Health state assessment method and device, electronic equipment and storage medium

Abstract

The invention discloses a health status assessment method and device, electronic equipment and a storage medium, and through the method and the device, traditional physiological index data and neurological function defect symptom data of a user can be collected at the same time by means of mobile terminal equipment, so that the blank of neglecting of digital collection of early symptoms of neurological function defect in an existing method is filled; then the two types of data are analyzed through a multi-dimensional health data fusion analysis model, internal association between the data and cerebrovascular disease outcome and relapse is fully mined, and the problems that only traditional physiological index monitoring is relied on, neglect is given to digital acquisition of early symptoms of neurological impairment, multi-modal data fusion analysis is lacked, and the accuracy of data analysis is poor are solved. The technical effects that early warning signals of the cerebrovascular diseases are accurately captured, the accuracy rate of health state evaluation and early warning is improved, a health management scheme meeting individual requirements is generated, the out-of-hospital rehabilitation monitoring and intervention effect is enhanced, and long-term scientific management of the cerebrovascular diseases is assisted are achieved.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and in particular to a health status assessment method and apparatus, electronic device and storage medium. Background Technology

[0002] Cerebrovascular disease health management, as an important component of chronic disease management, is widely used in outpatient rehabilitation and long-term monitoring. Among related technologies, a multi-dimensional health monitoring system has been constructed through the collaborative operation of wearable device data collection, AI analysis algorithms, and doctor-patient interaction systems.

[0003] Existing health management methods typically use traditional indicators such as blood pressure and blood sugar for monitoring. This monitoring method has limitations in digitally collecting early signs of neurological deficits, such as facial asymmetry and limited limb movement. It also lacks the fusion analysis of multimodal data physiological indicators and motion images, resulting in insufficient accuracy in early warning. Summary of the Invention

[0004] This disclosure provides a method, apparatus, electronic device, and storage medium for assessing health status.

[0005] According to a first aspect of this disclosure, a method for assessing health status is provided, comprising: The system collects users' physiological indicators and neurological deficit symptoms data through mobile devices; the neurological deficit symptoms data includes facial movement recognition data and limb movement trajectory analysis data. The physiological indicator data and neurological deficit symptom data are analyzed based on a multi-dimensional health data fusion analysis model to generate health status assessment results. Based on the health status assessment results, early warning information will be pushed out; A personalized health management plan is generated based on the early warning information and the health status assessment results.

[0006] Optionally, the collection of users' physiological indicator data and neurological deficit symptom data via mobile devices includes: A scoring algorithm is used to extract features from user facial motion recognition data and generate a facial NIHSS score. Based on the digital implementation of the scale, the system collects users' limb movement trajectory data through a mobile camera and calculates and generates a limb movement NIHSS score.

[0007] Optionally, the analysis of the physiological indicator data and neurological deficit symptom data based on the multi-dimensional health data fusion analysis model to generate health status assessment results includes: Deep learning models were used to analyze physiological indicators such as blood pressure, blood glucose, and electrocardiogram, along with facial function assessment values ​​and limb function assessment values, to identify potential stroke precursor patterns. The dynamic threshold early warning system combines individual patient baseline data with population medicine standards and uses an adaptive mechanism to adjust the early warning threshold range for different assessment values.

[0008] Optionally, the step of pushing early warning information based on the health status assessment results includes: The push notification method is selected based on the severity of outliers; the warning information includes a timestamp, a snapshot of the original data, and an analysis confidence score.

[0009] Optionally, the method further includes: Generate visual health trend reports, showing long-term changes in users' physiological indicators and neurological function assessment values ​​through trend curves and comparison charts.

[0010] According to a second aspect of this disclosure, a health status assessment device is provided, comprising: The data acquisition unit is used to collect users' physiological index data and neurological deficit symptom data through mobile devices; wherein, the neurological deficit symptom data includes facial motion recognition data and limb movement trajectory analysis data; The analysis unit is used to analyze the physiological indicator data and neurological deficit symptom data based on a multi-dimensional health data fusion analysis model to generate health status assessment results. The push unit is used to push early warning information based on the health status assessment results; The generation unit is used to generate a personalized health management plan based on the early warning information and the health status assessment results.

