Skin Type Determination Device

Abstract

The present invention relates to a skin type determining device. The skin type determining device according to one embodiment of the present invention includes a color sensor for measuring the color of a subject's skin, an elasticity sensor for measuring the elasticity of the subject's skin, one or more processors, a memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for determining a color index by converting color data measured by the color sensor to a value within a predetermined range, determining an elasticity index by converting elasticity data measured by the elasticity sensor to a value within a predetermined range, and determining a skin type of the subject based on the color index and the elasticity index.

Description

[Technical Field]

[0001] The present invention relates to a skin type determination device. [Background technology]

[0002] Previously, the 16-item Baumann Type Test was used as an important questionnaire item for classifying skin types (US Patent Publication US2014 / 0018634A1). The Baumann Type Test determines an individual's skin type by assessing oily or dry, sensitive or resistant, pigmented or non-pigmented, and whether wrinkles or elasticity are present.

[0003] However, the Baumann skin type has problems such as low reliability due to self-questionnaire assessment, complex type classification results, and inefficiency in serving as a basis for providing custom solutions. Summary of the Invention [Problem to be solved by the invention]

[0004] The problem that the present invention aims to solve is to provide a method for accurately and quickly determining relative skin type according to age.

[0005] The objects of the present invention are not limited to those mentioned above, and further objects not mentioned will be clearly understood by those skilled in the art from the following description. [Means for solving the problem]

[0006] In order to achieve the above object, a skin type determining device according to one embodiment of the present invention includes a color sensor that measures the color tone of a subject's skin; an elasticity sensor that measures the elasticity of the subject's skin; one or more processors; a memory; and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for determining a color tone index by converting color data measured by the color sensor to a value within a predetermined range, determining an elasticity index by converting elasticity data measured by the elasticity sensor to a value within a predetermined range, and determining the skin type of the subject based on the color tone index and the elasticity index. [Effects of the Invention]

[0007] According to the skin type determining device of the present invention, it is possible to accurately and quickly determine a relative skin type taking age into consideration.

[0008] The effects of the present invention are not limited to those mentioned above, and further effects not mentioned will be clearly understood by those skilled in the art from the claims. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a block diagram of a skin type determination device according to one embodiment of the present invention; [Figure 2] 10 is a graph showing subject results after normalizing color and elasticity data by a processor according to an embodiment of the present invention. [Figure 3] 10 is a graph showing oil content data and moisture content data for each subject after normalization by a processor according to an embodiment of the present invention. [Figure 4] 10 is a graph showing the distribution of subjects after the first evaluation index and the second evaluation index are determined by a processor according to one embodiment of the present invention. [Figure 5A] 10A and 10B are diagrams illustrating an example in which a processor according to an embodiment of the present invention identifies a point where a color index inversion phenomenon occurs. [Figure 5B]10 is a diagram illustrating an example in which a processor according to an embodiment of the present invention identifies a point where a reversal phenomenon of an elasticity index occurs. [Figure 5C] 10 is a diagram illustrating an example in which a processor according to an embodiment of the present invention identifies a point where an oil index reversal phenomenon occurs. [Figure 5D] 10 is a diagram illustrating an example in which a processor according to an embodiment of the present invention identifies a point where a moisture index inversion phenomenon occurs. [Figure 6A] This is clarified in the chart in Figure 5A, which lists the percentages by age and group. [Figure 6B] This is clarified in the chart in Figure 5B, which lists the percentages by age and group. [Figure 6C] This is clarified in the chart in Figure 5C, which lists the percentages by age and group. [Figure 6D] This is clarified in the chart in Figure 5D, which lists the percentages by age and group. [Figure 7A] The subject's color values ​​are expressed as a normal distribution curve. [Figure 7B] The elasticity values ​​of the subjects are expressed as a normal distribution curve. [Figure 7C] The moisture values ​​of the subjects are expressed as a normal distribution curve. [Figure 7D] The oil content values ​​of the subjects are expressed as a normal distribution curve. [Figure 8A] This is a dot graph showing the high and low groups divided by the values ​​obtained by multiplying the color tone data and elasticity data of the subjects in the YOUNG group (10-34 years old) after normalizing them. [Figure 8B] This is a dot graph showing the high and low groups divided by the values ​​obtained by multiplying the color tone data and elasticity data of the subjects in the Aging 1 group (35-50 years old) after normalizing them. [Figure 8C]This is a dot graph showing the high and low groups divided by the values ​​obtained by multiplying the color tone data and elasticity data of the subjects in the old group (51 to 71 years old) after normalizing them. [Figure 9] The distribution positions of subjects without applying the first age weighting and the second age weighting are shown in the graph in Figure 4, divided by age. [Figure 10] 1 is a flowchart of a method for determining a skin type according to an embodiment of the present invention. [Figure 11] 1 is a flowchart relating to a method for deriving age-stratified phenotypes. DETAILED DESCRIPTION OF THE INVENTION

[0010] The advantages and features of the present invention, as well as the methods for achieving them, will become more apparent with reference to the following detailed embodiments in conjunction with the accompanying drawings.

