Prediction device, prediction method, and prediction system

Abstract

To provide a technique capable of easily acquiring a shape of a foot of a person to be measured in a no-load state.SOLUTION: A prediction device 1 includes: a communication device 16 that acquires person-to-be-measured data including measurement data of a shape of a foot of a person to be measured in a load state; a storage 13 that stores first sample data in the load state and second sample data in a no-load state that are calculated from measurement data of the shape of the foot of a plurality of samples that are the same in the load state and the no-load state; and a processor 11 that predicts the shape of the foot of the person to be measured in the no-load state. The processor 11 calculates a difference between the person-to-be-measured data and the first sample data, and predicts the shape of the foot of the person to be measured in the no-load state on the basis of the difference and the second sample data.SELECTED DRAWING: Figure 8

Description

[Technical Field]

[0001] The present disclosure relates to a prediction device, a prediction method, and a prediction system for predicting the shape of a subject's foot in a no-load state. [Background technology]

[0002] Custom-made shoes or insoles (insoles) tailored to the shape of an individual's foot are generally made based on the shape of the foot, particularly the shape of the sole. The shape of the foot differs between a weight-bearing state in which weight is applied to the sole and a non-weight-bearing state in which no weight is applied to the sole. For example, the shape of the foot in the non-weight-bearing state does not deform due to weight, making it possible to make shoes or insoles that are more suited to the shape of the foot than the shape of the foot in the weight-bearing state.

[0003] A known method for measuring feet in a non-weight-bearing state involves having the subject lie face down on a bed, wrapping a plaster bandage (cast) around the subject's foot, and then pouring plaster into the hardened plaster bandage to create a mold of the foot shape. However, measuring foot shape using a plaster bandage requires a skilled expert and tends to take a long time to create the mold. Furthermore, it requires a bed for creating the mold and space to handle the plaster. For this reason, measuring foot shape using a plaster bandage is difficult to perform in a store such as a shoe store.

[0004] In this regard, Patent Document 1 discloses a method of obtaining pressurized state data, which is measurement data of the shape of the foot of a person being measured standing on a transparent plate, and unpressurized state data, which is measurement data of the shape of the foot of a person being measured lightly touching a transparent plate, and creating an insole based on the difference between the pressurized state data and the unpressurized state data. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Patent No. 5717894 Summary of the Invention [Problem to be solved by the invention]

[0006] According to the method disclosed in Patent Document 1, an insole suited to the shape of the subject's foot can be produced based on the shape of the subject's foot in both an unloaded and loaded state. However, the method disclosed in Patent Document 1 requires the store to measure the shape of the subject's foot both in an unloaded and loaded state, which can easily take a long time to complete. Furthermore, the method disclosed in Patent Document 1 requires the subject to keep the sole of their foot lightly in contact with a transparent plate to minimize the load on the sole, making it difficult to obtain accurate measurement data and requiring skilled store clerks to perform the measurements.

[0007] The present disclosure has been made to solve such problems, and its purpose is to provide a technology that can easily acquire the shape of a subject's foot in a no-load state. [Means for solving the problem]

[0008] A prediction device according to one aspect of the present disclosure includes an acquisition unit that acquires subject data including measurement data of the subject's foot shape under load, a storage unit that stores first sample data under load and second sample data under no load calculated from the measurement data of the same multiple samples of the foot shape under load and no load, and a prediction unit that predicts the subject's foot shape under no load. The prediction unit calculates the difference between the subject data and the first sample data, and predicts the subject's foot shape under no load based on the difference and the second sample data.

[0009] A prediction method according to an aspect of the present disclosure includes the steps of acquiring subject data including measurement data of the subject's foot shape in a loaded state, storing first sample data in a loaded state and second sample data in an unloaded state calculated from the same plurality of sample foot shape measurement data in both the loaded and unloaded states, and predicting the subject's foot shape in the unloaded state. The predicting step includes the steps of calculating a difference between the subject data and the first sample data, and predicting the subject's foot shape in the unloaded state based on the difference and the second sample data.

[0010] A prediction system according to one aspect of the present disclosure includes a measurement device that measures the foot shape of a subject under load and a prediction device that predicts the foot shape of the subject under no load. The prediction device includes an acquisition unit that acquires subject data including measurement data of the subject's foot shape under load from the measurement device, a memory unit that stores first sample data under load and second sample data under no load calculated from the foot shape measurement data of multiple identical samples under load and no load, and a prediction unit that predicts the subject's foot shape under no load. The prediction unit calculates the difference between the subject data and the first sample data and predicts the subject's foot shape under no load based on the difference and the second sample data. [Effects of the Invention]

[0011] According to the present disclosure, the shape of the subject's foot in a no-load state can be easily obtained. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a schematic diagram illustrating a configuration of a prediction system according to an embodiment. [Figure 2] 1A and 1B are diagrams showing the shape of the foot in an unloaded state and in a loaded state. [Figure 3] FIG. 1 is a diagram for explaining measurement items of foot shape. [Figure 4] FIG. 10 is a diagram for explaining the curve of a foot. [Figure 5] FIG. 1 is a cross-sectional view of a foot. [Figure 6] FIG. 10 is a diagram illustrating foot shape data. [Figure 7] FIG. 1 is a diagram for explaining a homology model. [Figure 8] 1 is a block diagram showing a configuration of a prediction device according to an embodiment. [Figure 9] FIG. 10 is a diagram showing an example of load-bearing foot shape data stored by the prediction device. [Figure 10] FIG. 10 is a diagram illustrating an example of acquisition of load-bearing foot shape data. [Figure 11] 10A and 10B are diagrams for explaining a reference foot length and a perpendicular foot width. [Figure 12] FIG. 10 is a diagram showing an example of no-weight footprint data stored in the prediction device. [Figure 13] FIG. 10 is a diagram showing an example of acquisition of no-load footprint data. [Figure 14] 10 is a flowchart illustrating a prediction process executed by a prediction device according to an embodiment. [Figure 15] FIG. 10 is a diagram for explaining calculation of the difference between subject data and first sample data. [Figure 16] FIG. 10 is a diagram illustrating a change in sample data based on a difference. [Figure 17] 10A and 10B are diagrams for explaining an example of making an insole. [Figure 18] FIG. 10 is a diagram for explaining a change in the second sample data based on an apparent arch. DETAILED DESCRIPTION OF THE INVENTION

[0013] Hereinafter, embodiments will be described with reference to the drawings. In the following description, the same components are denoted by the same reference numerals. The names and functions of the components are also the same. Therefore, detailed description thereof will not be repeated.

