Intelligent fitness guidance system for disease prevention of old people

Abstract

The invention discloses an intelligent fitness guidance system for disease prevention of old people. The intelligent fitness guidance system comprises a data acquisition module, a health assessment module, an exercise scheme generation module, an intelligent guidance module and a feedback adjustment module. According to the system, health data of old people are collected, a personalized exercise scheme is formulated after comprehensive assessment, real-time guidance is provided through intelligent equipment, the scheme is continuously optimized according to feedback, and therefore the purposes of preventing diseases and improving the health condition are achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent medical treatment and health management, and specifically relates to an intelligent fitness guidance system for disease prevention for the elderly. Background Art

[0002] In today's highly competitive healthcare environment, health management and disease prevention for the elderly, as the focus of social attention, are facing unprecedented challenges.

[0003] Traditional health management methods for the elderly often rely on regular physical examinations and passive medical interventions, but this method is difficult to accurately meet the personalized health needs of each elderly person. Due to the lack of in-depth health data analysis and personalized health labeling system, health management activities can often only provide general suggestions, which are poorly targeted and lead to waste of resources and poor prevention effects. The physical condition and health needs of the elderly are changing at an increasingly rapid pace, but traditional health management strategies often lag behind in adjustment and are difficult to respond to changes in health status in a timely manner. Fixed fitness guidance programs and strategies are difficult to adapt to the diverse health needs of the elderly, resulting in poor user experience and decreased compliance. Traditional health management decisions often rely on experience and intuition, lacking scientific data analysis and support. This decision-making method is not only inefficient, but also prone to ignoring potential health risks, making it difficult to achieve ideal results in complex health management for the elderly.

[0004] Therefore, it is necessary to provide an AI-based intelligent fitness guidance system for disease prevention in the elderly to solve the above technical problems. Through intelligent data collection, analysis and personalized guidance, the system can achieve accurate health management, respond quickly to changes, provide diversified strategies, support scientific decision-making, achieve active prevention, improve resource utilization efficiency, and enhance user participation. This AI-based system can not only solve many shortcomings of traditional health management methods, but also provide more accurate, efficient and personalized health management services for the elderly, thereby effectively preventing diseases and improving the quality of life. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides an intelligent fitness guidance system for disease prevention in the elderly based on artificial intelligence, which is used to solve the problems of poor pertinence of traditional health management methods leading to waste of resources and poor prevention effects, difficulty in adapting to the diverse health needs of the elderly, and low efficiency.

[0006] The present invention provides an artificial intelligence-based intelligent fitness guidance method for disease prevention in the elderly, the method comprising the following steps:

[0007] Periodically obtain health data of elderly users and generate user health tags;

[0008] Based on the generated user health labels, users with similar health conditions are grouped into one category through cluster analysis, and representative health characteristics are identified from each category of users;

[0009] Based on medical needs, weights are assigned to each representative health characteristic and the priority of the representative health characteristics is divided;

[0010] According to the priorities of the representative health characteristics, a matching fitness guidance plan is developed for each priority category through correlation analysis;

[0011] Set multiple extended exercise strategies for the developed fitness guidance plan, and set a corresponding matching coefficient for each extended strategy;

[0012] Obtain health data of elderly users in real time, extract and update user health tags, and dynamically assign fitness guidance plans that match their health characteristics to users;

[0013] Based on the set matching coefficient, an extended exercise strategy is selected for the fitness guidance program assigned to the user's health tag, and the corresponding fitness activities are performed.

[0014] Preferably, the periodic acquisition of health data of elderly users and generation of user health tags specifically includes the following steps:

[0015] Set a fixed time period to collect health data of elderly users from health monitoring devices, including but not limited to blood pressure, heart rate, blood sugar and exercise data;

[0016] Use data cleaning technology to remove noise and outliers from the user's health data and extract features related to the description of the user's health status;

[0017] The extracted features related to the user's health status are identified through machine learning algorithms to generate multi-dimensional user health labels.

[0018] Preferably, the method of classifying users with similar health conditions into one category based on the generated user health tags by a cluster analysis method, and identifying representative health characteristics from each category of users, specifically includes the following steps:

[0019] According to the similarity of users' health tags, use any clustering analysis method such as K-means or hierarchical clustering to divide users into different health categories;

[0020] By statistically calculating the frequency of each health feature among users of the same category, the feature with the highest frequency is counted from each category of users and marked as a representative health feature.