[0011] However, the present invention is not limited to the embodiments disclosed below, and may be embodied in various different forms. These embodiments are provided so that this disclosure will be complete and will fully convey the scope of the present invention to those skilled in the art. The present invention is defined only by the scope of the claims. Like reference characters refer to like elements throughout the specification.

[0012] The present invention will now be described with reference to the drawings.

[0013] FIG. 1 is a block diagram of a skin type determination device according to various embodiments of the present invention. FIG. 2 is a graph showing the results of subjects after normalizing color tone data and elasticity data by processor 210 according to various embodiments of the present invention. FIG. 3 is a graph showing the results of subjects after normalizing oil content data and moisture content data by processor 210 according to various embodiments of the present invention. FIG. 4 is a graph showing the distribution of subjects after determining first and second evaluation indexes by processor 210 according to various embodiments of the present invention.

[0014] 1 to 4, a skin type determination device or system according to various embodiments of the present invention includes a tone sensor 110 that measures the skin tone of a subject; an elasticity sensor 120 that measures the elasticity of the subject's skin; a memory 230 that stores tone data measured by the tone sensor 110 and elasticity data measured by the elasticity sensor 120; and one or more processors 210, wherein the one or more processors 210 are configured to: normalize the tone data to determine a tone value; normalize the elasticity data to determine an elasticity value; and determine a first evaluation index that defines the skin type of the subject based on the tone value and the elasticity value.

[0015] The color sensor 110 may be a color sensor or a light sensor. The color sensor 110 senses the color of the skin surface. The color sensor 110 receives color information from the skin.

[0016] The elasticity sensor 120 senses the elasticity of the skin. The elasticity sensor 120 senses the amount of contraction, the amount of recovery, and the recovery time of the skin. The elasticity sensor 120 may be a distance measurement sensor using a laser, an ultrasonic wave, or an infrared ray. The elasticity sensor 120 receives information on the contraction and recovery of the skin. The recovery time can be determined by the processor 210.

[0017] Memory 230 optionally includes one or more non-transitory computer-readable storage media, and optionally includes high-speed random access memory, and optionally includes non-volatile memory, such as one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state memory devices.

[0018] One or more processors 210 run or execute various software programs and / or instruction sets stored in memory 230 to perform various functions related to the skin type determination device and to process data. The processor 210 normalizes the data. Normalization shifts the range of data values ​​between 0 and 1. The processor 210 changes the units of the data values ​​to all be the same or to dimensionless numbers.

[0019] A skin type determination device or system according to various embodiments of the present invention includes an oil sensor 130, a moisture sensor 140, an elasticity sensor 120, a color sensor 110, and a main device 200. The main device 200 includes a process and memory 230. The various sensors (110, 120, 130, 140) and the main device 200 are connected via wire or wirelessly. The main device 200 may be installed on a cloud server. All or part of each component may be a portable mobile device.

[0020] 2, color data and elasticity data are distributed differently for each subject, and according to FIG. 3, oil content data and moisture data are distributed differently for each subject (H: High, M: Medium, L: Low). The processor 210 can normalize the color data and elasticity data for each subject and then store them in the memory 230.

[0021] The processor 210 determines a first evaluation index. The first evaluation index is an index that integrates the color data and the elasticity data. The processor 210 determines one first evaluation index based on the color data and the elasticity data, as shown in FIG.

[0022] The processor 210 performs an operation on two or more variables. The operation refers to a mathematical, logical, or other type of calculation using two or more variables. For example, the processor 210 may perform a mathematical operation. The mathematical operation may include the four arithmetic operations (addition, subtraction, multiplication, and division), exponential operations, logarithmic operations, differentiation, integration, etc. The processor 210 may multiply the color tone data and the elasticity data to determine a first evaluation index and a second evaluation index (described later).

[0023] As another example, processor 210 may perform a comparison operation. Processor 210 compares the value with a predetermined standard to evaluate at least one of "greater than, smaller than, less than, or exceeding," and returns a predetermined value according to the result. Hereinafter, a multiplication operation, which is one of the preferred examples of the operation, will be described as an example. However, other mathematical operations, logical operations, and comparison operations are also possible as long as they strengthen the correlation between data for determining skin type and are advantageous for data compression. Therefore, in the present invention, "operation" does not mean only a multiplication operation.

[0024] A skin type determination device or system according to various embodiments of the present invention includes a tone sensor 110 for measuring the tone of a subject's skin; an elasticity sensor 120 for measuring the elasticity of the subject's skin; one or more processors 210; a memory 230; and one or more programs stored in the memory 230 and configured to be executed by the one or more processors 210, the one or more programs including instructions for determining a tone value by converting tone data measured by the tone sensor 110 to a value within a defined range, determining an elasticity value by converting elasticity data measured by the elasticity sensor 120 to a value within a defined range, and determining a first evaluation index defining the subject's skin type based on the tone value and the elasticity value (one preferred example being a multiplication operation).