[0011] Optionally, the acquisition unit is further configured to: A scoring algorithm is used to extract features from user facial motion recognition data and generate a facial NIHSS score. Based on the digital implementation of the scale, the system collects users' limb movement trajectory data through a mobile camera and calculates and generates a limb movement NIHSS score.

[0012] Optionally, the analysis unit is further configured to: Deep learning models were used to analyze physiological indicators such as blood pressure, blood glucose, and electrocardiogram, along with facial function assessment values ​​and limb function assessment values, to identify potential stroke precursor patterns. The dynamic threshold early warning system combines individual patient baseline data with population medicine standards and uses an adaptive mechanism to adjust the early warning threshold range for different assessment values.

[0013] Optionally, the push unit is further configured to: The push notification method is selected based on the severity of outliers; the warning information includes a timestamp, a snapshot of the original data, and an analysis confidence score.

[0014] Optionally, the device further includes: The display unit is used to generate visual health trend reports, showing the long-term changes in users' physiological indicators and neurological function assessment values ​​through trend curves and comparison charts.

[0015] According to a third aspect of this disclosure, an electronic device is provided, comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect above.

[0016] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are configured to cause the computer to perform the method described in the first aspect above.

[0017] According to a fifth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method described in the first aspect above.

[0018] The health status assessment method, device, electronic device, and storage medium disclosed herein, through this application, can simultaneously collect users' traditional physiological indicator data and neurological deficit symptom data using mobile devices, filling the gap in existing methods that neglect the digital collection of early signs of neurological deficits. Furthermore, by employing a multi-dimensional health data fusion analysis model, the two types of data are deeply analyzed to fully explore their intrinsic correlation with the outcome and recurrence of cerebrovascular diseases. Therefore, it can solve the technical problems of existing cerebrovascular disease health management methods that rely solely on traditional physiological indicator monitoring, neglect the digital collection of early signs of neurological deficits, and lack multi-modal data fusion analysis, resulting in insufficient early warning accuracy. This achieves the technical effects of accurately capturing early warning signals of cerebrovascular diseases, improving the accuracy of health status assessment and early warning, generating health management plans tailored to individual needs, strengthening the effectiveness of outpatient rehabilitation monitoring and intervention, and assisting in the long-term scientific management of cerebrovascular diseases.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0020] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein: Figure 1 A schematic flowchart illustrating a health status assessment method provided in an embodiment of this disclosure; Figure 2 This is a schematic diagram of the structure of a health status assessment device provided in an embodiment of the present disclosure; Figure 3 This is a schematic diagram of another health status assessment device provided in an embodiment of the present disclosure; Figure 4 A schematic block diagram of an example electronic device provided for embodiments of this disclosure. Detailed Implementation

[0021] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0022] The following description, with reference to the accompanying drawings, outlines a health status assessment method, apparatus, electronic device, and storage medium according to embodiments of the present disclosure.

[0023] Figure 1 This is a schematic flowchart of a health status assessment method provided in an embodiment of the present disclosure.

[0024] like Figure 1 As shown, the method includes the following steps: Step 101: Collect users' physiological indicator data and neurological deficit symptom data through mobile devices; wherein, the neurological deficit symptom data includes facial movement recognition data and limb movement trajectory analysis data; The physiological indicators data cover the collection of basic health indicators such as blood pressure, blood sugar, and electrocardiogram. These indicators are important bases for judging the stability of the condition of patients with cerebrovascular disease. For example, blood pressure data can reflect changes in the pressure inside the patient's blood vessels, blood sugar data can reflect the body's metabolic state, and electrocardiogram data can help understand whether the heart function is normal, providing basic data support for subsequent health assessments.