[0021] Preferably, the weight allocation for each representative health characteristic based on medical needs and the priority classification of the representative health characteristics specifically include the following steps:

[0022] Comprehensively evaluate different representative health characteristics based on medical needs, and assign corresponding weights according to the scores of the comprehensive evaluation. Medical needs specifically include preventing chronic diseases and improving physical functions;

[0023] According to the results of weight allocation, the representative health characteristics are divided into different priority categories, where the priority categories include high risk, medium risk and low risk.

[0024] Preferably, the steps of formulating a matching fitness guidance program for each priority category through correlation analysis according to the priorities of the divided representative health characteristics specifically include the following steps:

[0025] Design corresponding fitness guidance plans based on the health characteristics and medical needs of each priority category, where the fitness guidance plans include exercise type and exercise intensity;

[0026] Data analysis tools are used to evaluate the correlation values ​​between the fitness guidance program and representative health characteristics, and the fitness guidance program is verified based on the preset correlation value evaluation threshold.

[0027] Preferably, the step of setting a plurality of extended exercise strategies for the formulated fitness guidance program and setting a corresponding matching coefficient for each extended strategy specifically includes the following steps:

[0028] Design multiple extended exercise strategies for each fitness guidance program, including aerobic exercise, strength training and balance training;

[0029] According to the security of the campaign strategy and the adaptability of the historical user group, a corresponding matching coefficient is set for each extension strategy.

[0030] Preferably, the real-time acquisition of health data of elderly users, extracting and updating user health tags, and dynamically assigning fitness guidance programs that match their health characteristics to users include the following steps:

[0031] Use real-time data processing technology to continuously monitor changes in user health data on health monitoring devices and extract current user health tags;

[0032] Update the user's health tag in real time based on the user's current health data, and conduct correlation analysis between the updated user's health tag and the existing fitness guidance plan;

[0033] Based on the updated user health tag and the correlation analysis result, a plan that best matches the updated user health tag is selected from multiple fitness guidance plans.

[0034] An intelligent fitness guidance system for disease prevention in the elderly based on artificial intelligence, the system comprising:

[0035] A data acquisition module is used to periodically acquire the health data of elderly users and generate user health tags;

[0036] A label classification module is used to classify users with similar health conditions into one category based on the generated user health labels through a cluster analysis method, and to identify representative health features from each category of users;

[0037] A weight allocation module is used to allocate weights to each representative health feature based on medical needs and prioritize the representative health features;

[0038] A program definition module is used to formulate a matching fitness guidance program for each priority category through correlation analysis according to the priorities of the representative health characteristics divided;

[0039] A strategy definition module is used to set multiple extended exercise strategies for the formulated fitness guidance program and set a corresponding matching coefficient for each extended strategy;

[0040] The allocation module is used to obtain the health data of elderly users in real time, extract and update the user's health tags, and dynamically allocate fitness guidance plans that match their health characteristics to the users;

[0041] The matching module is used to select an extended exercise strategy for the fitness guidance program assigned to the user's health tag based on the set matching coefficient and perform corresponding fitness activities.

[0042] Compared with related technologies, the artificial intelligence-based intelligent fitness guidance system for disease prevention for the elderly provided by the present invention has the following beneficial effects:

[0043] The present invention significantly improves the accuracy of health management and the efficiency of resource allocation by accurately analyzing the health data of elderly users, implementing cluster analysis and priority sorting. At the same time, it flexibly sets and dynamically adjusts fitness guidance plans and extension strategies, which not only enhances the flexibility of health management, but also greatly improves user participation and compliance. This series of measures jointly promotes data-driven decision-making, ensures that the system can flexibly respond to changes in the health status of the elderly, and continuously optimize the disease prevention effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 The present invention is a flow chart of the intelligent fitness guidance method for disease prevention in the elderly based on artificial intelligence.

[0045] Figure 2 This is a system block diagram of the artificial intelligence-based commodity marketing system of the present invention. DETAILED DESCRIPTION

[0046] The present invention will be further described below in conjunction with the accompanying drawings and implementation modes.