[0025] The value within the predetermined range may be between 0 and 1. As a specific example, the value within the predetermined range of the color tone data measured by the color tone sensor 110 may be between 0 and 1. As another specific example, the value within the predetermined range of the elasticity data measured by the elasticity sensor 120 may be between 0 and 1. The processor 210 changes the data column to a value between 0 and 1. The processor 210 can perform machine learning.

[0026] The processor 210 can standardize the data. Standardization can be performed under the assumption that the data follows a normal distribution (bell-shaped distribution). The processor 210 can transform the data so that the mean of the data is 0 and the standard deviation is 1.

[0027] The processor 210 can easily learn data by normalizing or standardizing it. The processor 210 learns columns equally rather than focusing on one column. After standardizing and normalizing the data and performing machine learning, the processor 210 can compare the results of the two cases to determine whether to standardize or normalize the data.

[0028] A skin type determination device or system according to various embodiments of the present invention includes an oil sensor 130 for measuring oil content on a subject's skin; a moisture sensor 140 for measuring moisture content on the subject's skin; one or more processors 210; a memory 230; and one or more programs stored in the memory 230 and configured to be executed by the one or more processors 210, the one or more programs including instructions for determining an oil value by converting oil data measured by the oil sensor 130 to a value within a predetermined range, determining a moisture value by converting moisture data measured by the moisture sensor 140 to a value within a predetermined range, and determining a second evaluation index that defines the subject's skin type based on the oil value and the moisture value (one preferred example of which is a multiplication operation).

[0029] 2, the oil content data and moisture content data are distributed differently for each subject (H: High, M: Medium, L: Low). The processor 210 can normalize the oil content data and moisture content data for each subject and then store the normalized data in the memory 230. The processor 210 can determine a second evaluation index that integrates the oil content and moisture content using the oil content data and moisture content data.

[0030] The processor 210 determines a second evaluation index. The second evaluation index is an index that integrates the oil content data and the moisture content data. The processor 210 determines one first evaluation index based on the color tone data and the elasticity data (one preferred example is a multiplication operation), as shown in FIG. 4. The processor 210 can display the subject's skin condition on one plane using the first evaluation index and the second evaluation index.

[0031] The moisture sensor 140 includes a first moisture measuring device that measures transepidermal water loss (TEWL) in the subject's skin; and a second moisture measuring device that measures hydration (HD) in the subject's skin; and one or more programs convert the water loss and moisture content into values ​​within a defined range and determine the moisture value using the following formula:

[0032] JPEG2025536029000002.jpg1633

[0033] where M is the moisture value, HD is the moisture content of the skin, and TEWL is the amount of water loss in the skin.

[0034] The processor 210 can determine a single moisture value by integrating the moisture content and moisture loss to generate an index representing the moisture status of the skin. The processor 210 determines that the lower the moisture loss of the skin, the better the skin barrier function is maintained. The processor 210 determines that the lower the moisture loss of the skin, the better the skin's moisturizing ability. The processor 210 determines that the higher the skin's hydration, the better the skin's moisturizing ability. The processor 210 determines that the higher the moisture value (M) value, the better the skin's moisturizing ability.

[0035] The first moisture measuring device measures the amount of moisture in the skin per minute to measure the amount of moisture lost per minute. The amount of moisture loss in the skin is the amount of moisture lost through the skin barrier present in the stratum corneum, which constitutes the outermost layer of the skin. The second moisture measuring device measures the amount of moisture contained in the skin.

[0036] FIG. 5A is a diagram illustrating an example of processor 210 according to various embodiments of the present invention determining when an inversion of a color index occurs. FIG. 5B is a diagram illustrating an example of processor 210 according to various embodiments of the present invention determining when an inversion of an elasticity index occurs. FIG. 5C is a diagram illustrating an example of processor 210 according to various embodiments of the present invention determining when an inversion of an oil index occurs. FIG. 5D is a diagram illustrating an example of processor 210 according to various embodiments of the present invention determining when an inversion of a moisture level occurs. FIG. 6A is a diagram of FIG. 5A with age and group percentages added. FIG. 6B is a diagram of FIG. 5B with age and group percentages added. FIG. 6C is a diagram of FIG. 5C with age and group percentages added. FIG. 6D is a diagram of FIG. 5D with age and group percentages added.

[0037] The skin type determining device according to various embodiments of the present invention includes an oil sensor 130 that measures oil on the subject's skin; and a moisture sensor 140 that measures moisture on the subject's skin.

[0038] The one or more programs include instructions to quantify the individual skin condition data measured by the sensor according to a predetermined standard, divide the data into groups, and select data types with similar patterns of specific gravity change according to age to determine the skin condition of the subject.

[0039] The pattern may be determined based on the similarity of age groups in which a reversal phenomenon in which the weight rankings of groups are reversed occurs. The type data having similar weight ranking change patterns of groups may be color data and elasticity data.

[0040] The meaning of the reversal phenomenon will be explained.

