the intercostal space, the posterior part of the sternum, and the region around the spine;

[0028] For each of the key regions, use the region masking technique to obtain the pressure-time curve corresponding to each key region from the thoracic cavity expansion information;

[0029] Based on the pressure-time curve, determine the pressure peak value and the pressure time integral corresponding to the key region, and use the pressure peak value and the pressure time integral as the thoracic cavity expansion data.

[0030] In a second aspect, an embodiment of the present application provides a data acquisition method for a remote data acquisition system based on early screening of ankylosing spondylitis. The system includes: a basic information acquisition unit, a posture information acquisition unit, a thoracic cavity expansion information acquisition unit, and an information extraction unit.

[0031] The basic information acquisition unit is configured to receive the symptom description information of the user;

[0032] The posture information acquisition unit is configured to acquire the continuous posture information of the user;

[0033] The thoracic cavity expansion information acquisition unit is configured to acquire the thoracic cavity expansion information of the user during the breathing process;

[0034] The method includes:

[0035] Extract the symptom description data, posture data, and thoracic cavity expansion data corresponding to the user based on the symptom description information, the posture information, and the thoracic cavity expansion information respectively.

[0036] In a third aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the method described in the embodiment of the present application is implemented.

[0037] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the method described in the embodiment of the present application is implemented.

[0038] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, characterized in that when the computer program is executed by a processor, it implements the method described in the embodiments of the present application.

[0039] The remote data acquisition system for ankylosing spondylitis provided by the embodiments of the present application can collect user information in at least three dimensions, namely basic information, posture information, and thoracic expansion information, effectively solving the problem of inability to make a definite diagnosis caused by insufficient available data dimensions in the early screening of ankylosing spondylitis. At the same time, the information extraction unit is used to extract data from the information in the three dimensions respectively, which can not only improve the accuracy and efficiency of the existing calculation methods, but also enhance the consistency of the data results, providing a reliable data basis for the doctor's subsequent diagnosis.

[0040] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be understood through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Other features, objects, and advantages of the present application will become more apparent by reading the detailed description of the non-limiting embodiments with reference to the following drawings:

[0042] Figure 1 The block diagram showing a remote data acquisition system for early screening of ankylosing spondylitis provided by an embodiment of the present application;

[0043] Figure 2 The flowchart showing the data acquisition method of the remote data acquisition system for early screening of ankylosing spondylitis provided by an embodiment of the present application;

[0044] Figure 3 The flowchart showing the data acquisition method of the remote data acquisition system for early screening of ankylosing spondylitis provided by another embodiment of the present application;

[0045] Figure 4 The flowchart showing the data acquisition method of the remote data acquisition system for early screening of ankylosing spondylitis provided by yet another embodiment of the present application;

[0046] Figure 5 The flowchart showing the data acquisition method of the remote data acquisition system for early screening of ankylosing spondylitis provided by still another embodiment of the present application;

[0047] Figure 6 The structural diagram showing a computer system of an electronic device or a server suitable for implementing the embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than limiting the invention. Additionally, it should be noted that for ease of description, only the parts related to the invention are shown in the accompanying drawings.

[0049] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and embodiments.

[0050] It should also be noted that the acquisition of various information involved in the present application complies with relevant laws and regulations. For example, information such as medical records and symptoms is acquired.

[0051] To further illustrate the technical solutions provided by the embodiments of the present application, the following will describe this in detail in conjunction with the accompanying drawings and specific implementation manners. Although the embodiments of the present application provide the method operation instruction steps as shown in the following embodiments or drawings, based on routine or non-creative labor, more or fewer operation instruction steps may be included in the method. In steps where there is no necessary causal relationship logically, the execution order of these steps is not limited to the execution order provided by the embodiments of the present application. When the method is actually processed or executed by the device, it can be executed in the order shown in the embodiments or drawings or executed in parallel.

