Alzheimer disease risk early warning method, device and system based on handwriting recognition

Abstract

The invention provides an Alzheimer's disease risk early warning method, device and system based on handwriting recognition, and relates to the technical field of health information processing, and the method comprises the steps: receiving handwriting recognition data in real time from a Bluetooth pen when an evaluator holds the Bluetooth pen to fill a paper diagnostic scale; calculating a diagnostic scale score of the testee according to the handwritten recognition data, and determining a first Alzheimer's disease risk level of the testee; and determining a second Alzheimer's disease risk level of the testee by using the Alzheimer's disease risk prediction model, and obtaining Alzheimer's disease risk early warning result data of the testee. The Alzheimer's disease risk early warning method and device can be suitable for the elderly and other people who are inconvenient to use electronic scales, can ensure the efficiency and automation degree of paper quality table data processing, can effectively improve the accuracy and reliability of Alzheimer's disease risk early warning, and can further better achieve auxiliary diagnosis of Alzheimer's disease of the testee.

Description

Technical Field

[0001] The present application relates to the technical field of health information processing, and in particular to a method, device and system for early warning of Alzheimer's disease risk based on handwriting recognition. Background Art

[0002] Alzheimer's disease is a common neurodegenerative disease in the elderly. The current trend is to move the window of diagnosis and intervention forward, that is, to focus on the early stages of Alzheimer's disease and conduct timely and effective intervention to slow down or even stop the progression to the dementia stage. The key to the diagnosis and treatment of Alzheimer's disease lies in early screening and identification. Neuropsychological testing through diagnostic scales is the core basis for the current clinical diagnosis of Alzheimer's disease.

[0003] Paper-based diagnostic scales are commonly used in clinical practice, but the traditional paper-and-pen recording method is difficult to achieve persistent storage and traceability, and the cumbersome scale evaluation criteria make it difficult for users to understand their own conditions, which can easily lead to difficulties in storing and sharing valuable medical information. Even if the scores of each scale are calculated manually and then input into the computer system, redundant data calculations are very likely to produce errors, and will also cause additional human resource expenses, and information such as handwriting characteristics cannot be fully utilized. Therefore, in the current digital age, digital scales that are independent of paper media have emerged as an effective tool for Alzheimer's disease screening, that is, developing corresponding scale systems in mobile devices, and displaying and filling out scales on electronic screens.

[0004] However, Alzheimer's disease-related scales are usually aimed at the elderly, but the learning cost of digital scales is high, time-consuming and the success rate is low for the elderly, which can easily lead to problems such as low patient cooperation and poor accuracy of assessment results. Therefore, there is an urgent need to design a new Alzheimer's disease risk warning method that can not only solve the problems of paper diagnostic scales that are time-consuming, high labor costs and low automation, but also solve the problem that digital scales are not suitable for people who are not convenient to use electronic scales, such as the elderly. Summary of the invention

[0005] In view of this, the embodiments of the present application provide an Alzheimer's disease risk warning method, device and system based on handwriting recognition to eliminate or improve one or more defects existing in the prior art.

[0006] One aspect of the present application provides an Alzheimer's disease risk early warning method based on handwriting recognition, comprising:

[0007] When the examiner fills in the diagnostic scale with preset dot codes for assisting diagnosis of Alzheimer's disease of the examinee with the Bluetooth pen, each set of handwriting recognition data is received in real time from the Bluetooth pen, wherein each set of the handwriting recognition data includes: the corresponding relationship between the scale content data determined by the Bluetooth pen through real-time recognition of the dot codes and the handwriting data recorded in real time by the Bluetooth pen;

[0008] Calculating the diagnostic scale score of the test subject according to each of the handwriting recognition data generated when the evaluator fills in all the diagnostic scales, and determining the first Alzheimer's disease risk level corresponding to the test subject based on the diagnostic scale score;

[0009] And, according to the handwriting data in each of the handwriting recognition data, a preset Alzheimer's disease risk prediction model is used to determine a second Alzheimer's disease risk level corresponding to the subject;

[0010] Based on the first Alzheimer's disease risk level and the second Alzheimer's disease risk level, Alzheimer's disease risk warning result data of the subject is obtained.

[0011] In some embodiments of the present application, the scale content data includes the page, question number and page area information of the dot matrix code currently contacted by the Bluetooth pen on the diagnostic scale;

[0012] The handwriting data includes: handwriting morphological feature data, writing pressure value and writing pause time; wherein, the handwriting morphological data includes: timestamp, coordinate point sequence of the current handwriting in the preset coordinate system corresponding to the diagnostic scale and stroke thickness value.

[0013] In some embodiments of the present application, the step of calculating the diagnostic scale score of the test subject according to each of the handwriting recognition data generated when the evaluator fills out all the diagnostic scales, and determining the first Alzheimer's disease risk level corresponding to the test subject based on the diagnostic scale score, includes:

[0014] According to the preset test task types to which each of the diagnostic scales belongs, the handwriting recognition data corresponding to the diagnostic scales whose test task type is a selective test task are divided into a selective test group, and the handwriting recognition data corresponding to the diagnostic scales whose test task type is a drawing test task are divided into a drawing test group;

[0015] Based on each of the handwriting recognition data in the selective test group, the total score corresponding to each of the diagnostic scales belonging to the selective test task is obtained, and the total score corresponding to each of the diagnostic scales belonging to the selective test task is summarized to obtain the selective test score corresponding to the subject;

[0016] And, based on each of the handwriting recognition data in the drawing type test group, the total score corresponding to each of the diagnostic scales belonging to the drawing type test task is obtained, and the total score corresponding to each of the diagnostic scales belonging to the drawing type test task is summarized to obtain the drawing type test score corresponding to the subject;

[0017] The selective test score and the drawing test score corresponding to the subject are determined as the diagnostic scale score of the subject, and the first Alzheimer's disease risk level corresponding to the subject is determined according to the diagnostic scale score of the subject from the diagnostic standard data used to record the correspondence between the diagnostic scale score and the Alzheimer's disease risk level.

[0018] In some embodiments of the present application, the obtaining of the total score corresponding to each of the diagnostic scales belonging to the selective test task based on each of the handwriting recognition data in the selective test group includes:

[0019] For each of the handwriting recognition data corresponding to each of the diagnostic scales belonging to the selective test task, a preset candidate box checking judgment step is respectively performed to determine the score of each checked candidate box in each of the diagnostic scales belonging to the selective test task, and the score of each checked candidate box in each of the diagnostic scales belonging to the selective test task is summarized to obtain the total score corresponding to each of the diagnostic scales belonging to the selective test task;

[0020] The step of checking and judging the candidate box includes:

[0021] Determine the page identifier in the diagnostic scale corresponding to the handwriting recognition data according to the scale content data in the current handwriting recognition data, and obtain the position information of each candidate box in the target page corresponding to the page identifier from the candidate box information corresponding to each of the preset diagnostic scales belonging to the selective test task;

[0022] Based on the handwriting data in the current handwriting recognition data and the position information of each candidate box in the target page, it is determined whether the distance between the handwriting data and any candidate box in the target page is less than a preset distance threshold, and if so, the candidate box whose distance to the handwriting data is less than the distance threshold is selected;

[0023] The score of the selected candidate box is determined from the scores corresponding to the respective candidate boxes stored in the candidate box information.