[0041] 5A and 6A, in the case of the subjects' color tone values, the weighting of the High and Low groups changes starting from age 34, and the pattern is completely reversed in the late 40s. In other words, the High group is in the majority for those under age 34, but the Low group gradually becomes the majority after age 34.

[0042] Again, Figures 5B and 6B show that in the case of the subjects' elasticity values, the weightings of the High and Low groups change starting from age 34, with the pattern completely reversing in the late 40s. In other words, the High group is in the majority below age 34, but from age 34 onwards, the Low group gradually becomes the majority.

[0043] In this way, the reversal phenomenon occurs in both the color tone value and the elasticity value, and the age group where they occur is the same. Therefore, the data on the color tone value and the elasticity value have the same pattern. The color tone value and the elasticity value are highly correlated data.

[0044] Therefore, when the color value and the elasticity value are multiplied, the characteristics are amplified, and the characteristics of the data can be more clearly distinguished.

[0045] On the other hand, referring to Figures 5C and 6C, the oil content value exhibits an inversion phenomenon, but is clearly distinguished from the tone and elasticity values ​​in terms of the age group of the skin. Referring to Figures 5D and 6D, the moisture value does not exhibit an inversion phenomenon. The moisture value represents the individual's skin condition, but has little correlation with age.

[0046] Figure 7A is a graph showing the subject's color value expressed as a normal distribution curve. Figure 7B is a graph showing the subject's elasticity value expressed as a normal distribution curve. Figure 7C is a graph showing the subject's moisture value expressed as a normal distribution curve. Figure 7D is a graph showing the subject's oil content value expressed as a normal distribution curve.

[0047] One or more programs according to various embodiments of the present invention include instructions for selecting remaining data having dissimilar specific gravity change patterns by group to determine the skin condition of the subject. The remaining data having dissimilar specific gravity change patterns by group are oil data measured by the oil sensor 130 and moisture data measured by the moisture sensor 140.

[0048] Referring to Figures 7A, 7B, and 7C, the median and average values ​​are consistent. According to Figure 7D, the average and median values ​​for oil content do not match. According to Figure 7D, the oil content values ​​are those in which the data distribution is asymmetric or has a long tail, and outliers or unusual values ​​affect the average value.

[0049] There are people (in the tail) who have abnormally high oil levels, and it is desirable to reduce the influence of such abnormal values.

[0050] According to various embodiments of the present invention, the oil value (oil data) is multiplied by the moisture value (moisture data) to reduce the influence of abnormal values ​​in the oil data and strengthen the normal data pattern. Therefore, the data quality and accuracy of the judgment are improved. In addition, the multiplication of the oil value (oil data) by the moisture value (moisture data) can be used as a judgment index for judging individual skin conditions regardless of age.

[0051] FIG. 8A is a dot graph showing High and Low groups separated by the values ​​obtained by multiplying the color tone data and elasticity data of subjects in the YOUNG group (10-34 years old) after normalizing the data. FIG. 8B is a dot graph showing High and Low groups separated by the values ​​obtained by multiplying the color tone data and elasticity data of subjects in the Aging 1 group (35-50 years old) after normalizing the data. FIG. 8C is a dot graph showing High and Low groups separated by the values ​​obtained by multiplying the color tone data and elasticity data of subjects in the Old group (51-71 years old) after normalizing the data.

[0052] Referring to Figures 8A, 8B, and 8C, if the number obtained by multiplying the X-axis value (tone value) and Y-axis value (elasticity value) of any one point is equal to or greater than a certain value, it is placed in the High group, and if it is less than a certain value, it is placed in the Low group.

[0053] Figure 8A shows an analysis of a relatively young group of subjects, with the dividing line (dotted line) between the High and Low groups located in the upper right corner, and as we move to Figures 8B and 8C, the dividing line moves to the lower left. The results of the subjects are generally clustered around the dividing line. This indicates that the results of multiplying the color and elasticity values ​​have few outliers and are suitable as a criterion for judging skin condition.

[0054] However, since skin condition has an absolute effect on age, it is meaningless to judge skin condition in an absolute manner ignoring age. The dividing line that moves according to age indicates that it is a standard for judging skin condition relative to age. Therefore, the result of multiplying the color value and elasticity value is a standard for judging skin condition relative to age.

[0055] According to various embodiments of the present invention, the condition of a subject can be determined by four phenotypes, taking age into consideration.

[0056] The processor 210 can classify the subject's skin condition into four groups: HH, HL, LH, and LL, using the results of multiplication of the color data and elasticity data, and the results of multiplication of the oil content data and moisture content data. The processor 210 can classify each of the four groups into HH (when color*elasticity and oil*moisture are all high), HL (when color*elasticity is high but oil and moisture content is low), LL (when color*elasticity and oil and moisture content are all low), and LH (when color*elasticity is low but oil and moisture content is high). The four groups have distinctive distributions on the graph.

[0057] The skin type determining device according to various embodiments of the present invention can classify the skin type of a subject by age group. That is, it can determine the relative skin condition by age group, rather than the absolute skin condition. Since skin condition has an absolute effect on age, a determination without considering age may be meaningless.

