TWI827958B - Evaluation system and evaluation method for suitable height range of high heels - Google Patents

Abstract

An evaluation system and an evaluation method for suitable height range of high heels are provided. The system includes a plurality of pressure sensors, a processor and a storage medium. The storage medium stores a regression model, wherein: a user's foot is placed on the plurality of pressure sensors, and a height from the back heel of the foot to a horizontal plane is at least one critical back heel height. The processor obtains at least one critical pressure value of the foot from at least one critical pressure sensor. The processor inputs the at least one critical pressure value to the regression model, and evaluates a suitable height range of high heels for the foot according to an output result of the regression model.

Description

High-heeled shoes suitable height range evaluation system and high-heeled shoes suitable height range evaluation method

The present disclosure relates to a system for evaluating the suitable height range of high heels and a method for evaluating the suitable height range of high heels.

Many women wear high heels for long periods of time due to beauty or work reasons. However, wearing high heels of inappropriate height will bring adverse burden and impact on the body. In addition, when women evaluate the suitable height range of high-heeled shoes, they usually choose according to their own preferences. However, the correct suitable height of high-heeled shoes must be diagnosed by a professional doctor, and it is impossible to effectively find out the suitable height for them in a shoe store. The heels are suitable for a range of heights. Therefore, an efficient high-heeled shoes suitable height range assessment system and a high-heeled shoes suitable height range assessment method are needed.

The present disclosure provides a system and method for evaluating the suitable height range of high heels, which can allow users to efficiently find out the suitable height range of high heels that they can withstand.

The disclosed high-heeled shoes suitable height range evaluation system includes multiple pressure sensors, a processor, and a storage medium. The processor communicates with multiple pressure sensors. The storage medium is coupled to the processor, and the storage medium stores the regression model, wherein: the sole of the user's foot is placed on a plurality of pressure sensors, wherein the height of the heel of the sole of the foot from the horizontal plane is at least a critical heel height, wherein a plurality of pressure sensors The sensors respectively obtain multiple sole pressure values of the sole of the foot; the processor obtains at least one key sole pressure value of the sole of the foot from at least one key pressure sensor, wherein the at least one key pressure sensor belongs to one of the plurality of pressure sensors. One, wherein at least one key pressure sensor obtains at least one key sole pressure value of the sole of the foot; and the processor inputs at least one key sole pressure value to the regression model, and evaluates the suitable height of the high-heeled shoes of the sole according to the output result of the regression model Scope.

The disclosed method for evaluating the suitable height range of high heels includes: placing the sole of the user's foot on multiple pressure sensors, wherein the height of the rear heel of the sole of the foot from the horizontal plane is at least a critical rear heel height, and the multiple pressure sensors obtain the Multiple sole pressure values of the sole of the foot; the processor obtains at least one key sole pressure value of the sole of the foot from at least one key pressure sensor, wherein the at least one key pressure sensor belongs to one of the plurality of pressure sensors, wherein At least one critical pressure sensor obtains at least one critical sole pressure value of the sole of the foot, wherein the storage medium stores the regression model; and the processor inputs at least one critical sole pressure value to the regression model, and evaluates the sole of the foot according to the output result of the regression model The heels are suitable for a range of heights.

Based on the above, the disclosed high-heeled shoes suitable height range evaluation system and the high-heeled shoes suitable height range evaluation method can obtain in advance the key factors for evaluating the suitable height range of high-heeled shoes on the soles of the feet. The key factors may include the key heel height and the key pressure corresponding to the key heel height. Sensors and key sole data, etc. Based on this, users only need to measure (provide) these key factors to effectively find out the suitable height range of high heels that the user can bear.

In order to make the above features and advantages of the present disclosure more obvious and understandable, embodiments are given below and described in detail with reference to the attached drawings.

FIG. 1 is a schematic diagram of a high-heeled shoe suitable height range evaluation system 100 according to an exemplary embodiment of the present disclosure. Referring to FIG. 1 , the high-heeled shoes suitable height range evaluation system 100 includes a plurality of pressure sensors (ie, pressure sensors 112-1 ~ pressure sensors 112-N, where N is a positive integer), a processor 122 and storage Media123.

