Exercise training reminding system based on physical examination data analysis and intelligent bracelet

Abstract

The invention belongs to the technical field of power anomaly detection, and discloses an exercise training reminding system based on physical examination data analysis and an intelligent bracelet. A user body state data set and user calorie intake data are collected and preprocessed, and the user body state data set comprises basic body data, important body data and body metabolism data; analyzing the user body state data set to obtain a user state reference value, and classifying the user state reference value to obtain a classification result; analyzing according to a classification result to obtain motion decision data; the exercise amount of the user and the calorie intake data of the user are analyzed to obtain a periodic exercise achievement report, the exercise amount of the user, the exercise decision data and the exercise achievement report are displayed through the smart bracelet, and generally, the method has the remarkable advantages of being good in user exercise assisting effect, high in exercise risk prevention capacity and high in decision assisting degree.

Description

Technical Field

[0001] The present invention relates to the technical field of power anomaly detection, and more specifically, to a sports training reminder system and a smart bracelet based on physical examination data analysis. Background Art

[0002] Currently, 90% of the main consumers of weight loss are young women who love beauty, and most of them are still women aged 20 to 50. However, the market structure will change in the future. The share of weight loss for beauty and the share of weight loss for health will each account for 50%, and the ratio of men and women will each account for half. The weight loss business is imminent. Under such a huge market demand, how to closely fit the most concerned weight loss issue at the moment, follow the trend, bring good news to many people who want to lose weight, so that everyone can lose weight easily and better manage and control themselves has become a new social issue.

[0003] The patent application publication number CN110314344B discloses a motion reminder method, device and system, which collects and extracts feature information of a three-dimensional model set of motion images, obtains standard feature information corresponding to the three-dimensional model set, compares the two, and when the difference between the feature information of the three-dimensional model set and the corresponding standard feature information is greater than a preset difference value, performs a reminder operation, so that the user can correct his or her motion posture based on the reminder operation, making the posture correction process more objective and thus improving the accuracy of posture correction.

[0004] Although the above-mentioned exercise reminder method, device and system collect, analyze and remind the user's exercise posture through a three-dimensional model set, and to a certain extent achieve the purpose of correcting the user's exercise posture standard, there are many problems involved in the current sports weight loss industry. For example, how do users control their own calorie intake, exercise according to their own condition and choose sports? There are many people who find it difficult to have professional knowledge, and blindly engaging in weight loss exercises can easily cause physical injuries, thereby bringing more problems.

[0005] In view of this, the present invention proposes a sports training reminder system and a smart bracelet based on physical examination data analysis to solve the above problems. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solutions: comprising:

[0007] A data collection module is used to collect a user's physical state data set and a user's caloric intake data, and to pre-process the user's physical state data set, including basic physical data, important physical data, and body metabolism data;

[0008] Furthermore, the method of collecting the user's physical status data set and the user's calorie intake data includes:

[0009] By reading the hospital physical examination data platform or manually inputting, the user's height, weight and gender are collected and substituted into the calculation formula: Get basic body data, where A2 is the user's weight and A3 is the user's height;

[0010] By reading the hospital physical examination data platform, the user's physical condition is collected to determine whether the user has any major diseases, and important physical data is obtained by referring to the exercise and disease comparison table;

[0011] Through metabolic rate testing, the user's body metabolism is collected to obtain the user's metabolic data;

[0012] Through the installed micro camera, the calories contained in the food consumed by the user each time are scanned and recorded, and the calories are added up and recorded in days to obtain the user's calorie intake data;

[0013] Preprocessing methods include data cleaning and data denoising;

[0014] The data processing module is used to analyze the user's physical status data set, obtain the user's status reference value, and classify it to obtain the classification result;

[0015] Furthermore, the step of analyzing the user's physical state data set includes:

[0016] Q1: Obtain the user's body reference value by substituting into the calculation formula: Aa = (A1 × B1) × (Ab × B2) × (Ac × B3), where Ab is important body data, Ac is body metabolism data, and B1, B2 and B3 are preset weight factors respectively;