[0024] In some embodiments of the present application, the obtaining of the total score corresponding to each of the diagnostic scales belonging to the drawing type test task based on each of the handwriting recognition data in the drawing type test group includes:

[0025] Converting the handwriting data corresponding to each of the handwriting recognition data in the drawing type test group into handwriting image data respectively;

[0026] Each of the handwriting image data is respectively input into a preset handwriting image scoring model to obtain the score corresponding to each of the handwriting recognition data in the drawing type test group, and based on the score corresponding to each of the handwriting recognition data in the drawing type test group, the total score corresponding to each of the diagnostic scales belonging to the drawing type test task is obtained.

[0027] In some embodiments of the present application, the method of determining the second Alzheimer's disease risk level corresponding to the subject according to the handwriting data in each of the handwriting recognition data using a preset Alzheimer's disease risk prediction model includes:

[0028] According to the handwriting morphological feature data in the handwriting data in each of the handwriting recognition data, respectively generate handwriting morphological feature image data corresponding to each of the handwriting recognition data;

[0029] Setting data samples corresponding to each of the handwriting recognition data, wherein each of the data samples includes the handwriting morphological feature image data, the writing pressure value and the writing pause time corresponding to the handwriting recognition data;

[0030] Inputting the data sample into a preset Alzheimer's disease risk prediction model so that the Alzheimer's disease risk prediction model outputs the Alzheimer's disease risk score of the subject correspondingly; the Alzheimer's disease risk prediction model is generated in advance by random forest model training;

[0031] A second Alzheimer's disease risk level corresponding to the subject is determined according to the Alzheimer's disease risk score.

[0032] In some embodiments of the present application, the step of obtaining the Alzheimer's disease risk warning result data of the subject based on the first Alzheimer's disease risk level and the second Alzheimer's disease risk level includes:

[0033] The first Alzheimer's disease risk level and the second Alzheimer's disease risk level are input into a preset diagnostic analysis report generation model so that the diagnostic analysis report generation model outputs a corresponding diagnostic analysis report, and the diagnostic analysis report is output as the Alzheimer's disease risk warning result data of the subject.

[0034] In some embodiments of the present application, it also includes:

[0035] The various handwriting recognition data generated when all the diagnostic scales filled in by the evaluator, the diagnostic scale scores of the subject, and the pre-acquired personal information of the subject are classified and stored in a relational database, and the various handwriting recognition data generated when all the diagnostic scales filled in by the evaluator, the diagnostic scale scores of the subject, and the pre-acquired personal information of the subject are extracted from the relational database, and the various handwriting recognition data generated when all the diagnostic scales filled in by the evaluator, the diagnostic scale scores of the subject, and the pre-acquired personal information of the subject are extracted from the relational database as training data to train the Alzheimer's disease risk prediction model or the handwriting image scoring model.

[0036] Another aspect of the present application also provides an Alzheimer's disease risk warning device based on handwriting recognition, comprising:

[0037] The data acquisition module is used to receive various handwriting recognition data from the Bluetooth pen in real time when the assessor fills in the diagnostic scale for assisting the diagnosis of Alzheimer's disease of the subject and provided with a preset dot matrix code, wherein each set of the handwriting recognition data includes: the corresponding relationship between the scale content data determined by the Bluetooth pen through real-time recognition of the dot matrix code and the handwriting data recorded in real time by the Bluetooth pen;

[0038] a scale score calculation module, configured to calculate the diagnostic scale score of the test subject according to each of the handwriting recognition data generated when the evaluator fills in all the diagnostic scales, and determine the first Alzheimer's disease risk level corresponding to the test subject based on the diagnostic scale score;

[0039] and a handwriting risk calculation module, for determining a second Alzheimer's disease risk level corresponding to the subject using a preset Alzheimer's disease risk prediction model according to the handwriting data in each of the handwriting recognition data;

[0040] The risk warning result acquisition module is used to obtain the Alzheimer's disease risk warning result data of the subject based on the first Alzheimer's disease risk level and the second Alzheimer's disease risk level.

[0041] The third aspect of the present application also provides an Alzheimer's disease risk warning system based on handwriting recognition, comprising: a diagnostic scale for assisting in diagnosing Alzheimer's disease of a subject and provided with a preset dot matrix code, a Bluetooth pen, and a mobile client device; the Bluetooth pen is in communication connection with the mobile client device;

[0042] The mobile client device is used to execute the Alzheimer's disease risk early warning method based on handwriting recognition;

[0043] The Bluetooth pen is provided with a sensor for collecting the handwriting data in real time.

[0044] The fourth aspect of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the Alzheimer's disease risk warning method based on handwriting recognition when executing the computer program.

[0045] A fifth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the Alzheimer's disease risk warning method based on handwriting recognition.

[0046] The sixth aspect of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the Alzheimer's disease risk warning method based on handwriting recognition.

[0047] The Alzheimer's disease risk warning method based on handwriting recognition provided by the present application comprises the following steps: in a process in which an assessor holds a Bluetooth pen and fills in a diagnostic scale with a preset dot matrix code for assisting in diagnosing the Alzheimer's disease of a subject, the Bluetooth pen receives various handwriting recognition data in real time, wherein each group of the handwriting recognition data includes: a correspondence between the scale content data determined by the Bluetooth pen through real-time recognition of the dot matrix code and the handwriting data recorded in real time by the Bluetooth pen; according to the various handwriting recognition data generated when the assessor fills in all the diagnostic scales, the diagnostic scale score of the subject is calculated, and based on the diagnostic scale score, the first Alzheimer's disease corresponding to the subject is determined. Alzheimer's disease risk level; and, according to the handwriting data in each of the handwriting recognition data, a preset Alzheimer's disease risk prediction model is used to determine a second Alzheimer's disease risk level corresponding to the subject; based on the first Alzheimer's disease risk level and the second Alzheimer's disease risk level, the Alzheimer's disease risk warning result data of the subject is obtained, which can be suitable for the subjects who are not convenient to use electronic scales, such as the elderly, and can ensure the efficiency and automation of paper quality scale data processing, can effectively improve the accuracy and reliability of Alzheimer's disease risk warning, and thus can better realize the auxiliary diagnosis of Alzheimer's disease for the subject.

[0048] Additional advantages, purposes, and features of the present application will be partially described in the following description, and will become partially apparent to those skilled in the art after studying the following, or may be learned from the practice of the present application. The purposes and other advantages of the present application can be achieved and obtained by the structures specifically pointed out in the specification and the drawings.

[0049] Those skilled in the art will understand that the purposes and advantages that can be achieved by the present application are not limited to the above specific description, and the above and other purposes that can be achieved by the present application will be more clearly understood based on the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The drawings described herein are used to provide a further understanding of the present application, constitute a part of the present application, and do not constitute a limitation of the present application. The components in the drawings are not drawn to scale, but are only for the purpose of illustrating the principles of the present application. In order to facilitate the illustration and description of some parts of the present application, the corresponding parts in the drawings may be enlarged, that is, they may become larger relative to other components in the exemplary device actually manufactured according to the present application. In the drawings:

[0051] Figure 1 This is a schematic diagram of a first flow chart of an Alzheimer's disease risk warning method based on handwriting recognition in an embodiment of the present application.

