CN112309575A - Body shape change prediction system - Google Patents
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Abstract
The invention relates to a body shape change prediction system, which utilizes a body shape three-dimensional scanning device to collect body shape data of different parts of a human body, a physiological data acquisition unit to collect personal physiological data, and an artificial intelligence operation host computer to perform learning operation on the collected body shape data and the physiological data to generate predicted body shape data and physiological data after the body shape is changed, and the predicted body shape data and the predicted physiological data are converted into a digital three-dimensional portrait model to be displayed on a display interface for the individual to watch and refer. The invention can estimate the future possible body shape variation quantity by collecting the personal body shape data and the physiological data.
Description
Technical Field
The present invention relates to a human body shape change prediction technology, and more particularly, to a prediction system for predicting the change of the shape according to the physiological characteristic parameters of the human body.
Background
Whether for health or aesthetic reasons, having a desirable posture is one of the goals sought by most people. Research shows that the human body is too fat or too thin and can become the genuine of many chronic diseases, for example, the risks of diabetes, metabolic syndrome, cardiovascular diseases and the like during the too fat are several times higher than those of normal people; on the other hand, insufficient body weight can easily cause fatigue, depression, muscle weakness, and even possibly reduce the immunity of the human body, so many people try to effectively control their own physical state through various ways.
For example, through the plans of continuous exercise, body building, professional nutrition management and the like, the aims of reducing body fat and increasing muscles are achieved, and the efficacy of sculpturing the body shape is achieved. The aforementioned various forms of management are progressively changing, and therefore require constant action by the parties to see the outcome.
However, when initially making a form management plan, the planned plan may be only a desired value (e.g., weight, body fat ratio), and the person concerned cannot know the form change and final appearance that may occur during the planning process, so that it is not easy to generate the motivation for the person concerned to act positively, and most plans may therefore be half-out.
Disclosure of Invention
The invention mainly aims to provide a body shape change trend prediction system which predicts the personal body state change according to continuously collected personal physiological characteristic parameters.
In order to achieve the above object, the system for predicting body shape variation trend of the present invention mainly comprises:
a body shape three-dimensional scanning device for scanning the body shape of an individual to obtain body shape data of different parts of the body;
a physiological data acquisition unit for measuring physiological data of an individual;
an artificial intelligence operation host computer contains:
the database stores the body shape data and the physiological data measured by different individuals by using the body shape three-dimensional scanning device and the physiological data acquisition unit;
a learning engine for accessing the body shape data and physiological data recorded in the database to generate predicted body shape data according to the body shape data measured by the individual and the physiological data;
and the display interface outputs the predicted body shape data generated by the learning engine, wherein the display interface displays a digital three-dimensional portrait model according to the predicted body shape data.
By the above system for predicting body shape change of the present invention, the future possible body shape change can be estimated by collecting personal body shape data and physiological data, for example, the system can be applied to users who intend to plan to change their own body shape by exercise, diet adjustment or other ways, and the future possible appearance can be known at the initial stage of planning to be used as a target.
Furthermore, the invention can continuously update the change state of the personal figure by recording the change of the same personal figure data and physiological data successively, thereby providing more instant reference information.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1: the invention discloses a block diagram of a body shape change trend prediction system.
FIG. 2: the present invention is a schematic diagram of the analysis of muscles of various parts.
FIG. 3: the invention discloses a schematic diagram for predicting the forward change or the backward change of the personal figure.
FIG. 4: the learning engine of the present invention generates a flow chart of the predicted body shape data.
FIG. 5: the learning engine of the present invention compares the latest data with the original predicted data in a flow chart.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention is a body shape trend prediction system, which includes a body shape three-dimensional scanning device 10, a physiological data acquisition unit 20 and an artificial intelligence operation host 30. For the purpose of changing the shape of a person who is planning to change his/her own shape, for example, the person wants to achieve the purpose of changing through the gradual actions such as weight loss through surgery (e.g. gastric bypass surgery), diet control or exercise, the present invention can predict the future changed body posture trend by measuring the related data of the person concerned, and provide the future possible appearance change trend as reference.
The body shape three-dimensional scanning device 10 is used for scanning the body shape of an individual to obtain body shape data of different parts of the body, such as the girth of each body part of the face, neck, chest, abdomen, buttocks, right arm, left arm, right thigh, left thigh and the like, and a digital three-dimensional body shape model representing the person concerned can be established according to the body shape data of the different parts.
