Assessment method for body composition data

Abstract

According to an assessment method for body composition data of the present disclosure, a three-dimensional human body scanner is first provided. The three-dimensional human body scanner is provided with a database, and the database stores reference data of different races. Next, a subject inputs parameters such as race, gender, age, and body weight into the three-dimensional human body scanner. Then, the three-dimensional human body scanner scans the body of the subject to obtain the body size of the subject. The three-dimensional human body scanner obtains measurement data on the basis of the obtained body size and the parameters input by the subject. Finally, the measurement data of the subject is analyzed and compared with the reference data in the database. In this way, the subject's body composition status within his or her specific race can be assessed, thereby meeting the body composition assessment needs of different races and improving the accuracy of the assessment results.

Description

Evaluation methods for body composition data Technical Field

[0001] This disclosure relates to the field of body composition data evaluation technology, and in particular to a method for evaluating body composition data of different ethnic groups using three-dimensional human body scanning technology. Background Technology

[0002] Body composition data (such as bone mineral percentage, fat percentage, muscle percentage, and bone mineral density) can be assessed using several different methods, such as advanced medical equipment like dual-energy X-ray absorptiometry (DXA), computed tomography (CT), and magnetic resonance imaging (MRI), or methods like underwater weighing, air displacement plethysmography (ADP), skinfold measurement, circumference measurement, isotope dilution, ultrasound, potassium-40 counting, and bioelectrical impedance analysis (BIA).

[0003] Because body composition data varies significantly across different races—for example, Black people tend to have higher muscle mass than other races, while Asians have higher body fat percentages—currently, among the aforementioned methods, there is a lack of assessment tools that evaluate body composition data across different races. This means that the accuracy of assessment results may be affected by racial differences. Therefore, traditional methods for evaluating body composition data still require improvement. Summary of the Invention

[0004] The main objective of this disclosure is to provide a method for evaluating body composition data, which uses three-dimensional human body scanning technology to evaluate body composition data of different ethnic groups, so as to meet the body composition evaluation needs of different ethnic groups and improve the accuracy of evaluation results.

[0005] To achieve the aforementioned main objectives, the body composition data assessment method provided in this disclosure includes the following steps: (a) providing a three-dimensional human body scanner having a database storing parent body data for different ethnic groups; (b) a subject inputting multiple parameters, including ethnicity, gender, age, and weight, into an input unit of the three-dimensional human body scanner; (c) the three-dimensional human body scanner scanning the subject's body to obtain the subject's body dimensions, including volume, girth, and length; (d) a data construction unit of the three-dimensional human body scanner obtaining measurement data of the subject based on the body dimensions obtained in step (c) and the multiple parameters input by the subject in step (b); and (e) a data analysis unit of the three-dimensional human body scanner analyzing and comparing the subject's measurement data with the parent body data in the database to assess the subject's body composition within their ethnic group.

[0006] As can be seen from the above, the assessment method disclosed herein can assess the body composition of the test subject within their respective ethnic group, thereby meeting the body composition assessment needs of different ethnic groups and improving the accuracy of the assessment results.

[0007] According to embodiments of this disclosure, the parent data consists of body composition data of Mexican, Black, White, Latino, Asian, and other races at different sexes and ages.

[0008] According to embodiments of this disclosure, the measurement data is at least one of bone mineral mass percentage, muscle mass percentage, fat mass percentage, and bone mineral density.

[0009] According to embodiments of this disclosure, after the analysis and comparison are completed, a dynamic update unit of the 3D human body scanner periodically updates the database based on the comparison results to improve the accuracy and reliability of the parent data.

[0010] According to embodiments of this disclosure, after the analysis and comparison are completed, an output unit of the 3D human body scanner generates a personalized body composition assessment report based on the comparison results. In addition to allowing the test subject to better understand their own body composition, the personalized body composition assessment report also enables the test subject to manage their personal body composition effectively.