[0052] Figure 2 This is a second flow chart of an Alzheimer's disease risk warning method based on handwriting recognition in one embodiment of the present application.

[0053] Figure 3 This is a third flow chart of the Alzheimer's disease risk warning method based on handwriting recognition in one embodiment of the present application.

[0054] Figure 4 This is a flowchart of the candidate box selection and judgment step in the Alzheimer's disease risk warning method based on handwriting recognition in one embodiment of the present application.

[0055] Figure 5 This is a schematic diagram of the first structure of an Alzheimer's disease risk warning device based on handwriting recognition in one embodiment of the present application.

[0056] Figure 6 This is a second structural diagram of an Alzheimer's disease risk warning device based on handwriting recognition in one embodiment of the present application. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the implementation modes and the accompanying drawings. Here, the illustrative implementation modes and descriptions of the present application are used to explain the present application, but are not intended to limit the present application.

[0058] It should also be noted here that in order to avoid obscuring the present application due to unnecessary details, only the structures and / or processing steps closely related to the scheme according to the present application are shown in the accompanying drawings, while other details that are not very relevant to the present application are omitted.

[0059] It should be emphasized that the term “include / comprises” when used herein refers to the presence of features, elements, steps or components, but does not exclude the presence or addition of one or more other features, elements, steps or components.

[0060] It should also be noted that, unless otherwise specified, the term “connection” herein may refer not only to a direct connection but also to an indirect connection with an intermediate.

[0061] Hereinafter, embodiments of the present application will be described with reference to the accompanying drawings. In the accompanying drawings, the same reference numerals represent the same or similar components, or the same or similar steps.

[0062] In order to achieve a balance between traditional paper-based scales and electronic scales that are completely based on software systems, the embodiments of the present application respectively provide an Alzheimer's disease risk warning method based on handwriting recognition, an Alzheimer's disease risk warning device based on handwriting recognition for executing the Alzheimer's disease risk warning method based on handwriting recognition, an Alzheimer's disease risk warning system based on handwriting recognition, an electronic device, a computer-readable storage medium and a computer program product, which can recognize the handwriting data of the dot code paper through a smart Bluetooth pen, transmit it to a mobile device via Bluetooth for real-time display, and further transmit the relevant information to the server for data analysis and storage, extract corresponding features according to the handwriting characteristics to calculate the risk of the disease, and generate an evaluation report to better realize the auxiliary diagnosis of Alzheimer's disease for the subject.

[0063] The details are described in detail through the following examples.

[0064] Based on this, the embodiment of the present application provides an Alzheimer's disease risk warning method based on handwriting recognition that can be implemented by an Alzheimer's disease risk warning device based on handwriting recognition, see Figure 1 The Alzheimer's disease risk warning method based on handwriting recognition specifically includes the following contents:

[0065] Step 100: When the evaluator holds a Bluetooth pen and fills out a diagnostic scale with a preset dot code for assisting in diagnosing the subject's Alzheimer's disease, each set of handwriting recognition data is received in real time from the Bluetooth pen, wherein each set of the handwriting recognition data includes: the correspondence between the scale content data determined by the Bluetooth pen through real-time recognition of the dot code and the handwriting data recorded in real time by the Bluetooth pen.

[0066] In one or more embodiments of the present application, the diagnostic scale is usually completed by both the doctor and the patient. In most cases, the doctor asks questions based on the scale content and fills in the rating scale based on the patient's answers, similar to the interview and record. When it comes to connecting lines and drawing tasks, the patient needs to fill it out. The evaluator mentioned in the present application may include the doctor and / or the patient. In one or more embodiments of the present application, the subject only refers to the patient.

[0067] In one or more embodiments of the present application, the Bluetooth pen is used as a data collection tool to collect the handwriting data of the Alzheimer's disease-related diagnostic scale filled by the subject for subsequent diagnostic analysis. Specifically, an existing smart Bluetooth pen that can connect to a client device via Bluetooth and transmit data can be used, and the Bluetooth pen can recognize the dot matrix code on the paper with the dot matrix code when in contact, and can collect handwriting data in real time through its own preset sensor. These are all existing functions of the existing Bluetooth pen, and no application or improvement is made, so the Bluetooth pen architecture is not described in detail.

[0068] It can be understood that the handwriting recognition data includes: the scale content data determined by the Bluetooth pen through real-time recognition of the dot matrix code, the handwriting data recorded by the Bluetooth pen in real time, and the correspondence between the scale content data and the handwriting data. Among them, the scale content data may include: the dot matrix code currently contacted by the Bluetooth pen on the diagnostic scale, the question number and the page area information, and the correspondence between the three;

[0069] The handwriting data includes: handwriting morphological feature data, writing pressure value, writing pause time and the corresponding relationship between the three; wherein, the handwriting morphological data includes: timestamp, the coordinate point sequence of the current handwriting in the diagnostic scale, the thickness value of the stroke and the corresponding relationship between the three. The preset coordinate system is the two-dimensional coordinate system (x, y).

[0070] It should also be noted that, although in step 100 of the present application, a paper diagnostic scale with a preset dot matrix code is used to better improve the scale filling experience and convenience for the elderly, the Alzheimer's disease risk warning method based on handwriting recognition proposed in the present application is also applicable to the electronic diagnostic scale for Alzheimer's disease developed based on a digital tablet.

[0071] In one or more embodiments thereof, the diagnostic scale may be referred to simply as a scale.

[0072] Step 200: Calculate the diagnostic scale score of the subject according to the handwriting recognition data generated when the evaluator fills in all the diagnostic scales, and determine the first Alzheimer's disease risk level corresponding to the subject based on the diagnostic scale score.

[0073] In step 200, the subject can fill in multiple different types of diagnostic scales, and examples of the types of diagnostic scales are shown in Table 1. Specifically, all or multiple of them can be used as diagnostic scales that the assessor needs to fill in in this application. At the same time, the diagnostic scale that can be used in this application is a combination of a series of neuropsychological assessment scales, and the version and quantity of the diagnostic scale can be added, deleted or modified according to actual needs.

[0074] Table 1: Examples of types of diagnostic scales

[0075]

[0076]

[0077] Each scale consists of many sub-items, and the total score of the sub-items needs to be calculated to represent the score of the corresponding scale to evaluate the various abilities of the subjects, such as cognitive level, depression level, memory ability, etc. The Mini-Mental State Examination (MMSE) is used to assist in the identification of dementia, while the Montreal Cognitive Assessment or Basic Scale (MoCA or MoCA-B) and the Addenbrooke's Cognitive Examination Scale 3rd Edition (ACE-Ⅲ) are used to identify mild cognitive impairment. There are also some commonly used tests to distinguish the assessment of impairment in different cognitive areas, such as the auditory verbal learning test (AVLT), the Boston naming test (BNT), the verbal fluency test (VFT), the symbol digit modalities test (SDMT), and the thread connection test (TMT), which reflect memory, language, attention, and executive function impairment respectively.

[0078] And, step 300: according to the handwriting data in each of the handwriting recognition data, a preset Alzheimer's disease risk prediction model is used to determine the second Alzheimer's disease risk level corresponding to the subject.