[0014] [Configuration of the forecasting system] 1 is a schematic diagram showing the configuration of a prediction system 100 according to an embodiment. Shoe stores and other such shops produce custom-made shoes or insoles tailored to the shape of an individual's foot. The shape of the foot in a no-load state is not deformed by a load, making it possible to produce shoes or insoles that are more suited to the shape of the foot than the shape of the foot in a loaded state.

[0015] For example, Figure 2 shows the shape of the foot in an unloaded state and in a loaded state. As shown in Figure 2(A), the contour line, medial and lateral ground contact lines, the transverse arch, medial arch and lateral arch, and the heel cup appear relatively clearly on the sole of the foot in an unloaded state.

[0016] The contour line is a line that shows the outer shape of the foot. The medial ground line is the ground line that appears on the inside of the foot, on the side of the first toe. The lateral ground line is the ground line that appears on the outside of the foot, on the side of the fifth toe. The medial arch is the arch formed from the calcaneus to the first metatarsal. The lateral arch is the arch formed from the calcaneus to the fifth metatarsal. The transverse arch is the arch formed between the medial arch and the lateral arch. The heel cup is the shape that appears on the heel part of the sole of the foot. Each of these parts of the sole has the role of absorbing shock when running and improving balance when standing.

[0017] On the other hand, as shown in Figure 2(B), when the sole of the foot is under load, the contour line, medial contact line, and lateral contact line can be distinguished, but the transverse arch, medial arch, lateral arch, and heel cup are difficult to distinguish.

[0018] In this disclosure, the term "loaded state" refers to a state that may affect the determination of the arch and heel cup, and includes, for example, a state in which the subject's foot is on the ground, as shown in FIG. 1 and FIG. 10(A) described later. In contrast, the term "unloaded state" refers to a state that does not affect the determination of the arch and heel cup, and includes, for example, a state in which the subject's foot is not on the ground, as shown in FIG. 13(A) described later. The "unloaded state" may also refer to a state in which a part of the subject's foot is in contact with the ground or some other object, as long as it does not affect the determination of the arch and heel cup.

[0019] Thus, in order to create shoes or insoles that fit the foot shape, it is preferable to acquire the foot shape in an unloaded state. However, acquiring the foot shape in an unloaded state in a store requires, for example, wrapping a plaster bandage around the subject's foot and pouring plaster into the hardened plaster bandage to capture the foot shape, which can take a long time to measure and requires the skill of the person taking the measurement. Therefore, the prediction system 100 according to the embodiment is configured to easily acquire the foot shape of the subject in an unloaded state.

[0020] 1, the prediction system 100 includes a measurement device 2 and a prediction device 1. Note that, although the embodiment shows an example in which data for producing an insole is generated using the prediction system 100, the technology of the present disclosure may also be applied to an example in which data for producing custom-made shoes is generated using the prediction system 100.

[0021] The measuring device 2 is, for example, a three-dimensional foot shape scanner that uses laser measurement, and includes a tabletop 21 and a laser measurement unit 22 that is installed so as to sandwich the tabletop. When the person being measured places their foot on the tabletop 21 in a standing position, their weight exerts a load from their foot onto the tabletop 21. In other words, the person being measured is in a state where their foot is under load. With the weight on the person being measured's foot, the measuring device 2 measures the shape of the foot while moving from the toes to the heel using the laser measurement unit 22. The measuring device 2 outputs to the prediction device 1 person data that includes measurement data (3D data) of the person's foot shape acquired by the laser measurement unit 22. Note that the person data only needs to include at least the measurement data of the foot shape acquired by the measuring device 2, and may also include other data (for example, personal data such as the person's gender or age).

[0022] The prediction device 1 acquires subject data from the measurement device 2 and predicts the shape of the subject's foot in a no-load state based on the subject data. The prediction of the subject's foot shape in a no-load state by the prediction device 1 will be described in detail later. The prediction device 1 outputs the predicted shape data of the subject's foot in a no-load state to a 3D printer 3 or the like that produces insoles.

[0023] In this way, in the prediction system 100, the prediction device 1 can predict the shape of the subject's foot in an unloaded state based on the shape of the subject's foot in a loaded state acquired by the measurement device 2. This allows the user of the prediction system 100 to easily acquire the shape of the subject's foot in an unloaded state.

[0024] [Foot shape measurement items] The measurement items of the foot shape using the measuring device 2 will be described with reference to Fig. 3. Fig. 3 is a diagram for explaining the measurement items of the foot shape. As shown in Fig. 3, the measurement items of the foot shape include foot length, foot circumference, orthogonal foot width, heel width, foot height, angle of the first toe, heel inclination angle, and arch height.

[0025] As shown in FIG. 3(A), the foot length is the distance from the rear end of the heel to the tip of the longest toe. As shown in FIG. 3(B), the foot circumference is the distance between a position on the fifth toe side that is A% of the foot length (e.g., the base of the fifth toe) and a position on the first toe side that is B% of the foot length (e.g., the base of the first toe). As shown in FIG. 3(C), the orthogonal foot width is the widthwise distance of the foot between a position on the fifth toe side that is C% of the foot length (e.g., the base of the fifth toe) and a position on the first toe side that is D% of the foot length (e.g., the base of the first toe). Note that A to D are values greater than 0 and may be predetermined by standards or may be arbitrarily set. Furthermore, A may have the same value as C, and B may have the same value as D.

[0026] As shown in FIG. 3(D), heel width is the widthwise length of the heel between a point on the fifth toe side that is E% of the foot length from the rear end of the heel and a point on the first toe side that is E% of the foot length from the rear end of the heel. As shown in FIG. 3(E), foot height is the height of the foot at a point on the first toe side that is F% of the foot length from the rear end of the heel. As shown in FIG. 3(F), the first toe side angle is the angle at which the first toe tilts toward the fifth toe. As shown in FIG. 3(G), the heel inclination angle is the angle at which the heel is inclined relative to a direction perpendicular to the ground. As shown in FIG. 3(H), arch height is the height from the ground to the navicular bone. Arch height may be calculated using a formula based on foot length, foot circumference, heel width, foot height, first toe side angle, and heel inclination angle. E and F are values greater than 0, and may be predetermined by standards or may be arbitrarily set.