[0017] Q2: Collect M groups of historical user body reference values ​​as sample sets, and divide the sample sets into 70%M training sets, 15%M test sets, and 15%M validation sets;

[0018] Based on the training set, a classification model is established, and three initial clustering centers K1, K2 and K2 are preset. By substituting into the calculation formula: Get the distance between data points, calculate the distance between the sub-data items in the training set and the initial cluster center, assign the sub-data items to the initial cluster center with the closest distance, and recalculate the means of the three initial cluster centers respectively, and use them as the new initial cluster center for secondary calculation, where x and y are the coordinate values ​​of the data points, xi and yi are the values ​​of the two data points on the i sub-data items respectively, and n is the number of sub-data items;

[0019] After reaching the preset number of iterations, a suggested motion classification model is obtained;

[0020] Q3: Input the user's body reference value into the recommended exercise classification model, and output the user's state reference value;

[0021] Further, the classification methods include:

[0022] The preset exercise suggestion threshold interval is [0, W1, W2). When the user status reference value is 0, a stop text message is sent to the user receiving end through the communication unit. When the user status reference value is greater than 0 and less than W1, a light text message is sent to the user receiving end through the unit. When the user status reference value is greater than W1 and less than W2, a general text message is sent to the user receiving end through the unit. When the user status reference value is greater than W2, a health text message is sent to the user interaction module through the unit.

[0023] Stop SMS includes information about the user's current poor physical condition and requires the user to temporarily stop exercising. Mild SMS includes information about the user's current poor physical condition and requires the user to always pay attention to whether the body is uncomfortable during exercise. General SMS includes information about the user's current good physical condition and recommends the user to choose appropriate sports during exercise. Health SMS includes information about the user's current excellent physical condition and reminds the user to pay attention to safety protection during exercise.

[0024] Pack stop text messages, light text messages, general text messages and health text messages to obtain classification results;

[0025] The intelligent allocation model exercise reminder module is used to analyze the classification results and obtain exercise decision data;

[0026] Further, the analysis methods based on the classification results include:

[0027] When the classification result is a stop message, a stop recommendation decision is generated; when the classification result is a mild message, a mild recommendation decision is generated; when the classification result is a general message, a general recommendation decision is generated; when the classification result is a healthy message, a healthy recommendation decision is generated

[0028] The stop suggestion decision includes explaining that it is recommended that the user cancel the exercise plan and stop exercising and that the user should seek reexamination and treatment from relevant medical institutions as soon as possible. The mild suggestion decision includes reading the user's physical status data set, formulating mild exercise programs based on the user's physical status data set, and avoiding conflicts with diseases in important physical data. The general suggestion decision includes reading the user's physical status data set, and formulating moderate and exercise programs for users with poor physical data based on the user's physical status data set. The health suggestion decision includes formulating exercise programs based on user preferences.

[0029] The stop recommendation decision, the mild recommendation decision, the general recommendation decision and the health recommendation decision are packaged to obtain the exercise decision data;

[0030] The user interaction module is used to analyze the user's exercise volume and user's calorie intake data, obtain a periodic exercise results report, and display the user's exercise volume, exercise decision data and exercise results report through a smart bracelet;

[0031] Furthermore, the method of analyzing the user's exercise volume and the user's body data includes:

[0032] Based on the exercise decision data, the calorie consumption value is obtained by substituting into the calculation formula: Ca = A1 × Af, where Af is the activity consumption factor;

[0033] By substituting into the calculation formula: Get the weight consumption value, where Cb is the user's calorie intake data and Cc is the static calorie consumption factor;

[0034] Based on the preset time unit, the user's exercise volume, calorie intake data and weight consumption value are packaged to generate a periodic exercise results report;

[0035] Further, S1: collecting a user's body state data set and a user's calorie intake data, and preprocessing the data, the user's body state data set including basic body data, important body data and body metabolism data;

[0036] S2: Analyze the user's physical status data set to obtain a user status reference value, and classify it to obtain a classification result;

[0037] S3: Analyze the classification results to obtain motion decision data;

[0038] S4: Analyze the user's exercise volume and calorie intake data to obtain a periodic exercise results report, and display the user's exercise volume, exercise decision data and exercise results report through a smart bracelet.