[0079] In one or more embodiments of the present application, the Alzheimer's disease risk prediction model can adopt a classification model for outputting an Alzheimer's disease risk score according to the input handwriting data. Specifically, the existing random forest algorithm can be adopted, and it can be implemented using a support vector machine, XGBoost or other integrated algorithms. It can also be implemented by integrating deep learning algorithms such as convolutional neural networks and recurrent neural networks and multiple algorithms. The specific selection is based on actual application needs. It is only necessary to adopt these existing model frameworks, and use handwriting data historical samples and labels used to represent Alzheimer's disease risk scores for training during the training phase.

[0080] Step 400: Acquire the Alzheimer's disease risk warning result data of the subject based on the first Alzheimer's disease risk level and the second Alzheimer's disease risk level.

[0081] In step 400, the first Alzheimer's disease risk level and the second Alzheimer's disease risk level can be directly output as the Alzheimer's disease risk warning result data of the subject, so as to assist the diagnostician in making a more accurate and comprehensive assessment of the subject's Alzheimer's disease risk based on the two risk levels.

[0082] In order to further improve the convenience and reliability of auxiliary diagnostic doctors in assessing the Alzheimer's disease risk of the subject, in another implementation of step 400, the existing large model can also be used to generate the first Alzheimer's disease risk level, the second Alzheimer's disease risk level, or the diagnostic analysis report text data corresponding to the first Alzheimer's disease risk level and the second Alzheimer's disease risk level.

[0083] From the above description, it can be seen that the Alzheimer's disease risk warning method based on handwriting recognition provided in the embodiment of the present application can be applicable to the tested population such as the elderly who are inconvenient to use electronic scales, and can ensure the efficiency and degree of automation of paper quality scale data processing, and can effectively improve the accuracy and reliability of Alzheimer's disease risk warning, thereby better realizing the auxiliary diagnosis of Alzheimer's disease.

[0084] In order to further improve the accuracy and effectiveness of calculating the diagnostic scale score of the subject, in the Alzheimer's disease risk warning method based on handwriting recognition provided in the embodiment of the present application, see Figure 2 Step 200 of the Alzheimer's disease risk warning method based on handwriting recognition specifically includes the following contents:

[0085] Step 210: According to the preset test task types to which each of the diagnostic scales belongs, the handwriting recognition data corresponding to the diagnostic scales whose test task type is a selective test task are divided into a selective test group, and the handwriting recognition data corresponding to the diagnostic scales whose test task type is a drawing test task are divided into a drawing test group.

[0086] Step 220: Based on each of the handwriting recognition data in the selective test group, obtain the total score corresponding to each of the diagnostic scales belonging to the selective test task, and summarize the total score corresponding to each of the diagnostic scales belonging to the selective test task to obtain the selective test score corresponding to the subject.

[0087] And, step 230: based on each of the handwriting recognition data in the drawing type test group, obtain the total score corresponding to each of the diagnostic scales belonging to the drawing type test task, and summarize the total score corresponding to each of the diagnostic scales belonging to the drawing type test task to obtain the drawing type test score corresponding to the subject.

[0088] Step 240: Determine the selective test score and the drawing test score corresponding to the subject as the diagnostic scale score of the subject, and determine the first Alzheimer's disease risk level corresponding to the subject according to the diagnostic scale score of the subject from the diagnostic standard data used to record the correspondence between the diagnostic scale score and the Alzheimer's disease risk level.

[0089] In order to further improve the accuracy and effectiveness of calculating the score of the test subject's selective test task, in the Alzheimer's disease risk warning method based on handwriting recognition provided in the embodiment of the present application, see Figure 3 , step 220 in the Alzheimer's disease risk warning method based on handwriting recognition specifically includes the following contents:

[0090] Step 221: Execute the preset candidate box checking judgment step for each of the handwriting recognition data corresponding to each of the diagnostic scales belonging to the selective test task to determine the score of each checked candidate box in each of the diagnostic scales belonging to the selective test task, and summarize the score of each checked candidate box in each of the diagnostic scales belonging to the selective test task to obtain the total score corresponding to each of the diagnostic scales belonging to the selective test task.

[0091] Among them, see Figure 4 The candidate box selection judgment step specifically includes the following contents:

[0092] Step 010: Determine the page identifier in the diagnostic scale corresponding to the handwriting recognition data based on the scale content data in the current handwriting recognition data, and obtain the position information of each candidate box in the target page corresponding to the page identifier from the candidate box information corresponding to each of the preset diagnostic scales belonging to the selective test task.

[0093] Step 020: Based on the handwriting data in the current handwriting recognition data and the position information of each candidate box in the target page, determine whether the distance between the handwriting data and any of the candidate boxes in the target page is less than a preset distance threshold. If so, determine that the candidate box whose distance to the handwriting data is less than the distance threshold is checked and execute step 030; if not, determine that the handwriting data is invalid data and complete the candidate box checking judgment step for the current handwriting recognition data.

[0094] Step 030: Determine the score of the selected candidate box from the scores corresponding to the respective candidate boxes stored in the candidate box information and complete the candidate box selection judgment step for the current handwriting recognition data.

[0095] Specifically, the Alzheimer's disease risk warning device based on handwriting recognition achieves scoring by corresponding the handwriting position with the coordinates of the score candidate box. First, determine the information of each candidate box used for score determination in the scale, including its center coordinates, frame size, corresponding score and number. Secondly, convert the handwriting data collected by the Bluetooth pen into a series of data points with continuous coordinates and time information. Take the mean of the horizontal and vertical coordinates of the data point to obtain the coordinates of its center point, calculate the distance between the center point of the handwriting and all candidate boxes on the current page, if the shortest distance is less than the sum of the side length of the candidate box and the error distance, then the handwriting is determined to correspond to this candidate box, otherwise the handwriting is determined to be invalid handwriting and does not participate in the total score calculation. By repeatedly iterating all the handwriting on a set of subscales, the check information in all candidate boxes on this scale can be obtained. Add the scores corresponding to the checked candidate boxes to get the total score of the sub-item.

[0096] In order to further improve the accuracy and effectiveness of calculating the test task score of the subject, in the Alzheimer's disease risk warning method based on handwriting recognition provided in the embodiment of the present application, see Figure 3 Step 230 in the Alzheimer's disease risk warning method based on handwriting recognition specifically includes the following contents:

[0097] Step 231: Convert the handwriting data corresponding to each of the handwriting recognition data in the drawing type test group into handwriting image data respectively.

[0098] Step 232: Input each of the handwriting image data into a preset handwriting image scoring model respectively to obtain the scores corresponding to each of the handwriting recognition data in the drawing type test group, and obtain the total scores corresponding to each of the diagnostic scales belonging to the drawing type test task based on the scores corresponding to each of the handwriting recognition data in the drawing type test group.

[0099] The handwriting image scoring model can be implemented by using a convolutional neural network, or it can be solved by using a convolutional neural network in combination with enhanced training data generated by a generative adversarial network (GAN).