The pressure sensors 112-1 to 112-N are, for example, Force Sensing Resistors (FSR), and the present disclosure does not limit the types of pressure sensors. After the soles of the user's feet are placed on the pressure sensors 112-1 to 112-N, the pressure sensors 112-1 to 112-N can respectively obtain multiple sole pressures of the soles of the user's feet. value.

The processor 122 is, for example, a central processing unit (CPU), or other programmable general-purpose or special-purpose microprocessor (Microprocessor), digital signal processor (Digital Signal Processor, DSP), programmable controller, Application Specific Integrated Circuit (ASIC), Field Programmable Gate Array (FPGA), Programmable Logic Controller (PLC) or other similar devices Or a combination of these devices, and can load and execute the program stored in the storage medium 123 to execute the high-heeled shoes suitable height range evaluation method according to the embodiment of the present disclosure. The processor 122 can communicate with (for example, in a wired or wireless manner) the pressure sensors 112-1 to 112-N.

The storage medium 123 is, for example, any type of fixed or removable random access memory (Random Access Memory, RAM), read-only memory (Read-Only Memory, ROM), flash memory (Flash Memory), hardware disk or other similar device, or a combination of these devices, for storing programs executable by the processor 122. The storage medium 123 may be coupled to the processor 122 . The storage medium 123 can store the regression model 121.

FIG. 2 is a schematic diagram of multiple pressure sensors (pressure sensors 112-1 to 112-42) according to an exemplary embodiment of the present disclosure.

Referring to FIG. 2 , the pressure sensors 112 - 1 to 112 - 42 may be provided, for example, in the left foot area 110L for the user to place the left foot and the right foot area 110R for the user to place the right foot. FIG. 2 takes N as 42 (that is, the left foot area 110L and the right foot area 110R include a total of 42 pressure sensors) as an example for illustration. The left foot area 110L may include pressure sensors 112-1 to 112-21, and the right foot area 110R may include pressure sensors 112-22 to 112-42. In other words, the left foot area 110L and the right foot area 110R may have the same number of pressure sensors, and the positions of the pressure sensors in the left foot area 110L and the positions of the pressure sensors in the right foot area 110R may be the same. One correspondence.

It should be noted here that although N is 42 in FIG. 2 as an illustration, the disclosure does not limit the number of N, nor does the disclosure limit the position of each pressure sensor.

The processor 122 may train the regression model 121 using the training data set. The training data set may include training sole data from each of a plurality of training data providers, respectively sensed by pressure at a plurality of rear heel heights (each of the plurality of rear heel heights indicating a height of the rear heel from the horizontal plane) The multiple training sole pressure values obtained by the device 112-1 ~ the pressure sensor 112-42 and the suitable height range of the training high-heeled shoes.

For example, if the training data providers are 100 women, the processor 122 can obtain the training data of the regression model 121 through each of the 100 women (ie, each adult woman).

Further, assume that the first woman among these 100 women is woman A. First, the training sole data of female A can be measured and recorded. The training sole information is, for example, female A's sole length, sole width, sole arch rate (Arch Rate), thumb valgus severity (Valgus of Toes) or rear foot heel bone varus angle (Calcaneus Varus Angle of Rearfoot), etc. . The processor 122 can obtain the training sole data. This disclosure does not limit the types of sole training materials.

FIG. 3A is a schematic diagram of the left foot area 110L according to an exemplary embodiment of the present disclosure. Although FIG. 3A illustrates the left foot area 110L, the right foot area 110R can also be implemented in the same manner as the left foot area 110L. It should be noted here that the left foot area 110L (and/or the right foot area 110R) shown in FIG. 3A is implemented in the form of an insole that can be placed in the high heels 200 . However, the present disclosure does not limit the left foot area 110L. (and/or right foot area 110R).

The woman A can place her soles on the left foot area 110L (and/or the right foot area 110R) at multiple heel heights H to obtain multiple measurements from the pressure sensors 112-1 to 112-42 respectively. A training foot pressure value. Multiple heel heights H can be sequentially increased from 0 cm by 0.5 cm each time until 10 cm (ie, a total of 21 different heel heights H). For example, as shown in FIG. 3 , the woman A can put 21 high-heeled shoes 200 with different heel heights H in the left foot area 110L (and/or the right foot area 110R), and then put the soles of her feet into the left foot area 110L (and/or the right foot area 110R). /or the right foot area 110R) respectively measures the sole pressure to obtain a plurality of training sole pressure values from the pressure sensors 112-1 to 112-42 respectively.