[0058] Figure 9 shows the distribution position of each subject by age group in the graph of Figure 4 without applying the first age weighting and the second age weighting.

[0059] The first evaluation index is the result of multiplying the color value and the elasticity value, and the second evaluation index is the result of multiplying the oil content value and the moisture value.

[0060] 9, it can be seen that the result values ​​(inverted triangle shapes) of the subject groups in their teens, twenties, thirties, forties, fifties, and sixties gradually move in groups, which means that the first and second evaluation indices intuitively show the relative results of comparing the subject's skin condition with a group of the same age group.

[0061] According to various embodiments of the present invention, even if a person is LL according to the YOUNG group, they may be HH or HL according to the OLD group, and a skin prescription suitable for that person can be provided.

[0062] The center or cluster area or cluster boundary that separates the phenotypes shifts depending on the age group. After determining the center or cluster characteristics, the processor 210 can classify the relative skin condition of each subject.

[0063] One or more programs according to various embodiments of the present invention calculate (in one preferred example, a multiplication operation) a first age weighting determined based on the subject's age with a first evaluation index to determine a first phenotype.

[0064] The one or more processors 210 are configured to calculate (in one preferred example, a multiplication operation) a second age weighting determined based on the subject's age with the second evaluation index to determine a second phenotype.

[0065] For example, the processor 210 may classify subjects into three groups: those aged 34 or younger as the YOUNG group, those aged 35 to 50 as the Aging I group, and those aged 51 or older as the Old group. The first age weighting and the second age weighting may differ depending on gender. The processor 210 may apply the first age weighting and the second age weighting differently depending on age.

[0066] The processor 210 may determine the first age weighting and the second age weighting differently depending on age through machine learning. The first age weighting and the second age weighting may be predetermined. The first age weighting and the second age weighting may be stored in the memory 230.

[0067] Processor 210 can compare the collected color indices of multiple subjects to determine the age at which the inversion phenomenon occurred. For example, processor 210 can determine that the majority of subjects under 35 years old have a color indices greater than 0.5, but that the majority of subjects over 35 years old have a color indices of 0.5 or less (in Figures 5A, 5B, 5C, and 5D, the majority is labeled "High" and the minority is labeled "Low," and the arrows indicate the time points at which the inversion phenomenon occurred).

[0068] In this case, processor 210 determines that age 35 is the age at which a reversal phenomenon occurs in the case of the color tone index. In this case, processor 210 may determine the first age weighting to be 1 for those 35 years of age or older and 0.7 for those under 35 years of age. Similarly, processor 210 determines that age 35 is the age at which a reversal phenomenon occurs in the case of the elasticity index.

[0069] The processor 210 can compare the collected oil content values ​​of multiple subjects to determine the age at which the reversal phenomenon occurred. For example, the processor 210 can determine the second age weighting by dividing subjects into three groups: under 30, between 30 and 50, and over 50.

[0070] For example, the processor 210 may determine that the majority of subjects up to the age of 15 have an oil content value of over 0.5, that the majority of subjects between the ages of 15 and 50 have an oil content value of 0.5 or less, and that the majority of subjects over the age of 50 have an oil content value of 0.5 or more.

[0071] In this case, processor 210 determines that the ages at which the reversal phenomenon occurs are 15 and 50. Processor 210 determines different second age weights for ages around 15 and 50.

[0072] The processor 210 applies the first and second age weightings to account for relative differences due to age, and the processor 210 applies the first and second age weightings to determine the skin condition of the subject relative to the same age.

[0073] 6, the skin conditions of subjects by age group are concentrated in certain areas. However, skin conditions must be judged relatively within the same age range. Therefore, the processor 210 must take age distribution into consideration.

[0074] For example, if processor 210 determines that a teenage subject has objectively better skin condition than a subject in their 70s, but less elasticity than other teens, processor 210 can compare the phenotype of the teenage subject with the same age group and assign a symbol representing a low, deficient, or poor first phenotype. Conversely, processor 210 can compare the phenotype of a subject in their 70s with the same age group and assign a symbol representing a high, sufficient, or excellent first phenotype.

[0075] The processor 210 may determine the second age weighting and the second phenotype in the same manner as the first age weighting and the first phenotype.

[0076] In some embodiments, memory 230 may pre-store information regarding the first age weighting and the second age weighting.

[0077] A skin type determination device according to various embodiments of the present invention is a device using a skin type determination device having a color sensor 110, an elasticity sensor 120, a moisture sensor 140, an oil sensor 130, and one or more processors 210, and includes steps of measuring the color, elasticity, oil, and moisture of a subject's skin; and determining a first evaluation index and a second evaluation index by the one or more processors 210, wherein the first evaluation index is determined by the processor 210 changing the color data to a value within a predetermined range to determine a color value, changing the elasticity data to a value within a predetermined range to determine an elasticity value, and calculating the color value and the elasticity value (a multiplication operation is one of the preferred examples); and the second evaluation index is determined by the processor 210 changing the oil data to a value within a predetermined range to determine an oil value, changing the moisture data to a value within a predetermined range to determine a moisture value, and calculating the oil value and the moisture value (a multiplication operation is one of the preferred examples).