[0039] Technical effects and advantages of the sports training reminder system and smart bracelet based on physical examination data analysis of the present invention:

[0040] By collecting the user's physical condition and calorie intake, the present invention can effectively obtain the user's physical condition information, which is convenient for providing the user with healthy and scientific exercise weight loss decisions based on the user's physical condition in the future. By analyzing the user's physical condition, the user's state reference value obtained can intuitively display the user's current suitable exercise level, avoid the user from choosing inappropriate sports due to ignorance of their own physical condition, and greatly reduce the risk of physical damage due to blind exercise. Scientific and healthy sports projects can be intuitively provided to the user through exercise decision data, greatly improving the user's usage experience. The periodic exercise achievement report can intuitively show the user's exercise results within the cycle, which is convenient for the user to make the next exercise level selection based on the periodic exercise achievement report. Finally, the smart bracelet enables the user to intuitively obtain the above data. Generally speaking, the present invention has the significant advantages of good auxiliary user exercise effect, strong ability to prevent exercise risks and high degree of auxiliary decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A schematic diagram of a sports training reminder system based on physical examination data analysis of the present invention;

[0042] Figure 2 It is a schematic diagram of the sports training reminder method based on physical examination data analysis of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] Example 1

[0045] See also Figure 1 As shown, the sports training reminder system based on physical examination data analysis described in this embodiment includes:

[0046] A data collection module is used to collect a user's physical state data set and a user's caloric intake data, and to pre-process the user's physical state data set, including basic physical data, important physical data, and body metabolism data;

[0047] Furthermore, the method of collecting the user's physical status data set and the user's calorie intake data includes:

[0048] By reading the hospital physical examination data platform or manually inputting, the user's height, weight and gender are collected and substituted into the calculation formula: Get basic body data, where A2 is the user's weight and A3 is the user's height;

[0049] By reading the hospital physical examination data platform, the user's physical condition is collected to determine whether the user has any major diseases, and important physical data is obtained by referring to the exercise and disease comparison table;

[0050] It should be explained that the exercise and disease comparison table is set up according to the disease conditions of the user and how to exercise safely. If the user has a more serious heart problem, the important body data value is 0; if the user has a common disease, the important body data value is 1; if the user is healthy, the important body data value is 2;

[0051] Through metabolic rate testing, the user's body metabolism is collected to obtain the user's metabolic data;

[0052] It should be explained that the user's body metabolism can be assigned a value through a preset body metabolism threshold interval (Z1, Z2). When the user's body metabolism is less than Z1, the user's metabolism data value is 1. When the user's body metabolism is greater than Z1 and less than Z2, the user's metabolism data value is 2. When the user's body metabolism is greater than Z2, the user's metabolism data value is 3.

[0053] Through the installed micro camera, the calories contained in the food consumed by the user each time are scanned and recorded, and the calories are added up and recorded in days to obtain the user's calorie intake data;

[0054] Preprocessing methods include data cleaning and data denoising;

[0055] The data processing module is used to analyze the user's physical status data set, obtain the user's status reference value, and classify it to obtain the classification result;

[0056] Further, the step of analyzing the user's physical state data set includes:

[0057] Q1: Obtain the user's body reference value by substituting into the calculation formula: Aa = (A1 × B1) × (Ab × B2) × (Ac × B3), where Ab is important body data, Ac is body metabolism data, and B1, B2 and B3 are preset weight factors respectively;

[0058] Q2: Collect M groups of historical user body reference values ​​as sample sets, and divide the sample sets into 70%M training sets, 15%M test sets, and 15%M validation sets;