[0011] Detailed construction, features, assembly, and usage of the methods for evaluating body composition data provided in this disclosure will be described in the following detailed description of embodiments. However, those skilled in the art will understand that these detailed descriptions and the specific embodiments listed in this disclosure are merely illustrative and not intended to limit the scope of the patent application. Attached Figure Description

[0012] The above and other objects, features, and advantages of this disclosure will become clearer from the following description of embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0013] Figure 1 is a flowchart of an evaluation method according to an embodiment of the present disclosure.

[0014] Figure 2 is a schematic diagram of the evaluation method according to an embodiment of the present disclosure, showing a three-dimensional human body scanner scanning the body of a test subject.

[0015] Figure 3 is a block diagram of an evaluation method according to an embodiment of the present disclosure.

[0016] Figure 4 is a graph of the evaluation method according to an embodiment of the present disclosure, showing the median body fat percentage of Asian men.

[0017] Figure 5 is a graph of the evaluation method according to an embodiment of the present disclosure, showing the median bone mineralization rate of the trunk of white women.

[0018] Figure 6 is a graph of the evaluation method according to an embodiment of the present disclosure, showing the standard deviation of the fat percentage of the left upper limb of a Mexican woman.

[0019] Figure 7 is a graph of the evaluation method according to an embodiment of the present disclosure, showing the standard deviation of bone mineralization rate of the right upper limb of a Latina woman.

[0020] Figure 8 is a graph of the evaluation method according to an embodiment of the present disclosure, showing the standard deviation of muscle rate in the right lower limb of a Black male.

[0021] Figure 9 is a graph of the evaluation method according to an embodiment of the present disclosure, showing the median muscle rate of the left lower limb of males of other races.

[0022] Figure 10 is a graph of the evaluation method according to an embodiment of the present disclosure, showing the median bone mineral density of a Black male.

[0023] Figure 10: 3D human body scanner; 12: Scanning unit; 20: Database; 22: Mother data; 30: Input unit; 40: Data construction unit; 50: Data analysis unit; 60: Dynamic update unit; 70: Output unit; 72: Personalized body composition assessment report; 80: Subject; 82: Measurement data; Detailed Implementation

[0024] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0025] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0026] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0027] When using expressions such as "at least one of A, B, and C", they should generally be interpreted in accordance with the meaning that is commonly understood by a person skilled in the art (e.g., "a system having at least one of A, B, and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B, and C, etc.).

[0028] The applicant hereby clarifies that throughout this specification, including the embodiments described below and the claims in the patent application, all directional terms are based on the directions shown in the drawings. Secondly, in the embodiments and drawings described below, the same component reference numerals represent the same or similar components or their structural features.

[0029] Please refer to Figure 1. The method for evaluating body composition data disclosed herein includes the following steps:

[0030] Step (a): As shown in S1 of Figure 1, a three-dimensional human body scanner 10 is provided. As shown in Figures 2 and 3, the three-dimensional human body scanner 10 has a database 20, which stores the parent data 22 of different races (curves shown in Figures 4 to 10).

[0031] The 3D human body scanner 10 provided in this step uses 3D human body scanning technology to scan the body of the subject 80 to obtain the body dimensions of the subject 80, including volume, circumference, and length. Since the detailed structure and operating principle of the 3D human body scanner 10 are not the focus of this case, they will not be elaborated here.

[0032] In addition, the parent data 22 in this embodiment is body composition data of Mexican, Black, White, Latino, Asian and other races at different sexes and ages. The aforementioned body composition data includes at least four types: bone mineral content percentage (BMC%), lean body mass percentage (LBM%), body fat percentage (BF%) and bone mineral density (BMD). Moreover, the aforementioned four types of data can be further subdivided into median, power transformation and standard deviation, as shown in the curves in Figures 4 to 10.