[0052] Please refer to Figure 1 , Figure 1 which shows a block diagram of a remote data acquisition system for early screening of ankylosing spondylitis provided by an embodiment of the present application. As Figure 1 shown, the system 100 includes: a basic information acquisition unit 101, a posture information acquisition unit 102, a thoracic expansion information acquisition unit 103, and an information extraction unit 104.

[0053] Among them, the basic information acquisition unit 101 is used to receive the symptom description information of the user.

[0054] The posture information acquisition unit 102 is used to acquire the continuous posture information of the user.

[0055] The thoracic expansion information acquisition unit 103 is used to acquire the thoracic expansion information of the user during the breathing process.

[0056] The information extraction unit 104 is used to extract the corresponding symptom description data, posture data, and thoracic expansion data of the user based on the symptom description information, posture information, and thoracic expansion information respectively.

[0057] It should be noted that the basic information collection unit 101 can be a device capable of obtaining user status information. In some embodiments, the basic information collection unit 101 can be the user's own terminal device. For example, the user can directly input their symptom description information through the terminal device according to the information framework or questions provided by, for example, a medical consultation platform, such as their pain perception, limb stiffness level, etc., to achieve the purpose of information collection. In other embodiments, the basic information collection unit 101 can also be the information storage unit of a medical institution, such as a physical examination institution, a community medical institution, etc. When the user undergoes a physical examination or medical consultation, the relevant medical staff inputs and uploads the user's symptom description information based on the examination or consultation results to achieve the purpose of information collection.

[0058] The posture information collection unit 102 is a device that determines the user's posture based on the user's image information, including but not limited to an image acquisition device and / or a spatial information acquisition device. Optionally, the posture information collection unit 102 can rely only on the image acquisition device to collect the user's posture, or only rely on the spatial information acquisition device to collect the user's posture, or also rely on both the image acquisition device and the spatial information acquisition device to collect the user's posture. This application does not make specific limitations. Among them, the image acquisition device can be an RGB-D camera, and the spatial information acquisition device can be a radar sensor (LiDAR sensor).

[0059] In a feasible embodiment, the posture information collection unit 102 can collect information when the user is unconscious. For example, the posture information collection unit 102 is set at a passage to facilitate the collection of posture information without the user's awareness during the walking process. Optionally, in order to further improve the standardization degree of posture information collection, the posture information collection unit 102 can be set to collect the posture information of the user in a specific area, and when collecting the posture information, the user assumes a specific posture, such as standing, swinging, etc. Thus, the posture information collection unit 102 can better capture the changes in the user's posture through the specific posture assumed by the user, providing a more reliable data basis for subsequent posture data extraction.

[0060] The thoracic expansion information collection unit 103 is used to collect the user's thoracic expansion information. It should be noted that the onset of ankylosing spondylitis will cause the problem of limited thoracic activity in patients, and it was included in the ankylosing spondylitis diagnostic criteria in 1984. Based on this, this application provides a new type of thoracic expansion information collection unit 103, which effectively overcomes the defect that imaging examinations are static data, that is, it can detect the changing thoracic expansion information during the user's breathing process, which can not only meet the data requirements for early screening of ankylosing spondylitis in users, but also reduce the examination cost, providing reliable support for remote data for early screening of ankylosing spondylitis.

[0061] In a feasible embodiment, the thoracic expansion information acquisition unit 103 can be a pressure sensing component based on 3D printing technology and hydrogel material. Through 3D printing technology, personalized customization for users can be achieved, enabling the thoracic expansion information acquisition unit 103 to better fit the user's body, improving the accuracy of thoracic expansion information acquisition. Moreover, the use of 3D printing technology can greatly reduce the customization cost. At the same time, the hydrogel material can ensure the irritation of the thoracic expansion information acquisition unit 103 to the skin and improve the comfort of the user's wearing.