[0100] Specifically, for drawing test tasks, the Alzheimer's disease risk warning device based on handwriting recognition converts the handwriting data collected by the Bluetooth pen into image information for data processing. Some relatively intuitive and simple scoring tasks are solved using functions such as polygon recognition of OpenCV (cross-platform computer vision library), and other difficult scoring tasks are solved using convolutional neural networks combined with enhanced training data generated by generative adversarial networks (GAN). When training the model, taking advantage of the Bluetooth pen's ability to collect multi-dimensional handwriting features, handwriting pressure, handwriting fluency information, etc. related to the scoring rules can be used as features for training, so that the trained model can obtain results closer to manual evaluation than previous classification tasks. Among them, intuitive and simple scoring tasks and difficult scoring tasks can be manually specified in advance.

[0101] In order to further improve the accuracy and effectiveness of handwriting risk calculation, in the Alzheimer's disease risk warning method based on handwriting recognition provided in the embodiment of the present application, see Figure 3 Step 300 of the Alzheimer's disease risk warning method based on handwriting recognition specifically includes the following contents:

[0102] Step 310: Generate handwriting morphological feature image data corresponding to each of the handwriting recognition data according to the handwriting morphological feature data in the handwriting data in each of the handwriting recognition data.

[0103] Step 320: setting data samples corresponding to each of the handwriting recognition data, wherein each of the data samples includes the handwriting morphological feature image data, the writing pressure value and the writing pause time corresponding to the handwriting recognition data.

[0104] Step 330: Input the data sample into a preset Alzheimer's disease risk prediction model so that the Alzheimer's disease risk prediction model outputs the Alzheimer's disease risk score of the subject accordingly; the Alzheimer's disease risk prediction model is generated in advance by random forest model training.

[0105] Step 340: Determine a second Alzheimer's disease risk level corresponding to the subject according to the Alzheimer's disease risk score.

[0106] Specifically, after obtaining the handwriting of the assessee, the Alzheimer's disease risk warning device based on handwriting recognition constructs features such as the original image, pressure, writing time, and completion, and uses the trained classifier to obtain the probability that the assessee has Alzheimer's disease as a risk score to assist in disease decision-making. This classifier is a random forest model. When training the model, the training data must first be divided into a training set and a test set, and the hyperparameters of the model must be optimized using nested cross-validation and grid search to select the optimal hyperparameter combination. The features of the input model include but are not limited to the above features, and the machine learning algorithm can also be implemented using deep learning methods such as convolutional neural networks and recurrent neural networks, or integrated algorithms.

[0107] In order to further improve the reliability and effectiveness of the generation of the Alzheimer's disease risk warning result, in the Alzheimer's disease risk warning method based on handwriting recognition provided in the embodiment of the present application, see Figure 3 Step 400 of the Alzheimer's disease risk warning method based on handwriting recognition specifically includes the following contents:

[0108] Step 410: Input the first Alzheimer's disease risk level and the second Alzheimer's disease risk level into a preset diagnostic analysis report generation model, so that the diagnostic analysis report generation model outputs a corresponding diagnostic analysis report, and outputs the diagnostic analysis report as the Alzheimer's disease risk warning result data of the subject.

[0109] The diagnostic analysis report generation model can be implemented using an existing large model, such as the moonshot (dark side of the moon) large model.

[0110] Specifically, the Alzheimer's disease risk warning device based on handwriting recognition can first take the first Alzheimer's disease risk level and the second Alzheimer's disease risk level as parameters, and encapsulate them in the JSON format required by the moonshot big model API interface; then call the API interface to send the JSON data to; the big model generates a diagnostic analysis report including potential cognitive problem analysis and treatment suggestions based on the received data and a pre-set template, and returns the results in JSON format; parses the returned JSON data, extracts keywords and corresponding content, and stores them in the corresponding positions in the pre-made Excel template; finally, converts the completed Excel table into PDF format for storage, and feeds the generated PDF report back to the front end for the experimenter and the subject, providing a reference for subsequent diagnosis and treatment.

[0111] In order to further improve the accuracy and application efficiency of Alzheimer's disease risk warning based on handwriting recognition, in an Alzheimer's disease risk warning method based on handwriting recognition provided in an embodiment of the present application, see Figure 3 , the step 400 in the Alzheimer's disease risk warning method based on handwriting recognition may further specifically include the following contents:

[0112] Step 500: Classify and store the various handwriting recognition data generated when the evaluator fills in all the diagnostic scales, the diagnostic scale scores of the subject, and the pre-acquired personal information of the subject in a relational database, extract the various handwriting recognition data generated when the evaluator fills in all the diagnostic scales, the diagnostic scale scores of the subject, and the pre-acquired personal information of the subject from the relational database, and use the various handwriting recognition data generated when the evaluator fills in all the diagnostic scales, the diagnostic scale scores of the subject, and the pre-acquired personal information of the subject from the relational database as training data to train the Alzheimer's disease risk prediction model or the handwriting image scoring model.

[0113] Specifically, the data stored in the Alzheimer's disease risk warning device based on handwriting recognition may include original handwriting, calculated total scale scores, and filled-in personal information. The above data is sorted according to different task types, scale types and other conditions, divided into multiple data tables, and stored in a MySQL relational database. This sub-table storage method not only helps to quickly retrieve the required data, but also facilitates the extraction of different features for different types of tasks. For example, drawing tasks can extract features such as the smoothness of lines and the regularity of shapes, and multiple-choice tasks can extract features such as the accuracy of the check position, thereby improving the efficiency and accuracy of model training. In addition, it can also reduce data redundancy, avoid storing duplicate information, and save storage space.

[0114] The present application also provides an Alzheimer's disease risk warning device based on handwriting recognition for executing all or part of the Alzheimer's disease risk warning method based on handwriting recognition, see Figure 5 The Alzheimer's disease risk warning device based on handwriting recognition specifically includes the following contents:

[0115] The data acquisition module 10 is used to receive various handwriting recognition data in real time from the Bluetooth pen when the evaluator holds the Bluetooth pen and fills in the diagnostic scale for assisting the diagnosis of Alzheimer's disease of the subject and provided with a preset dot matrix code, wherein each group of the handwriting recognition data includes: the correspondence between the scale content data determined by the Bluetooth pen through real-time recognition of the dot matrix code and the handwriting data recorded by the Bluetooth pen in real time.

[0116] Among them, the data acquisition module 10 involves a smart Bluetooth pen, a mobile client, and a matching Alzheimer's disease-related scale with a dot matrix code. The user uses the smart Bluetooth pen to fill in the Alzheimer's disease-related diagnostic scale on the scale paper printed with a specific dot matrix code. The smart Bluetooth pen can identify the dot matrix code on the paper during the writing process, determine the page, question number and page area information of the current writing (that is, each page is divided into multiple different areas in advance), and realize the association of handwriting data with the scale content. In addition, the smart Bluetooth pen has its own sensor to record the handwriting data in real time, including: the timestamp during the writing process, the sequence of coordinate points (x, y) passed by the handwriting, the thickness of the strokes, etc., which are used to restore the handwriting form; the writing pressure value, the pause time, etc., are used to reflect the strength and fluency of the writing. The above information can fully represent the writing characteristics of the user on the paper. When the user uses the Bluetooth pen to write, this information will be transmitted to the mobile client in real time, and the base map of the scale and the corresponding writing trajectory will be displayed in real time on the mobile client. Among them, the mobile client device can use an Android system tablet or other similar devices.

[0117] The scale score calculation module 20 is used to calculate the diagnostic scale score of the subject according to the various handwriting recognition data generated when the evaluator fills in all the diagnostic scales, and determine the first Alzheimer's disease risk level corresponding to the subject based on the diagnostic scale score.