[0033] Step (b): As shown in S2 of Figure 1, a subject 80 inputs multiple parameters into an input unit 30 of the 3D human body scanner 10. The multiple parameters include race, gender, age and weight.

[0034] In this step, the subject 80 uses the input unit 30 to input parameters such as race, gender, age and weight. The input unit 30 includes, but is not limited to, a keyboard or touch panel.

[0035] Step (c): As shown in S3 of Figure 1, the three-dimensional human body scanner 10 scans the body of the subject 80 to obtain the body dimensions of the subject 80.

[0036] In this step, the 3D human body scanner 10 uses a scanning unit 12 (e.g., a depth camera) to obtain the body dimensions of the subject 80, including height, body surface area, circumference (e.g., chest circumference, waist circumference, hip circumference, thigh circumference, upper arm circumference, and calf circumference), width (e.g., shoulder width and hip width), depth (e.g., chest depth and waist depth), and limb length (e.g., upper arm length, thigh length, and torso length).

[0037] Step (d): As shown in S4 of Figure 1, a data construction unit 40 of the 3D human body scanner 10 obtains a measurement data 82 of the subject 80 based on the body dimensions obtained in step (c) and the multiple parameters input by the subject 80 in step (b).

[0038] In this step, the data construction unit 40 of the 3D human body scanner 10 estimates the overall volume of the subject 80 based on the body dimensions obtained in step (c), and then calculates the data by combining all the parameters in step (b) to obtain a measurement data 82 of the subject 80. The measurement data 82 includes at least one of bone mineral mass percentage, muscle mass percentage, fat mass percentage and bone density. In this embodiment, it includes all four types of data.

[0039] The data construction unit 40 of the 3D human body scanner 10 calculates the standard score of the measurement data 82 using any of the following standardized formulas:

[0040] or And L = 0;

[0041] Where Z is the standard score of measurement data 82, X is measurement data 82, M is the median of population data 22, L is the power transformation of population data 22, and S is the standard deviation of population data 22.

[0042] When the measured data 82 represents bone mineral mass percentage, the relevant value of bone mineral mass percentage is calculated using the following formula: X Mi =a0+a1x+a2x 2 +a3x 3 +a4x 4 ......(1) X Li =b0+b1x+b2x 2 +b3x 3 +b4x 4 ......(2) X Si =c0+c1x+c2x 2 +c3x 3 +c4x 4 ......(3)

[0043] Among them, X M X represents the median of measurement data 82 (bone mineral content rate). L X represents the power transform of the measured data 82 (bone mineral mass percentage). S The standard deviation of the measurement data 82 (bone mineral mass ratio) is represented by i, which represents the whole body, right upper limb, left upper limb, trunk, right lower limb, or left lower limb of the subject. x represents the age of the subject. a0~a4, b0~b4, and c0~c4 are all regression coefficients. The regression coefficients mentioned above will vary depending on the item. The range of the regression coefficients mentioned above is between -100 and 100.

[0044] Use the above formula (1) to obtain the median bone mineral density percentage (X) of the whole body or each limb segment. Mi After that, the aforementioned median (X) Mi Substituting X into any of the above standardized formulas yields the median (X). Mi The standard score of bone mineral density is obtained by using the above formula (2) to obtain the power transform (X) of the bone mineral density percentage of the whole body or each limb segment. Li After that, the power transformation (X) will be performed. Li Substituting X into any of the above standardized formulas yields the power transform (X). Li The standard score of bone mineral density is obtained by using the above formula (3) to obtain the standard deviation (X) of bone mineral density of the whole body or each limb segment. Si After that, the standard deviation (X) Si Substituting X into any of the above standardized formulas, we can obtain the standard deviation (X). Si (Standard score)

[0045] When the measured data is 82, representing muscle rate, the relevant value of muscle rate is calculated using the following formula: X Mi =d0+d1x+d2x 2 +d3x 3 +d4x 4 ......(4) X Li =e0+e1x+e2x 2 +e3x 3 +e4x 4 ......(5) X Si =f0+f1x+f2x 2 +f3x 3 +f4x 4 ......(6)

[0046] Among them, X M X represents the median of the measured data (muscle rate). L X represents the power transform of the measurement data 82 (muscle rate). S 82 represents the standard deviation of the measurement data (muscle rate), i represents the whole body, right upper limb, left upper limb, trunk, right lower limb or left lower limb of the subject, x represents the age of the subject, d0~d4, e0~e4 and f0~f4 are all regression coefficients, which can vary depending on the item, and the regression coefficients range from -100 to 100.