[0062] Specifically, taking hydrogel as the 3D printing material, the three-dimensional information of the customized thoracic expansion information acquisition unit 103 for the user is determined by scanning the user's chest. Then, 3D printing is performed using a 3D printer to obtain a hydrogel structure that can adhere to the user's chest. Then, based on the need for thoracic expansion pressure information acquisition, at least one pressure sensor is arranged inside the hydrogel structure so that when the user wears the hydrogel structure, the pressure sensor can be between the hydrogel structure and the user's chest, enabling it to neither displace under the adhesion pressure of the hydrogel structure on the user's chest nor fully withstand the pressure changes generated by the expansion and contraction of the chest during the user's breathing, thus realizing the acquisition of thoracic expansion information.

[0063] The information extraction unit 104 is used to extract the corresponding symptom description data, posture data, and thoracic expansion data of the user based on the symptom description information, posture information, and thoracic expansion information respectively. Specifically, the information extraction unit 104 uses an optimized crayfish iterative algorithm to extract symptom description data based on the symptom description information, uses an optimized tracking algorithm to extract posture data based on the posture information, and provides an algorithm matching the thoracic expansion information to extract thoracic expansion data.

[0064] Therefore, the remote data acquisition system for early screening of ankylosing spondylitis provided by the embodiment of the present application can collect the user's information in at least three dimensions of basic information, posture information, and thoracic expansion information, effectively solving the problem of inability to diagnose due to insufficient available data dimensions in the early screening of ankylosing spondylitis. At the same time, using the information extraction unit to extract data from the information in the three dimensions respectively can not only improve the accuracy and efficiency of the existing calculation methods, but also enhance the consistency of the data results, providing a reliable data basis for the doctor's subsequent diagnosis.

[0065] In a feasible embodiment, the information extraction unit is specifically used to: obtain the symptom description information, perform iterative exploration in the symptom description information using an optimized crayfish iterative algorithm to obtain at least one candidate symptom acquisition strategy; evaluate at least one candidate symptom strategy based on a preset fitness function, and select the candidate symptom strategy with the highest fitness as the target symptom acquisition strategy; extract the symptom description data from the symptom description information based on the target symptom acquisition strategy.

[0066] This application uses an optimized crayfish iterative algorithm, which can dynamically search for strategies and make adjustments, enhancing the depth of understanding of the symptom description information by the model algorithm, thereby optimizing the symptom acquisition strategy.

[0067] Specifically, the optimized crayfish iterative algorithm is used to iteratively explore the symptom description information, including: obtaining the initial symptom description information for iterative exploration, iteratively exploring the initial symptom description information based on the crayfish iterative algorithm to obtain the symptom acquisition strategy corresponding to the initial symptom description; when the number of iterations in which the symptom acquisition strategy has not changed continuously reaches a preset number, taking the symptom acquisition strategy as a candidate symptom acquisition strategy, and returning to obtain new initial symptom description information.

[0068] Among them, the following formula can be used to obtain the initial symptom description information:

[0069] z = M min +(M max - M min )×Q

[0070] Among them, z is the initial description information, M min is the lower bound of the symptom description information, M max is the upper bound of the symptom description information, and Q is a random reference factor.

[0071] That is to say, in order to expand the search range of the crayfish iterative algorithm, by randomly generating feasible solutions, as much initial symptom description information as possible for iterative exploration is obtained to obtain a sufficiently rich candidate symptom acquisition strategy, thereby ensuring the reliability of the target symptom acquisition strategy. And when the number of iterations in which the symptom acquisition strategy has not changed continuously during the iterative exploration reaches a preset number, stopping the continuous iterative exploration of the symptom acquisition strategy can reduce the exploration pressure of the crayfish iterative algorithm, improve the determination efficiency of the candidate symptom acquisition strategy, and prevent the algorithm from prematurely falling into a local optimal solution.