[0025] The data on neurological deficit symptoms include facial motion recognition data and limb movement trajectory analysis data. Facial motion recognition data captures the user's facial muscle activity through the mobile device's camera, observing for abnormalities such as asymmetry in forehead wrinkles and nasolabial folds at rest, inability to close one eyelid properly, weakness in showing teeth on one side, and air leakage when puffing out cheeks. These abnormalities may indicate neurological impairment. Limb movement trajectory analysis data analyzes videos and, based on the NIHSS score for limb movement, quantitatively assesses the user's ability to maintain antigravity and voluntary motor control, providing crucial evidence for determining the degree of motor neuron dysfunction and developing follow-up strategies.

[0026] Step 102: Based on the multi-dimensional health data fusion analysis model, analyze the physiological indicator data and neurological deficit symptom data to generate a health status assessment result. This multi-dimensional health data fusion analysis model breaks down information barriers between different types of health data, avoiding judgment biases that may result from relying solely on a single data dimension for assessment, and allowing the value of both types of data to be synergistically utilized. The core of the model lies in combining abnormal physiological indicators such as blood pressure, blood sugar, and electrocardiogram with neurological deficit symptoms in the limbs and face to further reveal their intrinsic connection with disease progression and recurrence, thereby uncovering the deeper value of the data and more comprehensively and accurately capturing the dynamic evolution of users' health status.

[0027] The generated health status assessment results clearly reflect the user's current overall health status, including whether there are health risks related to cerebrovascular disease, and the physiological or neurological functional factors that may be involved in the risks. This provides valuable analytical basis for the formulation of subsequent health management measures and for doctors to conduct follow-up work, helping users and medical personnel to more accurately grasp health dynamics.

[0028] Step 103: Based on the health status assessment results, push out early warning information; The push notifications will be closely based on the key conclusions of the health status assessment. For example, when the assessment results show that physiological indicators are persistently abnormal (such as blood pressure exceeding the normal safe range for patients with cerebrovascular disease), or when there are new changes in the symptoms of neurological deficits (such as worsening of facial asymmetry or decreased limb motor ability), the system will generate targeted warning content, clearly indicating the type of risk and the possible associated health problems, allowing the recipient to quickly grasp the core risk points.

[0029] The push notification system for early warning information takes into account both timeliness and effectiveness, employing a push method suitable for mobile usage scenarios to ensure convenient reception by users. Simultaneously, based on actual needs, the warning information will also be pushed to the user's associated caregivers, allowing both parties to be aware of the health risks and forming a collaborative awareness. This early warning system helps users detect health abnormalities promptly and take timely measures such as rest and follow-up examinations. It also provides advance risk alerts for subsequent medical follow-ups, enabling doctors to more accurately focus on potential health problems during follow-ups, thus contributing to a coherent health management loop.

[0030] Step 104: Generate a personalized health management plan based on the warning information and the health status assessment results.

[0031] Personalized health management plans are generated based on health status assessment results and early warning information. The core principle is to combine a user's specific health risks with their overall health status to develop actionable health management content tailored to their individual needs, avoiding the limitations of generic plans that cannot adapt to the diverse health problems of patients with cerebrovascular diseases. The plan generation closely relies on the risk types clearly defined in the early warning information (such as abnormal physiological indicators or changes in neurological deficit symptoms) and the detailed conclusions in the health status assessment results. For example, if the early warning information indicates that indicators such as blood pressure and blood sugar repeatedly exceed safe ranges and the assessment results show a continuous decline in limb motor function, the plan will focus on the two core aspects of blood pressure and blood sugar control and limb function maintenance, rather than generalizing to cover all health dimensions.

[0032] Personalization is reflected in the targeted adjustments to the plan content: for users with mild abnormalities in physiological indicators, the plan may focus on optimizing the monitoring frequency (such as adjusting the blood pressure measurement frequency from once a day to once in the morning and once in the evening), dietary recommendations (such as controlling salt intake), and basic routines (such as fixing an early bedtime to avoid blood pressure fluctuations); for users with new changes in neurological deficit symptoms, the plan will closely monitor the user's changes and remind them to seek medical attention in a timely manner.