[0078] A skin type determination device or system according to various embodiments of the present invention includes an oil sensor 130 for measuring oil content on a subject's skin; a moisture sensor 140 for measuring moisture content on the subject's skin; a memory 230 for storing oil content data measured by the oil sensor 130 and moisture content data measured by the moisture sensor 140; and one or more processors 210, wherein the one or more processors 210 are configured to: normalize the oil content data to determine an oil value; normalize the moisture data to determine a moisture value; and determine a second evaluation index defining the subject's skin type based on the oil value and the moisture value (one preferred example being a multiplication operation).

[0079] A skin type determination device or system according to various embodiments of the present invention includes a tone sensor 110 for measuring the tone of a subject's skin; an elasticity sensor 120 for measuring the elasticity of the subject's skin; one or more processors 210; a memory 230; and one or more programs stored in the memory 230 and configured to be executed by the one or more processors 210, the one or more programs including instructions for determining a tone value by converting tone data measured by the tone sensor 110 to a value within a predetermined range, determining an elasticity value by converting elasticity data measured by the elasticity sensor 120 to a value within a predetermined range, and calculating the tone value and the elasticity value (one preferred example being a multiplication operation) to determine a first evaluation index defining the subject's skin type.

[0080] The value within the defined range may be between 0 and 1. The one or more programs calculate (in one preferred example, a multiplication operation) a first age weighting determined based on the age of the subject and the first evaluation index to determine a first phenotype, and store the first phenotype and a second phenotype in memory 230.

[0081] A skin type determination device or system according to various embodiments of the present invention includes a moisture sensor 140 for measuring moisture in a subject's skin; one or more processors 210; a memory 230; and one or more programs stored in the memory 230 and configured to be executed by the one or more processors 210, the one or more programs determining a moisture value by converting moisture data measured by the moisture sensor 140 into a value within a predetermined range; the moisture sensor 140 includes a first moisture measuring device for measuring transepidermal water loss (TEWL) in the subject's skin; and a second moisture measuring device for measuring hydration (HD) in the subject's skin; the one or more programs including instructions for converting the moisture loss and moisture content into values ​​within a predetermined range and determining a moisture value using the following formula to determine skin condition:

[0082] JPEG2025536029000003.jpg1633

[0083] where M is the moisture value, HD is the moisture content, and TEWL is the amount of water loss.

[0084] A skin type determination device or system according to various embodiments of the present invention includes a color sensor 110 for measuring the color of a subject's skin; an elasticity sensor 120 for measuring the elasticity of the subject's skin; an oil sensor 130 for measuring the oil content of the subject's skin; a moisture sensor 140 for measuring the moisture content of the subject's skin; a memory 230 for storing color data measured by the color sensor 110 and elasticity data measured by the elasticity sensor 120; and one or more processors 210, wherein the one or more processors 210 are configured to: determine a color value by changing the color data to a value within a predetermined range; determine an elasticity value by changing the elasticity data to a value within a predetermined range; determine a first evaluation index defining the subject's skin type by multiplying the color value and the elasticity value; determine the oil value by changing the oil data to a value within a predetermined range; determine a moisture value by changing the moisture data to a value within a predetermined range; and determine a second evaluation index defining the subject's skin type based on the oil value and the moisture value (one preferred example is a multiplication operation).

[0085] The memory 230 stores a standardization or normalization program that is executed by one or more processors 210 to change values ​​to within a defined range.

[0086] FIG. 10 is a flowchart of a method for determining a skin type according to various embodiments of the present invention.

[0087] Referring to FIG. 10, a skin type determination method according to various embodiments of the present invention uses a skin type determination device having a color sensor 110, an elasticity sensor 120, a moisture sensor 140, an oil sensor 130, and one or more processors 210. The method includes measuring the color, elasticity, oil, and moisture of a subject's skin (S100); and determining a first evaluation index and a second evaluation index by the one or more processors 210. The first evaluation index is determined by the processor 210 changing the color data to a value within a predetermined range to determine a color value, changing the elasticity data to a value within the predetermined range to determine an elasticity value (S110), and calculating the color value and the elasticity value (a preferred example is a multiplication operation) (S130). The second evaluation index is determined by the processor 210 changing the oil data to a value within a predetermined range to determine an oil value, changing the moisture data to a value within the predetermined range to determine a moisture value (S210), and calculating the oil value and the moisture value (a preferred example is a multiplication operation) (S230).

[0088] According to various embodiments of the present invention, the processor 210 performs step S150 of determining a first phenotype by multiplying a first evaluation index by a first age weighting determined based on the subject's age. According to various embodiments of the present invention, the processor 210 performs step S250 of determining a second phenotype by multiplying a second evaluation index by a second age weighting determined based on the subject's age. One or more processors 210 may change the amount of water loss and water content to values ​​within a predetermined range and use this to determine a water content value. The formula for determining a water content value has been described above.