[0059] Based on the training set, a classification model is established, and three initial clustering centers K1, K2 and K2 are preset. By substituting into the calculation formula: Get the distance between data points, calculate the distance between the sub-data items in the training set and the initial cluster center, assign the sub-data items to the initial cluster center with the closest distance, and recalculate the means of the three initial cluster centers respectively, and use them as the new initial cluster center for secondary calculation, where x and y are the coordinate values ​​of the data points, xi and yi are the values ​​of the two data points on the i sub-data items respectively, and n is the number of sub-data items;

[0060] After reaching the preset number of iterations, a suggested motion classification model is obtained;

[0061] Q3: Input the user's body reference value into the recommended exercise classification model, and output the user's state reference value;

[0062] Further classification methods include:

[0063] The preset exercise suggestion threshold interval is [0, W1, W2). When the user status reference value is 0, a stop text message is sent to the user receiving end through the communication unit. When the user status reference value is greater than 0 and less than W1, a light text message is sent to the user receiving end through the unit. When the user status reference value is greater than W1 and less than W2, a general text message is sent to the user receiving end through the unit. When the user status reference value is greater than W2, a health text message is sent to the user interaction module through the unit.

[0064] Stop SMS includes information about the user's current poor physical condition and requires the user to temporarily stop exercising. Mild SMS includes information about the user's current poor physical condition and requires the user to always pay attention to whether the body is uncomfortable during exercise. General SMS includes information about the user's current good physical condition and recommends the user to choose appropriate sports during exercise. Health SMS includes information about the user's current excellent physical condition and reminds the user to pay attention to safety protection during exercise.

[0065] Pack stop text messages, light text messages, general text messages and health text messages to obtain classification results;

[0066] The intelligent allocation model exercise reminder module is used to analyze the classification results and obtain exercise decision data;

[0067] Further, the analysis methods based on the classification results include:

[0068] When the classification result is a stop message, a stop recommendation decision is generated; when the classification result is a mild message, a mild recommendation decision is generated; when the classification result is a general message, a general recommendation decision is generated; when the classification result is a healthy message, a healthy recommendation decision is generated

[0069] The stop suggestion decision includes explaining that it is recommended that the user cancel the exercise plan and stop exercising and that the user should seek reexamination and treatment from relevant medical institutions as soon as possible. The mild suggestion decision includes reading the user's physical status data set, formulating mild exercise programs based on the user's physical status data set, and avoiding conflicts with diseases in important physical data. The general suggestion decision includes reading the user's physical status data set, and formulating moderate and exercise programs for users with poor physical data based on the user's physical status data set. The health suggestion decision includes formulating exercise programs based on user preferences.

[0070] It should be explained that, for poor user physical data, such as too high basic user physical data, too low metabolic rate, or too high metabolic rate, the user preference specifies an exercise direction for the user, such as reducing body fat, exercising leg strength, or exercising waist strength;

[0071] The stop recommendation decision, the mild recommendation decision, the general recommendation decision and the health recommendation decision are packaged to obtain the exercise decision data;

[0072] The user interaction module is used to analyze the user's exercise volume and user's calorie intake data, obtain a periodic exercise results report, and display the user's exercise volume, exercise decision data and exercise results report through a smart bracelet;

[0073] Further, the methods for analyzing the user's exercise volume and the user's body data include:

[0074] Based on the exercise decision data, the calorie consumption value is obtained by substituting into the calculation formula: Ca = A1 × Af, where Af is the activity consumption factor;

[0075] By substituting into the calculation formula: Get the weight consumption value, where Cb is the user's calorie intake data and Cc is the static calorie consumption factor;

[0076] Based on the preset time unit, the user's exercise volume, calorie intake data and weight consumption value are packaged to generate a periodic exercise results report;

[0077] It needs to be explained that the preset time unit is day, week or month;