That is, the female A can place the sole of her foot on the left foot area 110L (and/or the right foot area 110R) and the height H of the back heel is 0 cm to obtain the training foot sole pressure value 112-1-( 0 cm)-p, the pressure value of the training sole is obtained from the pressure sensor 112-2 112-2-(0 cm)-p, the pressure value of the training sole is obtained from the pressure sensor 112-3 112-3-(0 cm) )-p, and so on until the training foot sole pressure value 112-42-(0 cm)-p is obtained from the pressure sensor 112-42. Then, the female A can place the sole of her foot on the left foot area 110L (and/or the right foot area 110R) with the heel height H of 0.5 cm to obtain the training foot sole pressure value 112-1-( 0.5 cm)-p, the pressure value of the training sole is obtained by the pressure sensor 112-2 112-2-(0.5 cm)-p, the pressure value of the training sole is obtained by the pressure sensor 112-3 112-3-(0.5 cm) )-p, and so on until the training foot sole pressure value 112-42-(0.5 cm)-p is obtained from the pressure sensor 112-42. After repeating the above operation for different heel heights H), finally, the woman A can place the sole of her foot on the left foot area 110L (and/or the right foot area 110R) with the back heel height H of 10 cm to feel the pressure. The pressure sensor 112-1 obtains the training sole pressure value 112-1-(10 cm)-p, and the pressure sensor 112-2 obtains the training sole pressure value 112-2-(10 cm)-p. 112-3 obtains the training sole pressure value 112-3-(10 cm)-p, and so on, until the pressure sensor 112-42 obtains the training sole pressure value 112-42-(10 cm)-p. The processor 122 can obtain each training foot pressure value mentioned above.

As shown in FIG. 3A , the pressure sensors 112 - 1 to 112 - 42 in the left foot area 110L (and/or the right foot area 110R) can, for example, use the signal transmitter 124 to transmit the above-mentioned training sole pressure. The value is transmitted to processor 122 wirelessly.

FIG. 3B is a schematic diagram of the left foot area 110L according to another exemplary embodiment of the present disclosure. As shown in FIG. 3B , the pressure sensors 112 - 1 to 112 - 42 in the left foot area 110L (and/or the right foot area 110R) can, for example, transmit the above training sole pressure values in a wired manner. to processor 122.

In addition, this female A can obtain a suitable height range for training high heels based on the medical judgment of a doctor. Since a woman's navicular bone will deform when the height of wearing high heels reaches the extreme height, medical judgment may be, for example, that the doctor obtains the extreme height range of female A's high-heeled shoes after discovering that the navicular bone of female A's foot is deformed. Processor 122 may obtain a suitable height range for the training high heel shoe. This suitable height range for training heels (derived from medical judgment) can be considered as the answer in the training data used to train the regression model 121 .

The first female (female A) among the training data providers obtains the training data of the regression model 121 (i.e., the above-mentioned training sole data) by the pressure sensor 112-1~pressure at multiple rear heel heights H After the sensors 112-42 respectively obtain multiple training sole pressure values and training high heel shoes suitable height range), the processor 122 can obtain the training data of the regression model 121 from other training data providers (ie, other 99 women).

After obtaining the training data from all training data providers as mentioned above, the processor 122 can use the training data set including the training data to train the regression model 121 .

After the regression model 121 is trained (and established), the storage medium 123 may store the regression model 121 .

The processor 122 may determine the critical heel height based on the regression model 121 . The processor 122 may also determine the critical pressure sensor corresponding to the critical heel height based on the regression model 121 .