[0089] The skin type determination device and / or system according to various embodiments of the present invention can represent the first and second phenotypes by adding an age symbol rather than multiplying the age weighting as follows:

[0090] JPEG2025536029000004.jpg3972

[0091] The memory 230 can store the subject's skin condition by classifying it into phenotypes such as H / H, H / L, L / H, and L / L. The prefix H or L in the phenotype can mean high or low, or excellent or insufficient skin tone and elasticity. The suffix H or L in the phenotype can mean high or low, or excellent or insufficient oil and moisture content. The phenotype is used as data for improving the subject's skin condition. The above content can also be organized into mathematical formulas such as the following:

[0092] Referring to FIG. 11, stage A concerns the age group derivation algorithm (Female & 5 parameters, N=775).

[0093] There are various criteria for determining age, but in the above example, Young was defined as 34 years old or younger, Aging1 was defined as 35 to 50 years old, and Old was defined as 51 years old or older.

[0094] Next, sensors are used to measure five parameters: color, elasticity, oil content, moisture, and moisture loss. In step B of Figure 11, high and low levels are determined based on color and elasticity (Clinical data & Survey, N=705). In step C of Figure 11, high and low levels are determined based on oil content and moisture content.

[0095] The formula for standardizing various data according to the present invention is as follows:

[0096] JPEG2025536029000005.jpg1785

[0097] The above formula is for changing the data to a value within the range of 0 to 200.

[0098] JPEG2025536029000006.jpg20128

[0099] The above formula represents a multiplication operation of standardized or normalized data for color and elasticity.

[0100] JPEG2025536029000007.jpg29128

[0101] The above formula represents a multiplication operation of standardized or normalized data for oil and water.

[0102] JPEG2025536029000008.jpg11128

[0103] The above formula expresses the formula for deriving oil content data, and oil content is collected from the forehead, nose, and cheeks, and then the average value is used.

[0104] JPEG2025536029000009.jpg11128

[0105] The above formula expresses a formula for deriving moisture content data, and moisture is collected from the forehead and cheeks, and the average value is used.

[0106] JPEG2025536029000010.jpg11128

[0107] The above formula expresses a formula for deriving moisture loss data, and moisture is collected from the forehead and cheeks, and the average value is used.

[0108] The method for dividing the aging groups is as follows.

[0109] JPEG2025536029000011.jpg25128

[0110] Quantiles are tertile groups and were used to clearly separate the central group (Grey zone) from the remaining upper group (H) and lower group (L).

[0111] To explain the above formula, first, SS(x) of color, elasticity, oil content, and moisture content is calculated as GRP(x ss ) to execute the command.

[0112] JPEG2025536029000012.jpg1374

[0113] Next, age is extracted.

[0114] The percentage (%) of GRP(x) for each age group is determined from the clinical measurements. Then, the age at which the proportion of H type samples becomes lower than the proportion of L type samples is determined.

[0115] The derived example is as follows:

[0116] JPEG2025536029000013.jpg670

[0117] JPEG2025536029000014.jpg1980

[0118] The following is a mathematical formula that indicates that if the value is greater than or equal to the specified value, it is represented as High, and if it is less than the specified value, it is represented as Low.

[0119] JPEG2025536029000015.jpg1556

[0120] The following describes how the phenotype for color-springiness (TE) can be expressed as H or L as described above.

[0121] JPEG2025536029000016.jpg745

[0122] The following describes the phenotypes related to oil-moisture (OM), which can be expressed as H or L.

[0123] JPEG2025536029000017.jpg17128

[0124] Below are the age-appropriate groups along with the phenotype for tone-elasticity (TE): In the example below, the subject is in the Young group.

[0125] JPEG2025536029000018.jpg7128

[0126] Below are the relevant age groups along with their oil-moisture (OM) phenotypes. In the example below, the subject is in the Young group.

[0127] JPEG2025536029000019.jpg16146

[0128] The information on tone-elasticity (TE), oil-moisture (OM) and age can be expressed as follows:

[0129] JPEG2025536029000020.jpg7128

[0130] JPEG2025536029000021.jpg3839

[0131] The effects of the present invention are as follows.

[0132] According to the present invention, the processor 210 can obtain the first and second evaluation indexes by multiplying the four types of data again.

[0133] Furthermore, according to the present invention, the processor 210 can determine the first and second evaluation indexes to group the subjects, and the data can be used as machine learning data after normalization or standardization.

[0134] In addition, the processor 210 according to the present invention can perform a relative evaluation taking age into consideration by applying the first and second age weightings to color, elasticity, oiliness, and moisture.

[0135] Furthermore, the processor 210 according to the present invention can objectively evaluate skin conditions by processing only four types of data. Elasticity is highly correlated with pores and wrinkles. Therefore, the following first and second phenotypes can be considered to essentially include all six indices, including pores and wrinkles.