[0047] Embodiment 1

[0048] like Figure 1 As shown, the intelligent fitness guidance method for disease prevention in the elderly based on artificial intelligence includes the following steps:

[0049] S100, periodically obtaining health data of elderly users and generating user health tags;

[0050] In the specific implementation process, the specific steps of step S100 are:

[0051] S101, setting a fixed time period to collect health data of elderly users from health monitoring devices, including but not limited to blood pressure, heart rate, blood sugar and exercise volume data;

[0052] S102, using data cleaning technology to remove noise and outliers from the user's health data, and extracting features related to describing the user's health status;

[0053] S103, identifying the extracted features related to the user's health status through a machine learning algorithm to generate a multi-dimensional user health label;

[0054] S200, based on the generated user health tags, grouping users with similar health conditions into one category through a cluster analysis method, and identifying representative health features from each category of users;

[0055] In the specific implementation process, the specific steps of step S200 are:

[0056] S201, using K-means clustering method to classify users into different health categories;

[0057] S202, by statistically calculating the frequency of each health feature among users of the same category, the feature with the highest frequency is statistically obtained from each category of users, and marked as a representative health feature;

[0058] S300, based on medical needs, weighting each representative health feature and prioritizing the representative health features;

[0059] In the specific implementation process, the specific steps of step S300 are:

[0060] S301. Based on the needs of preventing chronic diseases and improving physical functions, conduct a comprehensive assessment of different representative health characteristics and assign weights according to the assessment results;

[0061] S302, according to the result of weight allocation, classify the representative health characteristics into three priority categories of high risk, medium risk and low risk;

[0062] S400, formulating a matching fitness guidance plan for each priority category through correlation analysis according to the priorities of the divided representative health characteristics;

[0063] In the specific implementation process, the specific steps of step S400 are:

[0064] S401. Design corresponding fitness guidance programs according to the health characteristics and medical needs of each priority category, including exercise type and exercise intensity;

[0065] S402. Using data analysis tools, evaluate the correlation between the fitness guidance program and representative health characteristics, and verify it;

[0066] S500, setting a plurality of extended exercise strategies for the formulated fitness guidance program, and setting a corresponding matching coefficient for each extended strategy;

[0067] In the specific implementation process, the specific steps of step S500 are:

[0068] S501, designing multiple extended exercise strategies for each fitness guidance program, including aerobic exercise, strength training, and balance training;

[0069] S502, setting a corresponding matching coefficient for each extension strategy according to the security of the movement strategy and the adaptability of the historical user group;

[0070] S600, real-time acquisition of health data of elderly users, and dynamic allocation of fitness guidance programs matching their health characteristics to users by extracting and updating their health tags;

[0071] S700: Based on the set matching coefficient, select an extended exercise strategy for the fitness guidance program assigned to the user's health tag, and perform corresponding fitness activities.

[0072] Example 2 Figure 2 As shown, the system applied to the intelligent fitness guidance method for disease prevention of the elderly based on artificial intelligence specifically includes:

[0073] The data acquisition module is used to periodically acquire the health data of elderly users and generate user health labels; the label classification module is used to classify users with similar health conditions into one category through a cluster analysis method based on the generated user health labels, and identify representative health characteristics from each category of users;

[0074] A weight allocation module is used to allocate weights to each representative health feature based on medical needs and prioritize the representative health features;

[0075] The solution definition module is used to prioritize representative health characteristics and conduct correlation analysis.

[0076] Develop a matching fitness guidance program for each priority category;

[0077] A strategy definition module is used to set multiple extended exercise strategies for the formulated fitness guidance program and set a corresponding matching coefficient for each extended strategy;

[0078] The allocation module is used to obtain the health data of elderly users in real time and update the user health tags by extracting them.

[0079] Dynamically assign users a fitness guidance program that matches their health characteristics;

[0080] The matching module is used to select an extended exercise strategy for the fitness guidance program assigned to the user's health tag based on the set matching coefficient and perform corresponding fitness activities.

[0081] The present application can implement each step of the above method by computer program instructions. These computer program instructions can be provided to a processor for execution, thereby generating a device for implementing the above method. These program instructions can be stored in a computer-readable storage medium.

Claims

1. An intelligent fitness guidance system for disease prevention in the elderly, characterized in that: The system comprises the following steps: Periodically obtain health data of elderly users and generate user health tags; Based on the generated user health labels, users with similar health conditions are grouped into one category through cluster analysis, and representative health characteristics are identified from each category of users; Based on medical needs, weights are assigned to each representative health characteristic and the priority of the representative health characteristics is divided; According to the priorities of the representative health characteristics, a matching fitness guidance plan is developed for each priority category through correlation analysis; Set multiple extended exercise strategies for the developed fitness guidance plan, and set a corresponding matching coefficient for each extended strategy; Obtain health data of elderly users in real time, extract and update user health tags, and dynamically assign fitness guidance plans that match their health characteristics to users; Based on the set matching coefficient, an extended exercise strategy is selected for the fitness guidance program assigned to the user's health tag, and the corresponding fitness activities are performed.