The physiological data acquisition unit 20 is used for measuring physiological data of an individual, and includes measurement items including, but not limited to, the following parameters:
| body composition analysis: | total body weight, total water weight, mineral weight, protein weight, and body fat weight |
| Obesity analysis: | body Mass Index (BMI), body fat Rate (PBF) |
| Analyzing physiological indexes: | blood pressure, blood glucose, heart rate, total cholesterol |
| Analyzing muscles of all parts: | right arm, left arm, trunk, right leg, left leg |
| Fat analysis of each part: | right arm, left arm, trunk, right leg, left leg |
The physiological data acquisition unit 20 may comprise a fixed measurement instrument or a wearable measurement instrument, and in a preferred embodiment, the measurement instrument is used to measure physiological data of a human body by a non-invasive measurement technique. Wherein, the total weight of the human body mainly comprises four types of weight, namely total water weight, mineral matter weight, protein weight and body fat weight. The muscle analysis of each part is used to measure the muscle mass or ratio occupied by each part of the body, please refer to fig. 2, mainly measure the muscle mass of the right arm a, the left arm B, the trunk C, the right leg D, and the left leg E; similarly, the fat analysis of each part is used to measure the amount or ratio of fat occupied by each part of the body, and the physiological data are closely related to the change of the personal shape data.
The artificial intelligence operation host 30 analyzes the future shape of the person according to the shape data of different parts measured by the three-dimensional body shape scanning device 10 and the physiological data obtained by the physiological data acquisition unit 20.
The artificial intelligence operation host 30 comprises a database 31 and a learning engine 32, wherein the database 31 stores the body shape data and the physiological data of different individuals, and the data not only comprises the latest body shape data/the latest physiological data which are measured by the same individual at the latest time, but also comprises historical body shape data/historical physiological data which are measured in the past; as the number of individuals stored in the database 31 increases, a huge amount of data of different shapes and physiological data can be gradually accumulated, so that the prediction accuracy of the learning engine 32 is gradually improved.
The learning engine 32 refers to the physical data and the physiological data in the database 31 to learn and generate the predicted physical data and the predicted physiological data of the individual. For example, the user a obtains the latest body shape data/latest physiological data through the body shape three-dimensional scanning device 10 and the physiological data acquisition unit 20, the learning engine 32 can find out a reference R closest to the data of the user a from the database 31, calculate and generate the predicted body shape data and the predicted physiological data of the user a after a period (e.g. 3 months, 6 months, etc.) according to the body shape change data recorded and retained by the reference R in the database 31, and establish a digital three-dimensional portrait model 50 according to the predicted body shape data and the predicted physiological data. The digital three-dimensional portrait model 50 may be further output to a display interface 40 for viewing reference by a user.
Referring to fig. 3, if the user first predicts the body shape, the measured data are stored in the database 31 as the original data, the learning engine 32 may further determine the change trend of the body shape according to a forward change parameter or a backward change parameter set by the user, the forward change parameter represents that the user expects to want to be thinned to reduce the body shape data, and when the user specifies the forward change, the predicted body shape data calculated by the learning engine 32 is the target value after being thinned. Conversely, the reverse variation parameter represents the expected desire of the user to enhance the shape data, and the learning engine 32 calculates the predicted shape data as the target value after the enhancement when the reverse variation is specified. When the learning engine 32 considers the forward variation parameter or the backward variation parameter, the predicted data after forward variation or backward variation can be determined by referring to the historical shape data/historical physiological data of different individuals recorded in the database 31.
As shown in fig. 4, a flowchart of the steps of the learning engine 32 generating the predicted shape data and the predicted physiological data according to the raw data of the party includes the following steps:
s41: the original shape data and the original physiological data of the party are obtained. In this step, the body shape three-dimensional scanning device 10 and the physiological data acquisition unit 20 are used to scan and measure the principal for the first time to obtain the original body shape data and the original physiological data, and the learning engine 32 is used to receive the original body shape data and the original physiological data to perform the subsequent operation.
S42: data of comparable gender is taken from the database 31 according to the gender of the party. Considering that there is a significant difference between the male and female body shapes, such as the chest circumference and hip circumference, the learning engine 32 will select the data of the same gender as the concerned from the database 31 as the basis for the subsequent operation.
S43: and acquiring the figure planning target value of the party. Before the person performs the action of changing the figure, a desired figure planning target value may be set, and for example, after the weight reduction is desired to be successful, figure data of each part may be obtained as the figure planning target value, and the figure planning target value may be incorporated into the learning engine 32. The learning engine 32 calculates the predicted figure data with reference to the proposed figure plan target value. When the figure planning target value is made, it can be further determined whether the figure planning target value of the party is within a reasonable human body safety range, and if the figure planning target value exceeds the safety range and is harmful to human health, the learning engine 32 can provide suggested data for the party to refer to.