[0118] The scale score calculation module 20 can automatically calculate the scores of each task after the scale test is completed.

[0119] For the selective test task, the scale score calculation module 20 realizes the scoring by matching the handwriting position with the coordinates of the score candidate box. First, determine the information of each candidate box used for score determination in the scale, including its center coordinates, frame size, corresponding score and number. Secondly, convert the handwriting data collected by the Bluetooth pen into a series of data points with continuous coordinates and time information. Take the mean of the horizontal and vertical coordinates of the data point to obtain the coordinates of its center point, calculate the distance between the center point of the handwriting and all candidate boxes on the current page, if the shortest distance is less than the sum of the side length of the candidate box and the error distance, then the handwriting is determined to correspond to this candidate box, otherwise the handwriting is determined to be invalid handwriting and does not participate in the total score calculation. By repeatedly iterating all the handwriting on a set of sub-scales, the check information in all the candidate boxes on this scale can be obtained. Add the scores corresponding to the checked candidate boxes to get the total score of the sub-item.

[0120] For drawing test tasks, the scale score calculation module 20 converts the handwriting data collected by the Bluetooth pen into image information for data processing. Some relatively intuitive and simple scoring tasks are solved using functions such as polygon recognition of opencv (cross-platform computer vision library), and other difficult scoring tasks are solved using enhanced training data generated by convolutional neural networks in conjunction with generative adversarial networks (GAN). When training the model, taking advantage of the Bluetooth pen's ability to collect multi-dimensional handwriting features, we train the handwriting pressure and handwriting fluency information related to the scoring rules as features, so that the trained model can obtain results closer to human evaluation than previous classification tasks.

[0121] Finally, the scale score calculation module 20 can quantify the scores based on the pre-acquired authoritative diagnostic standard data, and automatically classify and display the disease risk levels.

[0122] And, the handwriting risk calculation module 30 is used to determine the second Alzheimer's disease risk level corresponding to the subject according to the handwriting data in each of the handwriting recognition data using a preset Alzheimer's disease risk prediction model.

[0123] Among them, the handwriting risk calculation module 30 can construct the original image, pressure, writing time, completion and other features after obtaining the handwriting of the assessor, and use the trained classifier to obtain the probability that the assessor suffers from Alzheimer's disease as a risk score to assist in disease decision-making. This classifier is a random forest model. When training the model, the training data needs to be divided into a training set and a test set, and the hyperparameters of the model are optimized by nested cross-validation and grid search to select the optimal hyperparameter combination. The features of the input model include but are not limited to the above features, and the machine learning algorithm can also be implemented using deep learning methods such as convolutional neural networks and recurrent neural networks or integrated algorithms.

[0124] The risk warning result acquisition module 40 is used to obtain the Alzheimer's disease risk warning result data of the subject based on the first Alzheimer's disease risk level and the second Alzheimer's disease risk level.

[0125] Among them, the risk warning result acquisition module 40 can also be called the assessment result analysis module. First, the calculated total scores of each subscale are taken as parameters and encapsulated in the JSON format required by the moonshot big model API interface; then the API interface is called to send the JSON data to; the big model generates a diagnostic analysis report containing potential cognitive problem analysis and treatment suggestions based on the received data and the pre-set template, and returns the results in JSON format; parses the returned JSON data, extracts keywords and corresponding content, and stores them in the corresponding positions in the pre-made Excel template; finally, the completed Excel table is converted into PDF format for storage, and the generated PDF report is fed back to the front end for the main experimenter and the subject, providing a reference for subsequent diagnosis and treatment.

[0126] The embodiment of the Alzheimer's disease risk warning device based on handwriting recognition provided in the present application can be specifically used to execute the processing flow of the embodiment of the Alzheimer's disease risk warning method based on handwriting recognition in the above-mentioned embodiment. Its functions will not be repeated here, and reference can be made to the detailed description of the embodiment of the Alzheimer's disease risk warning method based on handwriting recognition.

[0127] The part of the Alzheimer's disease risk warning device based on handwriting recognition that performs the Alzheimer's disease risk warning based on handwriting recognition can be executed in the server or completed in the client device. The specific selection can be based on the processing capability of the client device and the limitations of the user's usage scenario. This application is not limited to this. If all operations are completed in the client device, the client device may also include a processor for specific processing of the Alzheimer's disease risk warning based on handwriting recognition.

[0128] The client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission with the server. The server may include a server on the task scheduling center side, and other implementation scenarios may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, or a server cluster consisting of multiple servers, or a server structure of a distributed device.

[0129] The server and the client device may communicate with each other using any suitable network protocol, including network protocols that have not yet been developed on the date of filing this application. The network protocols may include, for example, TCP / IP, UDP / IP, HTTP, HTTPS, etc. Of course, the network protocols may also include, for example, RPC (Remote Procedure Call Protocol) and REST (Representational State Transfer) protocols used on top of the above protocols.

[0130] From the above description, it can be seen that the Alzheimer's disease risk warning device based on handwriting recognition provided in the embodiment of the present application can be suitable for subjects such as the elderly who are not convenient to use electronic scales, and can ensure the efficiency and automation of paper quality scale data processing, and can effectively improve the accuracy and reliability of Alzheimer's disease risk warning, thereby better realizing auxiliary diagnosis of Alzheimer's disease for the subjects.

[0131] In order to further improve the accuracy and application efficiency of the Alzheimer's disease risk warning based on handwriting recognition, in an Alzheimer's disease risk warning device based on handwriting recognition provided in an embodiment of the present application, see Figure 6 The Alzheimer's disease risk warning device based on handwriting recognition may also specifically include the following contents:

[0132] The data storage module 50 is used to classify and store the various handwriting recognition data generated when the evaluator fills in all the diagnostic scales, the diagnostic scale scores of the subjects, and the pre-acquired personal information of the subjects in a relational database, to extract the various handwriting recognition data generated when the evaluator fills in all the diagnostic scales, the diagnostic scale scores of the subjects, and the pre-acquired personal information of the subjects from the relational database, and to use the various handwriting recognition data generated when the evaluator fills in all the diagnostic scales, the diagnostic scale scores of the subjects, and the pre-acquired personal information of the subjects extracted from the relational database as training data to train the Alzheimer's disease risk prediction model or the handwriting image scoring model.

[0133] Specifically, the data storage module 50 stores data including original handwriting, calculated total scale scores, and filled-in personal information. The above data is sorted according to different task types, scale types, and other conditions, divided into multiple data tables, and stored in a MySQL relational database. This sub-table storage method not only helps to quickly retrieve the required data, but also facilitates the extraction of different features for different types of tasks. For example, drawing tasks can extract features such as the smoothness of lines and the regularity of shapes, and multiple-choice tasks can extract features such as the accuracy of the check position, thereby improving the efficiency and accuracy of model training. In addition, it can also reduce data redundancy, avoid storing duplicate information, and save storage space.