Table 1 is an example of regression model 121. Table 1 An example of regression model 121 Key heel height: 10 cm Key pressure sensor (left foot) Pressure sensor 112-4 Pressure sensor 112-5 Pressure sensor 112-6 Pressure sensor 112-7 Pressure sensor 112-8 Pressure sensor 112-13 Pressure sensor 112-16 Pressure sensor 112-17 Key pressure sensor (right foot) Pressure sensor 112-23 Pressure sensor 112-25 Pressure sensor 112-26 Pressure sensor 112-27 Pressure sensor 112-30 Pressure sensor 112-35 Pressure sensor 112-38 Pressure sensor 112-41 Pressure sensor 112-42

The processor 122 can determine the critical heel height to be 10 centimeters based on the regression model 121 shown in Table 1 (that is, a total of 21 different heel heights that increase from 0 centimeters by 0.5 centimeters each time until 10 centimeters. Among the heel height H, the foot pressure value measured when the rear heel height H is 10 cm is a key factor in evaluating the suitable height range of high-heeled shoes.) In addition, the processor 122 can also determine based on the regression model 121 that the key pressure sensors corresponding to the key heel height H are the pressure sensor 112-4, the pressure sensor 112-5, and the pressure sensor 112-4. 112-6, pressure sensor 112-7, pressure sensor 112-8, pressure sensor 112-13, pressure sensor 112-16, pressure sensor 112-17, pressure sensor 112- 23. Pressure sensor 112-25, pressure sensor 112-26, pressure sensor 112-27, pressure sensor 112-30, pressure sensor 112-35, pressure sensor 112-38, Pressure sensors 112-41 and 112-42 (that is, among the foot pressure values measured when the heel height H is 10 cm, only the key foot pressure values obtained by these key pressure sensors are Pressure value is a key factor in evaluating the suitable height range of high heels on the sole of the foot).

It should be noted that the key heel height can be more than one height. For example, the processor 122 can determine based on the regression model 121 that the key heel heights are 0 cm and 10 cm (that is, a total of 21 different heel heights are sequentially increased from 0 cm by 0.5 cm each time until 10 cm). Among the heights H, the sole pressure value measured when the rear heel height H is 0 cm and the sole pressure value measured when the rear heel height H is 10 cm are the key factors for evaluating the suitable height range of high heels), and The key pressure sensors correspond to these two key heel heights (0 cm and 10 cm).

In another embodiment, the processor 122 can determine, through statistical analysis, which key sole data among the training sole data are based on the aforementioned training sole data and the suitable height range of training high heels (derived from medical judgment). Key factors in assessing the appropriate height range of high heels for your feet. As explained in the foregoing embodiments, the training sole data includes, for example, sole length, sole width, sole arch rate, big toe valgus severity, or rear heel bone varus angle. For example, the processor 122 can determine which key sole data among the training sole data are suitable for evaluating the suitability of high-heeled shoes based on whether the P value (P Value) in the statistical analysis model is less than a specific predetermined P value threshold. Key factor for height range. For example, the processor 122 may determine that the key sole information is sole length and rear heel bone varus angle.

Using the method described in the foregoing embodiments, the processor 122 can, for example, derive the regression model 121 shown in Table 2. That is, determine the key heel heights of 0 cm and 10 cm, the key pressure sensors corresponding to these two key heel heights (0 cm and 10 cm), as well as the sole length and heel bone varus angle (key Sole information) is a key factor in evaluating the suitable height range of high heels for the soles of the feet. Table 2 Another example of regression model 121 Key heel height: 0 cm and 10 cm Key pressure sensor (left foot) Pressure sensor 112-2 Pressure sensor 112-4 Pressure sensor 112-5 Pressure sensor 112-6 Pressure sensor 112-13 Pressure sensor 112-16 Pressure sensor 112-17 Key pressure sensor (right foot) Pressure sensor 112-23 Pressure sensor 112-24 Pressure sensor 112-25 Pressure sensor 112-26 Pressure sensor 112-27 Pressure sensor 112-30 Pressure sensor 112-31 Pressure sensor 112-35 Pressure sensor 112-38 Pressure sensor 112-41 Pressure sensor 112-42 Key sole information sole length Hindfoot heel bone varus angle