[0033] The plan clearly defines the implementation milestones and judgment criteria for each management measure. For example, it stipulates that the monitoring frequency can be fine-tuned after blood pressure has been within the target range for three consecutive days, allowing users to clearly grasp the implementation rhythm and ensuring the plan's effectiveness. The generated personalized health management plan not only echoes the risk warnings in the initial early warning information but also builds upon the overall conclusions of the health status assessment, providing clear guidance for users' subsequent daily health management and a reference basis for medical personnel to adjust intervention strategies during follow-up visits.

[0034] Optionally, the collection of users' physiological indicator data and neurological deficit symptom data via mobile devices includes: A scoring algorithm is used to extract features from user facial motion recognition data and generate a facial NIHSS score. Based on the digital implementation of the scale, the system collects users' limb movement trajectory data through a mobile camera and calculates and generates a limb movement NIHSS score.

[0035] When collecting users' physiological index data and neurological deficit symptom data through mobile devices, specific methods are used to conduct detailed collection and analysis of facial motion recognition data and limb movement trajectory data in the neurological deficit symptom data.

[0036] For facial movement recognition data, the system, based on the assessment principles of facial palsy in the NIHSS scoring, uses algorithms to analyze facial features when users perform standardized commands (such as "showing teeth" or "frowning"). Through computer vision technology, it qualitatively analyzes the symmetry of facial muscle activity, focusing on observing whether clinical features related to the NIHSS scoring criteria exist when performing specific expressions, such as unilateral shallowing of the nasolabial fold, asymmetry of the corners of the mouth, or weakness in eyelid closure. Through the identification and comprehensive analysis of these qualitative features, the algorithm ultimately outputs an assessment result corresponding to the NIHSS facial palsy grading standard, thus transforming facial movement data into a clinically significant neurological function score.

[0037] For limb movement trajectory data, the collection and analysis process relies on the digital implementation of the upper limb movement item in the NIHSS. The system guides users to complete standardized limb movement commands in front of a camera, recording the movement trajectory in real time via the mobile device's camera. Based on this trajectory data, the algorithm calculates three core indicators: limb suspension duration, movement trajectory stability, and falling speed. Among them, suspension duration directly reflects the limb's ability to maintain antigravity muscle strength and is key to distinguishing between mild drift and rapid fall in the NIHSS score; movement trajectory stability identifies abnormal shaking or deviation by analyzing the limb's vertical displacement; and falling speed quantifies the falling process when the limb cannot maintain its posture, providing a basis for distinguishing severe weakness. Finally, by comprehensively calculating these indicators, the model generates a limb function score that matches the NIHSS limb movement item standard, thereby accurately assessing the degree of motor neuron function deficit.

[0038] Optionally, the analysis of the physiological indicator data and neurological deficit symptom data based on the multi-dimensional health data fusion analysis model to generate health status assessment results includes: Deep learning models were used to analyze physiological indicators such as blood pressure, blood glucose, and electrocardiogram, along with facial function assessment values ​​and limb function assessment values, to identify potential stroke precursor patterns. The dynamic threshold early warning system combines individual patient baseline data with population medical standards and uses an adaptive mechanism to adjust the range of early warning thresholds.

[0039] When analyzing physiological indicators and neurological deficit symptoms using a multi-dimensional health data fusion analysis model to generate health status assessment results, a deep learning model is used for analysis. This model analyzes physiological indicators such as blood pressure, blood sugar, and electrocardiogram together with facial function assessment values ​​and limb function assessment values. By capturing changes in these two types of data, such as repeated fluctuations in a user's blood sugar, blood lipids, and coagulation function within a certain period, and a continuous decline in limb and facial function assessment values, potential stroke precursor patterns can be identified. The nonlinear fitting capability of the deep learning model can effectively uncover implicit correlations that are difficult to detect using traditional analysis methods, thereby improving the accuracy of stroke precursor identification.

