Multi-modal health data real-time monitoring and analysis system based on artificial intelligence

Abstract

The invention provides a multi-mode health data real-time monitoring and analysis system based on artificial intelligence, and relates to the technical field of health data real-time monitoring and analysis, the system comprises an X-ray irradiation module, an AI image analysis module and a report generation module, the output end of the X-ray irradiation module is connected with the AI image analysis module, and the output end of the AI image analysis module is connected with the report generation module. The output end of the AI image analysis module is connected with a report generation module, the output end of the report generation module is connected with a training plan making module, and the X-ray irradiation module is an X-ray photography system and can accurately detect the height of a child. The system has the advantages that the bone age and genetic height of a child are compared according to the AI image analysis module, and then the report generation module generates report data about the height according to a comparison analysis structure, so that the training plan making module can generate personalized training plans including courses of different difficulty levels, and the training efficiency is improved. Therefore, the system can make a more reasonable plan in a targeted manner.

Description

Technical Field

[0001] The present invention relates to the technical field of real-time monitoring and analysis of health data, and in particular to a multimodal real-time monitoring and analysis system for health data based on artificial intelligence. Background Art

[0002] As people's attention to health continues to increase, many people go to the hospital to develop special diet and exercise plans for their personal health, so as to adjust their health data through the combination of exercise and diet. This is especially true for some children's height. Because of exercise and diet problems in their childhood, their height cannot reach the health standard, so they need health testing and analysis to develop exercise and diet health plans.

[0003] Most of the existing health planning methods rely on a fixed diet template developed by a doctor, and slight changes are made to the template based on individual circumstances. Although this can achieve certain health adjustments, it is not very targeted, resulting in slow results, etc., which can easily lead people to give up halfway and lose the health adjustment effect. Summary of the Invention

[0004] The purpose of the present invention is to address the shortcomings of the existing technology and propose a multimodal health data real-time monitoring and analysis system based on artificial intelligence. The technical solution adopted by the present invention is:

[0005] An artificial intelligence-based multimodal health data real-time monitoring and analysis system, including an X-ray irradiation module, an AI image analysis module, a report generation module, a training plan development module, a dietary calculation module, a parent-side app, a real-time data acquisition module, a data recording module, a data comparison module, a doctor-side backend, an AI calculation and analysis module, and a difficulty adjustment module. The X-ray irradiation module is an X-ray photography system that can accurately detect a child's height.

[0006] The AI image analysis module can analyze the image emitted by the X-ray irradiation module to analyze the child's height and skeleton information;

[0007] The report generation module generates a report on height based on the data analyzed by the AI image analysis module, thereby facilitating subsequent use of the data and formulation of training plans;

[0008] The training plan formulation module will formulate a training plan based on the report generated by the report generation module, so that the plan generated by the system is more targeted.

[0009] As an improvement, the meal calculation module can calculate the amount and type of meals according to the training plan generated by the training plan formulation module, so as to reasonably distribute the meals to the child and facilitate nutritional matching.

[0010] The parent-side APP enables parents to view the training plan and meal plan in real time, making it easier to supervise their children to complete the training plan and diet.

[0011] As an improvement, the data real-time acquisition module is mainly a device such as a bracelet that can detect the child's movement data in real time. The child's real-time movement data can be sent to the system through the Internet, so that the system can perform dynamic analysis based on the child's real-time situation;

[0012] The data recording module can record the data collected in real time by the real-time data collection module, thereby storing the data to facilitate subsequent data inspection and comparison.

[0013] As an improvement, the data comparison module can compare the data collected in real time, so as to understand the child's training progress, making it easier to adjust the child's exercise intensity;

[0014] The doctor-side backend enables the doctor to view the training data in real time, thereby making it easier for the doctor to understand the child's condition, so that the doctor can make better suggestions.

[0015] As an improvement, the AI calculation and analysis module can perform intelligent analysis based on the data comparison results of the data comparison module, thereby making it easier for the difficulty adjustment module to make adjustments based on the analysis data.

[0016] As an improvement, the difficulty adjustment module can adjust the training plan formulation module according to the analysis structure of the AI calculation and analysis module, thereby changing the training plan so that the training plan can be changed according to the child's real-time situation and better suit the child's physical condition.

[0017] The beneficial effects of the present invention are:

[0018] The present invention provides a multimodal real-time health data monitoring and analysis system based on artificial intelligence. The system compares the bone age and genetic height of a child according to an AI image analysis module, and then, based on the comparative analysis structure, enables a report generation module to produce report data on the height, thereby enabling a training plan formulation module to generate personalized training plans, including courses of different difficulty levels, so that the system can formulate more reasonable plans in a targeted manner.