The aforementioned processor 122 obtains a plurality of training sole pressure values from the pressure sensors 112-1 to 112-42 to train (and establish) the regression model 121, which is only an example embodiment of the present disclosure. In another embodiment, multiple pressure sensors can be grouped according to their location. Taking the left foot area 110L shown in FIG. 2 as an example, multiple pressure sensors can be grouped into pressure sensor group A (original pressure sensor 112-1 and original pressure sensor 112-2) according to location, for example. , pressure sensor group B (original pressure sensor 112-3, original pressure sensor 112-4, original pressure sensor 112-7 and original pressure sensor 112-8), pressure sensor Group C (original pressure sensor 112-5, original pressure sensor 112-6, original pressure sensor 112-9, and original pressure sensor 112-10), pressure sensor group D (original pressure sensor Pressure sensor 112-11 ~ original pressure sensor 112-14), pressure sensor group E (original pressure sensor 112-15, original pressure sensor 112-16, original pressure sensor 112 -19 and the original pressure sensor 112-20) and the pressure sensor group F (the original pressure sensor 112-17, the original pressure sensor 112-18 and the original pressure sensor 112-21). The training sole pressure values of pressure sensor group A~pressure sensor group F can respectively be the average value of the training sole pressure values obtained by the original pressure sensors (for example, the training sole pressure value of pressure sensor group A The foot pressure value may be an average of the training foot pressure value obtained by the pressure sensor 112-1 and the training foot pressure value obtained by the pressure sensor 112-2). Based on this, the processor 122 can obtain a plurality of training foot pressure values from the pressure sensor group A to the pressure sensor group F respectively to train (and establish) the regression model 121 .

For users who want to know the suitable height range of high-heeled shoes, the disclosed high-heeled shoes suitable height range evaluation system 100 can be based on the key heel heights (in the regression model 121) of Table 2, key pressure sensors and key sole data. , evaluate the suitable height range of high heels for this user.

Figure 4 is a flow chart of a method for evaluating a suitable height range of high heels according to an exemplary embodiment of the present disclosure.

In step S401, the sole of the user's foot is placed on a plurality of pressure sensors, wherein the height of the rear heel of the sole of the foot from the horizontal plane is at least a critical heel height, and the plurality of pressure sensors respectively obtain multiple sole pressures of the sole of the foot. value.

In step S402, the processor obtains at least one key sole pressure value of the sole of the foot from at least one key pressure sensor, wherein the at least one key pressure sensor belongs to one of a plurality of pressure sensors, wherein at least one key The pressure sensor obtains at least one key sole pressure value of the sole of the foot, and the storage medium stores the regression model.

Specifically, the soles of the user's feet can be placed on the pressure sensors 112-1 to 112-N, and the height of the back heel of the user's soles from the horizontal plane is the critical heel height.

The processor 122 can obtain the key sole pressure value of the foot from the key pressure sensor, where the key pressure sensor belongs to one of the pressure sensor 112-1~pressure sensor 112-42, where the key pressure sensor The device obtains the key sole pressure value of the sole of the foot.

Taking the regression model 121 shown in Table 2 as an example, the user can measure the pressure of the sole of the foot by the pressure sensor 112-1~pressure sensor 112-42 when the height H of the heel of the sole of the foot is 0 cm from the heel of the horizontal plane. value, the processor 122 may obtain the key sole pressure value from each key pressure sensor shown in Table 2. For example, the processor 122 may obtain each key sole pressure value when the heel height H is 0 cm according to the implementation shown in FIG. 3A or FIG. 3B .

Then, the user can measure the pressure value of the sole of the foot again by the pressure sensor 112-1~pressure sensor 112-42 when the height H of the rear heel of the sole of the foot is 10 centimeters from the rear heel of the horizontal plane, and the processor 122 can measure the pressure value of the sole of the foot from the table. Each key pressure sensor shown in 2 obtains key foot pressure values respectively. For example, the processor 122 may obtain each key sole pressure value when the heel height H is 10 centimeters according to the implementation shown in FIG. 3A or FIG. 3B .

In step S403, the processor inputs at least one key sole pressure value into the regression model, and evaluates the suitable height range of the high heels for the sole of the foot based on the output result of the regression model. In other words, the processor 122 may input key plantar pressure values into the regression model 121 and, based on the output results of the regression model 121, evaluate the suitable height range of the high heel sole of the foot.

The processor 122 may further input key sole data corresponding to the sole of the foot into the regression model 121, and evaluate the suitable height range of the high-heeled shoes for the sole of the foot based on the output result of the regression model 121. Taking the regression model 121 shown in Table 2 as an example, the user can also provide the sole length and heel bone varus angle of the rear foot (ie, key sole data), and the processor 122 can combine the aforementioned key sole pressure values and these key sole values. The data is simultaneously input into the regression model 121, and based on the output results of the regression model 121, the suitable height range of the high-heeled shoes is evaluated.