[0027] [Degree of leg bending] The degree of bending of the foot will be described with reference to Figures 4 and 5. Figure 4 is a diagram illustrating the curve line of the foot. Figure 5 is a diagram showing a cross section of the foot. As shown in Figure 4, the curve line of the foot is defined within a range of the medial arch length corresponding to G% of the foot length, starting from the rear end of the heel, and is a line that indicates the degree of bending of the foot. Note that G is a value greater than 0, and is set to a predetermined value within the range of, for example, 70% to 75%. G may be predetermined according to a standard or may be set arbitrarily.

[0028] If the midpoint between the contour line and the inner ground line is X and the midpoint between the contour line and the outer ground line is Y, the line passing through the midpoint Z between point X and point Y will be the curved line.

[0029] FIG. 5 shows the A-A' cross section of the foot shown in FIG. 4. FIG. 5 also shows a cross section of the foot taken in an unloaded state. As shown in FIG. 5, if the line passing through the part of the foot that is in contact with the ground is taken as the lowest line, the medial ground contact point can be represented by the intersection of line L, which passes through a height of H mm from the lowest line, with the outline of the medial side of the foot. Because such medial ground contact points are set for each cross section, a line connecting multiple medial ground contact points set for each cross section in the foot length direction generally coincides with the medial ground contact line shown in FIG. 4. Furthermore, the lateral ground contact point can be represented by the intersection of line L with the outline of the lateral side of the foot. Because such lateral ground contact points are set for each cross section, a line connecting multiple lateral ground contact points set for each cross section in the foot length direction generally coincides with the lateral ground contact line shown in FIG. 4. Note that H is a value greater than 0 and may be predetermined by a standard or may be arbitrarily set.

[0030] The point of contact P1 between a line inclined at a degree from the ground toward the inside of the foot (medial a-degree line) and the outline of the inside of the foot becomes part of the top line of the insole on the inside of the foot. Also, the point of contact P2 between a line inclined at a degree from the ground toward the outside of the foot (lateral a-degree line) and the outline of the outside of the foot becomes part of the top line of the insole on the outside of the foot. Note that a is a value greater than 0 and is set to a predetermined value within the range of, for example, 45 degrees to 65 degrees. a may be predetermined according to a standard or the like, or may be set arbitrarily.

[0031] Furthermore, the point of contact between a line inclined at b degrees from the ground toward the inside of the foot (medial b-degree line) and the outline of the inside of the foot is defined as Q1, and the point of contact between a line inclined at b degrees from the ground toward the outside of the foot (lateral a-degree line) and the outline of the outside of the foot is defined as Q2. Note that b is a value greater than 0 and is set to a predetermined value within the range of, for example, 15 to 30 degrees. b may be predetermined according to standards or may be set arbitrarily.

[0032] The outer portion of the upper foot formed from contact point P1 to contact point P2 is also referred to as the instep. The outer portion of the lower foot formed from contact point P1 to contact point Q1 is also referred to as the inside of the roll-up. The outer portion of the lower foot formed from contact point P2 to contact point Q2 is also referred to as the outside of the roll-up. The outer portion of the lower foot formed from contact point Q1 to contact point Q2 is also referred to as the bottom.

[0033] [Foot shape data] Foot shape data used when producing an insole will be described with reference to Fig. 6. Fig. 6 is a diagram for explaining foot shape data. As shown in Fig. 6, the foot shape data includes position data for each of a plurality of configuration points arranged along the outline of the cross section of the foot.

[0034] For example, in the example of Figure 6, the shape data includes, in the cross section of the foot, 25 constituent points obtained by dividing the bottom part into 25 equal parts, 8 constituent points obtained by dividing the inside of the roll-up into 8 equal parts, 8 constituent points obtained by dividing the outside of the roll-up into 8 equal parts, and 40 constituent points obtained by dividing the instep part into 40 equal parts.

[0035] In the shape data, multiple constituent points (81 points in the example of FIG. 6) arranged on such cross sections are arranged at a predetermined interval (for example, every 1 mm) along the length of the foot. That is, for a foot with a foot length of 255 mm, the shape data includes position data for each of the 81 constituent points arranged on 255 cross sections. Note that the number of constituent points on each of the sole, inner roll-up, outer roll-up, and upper is not limited to the above numbers and can be set arbitrarily.

[0036] [Homologous model] The homologous model will be described with reference to FIG. 7. FIG. 7 is a diagram for explaining the homologous model. As shown in FIG. 7, the homologous model is a foot shape model that represents the shape of the foot using multiple lines. Specifically, 81 lines can be created in the foot length direction by connecting the constituent points of the sole, inner roll-up, outer roll-up, and instep obtained for each cross section of the foot as shown in FIG. 6 with lines in the foot length direction. A homologous model can be created using these 81 lines. For example, for a foot with a foot length of 255 mm, a homologous model including 20,655 constituent points (position data) (255 x 81 points) can be created.

[0037] [Configuration of prediction device] Fig. 8 is a block diagram showing the configuration of a prediction device 1 according to an embodiment. As shown in Fig. 8, the prediction device 1 includes a processor 11, a memory 12, a storage 13, an interface 14, a media reading device 15, and a communication device 16. These components are connected via a processor bus 17.

[0038] The processor 11 is an example of a "prediction unit." The processor 11 is a computer that reads out a program (for example, an OS (Operating System) 132 and a prediction program 131) stored in the storage 13, and deploys the read out program in the memory 12 and executes it. The processor 11 is configured, for example, by a CPU (Central Processing Unit), an FPGA (Field Programmable Gate Array), a GPU (Graphics Processing Unit), or an MPU (Multi Processing Unit). The processor 11 may also be configured by a processing circuitry.

[0039] The memory 12 is configured by a volatile memory such as a dynamic random access memory (DRAM) or a static random access memory (SRAM), or a non-volatile memory such as a read only memory (ROM) or a flash memory.

[0040] The storage 13 is an example of a "storage unit." The storage 13 is configured, for example, by a nonvolatile storage device such as a hard disk drive (HDD) or a solid state drive (SSD). The storage 13 stores a prediction program 131, an OS 132, load-bearing footprint data 133, and no-load-bearing footprint data 134.

[0041] The prediction program 131 is a program for executing a process (prediction process shown in Figure 14 described later) in which the prediction device 1 predicts the shape of the subject's foot in an unloaded state based on the shape of the subject's foot in an unloaded state obtained by the measurement device 2.