[0072] In a specific embodiment, first, the goal of acquiring symptom description data is clarified, such as maximizing the accuracy and coverage of symptom description data, and at the same time, constraints such as time and cost are determined. Then, a fitness function is set according to the level of detail of the symptom description data, the completeness of the diagnostic information, etc. When the symptom description data acquired based on the candidate symptom acquisition strategy is evaluated according to the fitness function, the higher the evaluation result, the higher the quality of the symptom description data acquired by the candidate symptom acquisition strategy, and the higher the accuracy for subsequent diagnosis. A certain number of initial symptom description information is set based on medical common sense, and a part of the initial symptom description information is generated by expanding the above-mentioned random reference factor. The initial symptom description information is iteratively explored in turn using the crayfish iteration algorithm to determine at least one candidate symptom acquisition strategy corresponding to each initial symptom description information, wherein, during the iteration process, if any symptom acquisition strategy stops changing during the iteration process, it is determined that the iteration of the symptom acquisition strategy is completed, determined as a complete candidate symptom acquisition strategy and scored using the fitness function. The candidate symptom strategy with the highest fitness function score among all candidate symptom strategies is used as the target symptom acquisition strategy to acquire the symptom description data corresponding to the user.

[0073] It should be understood that since basic information is usually input by users based on their own feelings, such as pain location, pain time, pain degree, etc., each user is different, and setting a fixed symptom acquisition strategy in advance can easily result in the lack of key symptoms in the symptom description data extracted from some patients, resulting in the problem of being unable to make an effective diagnosis. Based on this, the information extraction unit 104 proposed in this application can use the optimized crayfish iteration algorithm to iteratively explore the symptom description information input by each user, so as to obtain the target symptom acquisition strategy with the highest matching degree to the symptom description information that is most suitable for the user input, so that the symptom description data obtained according to the target symptom acquisition strategy can contain the key information of the user's symptoms as much as possible, avoid the lack of key information, and realize the personalized extraction of symptom description data, without restricting the user's symptom description, and fully guarantee the accuracy and reliability of the symptom description data under the premise of ensuring the user's freedom of expression, and provide a reliable data basis for the doctor's subsequent diagnosis.

[0074] In a feasible embodiment, the posture information acquisition unit includes a first sensor and a second sensor, and the information extraction unit is also used to: respectively obtain a first image frame collected by the first sensor and a second image frame collected by the second sensor at the same moment; perform three-dimensional data fusion on the first image frame and the second image frame, and use a CNN-based target edge detection algorithm to obtain the posture boundary corresponding to the moment; perform posture estimation on the image frame at the moment based on the posture boundary to obtain posture data.

[0075] Among them, the first sensor and the second sensor are an image acquisition device and a spatial information acquisition device respectively, such as an RGB-D camera and a LiDAR sensor.

[0076] It should be understood that when using different sensor devices for pose estimation, in order to improve the accuracy of pose estimation, sensor fusion needs to be performed first, that is, the two-dimensional features captured by the RGB-D camera are deeply correlated with the three-dimensional features collected by the LiDAR sensor to better predict the actual pose of the user.

[0077] In a feasible embodiment, the three-dimensional features (such as three-dimensional point cloud data) collected by the LiDAR sensor and the two-dimensional features (such as RGB images) captured by the RGB-D camera are input into the CNN algorithm model, and the object edge detection algorithm in the CNN algorithm model is used to extract the pose boundary. Optionally, the extracted pose boundary is a 3D bounding box. Preferably, the pose boundary is the smallest bounding rectangle corresponding to the user. Specifically, by performing object tracking on multiple frames of images at consecutive moments, the pose boundary representing the smallest bounding rectangle corresponding to the user is obtained.

[0078] In a feasible embodiment, in order to further reduce the size of the bounding rectangle used to represent the pose boundary, the projection points of at least one feature point in the three-dimensional point cloud data on the two-dimensional plane are obtained, and the projection points are matched and aligned with the pixel points in the two-dimensional features. Thus, the problem that the bounding rectangle corresponding to the three-dimensional point cloud data is larger than the bounding rectangle of the actual target object in the two-dimensional feature image can be effectively reduced, thereby effectively improving the accuracy of pose estimation and ensuring the coherence of object tracking and the accuracy of pose estimation.