[0078] The beneficial effects of this embodiment are that by collecting the user's physical condition and calorie intake, the user's physical condition information can be effectively obtained, which is convenient for providing the user with healthy and scientific exercise weight loss decisions based on the user's physical condition in the future. By analyzing the user's physical condition, the user's state reference value obtained can intuitively display the user's current suitable exercise level, avoiding the user from choosing inappropriate sports due to ignorance of their own physical condition, greatly reducing the risk of physical damage due to blind exercise, and can intuitively provide the user with scientific and healthy sports through exercise decision data, greatly improving the user's experience. Through the periodic exercise achievement report, the user's exercise results within the cycle can be intuitively displayed, which is convenient for the user to make the next exercise level selection based on the periodic exercise achievement report. Finally, the user can intuitively obtain the above data through the smart bracelet. Generally speaking, the present invention has the significant advantages of good auxiliary user exercise effect, strong ability to prevent exercise risks and high degree of auxiliary decision-making.

[0079] Example 2

[0080] See also Figure 2 As shown, the part not described in detail in this embodiment is described in Example 1, and a sports training reminder method based on physical examination data analysis is provided, including: S1: collecting a user's physical state data set and a user's calorie intake data, and preprocessing them, the user's physical state data set including basic physical data, important physical data and physical metabolism data;

[0081] S2: Analyze the user's physical status data set to obtain a user status reference value, and classify it to obtain a classification result;

[0082] S3: Analyze the classification results to obtain motion decision data;

[0083] S4: Analyze the user's exercise volume and calorie intake data to obtain a periodic exercise results report, and display the user's exercise volume, exercise decision data and exercise results report through a smart bracelet.

[0084] Example 3

[0085] The present embodiment discloses a smart bracelet, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the operation mode of the sports training reminder method based on physical examination data analysis provided above is implemented.

[0086] Since the electronic device introduced in this embodiment is an electronic device used to implement the sports training reminder method based on physical examination data analysis in the embodiment of the present application, based on the sports training reminder method based on physical examination data analysis introduced in the embodiment of the present application, a person skilled in the art can understand the specific implementation of the electronic device of the present embodiment and its various variations, so how the electronic device implements the method in the embodiment of the present application is not described in detail here. As long as a person skilled in the art implements the electronic device used in the sports training reminder method based on physical examination data analysis in the embodiment of the present application, it belongs to the scope of protection of the present application.

[0087] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0088] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technical users in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A sports training reminder system based on physical examination data analysis, characterized in that: include: A data collection module is used to collect a user's physical state data set and a user's caloric intake data, and to pre-process the user's physical state data set, including basic physical data, important physical data, and body metabolism data; The data processing module is used to analyze the user's physical status data set, obtain the user's status reference value, and classify it to obtain the classification result; The intelligent allocation model exercise reminder module is used to analyze the classification results and obtain exercise decision data; The user interaction module is used to analyze the user's exercise volume and calorie intake data, obtain a periodic exercise results report, and display the user's exercise volume, exercise decision data and exercise results report through a smart bracelet.

2. The sports training reminder system based on physical examination data analysis according to claim 1, characterized in that: Methods for collecting user physical status data sets and user calorie intake data include: By reading the hospital physical examination data platform or manually inputting, the user's height, weight and gender are collected and substituted into the calculation formula: Get basic body data, where A2 is the user's weight and A3 is the user's height; By reading the hospital physical examination data platform, the user's physical condition is collected to determine whether the user has any major diseases, and important physical data is obtained by referring to the exercise and disease comparison table; Through metabolic rate testing, the user's body metabolism is collected to obtain the user's metabolic data; Through the installed micro camera, the calories contained in the food consumed by the user each time are scanned and recorded, and the calories are added up and recorded in days to obtain the user's calorie intake data; Preprocessing methods include data cleaning and data denoising.