2. The intelligent fitness guidance system for disease prevention in the elderly according to claim 1, characterized in that: The method of periodically acquiring the health data of the elderly user and generating the user health tag specifically includes the following steps: Set a fixed time period to collect health data of elderly users from health monitoring devices, including but not limited to blood pressure, heart rate, blood sugar and exercise data; Use data cleaning technology to remove noise and outliers from the user's health data and extract features related to the description of the user's health status; The extracted features related to the user's health status are identified through machine learning algorithms to generate multi-dimensional user health labels.

3. The intelligent fitness guidance system for disease prevention in the elderly according to claim 1, characterized in that: The method, based on the generated user health tags, classifies users with similar health conditions into one category through a cluster analysis method, and identifies representative health features from each category of users, specifically including the following steps: According to the similarity of users' health tags, use any clustering analysis method such as K-means or hierarchical clustering to divide users into different health categories; By statistically calculating the frequency of each health feature among users of the same category, the feature with the highest frequency is counted from each category of users and marked as a representative health feature.

4. The intelligent fitness guidance system for disease prevention in the elderly according to claim 1, characterized in that: The method of allocating weights to the representative health characteristics based on medical needs and prioritizing the representative health characteristics specifically includes the following steps: A comprehensive assessment is conducted on different representative health characteristics based on medical needs, and corresponding weights are allocated according to the comprehensive assessment scores, where medical needs specifically include preventing chronic diseases and improving physical functions. According to the results of weight allocation, the representative health characteristics are divided into different priority categories, where the priority categories include high risk, medium risk and low risk.

5. The intelligent fitness guidance system for disease prevention in the elderly according to claim 1, characterized in that: According to the priorities of the representative health characteristics divided, a matching fitness guidance plan is formulated for each priority category through correlation analysis, which specifically includes the following steps: Design corresponding fitness guidance plans based on the health characteristics and medical needs of each priority category, where the fitness guidance plans include exercise type and exercise intensity; Data analysis tools are used to evaluate the correlation values ​​between the fitness guidance program and representative health characteristics, and the fitness guidance program is verified based on the preset correlation value evaluation threshold.

6. The intelligent fitness guidance system for disease prevention in the elderly according to claim 1, characterized in that: The step of setting a plurality of extended exercise strategies for the formulated fitness guidance program and setting a corresponding matching coefficient for each extended strategy specifically includes the following steps: Design multiple extended exercise strategies for each fitness guidance program, including aerobic exercise, strength training and balance training; According to the security of the campaign strategy and the adaptability of the historical user group, a corresponding matching coefficient is set for each extension strategy.

7. The intelligent fitness guidance system for disease prevention in the elderly according to claim 1, characterized in that: The method of acquiring the health data of the elderly user in real time, extracting and updating the user health tag, and dynamically assigning the user a fitness guidance program that matches his health characteristics includes the following steps: Use real-time data processing technology to continuously monitor changes in user health data on health monitoring devices and extract current user health tags; Update the user's health tag in real time based on the user's current health data, and conduct correlation analysis between the updated user's health tag and the existing fitness guidance plan; Based on the updated user health tag and the correlation analysis result, a plan that best matches the updated user health tag is selected from multiple fitness guidance plans.

8. An intelligent fitness guidance system for disease prevention in the elderly, applied to the intelligent fitness guidance method for disease prevention in the elderly as claimed in any one of claims 1 to 7, characterized in that: The system comprises: a data acquisition module, which is used to periodically acquire health data of elderly users and generate user health labels; a label classification module, which is used to classify users with similar health conditions into one category through a cluster analysis method based on the generated user health labels, and identify representative health features from each category of users; A weight allocation module is used to allocate weights to each representative health feature based on medical needs and prioritize the representative health features; A program definition module is used to formulate a matching fitness guidance program for each priority category through correlation analysis according to the priorities of the representative health characteristics divided; A strategy definition module is used to set multiple extended exercise strategies for the formulated fitness guidance program and set a corresponding matching coefficient for each extended strategy; The allocation module is used to obtain the health data of elderly users in real time, extract and update the user's health tags, and dynamically allocate fitness guidance plans that match their health characteristics to the users; The matching module is used to select an extended exercise strategy for the fitness guidance program assigned to the user's health tag based on the set matching coefficient and perform corresponding fitness activities.

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