[0079] In a feasible embodiment, since a doctor needs to understand the pose data of a patient in a static state and also needs to understand the pose information in a dynamic state when diagnosing ankylosing spondylitis to determine the degree of trouble caused by spinal fusion and muscle stiffness to the patient's behavior. Therefore, the information extraction unit also needs to track the pose boundaries in consecutive image frames to understand the pose changes of the user during the movement. Among them, in order to improve the accuracy of data association in consecutive image frames (including the first image frame and the second image frame) and ensure the coherence of object tracking and the accuracy of pose estimation, the present application also proposes to use the intersection over union matching method in the time domain to check the pose boundaries detected in consecutive image frames to ensure the accuracy and coherence of pose estimation in consecutive image frames.

[0080] It should be understood that the determination of the pose boundary can determine the action range of the user. After determining the pose boundary in the image frame, pose estimation is performed through action prediction to determine the pose data of the user in the image frame.

[0081] In a feasible embodiment, during the pose estimation process, a joint loss function is utilized to optimize the pose, and an optimal pose estimation value is obtained as pose data. The joint loss function at least includes a smoothing term, a scene-aware contact term, a pose prior term, and a mesh-to-point term.

[0082] Among them, the smoothing term is used to smooth the human motion by minimizing the acceleration and angular velocity of the joints. Specifically, the smoothing term focuses on the acceleration of the user's pelvic joint and the other 23 joints relative to the pelvis, and also takes into account the angular velocity of the user's other joints. Based on this, it is possible to avoid sudden changes in motion during pose estimation for consecutive image frames, making the pose estimation for consecutive image frames more accurate and more in line with the actual pose situation when the user collects pose information.

[0083] The scene-aware contact term is used to analyze the movement of the limb vertices, such as analyzing the movement of the foot vertices. Among them, in the embodiment of the present application, when it is analyzed from the movement of the foot vertices in consecutive image frames that the speed of the foot is lower than 0.1 m / s, it is considered that the user's foot is in a stable state. Then, the Chamfer distance between the foot and its closest surface is calculated as the loss of scene contact, which can ensure that the physical contact between the action and the scene conforms to the actual situation.

[0084] The pose prior term is used to constrain the pose during the optimization process to make it as close as possible to the initial value.

[0085] The mesh-to-point term is used to provide depth prior information by using the three-dimensional point cloud data generated by the dynamic radar sensor. By defining the Chamfer distance from the human body endpoints to the contact surface and considering the correspondence between the human body surface and the actual scene, the depth perception ability of pose estimation is enhanced.

[0086] In a specific embodiment, the gradient descent algorithm is used to gradually minimize the joint loss function. Through the iterative optimization process, the pose parameters of the pose estimation are continuously adjusted until the optimal solution that minimizes the loss function is found as the pose data.

[0087] Thus, the present application jointly performs pose estimation using two-dimensional images and three-dimensional point cloud data, effectively enriching the richness of pose information, fully considering the depth information in the three-dimensional environment, and using the joint loss function for pose estimation, which can achieve accurate estimation of the user's pose, improve the accuracy of pose data, provide more accurate judgment data for the early stage of ankylosing spondylitis with inconspicuous early lesions, and improve the reliability of early screening.

[0088] In a feasible embodiment, the information extraction unit 104 is further configured to: select at least one key area around the chest cavity, and for each key area, use the region masking technique to obtain a pressure-time curve corresponding to each key area from the chest cavity expansion information; based on the pressure-time curve, determine the pressure peak value and the pressure-time integral corresponding to the key area, and use the pressure peak value and the pressure-time integral as the chest cavity expansion data.