[0047] Use the above formula (4) to obtain the median (X) of the muscle rate of the whole body or each limb segment. Mi After that, the median (X) Mi Substituting X into any of the above standardized formulas yields the median (X). MiThe standard score of muscle rate is obtained by using the above formula (5) to obtain the power transform (X) of muscle rate of the whole body or each limb segment. Li After that, the power transformation (X) will be performed. Li Substituting X into any of the above standardized formulas yields the power transform (X). Li The standard score of muscle rate; the standard deviation (X) of the muscle rate of the whole body or each limb segment is obtained using the above formula (6). Si After that, the standard deviation (X) Si Substituting X into any of the above standardized formulas, we can obtain the standard deviation (X). Si (Standard score)

[0048] When the measured data is 82, representing body fat percentage, the relevant value of body fat percentage is calculated using the following formula: X Mi = g0 + g1x + g2x 2 +g3x 3 +g4x 4 ......(7) X Li =h0+h1x+h2x 2 +h3x 3 +h4x 4 ......(8) X Si =j0+j1x+j2x 2 +j3x 3 +j4x 4 ......(9)

[0049] Among them, X M X represents the median of the measured data 82 (body fat percentage). L X represents the power transform of the measured data 82 (body fat percentage). S 82 represents the standard deviation of the measurement data (fat percentage), i represents the whole body, right upper limb, left upper limb, trunk, right lower limb or left lower limb of the subject, x represents the age of the subject, g0~g4, h0~h4 and j0~j4 are all regression coefficients, which can vary depending on the item, and the regression coefficients range from -100 to 100.

[0050] Use the above formula (7) to obtain the median (X) of the body fat percentage or the percentage of fat in each limb. Mi After that, the median (X) Mi Substituting X into any of the above standardized formulas yields the median (X). Mi The standard score of fat percentage; the power transform (X) of the fat percentage of the whole body or each limb segment is obtained using the above formula (8). Li After that, the power transformation (X) will be performed. Li Substituting X into any of the above standardized formulas yields the aforementioned power transform (X). LiThe standard score of fat percentage; the standard deviation (X) of the body fat percentage or the fat percentage of each limb segment is obtained using the above formula (9). Si After that, the aforementioned standard deviation (X) Si Substituting X into any of the above standardized formulas, we can obtain the standard deviation (X). Si (Standard score)

[0051] When the measured data is 82, which is bone mineral density, the relevant value of bone mineral density is calculated by the following formula: X Mi =k0+k1x+k2x 2 +k3x 3 +k4x 4 ......(10) X Li =l0+l1x+l2x 2 +l3x 3 +l4x 4 ......(11) X Si =m0+m1x+m2x 2 +m3x 3 +m4x 4 ......(12)

[0052] Among them, X M X represents the median of the measured data (bone mineral density). L X represents the power transform of the measured data 82 (bone mineral density). S 82 represents the standard deviation of the measurement data (bone mineral density), i represents the whole body, right upper limb, left upper limb, trunk, right lower limb or left lower limb of the subject, x represents the age of the subject, k0~k4, l0~l4 and m0~m4 are all regression coefficients, which can vary depending on the item, and the regression coefficients range from -100 to 100.