[0134] That is to say, the Alzheimer's disease risk warning method and device based on handwriting recognition provided in the embodiment of the present application uses a smart Bluetooth pen as a data collection tool to collect handwriting data of Alzheimer's disease-related diagnostic scales filled out by the subjects for subsequent diagnostic analysis; calculates the scores of each evaluation indicator in multiple scales based on the handwriting data, and calculates the total score of the scale; rates the handwriting data, total score of the scale and case information of the evaluator according to the severity of the situation and stores them in categories; extracts relevant features of the evaluator's handwriting data, uses machine learning to obtain the risk score of the evaluator for Alzheimer's disease, and generates an evaluation report in combination with the scale results for auxiliary diagnosis.

[0135] Compared with the prior art, the Alzheimer's disease risk warning method and device based on handwriting recognition provided by the embodiment of the present application has the following beneficial effects:

[0136] 1. Electronic scale: The process of diagnosing Alzheimer's disease based on paper-based scales does not change. You only need to replace the pen and paper with a smart Bluetooth pen and paper with dot matrix codes. It is easy to promote and use. The data collection method based on the mobile terminal also helps to realize the remote diagnosis of Alzheimer's disease.

[0137] 2. Automatic calculation and storage of scores: effectively simplify the operating process, reduce manual operations, and efficiently trace and share data resources.

[0138] 3. Auxiliary recognition based on handwriting: Previous paper quality tables cannot effectively utilize handwriting data. The advantage of this system is that it can automatically evaluate the user's handwriting behavior characteristics, automatically determine their risk of Alzheimer's disease, and assist in disease screening.

[0139] 4. Assessment report generation: Based on the evaluator's scale assessment results and handwriting behavior analysis, an assessment report is generated that explains the patient's condition in detail, making it easier for the patient to understand his or her own condition.

[0140] Based on the above-mentioned Alzheimer's disease risk warning method and device based on handwriting recognition, the present application also provides an embodiment of an Alzheimer's disease risk warning system based on handwriting recognition, and the provided Alzheimer's disease risk warning system based on handwriting recognition specifically includes the following contents:

[0141] A diagnostic scale, a Bluetooth pen and a mobile client device for assisting in diagnosing Alzheimer's disease in a subject and provided with a preset dot matrix code; the Bluetooth pen and the mobile client device are in communication connection;

[0142] The mobile client device is used to execute the Alzheimer's disease risk warning method based on handwriting recognition provided in the above embodiment;

[0143] The Bluetooth pen is provided with a sensor for collecting the handwriting data in real time.

[0144] The developed system may include an Android client, a web page display terminal and a data storage terminal, which are respectively used to receive Bluetooth pen data, display and modify data, and store data.

[0145] That is to say, the embodiment of the present application designs an electronic scale system based on a smart Bluetooth pen. Compared with the current situation where there is no electronic scale solution for Alzheimer's disease related to a smart pen, the present application has developed a set of Android, web and server-side software systems based on the Bluetooth pen; it can obtain the score items corresponding to the handwriting by matching the candidate boxes with coordinates; it can automatically calculate through the scale score; it can use handwriting data to assist in the diagnosis of Alzheimer's disease, extract the handwriting of the evaluator, and use machine learning methods to automatically determine whether he has the risk of Alzheimer's disease, to assist in decision-making; and it can generate an evaluation report based on a large model.

[0146] The embodiment of the present application also provides an electronic device, which may include a processor, a memory, a receiver and a transmitter, wherein the processor is used to execute the Alzheimer's disease risk warning method based on handwriting recognition mentioned in the above embodiment, wherein the processor and the memory may be connected via a bus or other means, taking the bus connection as an example. The receiver may be connected to the processor and the memory via a wired or wireless manner.

[0147] The processor may be a central processing unit (CPU). The processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0148] As a non-transient computer-readable storage medium, the memory can be used to store non-transient software programs, non-transient computer executable programs and modules, such as the program instructions / modules corresponding to the Alzheimer's disease risk warning method based on handwriting recognition in the embodiment of the present application. The processor executes various functional applications and data processing of the processor by running the non-transient software programs, instructions and modules stored in the memory, that is, realizing the Alzheimer's disease risk warning method based on handwriting recognition in the above method embodiment.

[0149] The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created by the processor, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0150] The one or more modules are stored in the memory, and when executed by the processor, the Alzheimer's disease risk warning method based on handwriting recognition in the embodiment is executed.

[0151] In some embodiments of the present application, the user equipment may include a processor, a memory, and a transceiver unit, which may include a receiver and a transmitter. The processor, memory, receiver, and transmitter may be connected through a bus system. The memory is used to store computer instructions, and the processor is used to execute the computer instructions stored in the memory to control the transceiver unit to send and receive signals.

[0152] As an implementation method, the functions of the receiver and the transmitter in the present application can be considered to be implemented through a transceiver circuit or a dedicated chip for transceiver, and the processor can be considered to be implemented through a dedicated processing chip, a processing circuit or a general chip.

[0153] As another implementation method, it is possible to use a general-purpose computer to implement the server provided in the embodiment of the present application, that is, to store the program code for implementing the functions of the processor, receiver, and transmitter in a memory, and the general-purpose processor implements the functions of the processor, receiver, and transmitter by executing the code in the memory.

[0154] The embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the aforementioned Alzheimer's disease risk warning method based on handwriting recognition are implemented. The computer-readable storage medium can be a tangible storage medium, such as a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a floppy disk, a hard disk, a removable storage disk, a CD-ROM, or any other form of storage medium known in the technical field.

[0155] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the Alzheimer's disease risk warning method based on handwriting recognition.

[0156] It should be understood by those skilled in the art that the exemplary components, systems and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software or a combination of the two. Whether it is specifically performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier.

[0157] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0158] In the present application, features described and / or illustrated for one embodiment may be used in the same manner or in a similar manner in one or more other embodiments, and / or combined with features of other embodiments or replace features of other embodiments.

[0159] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the embodiments of the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A risk warning method for Alzheimer's disease based on handwriting recognition, characterized in that: include: When the examiner fills in the diagnostic scale with preset dot codes for assisting diagnosis of Alzheimer's disease of the examinee with the Bluetooth pen, each set of handwriting recognition data is received in real time from the Bluetooth pen, wherein each set of the handwriting recognition data includes: the corresponding relationship between the scale content data determined by the Bluetooth pen through real-time recognition of the dot codes and the handwriting data recorded in real time by the Bluetooth pen; Calculating the diagnostic scale score of the test subject according to each of the handwriting recognition data generated when the evaluator fills in all the diagnostic scales, and determining the first Alzheimer's disease risk level corresponding to the test subject based on the diagnostic scale score; And, according to the handwriting data in each of the handwriting recognition data, a preset Alzheimer's disease risk prediction model is used to determine a second Alzheimer's disease risk level corresponding to the subject; Based on the first Alzheimer's disease risk level and the second Alzheimer's disease risk level, Alzheimer's disease risk warning result data of the subject is obtained.

2. The Alzheimer's disease risk warning method based on handwriting recognition according to claim 1, characterized in that: The scale content data includes the page, question number and page area information of the dot matrix code currently contacted by the Bluetooth pen on the diagnostic scale; The handwriting data includes: handwriting morphological feature data, writing pressure value and writing pause time; wherein, the handwriting morphological data includes: timestamp, coordinate point sequence of the current handwriting in the preset coordinate system corresponding to the diagnostic scale and stroke thickness value.