[0042] The load-bearing foot shape data 133 is an example of "first sample data." The load-bearing foot shape data 133 includes data calculated from measurement data of the foot shapes of a plurality of samples in a load-bearing state. The load-bearing foot shape data 133 will be described later with reference to FIGS. 9 to 11.

[0043] The no-load foot shape data 134 is an example of "second sample data." The no-load foot shape data 134 includes data calculated from measurement data of the foot shapes of multiple samples in a no-load state. The no-load foot shape data 134 will be described later with reference to FIGS. 12 and 13.

[0044] The interface 14 receives input from the user of the prediction device 1 and is composed of a keyboard, a mouse, a touch device, and the like.

[0045] The media reader 15 accepts a storage medium such as a removable disk 18 and obtains data stored on the removable disk 18 .

[0046] The communication device 16 is an example of an "acquisition unit." The communication device 16 transmits and receives data to and from other devices by performing wired or wireless communication. For example, the communication device 16 communicates with the measurement device 2 to acquire foot shape measurement data acquired by the measurement device 2 from the measurement device 2. The communication device 16 communicates with the 3D printer 3 to output foot shape data to be used to create an insole to the 3D printer 3.

[0047] Note that prediction device 1 is not limited to acquiring foot shape measurement data from measurement device 2 using communication device 16. For example, prediction device 1 may acquire foot shape measurement data input by a user using interface 14. In this case, interface 14 is an example of an "acquisition unit." Alternatively, prediction device 1 may read foot shape measurement data stored on removable disk 18 using media reading device 15. In this case, media reading device 15 is an example of an "acquisition unit."

[0048] [Load footprint data] Referring to FIGS. 9 to 11, the load footprint data 133 will be described. FIG. 9 is a diagram showing an example of the load footprint data stored in the prediction device 1. FIG. 10 is a diagram showing an example of the acquisition of the load footprint data. FIG. 11 is a diagram for explaining the reference foot length and the orthogonal foot width.

[0049] As shown in FIG. 9, the load footprint data includes data obtained by classifying measurement data of the shape of the foot of a plurality of samples in a load state (specifically, a homologous model created based on the measurement data) for each of a plurality of foot types based on at least one feature amount related to the shape of the foot. In the example shown in FIG. 9, as the at least one feature amount, the arch height ratio and the heel inclination angle (the angle of falling inward of the heel) are used. Then, the load footprint data is classified into a total of nine foot types by three arch types classified based on the arch height ratio and three heel inclination types classified based on the heel inclination angle.

[0050] The arch type is classified by the arch height ratio calculated by dividing the arch height (FIG. 3(H)) by the foot length (FIG. 3(A)). When the arch height ratio of the sample foot is less than A1%, the sample foot is classified as flat. When the arch height ratio of the sample foot is A1% or more and less than A2%, the sample foot is classified as average. When the arch height ratio of the sample foot is A2% or more, the sample foot is classified as high arch. Here, A1 and A2 are values greater than 0 (0 < A1 < A2). For example, A1 is set to a predetermined value within the range of 12% to 16%, and A2 is set to a predetermined value within the range of 18% to 22%. These A1 and A2 may be determined in advance according to standards or the like, or may be set arbitrarily.

[0051] The heel inclination type is calculated based on the heel inclination angle (Figure 3(G)). If the heel inclination angle of the sample foot is less than B1 degrees, the sample foot is classified as inversion. If the heel inclination angle of the sample foot is equal to or greater than B1 degrees and less than B2 degrees, the sample foot is classified as average. If the heel inclination angle of the sample foot is equal to or greater than B2 degrees, the sample foot is classified as eversion. For example, B1 is set to a predetermined value within the range of -2 degrees to 0 degrees, and B2 is set to a predetermined value within the range of 2 degrees to 5 degrees. B1 and B2 may be predetermined by standards or may be arbitrarily set.

[0052] In generating the weight-bearing foot shape data, first, the measurement device 2 acquires the foot shapes of each of a plurality of sample subjects in a weight-bearing state, as shown in Fig. 10(A). The method for measuring the sample subjects' feet using the measurement device 2 is the same as the method for measuring the subject's feet using the measurement device 2 shown in Fig. 1. Specifically, when the sample subject places their feet on the tabletop 21 in a standing position, a load is applied from the sample subject's feet to the tabletop 21 due to the sample subject's weight. With the sample subject's feet under weight, the measurement device 2 measures the foot shapes by moving from the toes to the heels using the laser measurement unit 22.

[0053] As shown in Fig. 10(B), the measurement data of the sample's foot shape acquired by the measurement device 2 is classified into a plurality of foot shape types based on at least one feature value (in this example, arch height ratio and heel tilt angle) related to the sample's foot shape. Specifically, a homologous model as shown in Fig. 7 is created based on the measurement data of the sample's foot shape acquired by the measurement device 2, and the created homologous model is classified into a plurality of foot shape types.

[0054] The homologous models of the sample feet classified into multiple footprint types are modified to fit the reference foot length and orthogonal foot width.

[0055] The reference foot length and orthogonal foot width will be described with reference to Figure 11. As shown in Figure 11(A), multiple foot shape types can be set based on the arch type and heel inclination type. As shown in Figure 11(B), when the reference foot length is X mm (for example, 255 mm), the orthogonal foot width is predetermined according to the foot shape type. Note that X and Y1 to Y9 are values greater than 0 and may be predetermined according to standards or may be arbitrarily set.

[0056] By changing the homology model of the sample foot to match the reference foot length and orthogonal foot width, the foot curve line shown in Figure 4, which can be calculated from the homology model (measurement data), is also changed.

[0057] Returning to Fig. 10, after the homologous model of the sample foot shape is modified to match the reference foot length and orthogonal foot width, the average value of the homologous model of the modified sample foot shape is calculated for each of the multiple foot shape types. As shown in Fig. 10(C), the average values calculated for each of the multiple foot shape types are included in the load-bearing foot shape data 133 as data A1 to A9.

[0058] In this way, the designer of the prediction device 1 classifies the homologous models created based on the measurement data of sample foot shapes acquired by the measurement device 2 into types based on at least one feature (in this example, arch height ratio and heel inclination angle), modifies the classified homologous models to match the reference foot length and orthogonal foot width, and calculates the average value from the modified homologous models to acquire sample data in a loaded state for each of the multiple foot shape types. The acquired sample data in a loaded state is stored in advance in the storage 13 as loaded foot shape data 133 (first sample data). In other words, the loaded foot shape data 133 (first sample data) stored in the storage 13 includes average foot shape data in a loaded state for each of the multiple foot shape types.