[0019] The data real-time acquisition module of the present invention can track exercise data in real time, and then analyze the child's diet through the dietary calculation module, so as to dynamically adjust the recipe based on the association of nutritional recommendations with exercise consumption, thereby being able to provide better dietary recommendations for the child. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a flow chart of the system of the present invention.

[0021] In the figure: 1. X-ray irradiation module; 2. AI image analysis module; 3. Report generation module; 4. Training plan formulation module; 5. Diet calculation module; 6. Parent-side APP; 7. Real-time data collection module; 8. Data recording module; 9. Data comparison module; 10. Doctor-side backend; 11. AI calculation and analysis module; 12. Difficulty adjustment module. DETAILED DESCRIPTION

[0022] In order to make the contents of the present invention more clearly understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0023] like Figure 1 As shown, it includes an X-ray irradiation module 1, an AI image analysis module 2, a report generation module 3, a training plan formulation module 4, a dietary calculation module 5, a parent-side APP 6, a real-time data acquisition module 7, a data recording module 8, a data comparison module 9, a doctor-side backend 10, an AI calculation and analysis module 11, and a difficulty adjustment module 12. The output end of the X-ray irradiation module 1 is connected to the AI image analysis module 2, the output end of the AI image analysis module 2 is connected to the report generation module 3, and the output end of the report generation module 3 is connected to the training plan formulation module 4. The X-ray irradiation module 1 is an X-ray photography system that can accurately detect the child's height;

[0024] The AI image analysis module 2 can analyze the image emitted by the X-ray irradiation module 1 to analyze the child's height and skeleton information;

[0025] The report generation module 3 will generate a report on height based on the data analyzed by the AI image analysis module 2, so as to facilitate the subsequent use of the data and the formulation of training plans;

[0026] The training plan formulation module 4 will formulate a training plan based on the report generated by the report generation module 3, so that the plan generated by the system is more targeted;

[0027] The output end of the training plan formulation module 4 is connected to the meal calculation module 5, and the output end of the meal calculation module 5 is connected to the parent end APP 6. The meal calculation module 5 can calculate the amount and type of meals in a targeted manner according to the training plan generated by the training plan formulation module 4, so as to reasonably distribute the child's meals and make nutritional matching more convenient;

[0028] The child's bone age and genetic height are compared according to the AI image analysis module 2, and then based on the comparative analysis structure, the report generation module 3 produces report data on the height, so that the training plan formulation module 4 can generate a personalized training plan, including courses of different difficulty levels, so that the system can formulate a more reasonable plan in a targeted manner.

[0029] The parent-side APP6 allows parents to view the training plan and meal plan in real time, making it easier to supervise their children to complete the training plan and diet;

[0030] The output end of the real-time data acquisition module 7 is connected to the data recording module 8. The real-time data acquisition module 7 is mainly a device such as a wristband that can detect the child's motion data in real time. The child's real-time motion data can be sent to the system through the Internet, so that the system can perform dynamic analysis based on the child's real-time situation.

[0031] The data recording module 8 can record the data collected in real time by the data real-time acquisition module 7, thereby storing the data to facilitate subsequent data inspection and comparison;

[0032] The output end of the data recording module 8 is connected to the data comparison module 9, and the output end of the data comparison module 9 is connected to the doctor's backend 10. The data comparison module 9 can compare the real-time collected data, so as to understand the child's training progress, thereby making it easier to adjust the child's exercise intensity;

[0033] The data real-time acquisition module 7 can track the exercise data in real time, and then analyze the child's diet through the dietary calculation module 5, so as to dynamically adjust the recipe based on the association between nutritional recommendations and exercise consumption, so as to provide better dietary recommendations for the child.

[0034] The doctor's backend 10 can facilitate the doctor to view the training data in real time, so that the doctor can understand the child's condition, so that the doctor can make better suggestions;

[0035] The output end of the data comparison module 9 is connected to the AI calculation and analysis module 11. The AI calculation and analysis module 11 is connected in parallel with the doctor-side backend 10. The AI calculation and analysis module 11 can perform intelligent analysis based on the data comparison results of the data comparison module 9, thereby making it easier for the difficulty adjustment module 12 to adjust according to the analysis data;

[0036] The output end of the AI computing and analysis module 11 is connected to a difficulty adjustment module 12. The difficulty adjustment module 12 can adjust the training plan formulation module 4 according to the analysis structure of the AI computing and analysis module 11, thereby changing the training plan so that the training plan can be changed according to the child's real-time situation and better suit the child's physical condition.