[0136] Additionally, according to the present invention, the processor 210 can use the moisture value to independently determine the skin moisture status without taking into account age groups.

[0137] Although the preferred embodiments of the present invention have been illustrated and described above, the present invention is not limited to the specific embodiments described above. Of course, various modifications can be made by a person having ordinary skill in the art to which the invention pertains without departing from the gist of the present invention as claimed in the claims, and such modifications should not be understood individually from the technical ideas and perspectives of the present invention.

Claims

1. a color tone sensor for measuring the skin tone of the subject; an elasticity sensor for measuring the elasticity of the subject's skin; one or more processors; Memory and one or more programs stored in the memory and configured to be executed by the one or more processors; The one or more programs determining a color index by converting the color data measured by the color sensor into a value within a predetermined range; determining an elasticity index by converting the elasticity data measured by the elasticity sensor into a value within a predetermined range; a skin type determining device including instructions for defining a subject's skin type based on said tone index and said elasticity index;

2. The skin type determining device of claim 1 , wherein the value within the defined range is between 0 and 1.

3. The one or more programs The skin type determination device according to claim 1, wherein the first phenotype is determined by calculating a first age weighting determined based on the age of the subject and a first evaluation index obtained based on the color tone index and the elasticity index.

4. an oil sensor for measuring the oil content of the subject's skin; further comprising a moisture sensor for measuring moisture on the subject's skin; The one or more programs The individual skin condition data measured by the sensor is quantified according to a predetermined standard and divided into groups; The skin type determining device according to claim 1 , further comprising an instruction to select type data having similar patterns of specific gravity change by group according to age to determine the skin condition of the subject.

5. The pattern is The skin type determining device according to claim 4, wherein the similarity is the similarity of age groups in which a reversal phenomenon occurs, in which the ranking of weights by group is reversed.

6. The skin type determining device according to claim 4 , wherein the type data having similar group-specific gravity change patterns are the color data and the elasticity data.

7. The one or more programs The skin type determining device according to claim 6 , further comprising an instruction to select the remaining data in which the group-by-group specific gravity change patterns are dissimilar to determine the skin condition of the subject.

8. The device for determining a skin type according to claim 7, wherein the remaining data having dissimilar group-by-group specific gravity change patterns are oil data measured by the oil sensor and moisture data measured by the moisture sensor.

9. The one or more programs changing the oil content data to a value within a predetermined range to determine an oil content index; changing the moisture data to a value within a predetermined range to determine a moisture index; The skin type determining device of claim 4 , further comprising instructions for defining a subject's skin type based on the oil index and the moisture index.

10. an oil sensor for measuring the oil content of the subject's skin; a moisture sensor for measuring moisture in the subject's skin; a memory for storing oil content data measured by the oil sensor and moisture content data measured by the moisture sensor; one or more processors; The one or more processors: normalizing the oil content data to determine an oil content index; normalizing the moisture data to determine a moisture index; A skin type determination device configured to multiply the oil index and the moisture index to determine the skin type of the subject.

11. The memory includes: The skin type determination device of claim 10, further comprising a standardization or normalization program executed by the one or more processors so that the measured individual subject data can be changed to values ​​within a defined range.

12. an oil sensor for measuring the oil content of the subject's skin; a moisture sensor for measuring moisture in the subject's skin; a color tone sensor for measuring the skin tone of the subject; an elasticity sensor for measuring the elasticity of the subject's skin; one or more processors; Memory and one or more programs stored in the memory and configured to be executed by the one or more processors; The one or more programs determining an oil content index by changing the oil content data measured by the oil content sensor to a value within a predetermined range; determining a moisture index by converting the moisture data measured by the moisture sensor into a value within a predetermined range; determining a color index by converting the color data measured by the color sensor into a value within a predetermined range; determining an elasticity index by converting the elasticity data measured by the elasticity sensor into a value within a predetermined range; multiplying the tone index and the elasticity index to determine the subject's skin type; The skin type determining device includes instructions for multiplying the oil index and the moisture index to determine the skin type of the subject.

13. The one or more programs determining a first phenotype by multiplying a first evaluation index obtained by multiplying the color index and the elasticity index by a first age weighting determined based on the age of the subject; a second age weighting determined based on the age of the subject and a second evaluation index obtained by multiplying the oil index and the moisture index to determine a second phenotype; The skin type determination device of claim 12 , wherein the first phenotype and the second phenotype are stored in the memory.

14. The moisture sensor is a first moisture measuring device for measuring transepidermal water loss (TEWL) of a subject; a second moisture measuring device for measuring the moisture content (hydration, HD) of the subject's skin; The one or more programs changing the amount of water loss and the amount of water content to values ​​within a defined range; The moisture index is determined using the following formula:

13. The skin type determining device of claim 12, wherein M is the moisture index, HD is the skin moisture content, and TEWL is the skin water loss.

15. The memory includes: The skin type determination device of claim 12, further comprising a standardization or normalization program executed by the one or more processors so that the measured individual subject data can be changed to values ​​within a defined range.

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