[0040] On the other hand, the system optimizes evaluation criteria based on a dynamic threshold early warning system. The core of this mechanism lies in combining individualized baselines with large-sample population standards and introducing key indicators. The system establishes a personal health baseline profile for each user, including the historical normal fluctuation range of their physiological indicators and the baseline level of their neurological functions (such as facial symmetry, limb motor muscle strength, etc.). Based on this, the system incorporates a population-based medical knowledge base in the field of cerebrovascular diseases and dynamically adjusts the early warning threshold through an adaptive algorithm. For example, for a user with a long history of hypertension, the system will refer to their higher individual baseline when issuing blood pressure warnings, appropriately raising the threshold to avoid over-alarms. Simultaneously, the system continuously analyzes the changing trends of their limb motor assessment values. If it finds a progressive shortening of their "upper limb suspension time" or a drift in their "motor trajectory deviation" baseline level, even if these values ​​have not yet exceeded the population-based general threshold, because they show a continuous deterioration trend and have a high weight in predicting the risk of cerebrovascular disease recurrence, the system will give them higher weight and promptly alert the user to the risk. This mechanism, which combines individualization and standardization and deeply integrates different types of data analysis, significantly reduces misjudgments and omissions caused by individual differences.

[0041] Optionally, the step of pushing early warning information based on the health status assessment results includes: The push notification method is selected based on the severity of outliers; the warning information includes a timestamp, a snapshot of the original data, and an analysis confidence score.

[0042] When analyzing physiological indicators and neurological deficit symptoms based on a multi-dimensional health data fusion analysis model to generate health status assessment results, a deep learning model is used for analysis. This model analyzes physiological indicators such as blood pressure, blood sugar, and electrocardiogram together with facial function assessment values ​​and limb function assessment values. The nonlinear fitting ability of the deep learning model can effectively uncover implicit correlations that are difficult to detect by traditional analysis methods, thereby improving the accuracy of stroke precursor identification.

[0043] On the other hand, relying on a dynamic threshold early warning system to optimize assessment standards, the system first establishes individual patient baseline data, that is, determines the normal fluctuation range of physiological indicators and neurological function based on the patient's historical health data. Then, it combines the population medicine standards in the field of cerebrovascular disease and adjusts the early warning threshold range in real time through an adaptive mechanism. This avoids misjudgments or omissions caused by ignoring individual patient differences when only a uniform population standard is used. For example, for patients with high baseline blood pressure, the system will appropriately increase the early warning threshold based on the population normal blood pressure threshold and the individual patient's baseline, ensuring that the threshold is more in line with the patient's actual health condition. Through this dual analysis mechanism, the final health status assessment result can accurately indicate potential stroke risk and fully adapt to the individual patient's situation, providing a reliable basis for subsequent health management.

[0044] Optionally, the method further includes: Generate visual health trend reports, showing long-term changes in users' physiological indicators and neurological function assessment values ​​through trend curves and comparison charts.

[0045] The core of generating visualized health trend reports is to transform users' long-term accumulated physiological indicator data and neurological function assessment values ​​into intuitive graphical content. This helps users and medical personnel quickly grasp the patterns of health changes and avoid misjudgments of health trends caused by the difficulty in integrating fragmented data. The trend curves in the report will be presented separately for each key indicator. For example, the blood pressure curve uses time as the horizontal axis (which can be divided into weekly or monthly periods) and blood pressure measurement values ​​as the vertical axis, clearly outlining the daily and weekly fluctuations of blood pressure with continuous lines. The blood glucose curve will mark the changes in values ​​under different measurement scenarios such as fasting and postprandial, intuitively reflecting the trend of blood glucose changes within a day or over a long period. The curves related to neurological function assessment values ​​will correspond to facial function assessment values ​​and limb function assessment values ​​respectively, showing the rise and fall of these two types of assessment values ​​over time through the fluctuation of lines. For example, whether the facial function assessment value has gradually recovered from a low level, or whether the limb function assessment value has shown a phased decline.