[0059] [No-weight foot shape data] The no-load footprint data 134 will be described with reference to Fig. 12 and Fig. 13. Fig. 12 is a diagram showing an example of no-load footprint data stored in the prediction device 1. Fig. 13 is a diagram showing an example of acquisition of no-load footprint data.

[0060] As shown in Fig. 12, the no-load foot shape data includes data obtained by classifying measurement data of the foot shapes of a plurality of samples in a no-load state (specifically, homologous models created based on the measurement data) into a plurality of foot shape types based on at least one feature quantity related to the foot shape. In the example shown in Fig. 12, arch height ratio and heel inclination angle are used as at least one feature quantity. The no-load foot shape data is then classified into a total of nine foot shape types, based on the three arch types classified based on the arch height ratio and the three heel inclination types classified based on the heel inclination angle.

[0061] In generating the no-load foot shape data, first, the foot shapes of each of the multiple sample subjects in the no-load state are obtained using plaster bandages, as shown in Fig. 13(A). The multiple sample subjects in the no-load state are the same as the multiple sample subjects in the loaded state in Fig. 10. The obtained plaster foot shapes are then measured with the measuring device 2 to obtain the foot shapes of each of the multiple sample subjects in the no-load state.

[0062] As shown in Figure 13(B), the measurement data of the sample's foot shape acquired using a plaster bandage is classified into multiple foot shape types based on at least one feature of the sample's foot shape (in this example, arch height ratio and heel tilt angle). Note that the multiple sample subjects in the loaded state in Figure 10 are the same as the multiple sample subjects in the unloaded state in Figure 13, so the measurement data of the multiple sample subjects' foot shapes in the unloaded state can be classified according to the foot shape types into which the measurement data of the multiple sample subjects' foot shapes in the loaded state was classified. Specifically, a homologous model such as that shown in Figure 7 is created based on the measurement data of the sample's foot shape acquired by measurement device 2, and the created homologous model is classified into the same foot shape type as the homologous model of the sample subject's foot in the loaded state.

[0063] The homologous foot models of the samples classified into multiple foot shape types are modified to match the reference foot length and orthogonal foot width. The modification of the homologous foot models of the samples in the no-load state is the same as the modification of the homologous foot models of the samples in the loaded state in Figure 10.

[0064] By changing the homology model of the sample foot to match the reference foot length and orthogonal foot width, the foot curve line shown in Figure 4, which can be calculated from the homology model (measurement data), is also changed.

[0065] After the homologous model of the sample foot is modified to match the reference foot length and orthogonal foot width, the average value of the modified homologous model of the sample foot is calculated for each of the multiple foot shape types. As shown in Fig. 13(C), the average values calculated for each of the multiple foot shape types are included in the no-weight-bearing foot shape data 134 as data B1 to B9.

[0066] As a result, the designer of the prediction device 1 classifies homologous models created based on measurement data of sample foot shapes acquired using plaster bandages into types based on at least one feature (in this example, arch height ratio and heel inclination angle), modifies the classified homologous models to match the reference foot length and orthogonal foot width, and calculates the average value from the modified homologous models to acquire sample data in a no-load state for each of the multiple foot shape types. The acquired sample data in a no-load state is stored in advance in the storage 13 as no-load foot shape data 134 (second sample data). In other words, the no-load foot shape data 134 (second sample data) stored in the storage 13 includes average foot shape data in a no-load state for each of the multiple foot shape types.

[0067] [Prediction of subject's foot shape] 14 to 16, the process of predicting the shape of the subject's foot in an unloaded state by the prediction device 1 will be described. FIG. 14 is a flowchart showing the prediction process executed by the prediction device 1 according to the embodiment. FIG. 15 is a diagram for explaining calculation of the difference between the subject data and the first sample data. FIG. 16 is a diagram for explaining modification of the second sample data based on the difference. Each step (hereinafter indicated by "S") shown in FIG. 14 is realized by the processor 11 of the prediction device 1 executing the prediction program 131.

[0068] As shown in FIG. 14, prediction device 1 acquires subject data including measurement data of the subject's foot shape under load acquired by measurement device 2 (S1). Based on the subject data, prediction device 1 creates a homology model as shown in FIG. 7 and extracts feature quantities (in this example, arch height ratio and heel inclination angle) of the subject data (S2). Based on the feature quantities of the subject data, prediction device 1 selects a footprint type of the subject's foot (S3). For example, prediction device 1 selects one footprint type from the footprint types shown in FIG. 11(A), which are classified based on the subject's foot arch type and heel inclination type.

[0069] The prediction device 1 extracts (S4) first sample data in a load condition that matches the foot shape type of the person being measured selected in S3 from the load-bearing foot shape data 133 stored in the storage 13. The extracted first sample data includes average foot shape data (homology model) in a load condition that matches the foot shape type of the person being measured.

[0070] Furthermore, the prediction device 1 extracts (S5) second sample data in a no-load state that matches the foot shape type of the subject selected in S3 from the no-load foot shape data 134 stored in the storage 13. The extracted second sample data includes average foot shape data (homologous model) in a no-load state that matches the foot shape type of the subject.

[0071] The prediction device 1 matches the foot length and orthogonal foot width of the first sample data in the loaded state extracted in S4 to the foot length and orthogonal foot width of the homologous model created based on the subject data (S6). As a result, the average foot curve line in the loaded state corresponding to the first sample data is changed to match the foot length and orthogonal foot width of the subject.

[0072] Furthermore, the prediction device 1 adjusts the foot length and orthogonal foot width of the second sample data in the no-load state extracted in S5 to the foot length and orthogonal foot width of the homologous model created based on the subject data (S7). As a result, the average foot curve line in the no-load state corresponding to the second sample data is changed to match the foot length and orthogonal foot width of the subject.

[0073] The prediction device 1 calculates the foot curve line based on each of the subject data and the first sample data changed in S6, and calculates the difference between the foot curve line in the subject data and the foot curve line in the load-bearing state corresponding to the first sample data changed in S6 (S8).