S44: and screening out corresponding reference samples from the database. The learning engine 32 takes a corresponding reference sample from the database 31 according to the basic information and the shape planning target value of the person concerned, for example, according to the height, age or other basic information of the person concerned, selects the data of other people corresponding to the basic information from the database 31 as the reference sample, assuming that the height of the person concerned is 172cm, can find out the sample with the height of 171-175 cm from the database 31, and further filters out the data of other people with similar ages as the reference sample for subsequent learning operation. The learning engine 32 may further select a reference sample according to the form change mode adopted by the party, such as surgical weight loss, diet control or exercise.
S45: the characteristics of each part of the body are identified. The learning engine 32 can recognize the circumference values of the respective parts of the body, for example, the face, the neck, the chest, the abdomen, the buttocks, the right arm, the left arm, the right thigh, and the left thigh, from the original body shape data acquired in step S41, and the circumference value of each part is regarded as one feature value.
S46: calculating and modulating the characteristic values of each part of the body to generate the predicted body shape data. The learning engine 32 calculates a future variation trend of the feature values of the body parts of the person to be studied based on the reference samples screened from the database 31. In a preferred embodiment, the learning engine 32 calculates predicted values of the feature values of each part at different time points in the future, for example, calculates possible values at 1 st month, 3 rd month, 6 th month, 9 th month and 12 th month in the future to obtain multiple sets of predicted values at different time stages. When the learning engine 32 calculates the predicted value, one of the methods is to calculate the predicted value of the person concerned according to the value change rate of the corresponding body part in the reference samples, for example, the predicted data of the abdominal circumference of the person concerned at different time points in the future is calculated according to the change amount (e.g. average decrease of 2% per month) of the abdominal circumference data in the selected reference samples as a reference, which is just one of the above-mentioned examples, and the learning engine 32 can adopt other different learning methods to perform the calculation.
S47: the physiological data is calculated and modulated to generate physiological prediction data. The learning engine 32 calculates the predicted values of the feature values of the respective parts at different time points in the future in step S46, and then, similarly, refers to the data of the reference samples to calculate the physiological data at different time stages from the original physiological data, thereby generating the predicted physiological data.
After the user performs the first body shape prediction, after a certain time (for example, every 1 to 2 months), the latest body shape data/latest physiological data are measured again by the three-dimensional body shape scanning device 10 and the physiological data acquisition unit 20, and the latest data are stored in the database 31. The learning engine 32 compares the original predicted data to determine the variation of the current shape data and the difference from the target value. Referring to fig. 5, the specific process includes the following steps:
s51: and acquiring the latest shape data/latest physiological data of the party. This step is the latest data obtained after the body shape three-dimensional scanning device 10 and the physiological data acquisition unit 20 scan and measure the person concerned.
S52: and comparing the latest shape data with the predicted shape data to obtain the difference. The learning engine 32 compares the latest shape data with the previously calculated predicted shape data, and when comparing, the predicted shape data of the same period is used as the basis, that is, if the person concerned obtains the latest data measured in 3 rd month after the plan is started, the latest data is also compared with the predicted data of 3 rd month.
S53: judging whether the difference is in a set range. The learning engine 32 determines whether the difference is within a preset range, if so, it represents that the principal is close to the predicted figure, and if not, it represents that the principal is not close to the predicted figure.
S54: if the difference is within the set range, the latest shape data/latest physiological data of the party is stored in the database 31.
S55: if the difference is out of the set range, the learning engine 32 re-identifies the feature values of each part of the body, i.e., identifies the latest values of each part of the body, such as the face, the neck, the chest, the abdomen, the buttocks, the right arm, the left arm, the right thigh, and the left thigh. And calculating the deformation parameters of the parts according to the newly identified latest data, wherein the calculation operation comprises calculating the deformation ratio of the parts of the current body shape and recalculating the physiological data according to the calculated data of the parts.
S56: and storing the latest body shape data and the latest physiological data. Because the difference is within the set range, which represents that the present shape of the party is not similar to the predicted shape, the learning engine 32 adds the latest data measured this time to the database 31 as a new set of available data, so as to add the samples in the database 31 and expand the available information in the database 31.