[0037] During use, the child is first irradiated with the X-ray irradiation module 1 to obtain the child's skeleton information, so that the AI image analysis module 2 can perform intelligent analysis based on the skeleton image to obtain the current state of the child's skeleton. Then, the report generation module 3 generates a report based on the analysis results of the AI image analysis module 2, which facilitates the training plan formulation module 4 to analyze the report and formulate a training plan. Then, the dietary calculation module 5 will produce targeted dietary recipes based on the training plan formulated by the training plan formulation module 4, and then send both the recipes and the training plan to the parent-side APP 6, so that parents can view the training plan and dietary recipes in real time, which is more convenient for parents to cooperate with their children.

[0038] During the child's exercise, the real-time data acquisition module 7 will monitor the child's exercise status in real time, thereby recording the exercise status at that time, and then transmit the recorded data to the data recording module 8 for recording and storage. The data comparison module 9 will intermittently extract historical data from the data recording module 8 to obtain the child's exercise status, and then send the exercise status to the doctor's backend 10, so that the doctor can understand the child's exercise status and make feasible suggestions based on the exercise status.

[0039] The data compared by the data comparison module 9 will also be transmitted to the AI calculation and analysis module 11 for analysis, so as to analyze the child's current state according to the movement state, and control the difficulty adjustment module 12, so that the difficulty adjustment module 12 controls the training plan formulation module 4, thereby modifying the training plan, facilitating real-time updating of the training plan. At the same time, the doctor can also control the difficulty adjustment module 12 from the doctor's backend 10 to modify the training plan.

[0040] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included in the scope of protection of the present invention.

Claims

1. A multimodal health data real-time monitoring and analysis system based on artificial intelligence, characterized by: The system comprises an X-ray irradiation module (1), an AI image analysis module (2), a report generation module (3), a training plan formulation module (4), a meal calculation module (5), a parent-side APP (6), a real-time data acquisition module (7), a data recording module (8), a data comparison module (9), a doctor-side backend (10), an AI calculation and analysis module (11), and a difficulty adjustment module (12). The X-ray irradiation module (1) is an X-ray photography system that can accurately detect the height of a child. The AI image analysis module (2) can analyze the image irradiated by the X-ray irradiation module (1), thereby analyzing the child's height and skeleton information; The report generation module (3) generates a report on height based on the data analyzed by the AI image analysis module (2), thereby facilitating subsequent use of the data and formulation of training plans; The training plan formulation module (4) will formulate a training plan based on the report generated by the report generation module (3), so that the plan generated by the system is more targeted.

2. The artificial intelligence-based multimodal health data real-time monitoring and analysis system according to claim 1, characterized in that: The meal calculation module (5) can calculate the amount and type of meals in a targeted manner according to the training plan generated by the training plan formulation module (4), thereby being able to reasonably distribute the meals to the child and making nutritional matching more convenient; The parent-side APP (6) enables parents to view the training plan and meal plan in real time, thereby making it easier to supervise the completion of the training plan and diet of the child.

3. The artificial intelligence-based multimodal health data real-time monitoring and analysis system according to claim 1, characterized in that: The data real-time acquisition module (7) is mainly a device such as a wristband that can detect the child's movement data in real time. The child's real-time movement data can be sent to the system through networking, so that the system can perform dynamic analysis based on the child's real-time situation. The data recording module (8) can record the data collected in real time by the real-time data collection module (7), thereby storing the data to facilitate subsequent data inspection and comparison.

4. The artificial intelligence-based multimodal health data real-time monitoring and analysis system according to claim 1, characterized in that: The data comparison module (9) can compare the data collected in real time, so as to know the child's training progress, thereby making it easier to adjust the child's exercise intensity; The doctor-side backend (10) enables the doctor to view the training data in real time, thereby facilitating the doctor to understand the child's condition and thus enabling the doctor to make better suggestions.

5. The artificial intelligence-based multimodal health data real-time monitoring and analysis system according to claim 1, characterized in that: The AI calculation and analysis module (11) can perform intelligent analysis based on the data comparison results of the data comparison module (9), thereby making it easier for the difficulty adjustment module (12) to perform adjustments based on the analysis data.

6. The artificial intelligence-based multimodal health data real-time monitoring and analysis system according to claim 1, characterized in that: The difficulty adjustment module (12) can adjust the training plan formulation module (4) according to the analysis structure of the AI calculation analysis module (11), thereby changing the training plan so that the training plan can be changed according to the child's real-time situation and can better meet the child's physical condition.

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