[0046] Meanwhile, the comparative charts in the report further enhance the readability of the trends. They are often presented in the form of bar charts or line charts, comparing the user's indicator data with two types of standards: one is the user's individual baseline data, which is the normal fluctuation range determined based on the user's historical health data. By comparing, it is clear whether the current indicators deviate from their normal state, such as whether the limb function assessment value is lower than their average level over the past 3 months; the other is the group medical standard for cerebrovascular disease patients. By comparing, users can understand their own health status in the same group, such as whether their blood pressure control level meets the recommended management standards for cerebrovascular disease patients.

[0047] These visualization elements enhance readability through clear color differentiation (e.g., different indicators are marked with different colored curves) and key node annotations (e.g., the time point of an abnormal indicator or the time point after adjusting health management measures). Users do not need professional data interpretation skills to quickly identify key information in health changes, providing direction for subsequent health adjustments. At the same time, they also provide doctors with intuitive and easy-to-understand references for analyzing changes in the condition and adjusting treatment plans during follow-up visits.

[0048] Corresponding to the above-described health status assessment method, this invention also proposes a health status assessment device. Since the device embodiments of this invention correspond to the above-described method embodiments, details not disclosed in the device embodiments can be referred to the above-described method embodiments, and will not be repeated here.

[0049] Figure 2 This is a schematic diagram of the structure of a health status assessment device provided in an embodiment of the present disclosure, as shown below. Figure 2 As shown, it includes: The data acquisition unit 21 is used to collect users' physiological index data and neurological deficit symptom data through a mobile terminal device; wherein, the neurological deficit symptom data includes facial movement recognition data and limb movement trajectory analysis data; Analysis unit 22 is used to analyze the physiological indicator data and neurological deficit symptom data based on a multi-dimensional health data fusion analysis model to generate health status assessment results; Push unit 23 is used to push early warning information based on the health status assessment results; The generation unit 24 is used to generate a personalized health management plan based on the early warning information and the health status assessment results.

[0050] Furthermore, in one possible implementation of the embodiments of this disclosure, such as Figure 3 As shown, the acquisition unit 21 is also used for: A scoring algorithm is used to extract features from user facial motion recognition data and generate a facial NIHSS score. Based on the digital implementation of the scale, the system collects users' limb movement trajectory data through a mobile camera and calculates and generates a limb movement NIHSS score.

[0051] Furthermore, in one possible implementation of the embodiments of this disclosure, such as Figure 3 As shown, the analysis unit 22 is further used for: Deep learning models were used to analyze physiological indicators such as blood pressure, blood glucose, and electrocardiogram, along with facial function assessment values ​​and limb function assessment values, to identify potential stroke precursor patterns. The dynamic threshold early warning system combines individual patient baseline data with population medicine standards and uses an adaptive mechanism to adjust the early warning threshold range for different assessment values.

[0052] Furthermore, in one possible implementation of the embodiments of this disclosure, such as Figure 3 As shown, the push unit 23 is also used for: The push notification method is selected based on the severity of outliers; the warning information includes a timestamp, a snapshot of the original data, and an analysis confidence score.

[0053] Furthermore, in one possible implementation of the embodiments of this disclosure, such as Figure 3 As shown, the device further includes: Display unit 25 is used to generate a visual health trend report, which displays the long-term changes in users' physiological indicators and neurological function assessment values ​​through trend curves and comparison charts.

[0054] It should be noted that the foregoing explanation of the method embodiments also applies to the apparatus of the embodiments of this disclosure, and the principle is the same. Therefore, the embodiments of this disclosure are not limited thereto.

[0055] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0056] Figure 4 A schematic block diagram of an example electronic device 400 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0057] Figure 4 A schematic block diagram of an example electronic device 400 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0058] like Figure 4 As shown, device 400 includes a computing unit 401, which can perform various appropriate actions and processes based on a computer program stored in ROM (Read-Only Memory) 402 or a computer program loaded from storage unit 408 into RAM (Random Access Memory) 403. RAM 403 may also store various programs and data required for the operation of device 400. The computing unit 401, ROM 402, and RAM 403 are interconnected via bus 404. I / O (Input / Output) interface 405 is also connected to bus 404.