3. The Alzheimer's disease risk warning method based on handwriting recognition according to claim 2, characterized in that: The step of calculating the diagnostic scale score of the test subject according to each of the handwriting recognition data generated when the evaluator fills in all the diagnostic scales, and determining the first Alzheimer's disease risk level corresponding to the test subject based on the diagnostic scale score, comprises: According to the preset test task types to which each of the diagnostic scales belongs, the handwriting recognition data corresponding to the diagnostic scales whose test task type is a selective test task are divided into a selective test group, and the handwriting recognition data corresponding to the diagnostic scales whose test task type is a drawing test task are divided into a drawing test group; Based on each of the handwriting recognition data in the selective test group, the total score corresponding to each of the diagnostic scales belonging to the selective test task is obtained, and the total score corresponding to each of the diagnostic scales belonging to the selective test task is summarized to obtain the selective test score corresponding to the subject; And, based on each of the handwriting recognition data in the drawing type test group, the total score corresponding to each of the diagnostic scales belonging to the drawing type test task is obtained, and the total score corresponding to each of the diagnostic scales belonging to the drawing type test task is summarized to obtain the drawing type test score corresponding to the subject; The selective test score and the drawing test score corresponding to the subject are determined as the diagnostic scale score of the subject, and the first Alzheimer's disease risk level corresponding to the subject is determined according to the diagnostic scale score of the subject from the diagnostic standard data used to record the correspondence between the diagnostic scale score and the Alzheimer's disease risk level.

4. The Alzheimer's disease risk warning method based on handwriting recognition according to claim 3, characterized in that: The obtaining, based on each of the handwriting recognition data in the selective test group, a total score corresponding to each of the diagnostic scales belonging to the selective test task comprises: For each of the handwriting recognition data corresponding to each of the diagnostic scales belonging to the selective test task, a preset candidate box checking judgment step is respectively performed to determine the score of each checked candidate box in each of the diagnostic scales belonging to the selective test task, and the score of each checked candidate box in each of the diagnostic scales belonging to the selective test task is summarized to obtain the total score corresponding to each of the diagnostic scales belonging to the selective test task; The step of checking and judging the candidate box includes: Determine the page identifier in the diagnostic scale corresponding to the handwriting recognition data according to the scale content data in the current handwriting recognition data, and obtain the position information of each candidate box in the target page corresponding to the page identifier from the candidate box information corresponding to each of the preset diagnostic scales belonging to the selective test task; Based on the handwriting data in the current handwriting recognition data and the position information of each candidate box in the target page, it is determined whether the distance between the handwriting data and any candidate box in the target page is less than a preset distance threshold, and if so, the candidate box whose distance to the handwriting data is less than the distance threshold is selected; The score of the selected candidate box is determined from the scores corresponding to the respective candidate boxes stored in the candidate box information.

5. The Alzheimer's disease risk warning method based on handwriting recognition according to claim 3, characterized in that: The obtaining, based on each of the handwriting recognition data in the drawing type test group, a total score corresponding to each of the diagnostic scales belonging to the drawing type test task comprises: Convert the handwriting data corresponding to each of the handwriting recognition data in the drawing type test group into handwriting image data respectively; Each of the handwriting image data is respectively input into a preset handwriting image scoring model to obtain the score corresponding to each of the handwriting recognition data in the drawing type test group, and based on the score corresponding to each of the handwriting recognition data in the drawing type test group, the total score corresponding to each of the diagnostic scales belonging to the drawing type test task is obtained.

6. The Alzheimer's disease risk warning method based on handwriting recognition according to claim 2, characterized in that: The method of determining the second Alzheimer's disease risk level corresponding to the subject according to the handwriting data in each of the handwriting recognition data by using a preset Alzheimer's disease risk prediction model comprises: According to the handwriting morphological feature data in the handwriting data in each of the handwriting recognition data, respectively generate handwriting morphological feature image data corresponding to each of the handwriting recognition data; Setting data samples corresponding to each of the handwriting recognition data, wherein each of the data samples includes the handwriting morphological feature image data, the writing pressure value and the writing pause time corresponding to the handwriting recognition data; Inputting the data sample into a preset Alzheimer's disease risk prediction model so that the Alzheimer's disease risk prediction model outputs the Alzheimer's disease risk score of the subject correspondingly; the Alzheimer's disease risk prediction model is generated in advance by random forest model training; A second Alzheimer's disease risk level corresponding to the subject is determined according to the Alzheimer's disease risk score.

7. The Alzheimer's disease risk warning method based on handwriting recognition according to claim 1, characterized in that: The step of obtaining the Alzheimer's disease risk warning result data of the subject based on the first Alzheimer's disease risk level and the second Alzheimer's disease risk level includes: The first Alzheimer's disease risk level and the second Alzheimer's disease risk level are input into a preset diagnostic analysis report generation model so that the diagnostic analysis report generation model outputs a corresponding diagnostic analysis report, and the diagnostic analysis report is output as the Alzheimer's disease risk warning result data of the subject.

8. The Alzheimer's disease risk warning method based on handwriting recognition according to claim 5, characterized in that: Also includes: The various handwriting recognition data generated when all the diagnostic scales filled in by the evaluator, the diagnostic scale scores of the subject, and the pre-acquired personal information of the subject are classified and stored in a relational database, and the various handwriting recognition data generated when all the diagnostic scales filled in by the evaluator, the diagnostic scale scores of the subject, and the pre-acquired personal information of the subject are extracted from the relational database, and the various handwriting recognition data generated when all the diagnostic scales filled in by the evaluator, the diagnostic scale scores of the subject, and the pre-acquired personal information of the subject are extracted from the relational database as training data to train the Alzheimer's disease risk prediction model or the handwriting image scoring model.

9. An Alzheimer's disease risk warning device based on handwriting recognition, characterized in that: include: The data acquisition module is used to receive various handwriting recognition data from the Bluetooth pen in real time when the assessor fills in the diagnostic scale for assisting the diagnosis of Alzheimer's disease of the subject and provided with a preset dot matrix code, wherein each set of the handwriting recognition data includes: the corresponding relationship between the scale content data determined by the Bluetooth pen through real-time recognition of the dot matrix code and the handwriting data recorded in real time by the Bluetooth pen; a scale score calculation module, configured to calculate the diagnostic scale score of the test subject according to each of the handwriting recognition data generated when the evaluator fills in all the diagnostic scales, and determine the first Alzheimer's disease risk level corresponding to the test subject based on the diagnostic scale score; and a handwriting risk calculation module, for determining a second Alzheimer's disease risk level corresponding to the subject using a preset Alzheimer's disease risk prediction model according to the handwriting data in each of the handwriting recognition data; The risk warning result acquisition module is used to obtain the Alzheimer's disease risk warning result data of the subject based on the first Alzheimer's disease risk level and the second Alzheimer's disease risk level.

10. An Alzheimer's disease risk warning system based on handwriting recognition, characterized in that: include: A diagnostic scale, a Bluetooth pen, and a mobile client device for assisting in diagnosing Alzheimer's disease in a subject and provided with a preset dot matrix code; The Bluetooth pen is in communication connection with the mobile client device; The mobile client device is used to execute the Alzheimer's disease risk warning method based on handwriting recognition according to any one of claims 1 to 8; The Bluetooth pen is provided with a sensor for collecting the handwriting data in real time.

Images