3. The sports training reminder system based on physical examination data analysis according to claim 2, characterized in that: The steps of analyzing the user's physical status dataset include: Q1: Obtain the user's body reference value by substituting into the calculation formula: Aa = (A1 × B1) × (Ab × B2) × (Ac × B3), where Ab is important body data, Ac is body metabolism data, and B1, B2 and B3 are preset weight factors respectively; Q2: Collect M groups of historical user body reference values ​​as sample sets, and divide the sample sets into 70%M training sets, 15%M test sets, and 15%M validation sets; Based on the training set, a classification model is established, and three initial clustering centers K1, K2 and K2 are preset. By substituting into the calculation formula: Get the distance between data points, calculate the distance between the sub-data items in the training set and the initial cluster center, assign the sub-data items to the initial cluster center with the closest distance, and recalculate the means of the three initial cluster centers respectively, and use them as the new initial cluster center for secondary calculation, where x and y are the coordinate values ​​of the data points, xi and yi are the values ​​of the two data points on the i sub-data items respectively, and n is the number of sub-data items; After reaching the preset number of iterations, a suggested motion classification model is obtained; Q3: Input the user's body reference value into the recommended exercise classification model, and output the user's state reference value.

4. The sports training reminder system based on physical examination data analysis according to claim 3, characterized in that: The classification methods include: The preset exercise suggestion threshold interval is [0, W1, W2). When the user status reference value is 0, a stop text message is sent to the user receiving end through the communication unit. When the user status reference value is greater than 0 and less than W1, a light text message is sent to the user receiving end through the unit. When the user status reference value is greater than W1 and less than W2, a general text message is sent to the user receiving end through the unit. When the user status reference value is greater than W2, a health text message is sent to the user interaction module through the unit. Stop SMS includes information about the user's current poor physical condition and requires the user to temporarily stop exercising. Mild SMS includes information about the user's current poor physical condition and requires the user to always pay attention to whether the body is uncomfortable during exercise. General SMS includes information about the user's current good physical condition and recommends the user to choose appropriate sports during exercise. Health SMS includes information about the user's current excellent physical condition and reminds the user to pay attention to safety protection during exercise. Stop text messages, light text messages, general text messages and healthy text messages are packaged to obtain classification results.

5. The sports training reminder system based on physical examination data analysis according to claim 4, characterized in that: The analysis methods based on the classification results include: When the classification result is a stop message, a stop recommendation decision is generated; when the classification result is a mild message, a mild recommendation decision is generated; when the classification result is a general message, a general recommendation decision is generated; when the classification result is a healthy message, a healthy recommendation decision is generated The stop suggestion decision includes explaining that it is recommended that the user cancel the exercise plan and stop exercising and that the user should seek reexamination and treatment from relevant medical institutions as soon as possible. The mild suggestion decision includes reading the user's physical status data set, formulating mild exercise programs based on the user's physical status data set, and avoiding conflicts with diseases in important physical data. The general suggestion decision includes reading the user's physical status data set, and formulating moderate and exercise programs for users with poor physical data based on the user's physical status data set. The health suggestion decision includes formulating exercise programs based on user preferences. The stop recommendation decision, light recommendation decision, general recommendation decision and health recommendation decision are packaged to obtain the exercise decision data.

6. A smart bracelet, based on the reminder system according to any one of claims 1 to 5, characterized in that: Methods for analyzing user exercise volume and user body data include: Based on the exercise decision data, the calorie consumption value is obtained by substituting into the calculation formula: Ca = A1 × Af, where Af is the activity consumption factor; By substituting into the calculation formula: Get the weight consumption value, where Cb is the user's calorie intake data and Cc is the static calorie consumption factor; Based on the preset time unit, the user's exercise volume, calorie intake data and weight consumption value are packaged to generate a periodic exercise results report.

7. A sports training reminder method based on physical examination data analysis, wherein the reminder system according to any one of claims 1 to 6 is implemented by a smart bracelet, characterized in that: S1: Collecting a user's physical state data set and a user's calorie intake data, and preprocessing the data. The user's physical state data set includes basic physical data, important physical data, and body metabolism data; S2: Analyze the user's physical status data set to obtain a user status reference value, and classify it to obtain a classification result; S3: Analyze the classification results to obtain motion decision data; S4: Analyze the user's exercise volume and calorie intake data to obtain a periodic exercise results report, and display the user's exercise volume, exercise decision data and exercise results report through a smart bracelet.

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