[0059] Multiple components in device 400 are connected to I / O interface 405, including: input unit 406, such as keyboard, mouse, etc.; output unit 407, such as various types of monitors, speakers, etc.; storage unit 408, such as disk, optical disk, etc.; and communication unit 409, such as network card, modem, wireless transceiver, etc. Communication unit 409 allows device 400 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0060] The computing unit 401 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, CPUs (Central Processing Units), GPUs (Graphics Processing Units), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processors), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as health status assessment methods. For example, in some embodiments, the health status assessment method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on device 400 via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by the computing unit 401, one or more steps of the methods described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the aforementioned health status assessment method by any other suitable means (e.g., by means of firmware).

[0061] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application-Specific Standard Products), SOCs (System-on-Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0062] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0063] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0064] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0065] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include LANs (Local Area Networks), WANs (Wide Area Networks), the Internet, and blockchain networks.

[0066] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service system that addresses the shortcomings of traditional physical hosts and VPS (Virtual Private Server) services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0067] It's important to note that artificial intelligence (AI) is the study of enabling computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily include computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graph technologies.

[0068] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0069] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for assessing health status, characterized in that, include: The system collects users' physiological indicators and neurological deficit symptoms data through mobile devices; the neurological deficit symptoms data includes facial movement recognition data and limb movement trajectory analysis data. Based on a multi-dimensional health data fusion analysis model, the physiological indicator data and neurological deficit symptom data are analyzed to generate health status assessment results. Based on the health status assessment results, early warning information will be pushed out; A personalized health management plan is generated based on the early warning information and the health status assessment results.

2. The method according to claim 1, characterized in that, The collection of users' physiological index data and neurological deficit symptom data via mobile devices includes: A scoring algorithm is used to extract features from user facial motion recognition data and generate a facial NIHSS score. Based on the digital implementation of the scale, the system collects users' limb movement trajectory data through a mobile camera and calculates and generates a limb movement NIHSS score.

3. The method according to claim 1, characterized in that, The multi-dimensional health data fusion analysis model analyzes the physiological indicator data and neurological deficit symptom data to generate health status assessment results, including: Deep learning models were used to analyze physiological indicators such as blood pressure, blood glucose, and electrocardiogram, along with facial function assessment values ​​and limb function assessment values, to identify potential stroke precursor patterns. The dynamic threshold early warning system combines individual patient baseline data with population medicine standards and uses an adaptive mechanism to adjust the early warning threshold range for different assessment values.

4. The method according to claim 1, characterized in that, The step of pushing out early warning information based on the health status assessment results includes: The push notification method is selected based on the severity of outliers; the warning information includes a timestamp, a snapshot of the original data, and an analysis confidence score.

5. The method according to claim 1, characterized in that, The method further includes: Generate visual health trend reports, showing long-term changes in users' physiological indicators and neurological function assessment values ​​through trend curves and comparison charts.

6. A health status assessment device, characterized in that, include: The data acquisition unit is used to collect users' physiological index data and neurological deficit symptom data through mobile devices; wherein, the neurological deficit symptom data includes facial motion recognition data and limb movement trajectory analysis data; The analysis unit is used to analyze the physiological indicator data and neurological deficit symptom data based on a multi-dimensional health data fusion analysis model to generate health status assessment results. The push unit is used to push early warning information based on the health status assessment results; The generation unit is used to generate a personalized health management plan based on the early warning information and the health status assessment results.

7. The apparatus according to claim 6, characterized in that, The acquisition unit is also used for: A scoring algorithm is used to extract features from user facial motion recognition data and generate a facial NIHSS score. Based on the digital implementation of the scale, the system collects users' limb movement trajectory data through a mobile camera and calculates and generates a limb movement NIHSS score.

8. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-5.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1-5.

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