[0089] It should be understood that during a respiratory cycle, the expansion and contraction of the lungs will change the pressure around the chest cavity, and different regions will form different pressure ranges and change conditions based on the lung respiratory cycle. Therefore, dividing the area around the chest cavity into multiple key areas and extracting chest cavity expansion data separately can ensure the reliability of data extraction and provide high-precision data guarantee for subsequent doctor diagnosis, that is, effectively prevent the loss of abnormal data caused by operations such as data averaging. Among them, the key areas at least include the intercostal space, the posterior part of the sternum, and the area around the spine.

[0090] In the embodiment of the present application, for the planned multiple key areas, the region masking technique is used to obtain the pressure-time curve corresponding to each key area from the chest cavity expansion information. Specifically, the chest cavity expansion information acquisition unit can pay attention to multiple areas around the chest cavity. When extracting the pressure-time curve, the region masking technique is used to distinguish the current area of interest from other areas. For example, other areas are set to 0, so that the pressure data of the current key area can be prominently displayed, thereby realizing the extraction of pressure data by region, and drawing the pressure-time curve corresponding to the key area according to time.

[0091] For each key area, after obtaining the pressure-time curve, the pressure data is constructed into a pressure matrix according to the acquisition frequency of the chest cavity expansion information acquisition unit, and then the pressure peak value max(x ij ) is determined in the pressure matrix X corresponding to the key area.

[0092] The pressure-time integral is the integral of the chest cavity expansion pressure over time and is used to characterize the mechanical load generated by the chest cavity expansion. Specifically, the pressure-time integral can be obtained using the following formula:

[0093]

[0094] where F is the pressure value, t is the time, and A is the area of the key area.

[0095] It should be understood that since the early lesions of ankylosing spondylitis are prone to have a relatively mild degree of spinal fusion and have not yet affected the chest cavity function and thus have not caused obvious changes in the pressure value, but spinal fusion will lead to an extension of the reaction time of bones and muscles, that is, an increase in the pressure-time integral.

[0096] Therefore, the embodiments of the present application use the thoracic expansion information collected by the thoracic expansion information collection unit with high accuracy to extract thoracic expansion data, effectively ensuring the reliability of the pressure data and providing reliable data support for subsequent medical diagnosis. At the same time, using key area partition extraction can effectively avoid the problem of missing key data during global extraction caused by the insignificant impact of early lesions, enabling the effective discovery of local abnormalities under the influence of early lesions, thereby improving the accuracy and reliability of early screening. Selecting the pressure-time integral as the thoracic expansion data for data extraction can identify thoracic expansion abnormalities caused by ankylosing spondylitis earlier, providing a data basis for the early screening of ankylosing spondylitis lesions and improving the accuracy of early screening of ankylosing spondylitis.

[0097] In summary, the remote data collection system for early screening of ankylosing spondylitis provided by the embodiments of the present application can collect user information in at least three dimensions: basic information, posture information, and thoracic expansion information, effectively solving the problem of inability to diagnose due to insufficient available data dimensions for early screening of ankylosing spondylitis. At the same time, using the information extraction unit to extract data from the information in three dimensions respectively can not only improve the accuracy and efficiency of the existing calculation methods, but also enhance the consistency of the data results, providing a reliable data basis for doctors' subsequent diagnosis.

[0098] Among the several modules or units mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of the two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0099] Figure 2 The flowchart of the data collection method of the remote data collection system for early screening of ankylosing spondylitis provided by an embodiment of the present application is shown.

[0100] As Figure 2 shown, the method includes:

[0101] Step 201, obtain symptom description information, posture description information, and thoracic expansion information.

[0102] Step 202, respectively extract the corresponding symptom description data, posture data, and thoracic expansion data of the user based on the symptom description information, posture description information, and thoracic expansion information.

[0103] In a feasible embodiment, as Figure 3 shown, extracting symptom description data based on the symptom description information includes:

[0104] Step 301: Obtain symptom description information, and use the optimized crayfish iteration algorithm to iteratively explore in the symptom description information to obtain at least one candidate symptom acquisition strategy.