[0074] Specifically, as shown in Figure 15, the prediction device 1 calculates the difference between the curve lines of the foot in the length direction of the foot by comparing the curve line of the foot in the subject data, represented by a solid line, with the curve line of the foot in a loaded state corresponding to the first sample data, represented by a dotted line.

[0075] Here, the foot curve line of the first sample data in a loaded state and the foot curve line of the subject data can be calculated from the shape of the sole of the foot acquired by the measurement device 2. Specifically, as shown in FIG. 2, the contour line, inner ground line, and outer ground line can be distinguished even in a loaded state. Therefore, as described with reference to FIG. 4, the foot curve line of the first sample data in a loaded state and the foot curve line of the subject data can be calculated based on the contour line, inner ground line, and outer ground line. Note that the foot curve line of the first sample data may be the foot curve line of the second sample data in a no-load state. For example, as described with reference to FIGS. 4 and 5, the inner ground line and outer ground line can be calculated using the intersection of line L, which passes through a height of H mm from the lowest line, with the outline of the medial or lateral side of the foot. The calculated inner ground line and outer ground line can then be used to calculate the foot curve line of the second sample data, and the calculated curve line can be used as the foot curve line of the first sample data.

[0076] The prediction device 1 calculates the shape (homology model) of the subject's foot in the no-load state by changing the degree of bending of the foot (curve line) in the no-load state corresponding to the second sample data changed in S6 based on the calculated difference of the curve line (S9). After that, the prediction device 1 ends this process.

[0077] 16, the prediction device 1 moves the plurality of constituent points arranged along the outline of the cross section of the foot in the second sample data in a direction that cancels out the difference calculated in S8. That is, the prediction device 1 moves the plurality of constituent points arranged along the outline of the cross section of the foot in the second sample data so that the curve line of the foot in the second sample data coincides with the curve line of the foot in the subject data. The prediction device 1 moves the plurality of constituent points in this way at predetermined intervals (for example, every 1 mm) along the length of the foot.

[0078] This allows the prediction device 1 to calculate data (homology model) of the shape of the subject's foot in a no-load state from the measurement data of the subject's foot shape in a loaded state acquired by the measurement device 2.

[0079] [Making insoles] An example of insole fabrication will be described with reference to Fig. 17. Fig. 17 is a diagram for explaining an example of insole fabrication. Fig. 17 shows a cross section of a homologous model representing the shape of the subject's foot in an unloaded state, which is acquired by the prediction device 1 through the prediction process of Fig. 14. In Fig. 17, as in Fig. 5, a point on the top line on the inside of the homologous model is designated as P1, a point on the top line on the outside of the homologous model is designated as P2, the point of contact between the medial b-degree line and the outline of the homologous model on the inside is designated as Q1, and the point of contact between the lateral b-degree line and the outline of the homologous model on the outside is designated as Q2.

[0080] The insole designer measures the length T1 of the contact point Q1 with the bottom surface of the insole, and obtains point U by adding a support adjustment amount T2 to length T1. The designer can obtain the surface shape of the insole in one cross section of the homologous model by drawing lines connecting point P1 of the top line on the inside of the homologous model, point U, the lowest point V of the homologous model, and point P2 of the top line on the outside of the homologous model. The designer can create the insole by performing this process for multiple cross sections in the foot length direction.

[0081] [Variations] The present disclosure is not limited to the above-described embodiments, and various modifications and applications are possible. Modifications applicable to the present disclosure will be described below.

[0082] In the prediction process, the prediction device 1 according to the embodiment uses measurement data of the foot shape measured by the subject in a standing position as shown in FIG. 1 and first sample data calculated from measurement data of the foot shape measured by the sample subject in a standing position as shown in FIG. 10, but the prediction process is not limited to being performed using data measured in such a standing position.

[0083] For example, the prediction device 1 may perform a prediction process using measurement data of the foot shape measured by the subject in a seated position and first sample data calculated from the measurement data of the foot shape measured by the sample subject in a seated position.

[0084] The prediction device 1 according to the embodiment classifies the subject data, the first sample data, and the second sample data based on at least one feature, namely, the arch height ratio and the heel inclination angle, but the classification of data is not limited to based on the arch height ratio and the heel inclination angle.

[0085] For example, the prediction device 1 may classify the subject data, the first sample data, and the second sample data based on at least one of the following features: arch height ratio, heel inclination angle, foot circumference, degree of foot curvature (curve line), toe shape, and age. All of these features are parameters that can affect the shape of the foot, and the prediction device 1 can classify the subject data, the first sample data, and the second sample data with high accuracy by using any of these features.

[0086] In the prediction system 100 according to the embodiment, the prediction device 1 may be installed in a store where the measuring device 2 is installed, or may exist as a server device on the cloud. Furthermore, the prediction device 1 existing as a server device on the cloud may be communicably connected to the measuring devices 2 installed in each of a plurality of stores, and may predict the foot shape of each subject in a no-load state based on subject data acquired from each measuring device 2.

[0087] In the prediction device 1 according to the embodiment, in S6, the foot length and orthogonal foot width of the first sample data in a loaded state are matched to the foot length and orthogonal foot width of the homologous model created based on the subject data, and in S7, the foot length and orthogonal foot width of the second sample data in an unloaded state are matched to the foot length and orthogonal foot width of the homologous model created based on the subject data, but the items to be matched may be other than foot length and orthogonal foot width.

[0088] For example, in S6, the prediction device 1 may adjust the foot length and orthogonal foot width of the first sample data in a loaded state to those of a homologous model created based on the subject data, and then correct the changed first sample data based on the apparent arch length of the homologous model in the subject data.Furthermore, in S7, the prediction device 1 may adjust the foot length and orthogonal foot width of the second sample data in an unloaded state to those of the subject data, and then correct the changed second sample data based on the apparent arch length of the homologous model in the subject data.

[0089] For example, Figure 18 is a diagram for explaining the modification of sample data based on the apparent arch. As shown in Figure 18(A), in the cross section of the foot, the point of contact between a line inclined a degree from the ground toward the inside of the foot (medial a-degree line) and the outline of the inside of the foot is defined as P1. As shown in Figure 18(B), the points of contact P1 are obtained at predetermined intervals (for example, every 1 mm) along the length of the foot, and the line connecting the obtained points of contact P1 is defined as the apparent arch. The length between the end point of the apparent arch and the heel point where the heel is located is defined as the length of the apparent arch.