The body shape change trend prediction system of the invention can estimate future body shape change according to personal body shape data and physiological data, and the artificial intelligence operation host 30 generates the predicted body shape according to the measured data and referring to massive data stored in the database 31. In practical application, a doctor, a dietician or a professional can use the invention to predict the future changed posture of a user who intentionally changes the self-body shape, so that the user can know the future possible appearance in advance, and the motivation of the user for continuous action is improved; in the process, the change data of the future figure can be updated at any time by regularly tracking the change of the figure data and the physiological data of the individual.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes may be made to the embodiment of the present invention by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A system for predicting a change in shape, comprising:
a body shape three-dimensional scanning device for scanning the body shape of a party to obtain body shape data of different parts of the body;
a physiological data acquisition unit for measuring physiological data of the party;
an artificial intelligence operation host computer contains:
the database stores the body shape data and the physiological data of different parties measured by the body shape three-dimensional scanning device and the physiological data acquisition unit;
a learning engine for accessing the body shape data and physiological data recorded in the database to generate predicted body shape data according to the body shape data measured by the party and the physiological data;
and the display interface outputs the predicted body shape data generated by the learning engine, wherein the display interface displays a digital three-dimensional portrait model according to the predicted body shape data.
2. The system of claim 1, wherein the learning engine further learns to generate predicted physiological data based on the measured shape data and the physiological data of the party; the display interface further outputs the predicted physiological data and displays the digital three-dimensional portrait model according to the predicted body shape data and the predicted physiological data.
3. The system of claim 2, wherein the learning engine generates the predicted body data and the predicted physiological data after slimming according to a forward variation parameter calculation; or the predicted body shape data and the predicted physiological data after the body shape is strengthened are generated according to a set reverse variation parameter operation.
4. The system of claim 2, wherein the learning engine performs the following steps to generate the predicted shape data and the predicted physiological data:
acquiring original body shape data and original physiological data of a party, wherein the original body shape data is obtained by scanning the party for the first time by the body shape three-dimensional scanning device, and the original physiological data is obtained by acquiring the party for the first time by the physiological data acquisition unit;
according to the sex of the party, the body shape data and the physiological data of the equivalent sex are taken from the database;
obtaining a body shape plan target value preset by a party;
screening out corresponding shape data and physiological data from the database as reference samples according to the basic information of the party and the shape plan target value;
identifying the circumference value of each part in the original body shape data;
calculating and modulating the circumference value of each part to generate the predicted body shape data;
according to the predicted body shape data, the original physiological data is calculated and modulated to generate the predicted physiological data.
5. The system of claim 4, wherein the predicted shape data comprises a plurality of sets of data at different time periods; the predicted physiological data comprises a plurality of sets of data at different time periods.
6. The system of claim 1, wherein the three-dimensional body shape scanning device generates the circumference values of the face, neck, chest, abdomen, kidney, right arm, left arm, right thigh and left thigh of the human body.
7. The system of claim 1, wherein the physiological data measured by the physiological data measuring unit comprises:
body composition analysis: total body weight, total water weight, mineral matter weight, protein weight, body fat weight;
obesity analysis: body Mass Index (BMI), body fat Percentage (PBF);
analyzing physiological indexes: blood pressure, blood glucose, heart rate, total cholesterol;
analyzing muscles of all parts: a right arm, a left arm, a torso, a right leg, and a left leg;
fat analysis of each part: right arm, left arm, trunk, right leg, left leg.
8. The system of claim 7, wherein the physiological data measuring unit comprises a fixed measuring device and a wearable physiological measuring device.
9. The system of claim 1, wherein the database stores historical shape data/historical physiological data of the same party measured by the three-dimensional shape scanning device and the physiological data measuring unit each time.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113616402A (en) * | 2021-08-05 | 2021-11-09 | 首都医科大学宣武医院 | Nursing device to back |
| CN113616401A (en) * | 2021-08-05 | 2021-11-09 | 首都医科大学宣武医院 | Nursing device |
| CN114271797A (en) * | 2022-01-25 | 2022-04-05 | 泰安市康宇医疗器械有限公司 | System for measuring human body components by using body state density method based on three-dimensional modeling technology |
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| CN113616402B (en) * | 2021-08-05 | 2022-11-04 | 首都医科大学宣武医院 | Nursing device to back |
| CN113616401B (en) * | 2021-08-05 | 2022-11-29 | 首都医科大学宣武医院 | Nursing device |
| CN114271797A (en) * | 2022-01-25 | 2022-04-05 | 泰安市康宇医疗器械有限公司 | System for measuring human body components by using body state density method based on three-dimensional modeling technology |
| CN114271797B (en) * | 2022-01-25 | 2023-04-04 | 泰安市康宇医疗器械有限公司 | System for measuring human body components by using body state density method based on three-dimensional modeling technology |
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