[0053] Use the above formula (10) to obtain the median bone mineral density (X) of the whole body or each limb segment. Mi After that, the median (X) Mi Substituting X into any of the above standardized formulas, we can obtain the aforementioned median (X). Mi The standard score of bone mineral density; the power transform (X) of the whole body or each limb segment is obtained using the above formula (11). Li After that, the power transformation (X) will be performed. Li Substituting X into any of the above standardized formulas yields the power transform (X). Li The standard score of bone mineral density; the standard deviation (X) of bone mineral density of the whole body or each limb segment is obtained using the above formula (12). Si After that, the standard deviation (X) Si Substituting X into any of the above standardized formulas, we can obtain the standard deviation (X). Si (Standard score)

[0054] Step (e): As shown in S5 of Figure 1, a data analysis unit 50 of the 3D human body scanner 10 analyzes and compares the measurement data 82 of the subject 80 with the parent data 22 of the database 20 to evaluate the body composition of the subject 80 within its race.

[0055] In this step, to facilitate the explanation of how to analyze and compare, the following illustrations show several implementation patterns of different ethnicities on different body parts:

[0056] Please refer to Figure 4. The curve shown in Figure 4 represents the median body fat percentage of male maternal data 22. The ethnicity of maternal data 22 is Asian, and the test site for maternal data 22 is the whole body. When the first subject 80 inputs the same ethnicity, gender, and age as the maternal data 22 in Figure 4 into the input unit 30, the median body fat percentage can be calculated by the above formula (7). Then, by analyzing and comparing the measured data 82 and the maternal data 22, the difference between them can be known, thereby allowing the first subject 80 to assess the body fat percentage of their ethnicity.

[0057] Please refer to Figure 5. The curve shown in Figure 5 represents the median bone mineral density percentage of female maternal data 22. The ethnicity of maternal data 22 is Caucasian, and the measured part of the aforementioned maternal data 22 is the torso. When the second subject 80 inputs the same ethnicity, gender, and age parameters as the maternal data 22 in Figure 5 into the input unit 30, the median bone mineral density percentage can be calculated using the above formula (1). Then, by analyzing and comparing the aforementioned measurement data 82 with the maternal data 22, the difference between them can be determined, thereby allowing the second subject 80 to assess the status of their own torso's bone mineral density percentage within their ethnic group.

[0058] Please refer to Figure 6. The curve shown in Figure 6 represents the standard deviation of the female maternal data 22 for body fat percentage. The ethnicity of the aforementioned maternal data 22 is Mexican, and the test site for the aforementioned maternal data 22 is the left upper limb. When the third subject 80 inputs the same ethnicity, gender, and age as the maternal data 22 in Figure 6 into the input unit 30, the standard deviation of body fat percentage can be calculated using the above formula (9). Then, by analyzing and comparing the measured data 82 with the maternal data 22, the difference between the measured data 82 and the maternal data 22 can be determined, thereby allowing the third subject 80 to assess the body fat percentage of the left upper limb within the range of their ethnicity.

[0059] Please refer to Figure 7. The curve shown in Figure 7 represents the standard deviation of the bone mineral density ratio for male maternal data 22. The ethnicity of maternal data 22 is Latino, and the test site for maternal data 22 is the right upper limb. When the fourth subject 80 inputs the same ethnicity, gender, and age as the maternal data 22 in Figure 7 into the input unit 30, the standard deviation of the bone mineral density ratio can be calculated using the above formula (3). Then, by analyzing and comparing the measured data 82 with the maternal data 22, the difference between the measured data 82 and the maternal data 22 can be determined, thereby allowing the fourth subject 80 to assess the bone mineral density ratio of the right upper limb within their ethnic group.

[0060] Please refer to Figure 8. The curve shown in Figure 8 is the male parent data 22 of the standard deviation of muscle rate. The ethnicity of parent data 22 is Black, and the test site of parent data 22 is the right lower limb. When the fifth test subject 80 inputs the same ethnicity, gender, and age as parent data 22 in Figure 8 into the input unit 30, the standard deviation of muscle rate can be calculated by the above formula (4). Then, by analyzing and comparing the measured data 82 and parent data 22, the difference between them can be known, so that the fifth test subject 80 can assess the status of the muscle rate of the right lower limb in his / her ethnicity.