[0090] In S6, the prediction device 1 matches the foot length and orthogonal foot width of the first sample data in a weight-bearing state to the foot length and orthogonal foot width of the homologous model created based on the subject data, thereby changing the average foot curve line in a weight-bearing state corresponding to the first sample data to match the foot length and orthogonal foot width of the subject. Furthermore, the prediction device 1 corrects the foot curve line in the first sample data by matching the apparent arch length in the changed first sample data to the apparent arch length of the homologous model in the subject data.

[0091] In addition, in S7, the prediction device 1 matches the foot length and orthogonal foot width of the second sample data in a no-load state to the foot length and orthogonal foot width of the homologous model created based on the subject data, thereby changing the average foot curve line in a no-load state corresponding to the second sample data to match the foot length and orthogonal foot width of the subject. Furthermore, the prediction device 1 corrects the foot curve line in the second sample data by matching the apparent arch length in the changed second sample data to the apparent arch length of the homologous model in the subject data.

[0092] By performing such a correction, the prediction device 1 can adjust the leg length of the sample data before correction, represented by the dotted line, to the leg length of the sample data after correction, represented by the solid line, as shown in Figure 18(C). Thereafter, the prediction device 1 may execute the processes from S8 onwards.

[0093] [summary] As shown in Fig. 8, the prediction device 1 includes a communication device 16 that acquires subject data including measurement data of the subject's foot shape under load, a storage 13 that stores first sample data under load and second sample data under no load calculated from the same plurality of sample foot shape measurement data under load and no load, and a processor 11 that predicts the subject's foot shape under no load. As shown in Fig. 14, the processor 11 calculates the difference between the subject data and the first sample data (S8), and predicts the subject's foot shape under no load based on the difference and the second sample data (S9).

[0094] Thus, by using the first sample data in a loaded state and the second sample data in an unloaded state, the prediction device 1 can predict the shape of the subject's foot in an unloaded state from the measurement data of the shape of the subject's foot in a loaded state. Therefore, a user of the prediction device 1 can easily obtain the shape of the subject's foot in an unloaded state by using the prediction device 1 simply by measuring the shape of the subject's foot in a loaded state.

[0095] As shown in Fig. 10, the first sample data is data in which measurement data of a plurality of samples of foot shapes in a loaded state are classified into a plurality of foot shape types based on at least one feature amount related to the foot shape. As shown in Fig. 13, the second sample data is data in which measurement data of a plurality of samples of foot shapes in a no-load state are classified into a plurality of foot shape types based on at least one feature amount. As shown in Fig. 14, the processor 11 selects one of the plurality of foot shape types based on the subject data (S3), calculates the difference between the subject data and the first sample data belonging to the selected foot shape type (S8), and predicts the subject's foot shape in a no-load state based on the difference and the second sample data belonging to the selected foot shape type (S9).

[0096] This allows the prediction device 1 to predict the foot shape of the subject in a no-load state using the first sample data and the second sample data that match the foot shape type of the subject in a no-load state, thereby improving the prediction accuracy of the foot shape of the subject in a no-load state.

[0097] As shown in Fig. 10, the first sample data is data obtained by averaging the measurement data of multiple samples of foot shapes in a loaded state for each of multiple foot shape types. As shown in Fig. 13, the second sample data is data obtained by averaging the measurement data of multiple samples of foot shapes in a no-load state for each of multiple foot shape types.

[0098] This allows the prediction device 1 to predict the foot shape of the person being measured in the no-load state using the averaged first and second sample data that match the foot shape type of the person being measured in the load state. Therefore, the prediction device 1 does not need to store a huge amount of first and second sample data, and therefore can prevent an increase in the capacity consumed by the storage 13.

[0099] As shown in Figure 14, the processor 11 calculates modified first sample data by changing the foot length and width included in the first sample data belonging to one foot shape type to match the foot length and width included in the subject data (S6), calculates modified second sample data by changing the foot length and width included in the second sample data belonging to one foot shape type to match the foot length and width included in the subject data (S7), calculates the difference by comparing the degree of foot bending included in the subject data with the degree of foot bending included in the modified first sample data (S8), and predicts the shape of the subject's foot in an unloaded state by changing the degree of foot bending included in the modified second sample data based on the difference (S9).

[0100] This allows the prediction device 1 to predict the shape of the subject's feet in a no-load state by changing the length and width of the feet in each of the first and second sample data to match the length and width of the feet included in the subject data, and then changing the degree of foot curvature in the second sample data to match the degree of foot curvature included in the subject data.As a result, the prediction device 1 can further improve the accuracy of predicting the shape of the subject's feet in a no-load state.

[0101] As shown in Figure 18, the processor 11 corrects the changed first sample data based on the length of the lateral arch of the foot (apparent arch) included in the subject data, and corrects the changed second sample data based on the length of the lateral arch of the foot (apparent arch) included in the subject data.

[0102] As a result, the prediction device 1 corrects the first sample data and the second sample data based on the apparent arch length of the foot included in the subject data, and can make the first sample data and the second sample data closer to the foot of the subject data. Therefore, the prediction device 1 can further improve the prediction accuracy of the foot shape of the subject in a no-load state.

[0103] The at least one feature amount includes any one of arch height ratio, heel inclination angle, foot circumference, degree of foot curvature, toe shape, and age.

[0104] This allows the prediction device 1 to classify the subject data, the first sample data, and the second sample data based on any one of the arch height ratio, heel inclination angle, foot circumference, degree of foot curvature, toe shape, and age. Furthermore, by increasing the number of types of feature amounts used for classification, the prediction device 1 can classify the subject data, the first sample data, and the second sample data in more detail.

[0105] As shown in Fig. 1, the subject data includes measurement data of the foot shape measured by the subject in a standing position. As shown in Fig. 10, the first sample data is data calculated from measurement data of the foot shapes of multiple samples measured by multiple sample subjects in a standing position.

[0106] As a result, a user of the prediction device 1 can easily obtain the foot shape of a person under test in a no-load state by simply measuring the feet of the person under test who visits the store in a standing position using the prediction device 1. Therefore, it is not necessary for a skilled expert to measure the foot shape of the person under test, which improves convenience in the store.