[0105] Step 302: Evaluate at least one candidate symptom acquisition strategy based on a preset fitness function, and select the candidate symptom acquisition strategy with the highest fitness as the target symptom acquisition strategy.

[0106] Step 303: Extract symptom description data from the symptom description information based on the target symptom acquisition strategy.

[0107] In a feasible embodiment, as Figure 4 shown, extract pose data based on pose information, including:

[0108] Step 401: Obtain the first image frame collected by the first sensor and the second image frame collected by the second sensor at the same moment respectively.

[0109] Step 402: Perform three-dimensional data fusion on the first image frame and the second image frame, and use the CNN-based target edge detection algorithm to obtain the pose boundary corresponding to this moment.

[0110] Step 403: Perform pose estimation on the image frame at this moment based on the pose boundary to obtain pose data.

[0111] In a feasible embodiment, as Figure 5 shown, extract thoracic expansion data based on thoracic expansion information, including:

[0112] Step 501: Select at least one key area around the chest; the key area includes at least the intercostal space, the posterior part of the sternum, and the area around the spine.

[0113] Step 502: For each key area, use the region masking technique to obtain the pressure-time curve corresponding to each key area from the thoracic expansion information.

[0114] Step 503: Based on the pressure-time curve, determine the pressure peak and the pressure time integral corresponding to the key area, and use the pressure peak and the pressure time integral as the thoracic expansion data.

[0115] In summary, the data acquisition method of the remote data acquisition system for early screening of ankylosing spondylitis provided by the embodiments of the present application extracts data from the information in three dimensions of basic information, pose information, and thoracic expansion information respectively, which can not only improve the accuracy and efficiency of the existing calculation methods, but also improve the consistency of the data results, providing a reliable data basis for the doctor's subsequent diagnosis.

[0116] Next, refer to Figure 6 , Figure 6The figure shows a schematic structural diagram of a computer system suitable for an electronic device or a server for implementing the embodiments of the present application.

[0117] As Figure 6 shown, the computer system includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 602 or the program loaded from the storage section 608 into the random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation instructions of the system are also stored. The CPU 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. The input / output (I / O) interface 605 is also connected to the bus 604.

[0118] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as required. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as required so that a computer program read from it can be installed into the storage section 608 as required.

[0119] Specifically, according to the embodiments of the present application, the process described above with reference to the flowchart Figure 2 can be implemented as a computer software program. For example, the embodiments of the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program codes for performing the method shown in the flowchart. In such an embodiment, the computer program includes program codes for performing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication section 609, and / or installed from the removable medium 611. When the computer program is executed by the central processing unit (CPU) 601, the above functions defined in the system of the present application are executed.

[0120] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, a computer-readable storage medium can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device. And in this application, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0121] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operation instructions of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram can represent a module, a program segment, or a part of code, and the foregoing module, program segment, or part of code contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two connected blocks can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for executing the specified functions or operation instructions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0122] As another aspect, the present application also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments, or may exist alone without being assembled into the electronic device. The above computer-readable storage medium stores one or more programs, and when the above programs are executed by one or more processors, they are used to perform the data acquisition method of the remote data acquisition system based on ankylosing spondylitis described in the present application.

[0123] The above description is only a preferred embodiment of the present application and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the foregoing disclosure concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) having similar functions disclosed in the present application.