[0061] Please refer to Figure 9. The curve shown in Figure 9 represents the male population data 22 of the standard deviation of muscle rate. The ethnicity of population data 22 includes all ethnicities except Mexican, Black, White, Latino, and Asian. The test site for population data 22 is the left lower limb. When the sixth subject 80 inputs the same ethnicity, gender, and age as population data 22 in the input unit 30, the standard deviation of muscle rate can be calculated using the above formula (6). Then, by analyzing and comparing the measured data 82 with population data 22, the difference between the measured data 82 and population data 22 can be determined, allowing the subject 80 to assess the distribution of muscle rate in the left lower limb within their ethnicity.

[0062] Please refer to Figure 10. The curve shown in Figure 10 represents the median bone mineral density of a female maternal data 22. The ethnicity of maternal data 22 is Black, and the measurement site of maternal data 22 is the whole body. When the seventh subject 80 inputs the same ethnicity, gender, and age as the maternal data 22 in Figure 10 into the input unit 30, the median bone mineral density can be calculated by the above formula (10). Then, by analyzing and comparing the measured data 82 with the maternal data 22, the difference between them can be known, thereby allowing the subject 80 to assess the distribution of bone mineral density throughout the body within their ethnicity.

[0063] The above is for illustrative purposes only and is not intended to limit the race or test site of the test subject 80. The test subject 80 can choose the test site according to their own needs. They only need to enter their race, gender, age and weight to obtain the comparison results of the body composition data of the test site and the parent body data 22.

[0064] In addition, during this step, a dynamic update unit 60 of the 3D human body scanner 10 will periodically update the database 20 according to the comparison results, so that the measurement data 82 becomes part of the parent data 22, thereby improving the accuracy and reliability of the parent data 22.

[0065] On the other hand, as shown in Figure 3, after the analysis and comparison are completed, an output unit 70 of the 3D human body scanner 10 will generate a personalized body composition assessment report 72 based on the comparison results. In addition to allowing the test subject 80 to have a clearer understanding of their own body composition, the personalized body composition assessment report 72 also allows the test subject 80 to manage their personal body composition.

[0066] In summary, this disclosure utilizes three-dimensional human body scanning technology to evaluate the body composition data of subject 80, enabling subject 80 to understand their body composition status within their own ethnic group, thereby meeting the body composition assessment needs of different ethnic groups and improving the accuracy of the assessment results.

[0067] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this disclosure can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments and / or claims of this disclosure can be combined and / or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0068] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. The scope of this disclosure is defined by the appended claims and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. A method for evaluating body composition data, comprising the following steps: (a) Provide a three-dimensional human body scanner having a database containing parent data of different ethnic groups; (b) A subject inputs multiple parameters into an input unit of the three-dimensional human body scanner, the multiple parameters including race, sex, age and weight; (c) The three-dimensional human body scanner scans the body of the subject to obtain the body dimensions of the subject, which include volume, circumference and length; (d) A data construction unit of the three-dimensional human scanner obtains measurement data of the subject based on the body dimensions obtained in step (c) and the multiple parameters input by the subject in step (b); and (e) A data analysis unit of the three-dimensional human body scanner analyzes and compares the measurement data of the subject with the parent data in the database to assess the subject's body composition within their ethnic group.

2. The method for evaluating body composition data according to claim 1, wherein in step (a), the parent data is body composition data of Mexican, Black, White, Latino, Asian and other races at different sexes and ages.

3. The method for evaluating body composition data according to claim 1, wherein in step (c), the standard score of the measurement data is calculated by any of the following formulas: or And L = 0, in, Z is the standard score of the measured data, X is the measured data, M is the median of the population data, L is the power transform of the population data, and S is the standard deviation of the population data.