[0107] The prediction method by processor 11 for predicting the subject's foot shape in the no-load state includes the steps of acquiring subject data including measurement data of the subject's foot shape in the loaded state, storing first sample data in the loaded state and second sample data in the no-load state calculated from the measurement data of the same multiple samples of foot shape in the loaded state and the no-load state, and predicting the subject's foot shape in the no-load state. As shown in Fig. 14, the prediction step includes a step (S8) of calculating the difference between the subject data and the first sample data, and a step (S9) of predicting the subject's foot shape in the no-load state based on the difference and the second sample data.

[0108] Thus, by using the first sample data in a loaded state and the second sample data in an unloaded state, the prediction device 1 can predict the shape of the subject's foot in an unloaded state from the measurement data of the shape of the subject's foot in a loaded state. Therefore, a user of the prediction device 1 can easily obtain the shape of the subject's foot in an unloaded state by using the prediction device 1 simply by measuring the shape of the subject's foot in a loaded state.

[0109] As shown in Fig. 1, prediction system 100 includes a measurement device 2 that measures the subject's foot shape under load and a prediction device 1 that predicts the subject's foot shape under no load. As shown in Fig. 8, prediction device 1 includes a communication device 16 that acquires subject data, including measurement data of the subject's foot shape under load, from measurement device 2; storage 13 that stores first sample data under load and second sample data under no load, calculated from the measurement data of the same multiple samples of foot shape under load and no load; and processor 11 that predicts the subject's foot shape under no load. As shown in Fig. 14, processor 11 calculates the difference between the subject data and the first sample data (S8) and predicts the subject's foot shape under no load based on the difference and the second sample data (S9).

[0110] Thus, by using the first sample data in a loaded state and the second sample data in an unloaded state, the prediction system 100 can predict the foot shape of the subject in an unloaded state from the measurement data of the foot shape of the subject in an loaded state. Therefore, a user of the prediction system 100 can easily obtain the foot shape of the subject in an unloaded state using the prediction device 1, simply by measuring the foot shape of the subject in an loaded state.

[0111] The embodiments disclosed herein should be considered to be illustrative in all respects and not restrictive. The scope of the present invention is defined by the claims, not by the above description, and is intended to include all modifications within the meaning and scope of the claims. [Explanation of symbols]

[0112] 1 prediction device, 2 measurement device, 3 3D printer, 11 processor, 12 memory, 13 storage, 14 interface, 15 media reading device, 16 communication device, 17 processor bus, 18 removable disk, 21 tabletop, 22 laser measurement unit, 100 prediction system, 131 prediction program, 133 loaded foot shape data, 134 unloaded foot shape data.

Claims

1. A prediction device for predicting the shape of a subject's foot in a no-load state, an acquisition unit that acquires subject data including measurement data of the subject's foot shape in a weight-bearing state; a storage unit that stores first sample data in the loaded state and second sample data in the unloaded state, the first sample data being calculated from measurement data of the foot shapes of the same plurality of samples in the loaded state and the unloaded state; a prediction unit that predicts the shape of the subject's foot in the no-load state, The prediction unit calculating a difference between the subject data and the first sample data; A prediction device that predicts the shape of the subject's foot in the no-load state based on the difference and the second sample data.

2. the first sample data is data obtained by classifying measurement data of the plurality of samples of foot shapes in the load-bearing state into a plurality of foot shape types based on at least one feature amount related to foot shape, the second sample data is data obtained by classifying measurement data of the foot shapes of the plurality of samples in the no-load state into the plurality of foot shape types based on the at least one feature amount, The prediction unit selecting one foot shape type from the plurality of foot shape types based on the subject data; calculating a difference between the subject data and the first sample data belonging to the one foot shape type; The prediction device according to claim 1 , wherein the device predicts the foot shape of the subject in the no-load state based on the difference and the second sample data belonging to the one foot shape type.

3. the first sample data is data obtained by averaging measurement data of the plurality of samples of foot shapes in the load-bearing state for each of the plurality of foot shape types; The prediction device according to claim 2 , wherein the second sample data is data obtained by averaging measurement data of the plurality of samples of foot shapes in the no-load state for each of the plurality of foot shape types.

4. The prediction unit calculating modified first sample data by modifying the foot length and width included in the first sample data belonging to the one foot shape type to match the foot length and width included in the subject data; calculating modified second sample data by modifying the foot length and width included in the second sample data belonging to the one foot shape type to match the foot length and width included in the subject data; calculating the difference by comparing the degree of leg bending included in the subject data with the degree of leg bending included in the changed first sample data; 4. The prediction device according to claim 3, wherein the shape of the subject's foot in the no-load state is predicted by changing the degree of foot bending contained in the changed second sample data based on the difference.

5. The prediction unit correcting the changed first sample data based on the length of the lateral arch of the foot included in the subject data; The prediction device according to claim 4 , wherein the second sample data after the change is corrected based on the length of the lateral arch of the foot included in the subject data.

6. The prediction device according to any one of claims 2 to 5, wherein the at least one feature includes any one of arch height ratio, heel inclination angle, foot circumference, degree of foot curvature, toe shape, and age.

7. the subject data includes measurement data of the shape of the foot of the subject measured in a standing position, The prediction device according to any one of claims 1 to 6, wherein the first sample data is data calculated from measurement data of the foot shapes of the plurality of samples measured by a plurality of sample subjects in the standing posture.

8. A method for predicting the shape of a subject's foot in a no-load state by a computer, comprising: acquiring subject data including measurement data of the subject's foot shape in a weight-bearing state; storing first sample data in the loaded state and second sample data in the unloaded state, the first sample data being calculated from measurement data of the foot shapes of the same plurality of samples in the loaded state and the unloaded state; a step of predicting the shape of the subject's foot in the no-load state, The predicting step includes: calculating a difference between the subject data and the first sample data; and predicting the shape of the subject's foot in the no-load state based on the difference and the second sample data.

9. A prediction system for predicting the shape of a subject's foot in a no-load state, a measuring device for measuring the shape of the subject's foot in a weight-bearing state; a prediction device for predicting the shape of the subject's foot in the no-load state, The prediction device includes: an acquisition unit that acquires subject data including measurement data of the subject's foot shape in the load-bearing state from the measurement device; a storage unit that stores first sample data in the loaded state and second sample data in the unloaded state, the first sample data being calculated from measurement data of the foot shapes of the same plurality of samples in the loaded state and the unloaded state; a prediction unit that predicts the shape of the subject's foot in the no-load state, The prediction unit calculating a difference between the subject data and the first sample data; A prediction system that predicts the shape of the subject's foot in the no-load state based on the difference and the second sample data.

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