Claims

1. A remote data acquisition system for early screening of ankylosing spondylitis, characterized in that: include: Basic information collection unit, posture information collection unit, chest expansion information collection unit and information extraction unit, The basic information collection unit is used to receive symptom description information of the user; The posture information collection unit is used to collect continuous posture information of the user; The chest expansion information collection unit is used to collect chest expansion information during the user's breathing process; The information extraction unit is used to extract the symptom description data, posture data and chest expansion data corresponding to the user based on the symptom description information, the posture information and the chest expansion information respectively; Wherein, the information extraction unit is further used for: Selecting at least one key area around the thorax; the key area at least includes the intercostal space, the posterior part of the sternum, and the area around the spine; For each of the key areas, a pressure-time curve corresponding to each of the key areas is obtained from the chest expansion information using a regional masking technique; Based on the pressure-time curve, the pressure peak value and the pressure-time integral corresponding to the key area are determined, and the pressure peak value and the pressure-time integral are used as the chest expansion data.

2. The remote data acquisition system for early screening of ankylosing spondylitis according to claim 1, characterized in that: The information extraction unit is further used for: Acquire the symptom description information, and perform iterative exploration in the symptom description information using an optimized crayfish iterative algorithm to obtain at least one candidate symptom acquisition strategy; Evaluate the at least one candidate symptom acquisition strategy based on a preset fitness function, and select the candidate symptom acquisition strategy with the highest fitness as the target symptom acquisition strategy; Based on the target symptom acquisition strategy, the symptom description data is extracted from the symptom description information.

3. The remote data acquisition system for early screening of ankylosing spondylitis according to claim 2, characterized in that: The information extraction unit is further used for: Obtain initial symptom description information for iterative exploration; Iteratively explore the initial symptom description information based on the crayfish iterative algorithm to obtain a symptom acquisition strategy corresponding to the initial symptom description; When the number of iterations in which the symptom acquisition strategy remains unchanged continuously reaches a preset number, the symptom acquisition strategy is used as the candidate symptom acquisition strategy, and the new initial symptom description information is returned to be acquired.

4. The remote data acquisition system for early screening of ankylosing spondylitis according to claim 1, characterized in that: The posture information acquisition unit includes a first sensor and a second sensor, and the information extraction unit is further used for: Respectively acquiring a first image frame acquired by the first sensor and a second image frame acquired by the second sensor at the same moment; Performing three-dimensional data fusion on the first image frame and the second image frame, and using a CNN-based target edge detection algorithm to obtain a posture boundary corresponding to the moment; The posture data is obtained by performing posture estimation on the image frame at the moment based on the posture boundary.

5. The remote data acquisition system for early screening of ankylosing spondylitis according to claim 4, characterized in that: The information extraction unit is further used for: In the posture estimation process, a joint loss function is used to perform posture optimization to obtain an optimal posture estimation value as the posture data; The joint loss function includes at least a smoothness term, a scene-aware contact term, a pose prior term, and a mesh-to-point term.

6. A data collection method based on a remote data collection system for early screening of ankylosing spondylitis, characterized in that: The system comprises: a basic information acquisition unit, a posture information acquisition unit, a chest expansion information acquisition unit and an information extraction unit. The basic information collection unit is used to receive symptom description information of the user; The posture information collection unit is used to collect continuous posture information of the user; The chest expansion information collection unit is used to collect chest expansion information during the user's breathing process; The method comprises: Acquiring the symptom description information, the posture information and the chest expansion information; extracting symptom description data, posture data and chest expansion data corresponding to the user based on the symptom description information, the posture information and the chest expansion information respectively; The method further includes: selecting at least one key area around the thorax; the key area at least includes the intercostal space, the rear of the sternum and the area around the spine; For each of the key areas, a pressure-time curve corresponding to each of the key areas is obtained from the chest expansion information using a regional masking technique; Based on the pressure-time curve, the pressure peak value and the pressure-time integral corresponding to the key area are determined, and the pressure peak value and the pressure-time integral are used as the chest expansion data.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the data collection method of the remote data collection system based on early screening of ankylosing spondylitis as claimed in claim 6 is implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the data collection method of the remote data collection system based on early screening of ankylosing spondylitis as claimed in claim 6 is implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the data collection method of the remote data collection system based on early screening of ankylosing spondylitis as described in claim 6 is implemented.

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