4. The method for evaluating body composition data according to claim 3, wherein, The measurement data includes at least one of bone mineral mass percentage, muscle mass percentage, fat mass percentage, and bone mineral density.

5. The method for evaluating body composition data according to claim 4, wherein when the measurement data is bone mineral mass percentage, the median of the measurement data is calculated by the following formula: X Mi =a0+a1x+a2x 2 +a3x 3 +a4x 4 ; The power transform of the measured data is calculated using the following formula: X Li =b0+b1x+b2x 2 +b3x 3 +b4x 4 ; The standard deviation of the measurement data is calculated using the following formula: X Si =c0+c1x+c2x 2 +c3x 3 +c4x 4 ; in, X M X represents the median of the measured data. L X represents the power transform of the measured data. S The standard deviation of the measurement data is represented by , i represents the whole body, right upper limb, left upper limb, trunk, right lower limb or left lower limb of the subject, x represents the age of the subject, and a0~a4, b0~b4 and c0~c4 are regression coefficients.

6. The method for evaluating body composition data according to claim 1, wherein when the measured data is muscle rate, the median of the measured data is calculated by the following formula: X Mi =d0+d1x+d2x 2 +d3x 3 +d4x 4 ; The power transform of the measured data is calculated using the following formula: X Li =e0+e1x+e2x 2 +e3x 3 +e4x 4 ; The standard deviation of the measurement data is calculated using the following formula: X Si =f0+f1x+f2x 2 +f3x 3 +f4x 4 ; in, X M X represents the median of the measured data. L X represents the power transform of the measured data. S The standard deviation of the measurement data is represented by , i represents the whole body, right upper limb, left upper limb, trunk, right lower limb or left lower limb of the subject, x represents the age of the subject, and d0~d4, e0~e4 and f0~f4 are regression coefficients.

7. The method for evaluating body composition data according to claim 4, wherein when the measured data is body fat percentage, the median of the measured data is calculated by the following formula: X Mi = g0 + g1x + g2x 2 +g3x 3 +g4x 4 ; The power transform of the measured data is calculated using the following formula: X Li =h0+h1x+h2x 2 +h3x 3 +h4x 4 ; The standard deviation of the measurement data is calculated using the following formula: X Si =j0+j1x+j2x 2 +j3x 3 +j4x 4 ; in, X M X represents the median of the measured data. L X represents the power transform of the measured data. S The standard deviation of the measurement data is represented by , i represents the whole body, right upper limb, left upper limb, trunk, right lower limb or left lower limb of the subject, x represents the age of the subject, and g0~g4, h0~h4 and j0~j4 are regression coefficients.

8. The method for evaluating body composition data according to claim 4, wherein, When the measured data is bone mineral density, the median of the measured data is calculated using the following formula: X Mi =k0+k1x+k2x 2 +k3x 3 +k4x 4 ; The power transform of the measured data is calculated using the following formula: X Li =l0+l1x+l2x 2 +l3x 3 +l4x 4 ; The standard deviation of the measurement data is calculated using the following formula: X Si =m0+m1x+m2x 2 +m3x 3 +m4x 4 ; Among them, X M X represents the median of the measured data. L X represents the power transform of the measured data. S The standard deviation of the measurement data is represented by , i represents the whole body, right upper limb, left upper limb, trunk, right lower limb or left lower limb of the subject, x represents the age of the subject, and k0~k4, l0~l4 and m0~m4 are regression coefficients.

9. In the method for evaluating body composition data according to claim 1, in step (d), a dynamic update unit of the three-dimensional human body scanner periodically updates the database based on the comparison results.

10. The method for evaluating body composition data according to claim 1, in step (d), an output unit of the three-dimensional human body scanner generates a humanized body composition evaluation report based on the comparison result of step (e).

Images