To sum up, the high-heeled shoes suitable height range evaluation system and the high-heeled shoes suitable height range evaluation method disclosed in the present disclosure can obtain in advance the key factors for evaluating the suitable height range of high-heeled shoes for the soles of the feet. The key factors may include the key rear heel height and the corresponding key rear heel height. Main pressure sensors and main sole data, etc. Based on this, users only need to measure (provide) these key factors to effectively find out the suitable height range of high heels that the user can bear.

Although the disclosure has been disclosed above through embodiments, they are not intended to limit the disclosure. Anyone with ordinary knowledge in the technical field may make slight changes and modifications without departing from the spirit and scope of the disclosure. Therefore, The scope of protection of this disclosure shall be determined by the scope of the appended patent application.

100: High-heeled shoes suitable height range evaluation system 112-1~112-N, 112-1~112-42: Pressure sensor 121:Regression model 122: Processor 123:Storage media 110L: Left foot area 110R: Right foot area 124:Signal transmitter H: Heel height 200:High heels S401~S403: steps

FIG. 1 is a schematic diagram of a high-heeled shoe suitable height range evaluation system according to an exemplary embodiment of the present disclosure. FIG. 2 is a schematic diagram of multiple pressure sensors according to an exemplary embodiment of the present disclosure. FIG. 3A is a schematic diagram of the left foot area according to an exemplary embodiment of the present disclosure. FIG. 3B is a schematic diagram of the left foot area according to another exemplary embodiment of the present disclosure. FIG. 4 is a flow chart of a method for evaluating the suitable height range of high heels according to an exemplary embodiment of the present disclosure.

S401~S403: steps

Claims (8)

A high-heeled shoes suitable height range evaluation system includes: a plurality of pressure sensors; a processor that communicates with the plurality of pressure sensors; and a storage medium that is coupled to the processor, and the storage medium stores a regression model, Wherein: the sole of the user's foot is placed on the plurality of pressure sensors, wherein the height of the rear heel of the sole of the foot from the horizontal plane includes a plurality of critical heel heights, wherein the plurality of pressure sensors respectively obtain the Multiple sole pressure values of the sole of the foot; the processor determines the multiple critical heel heights based on the regression model; the processor obtains multiple critical sole pressure values of the sole of the foot from at least one critical pressure sensor , wherein the at least one key pressure sensor belongs to one of the plurality of pressure sensors, wherein the at least one key pressure sensor obtains the plurality of key sole pressure values of the sole of the foot, wherein The plurality of critical sole pressure values include a first critical sole pressure value and a second critical sole pressure value, the plurality of critical heel heights include a first critical heel height and a second critical heel height, and the first The critical heel height is different from the second critical heel height, and the first critical sole pressure value is the height of the heel of the sole of the foot from the horizontal plane, which is the first critical heel height. The second critical foot sole pressure value is obtained when the height of the rear heel of the sole of the foot from the horizontal plane is the second critical rear heel. The processor inputs the multiple key plantar pressure values into the regression model, and evaluates the quality of the high heels suitable for the user based on the output results of the regression model. height range. The high-heeled shoes suitable height range evaluation system according to claim 1, wherein the processor further inputs key sole data corresponding to the sole of the foot into the regression model, and evaluates the The height range of the high heels suitable for wearing by the user. The high-heeled shoe fit height range evaluation system of claim 1, wherein the processor trains the regression model using a training data set, wherein the training data set includes training sole data from each of a plurality of training data providers. , a plurality of training sole pressure values respectively obtained by the plurality of pressure sensors at a plurality of rear heel heights and a training high heel suitable height range, wherein each of the plurality of rear heel heights indicates a rear heel distance from a horizontal plane the height of. The high-heeled shoe fit height range evaluation system of claim 1, wherein the processor determines the at least one key pressure sensor corresponding to the plurality of key heel heights based on the regression model. A method for assessing the suitable height range of high heels, including: Place the soles of the user's feet on a plurality of pressure sensors, wherein the height of the heel of the sole of the foot from the horizontal plane includes a plurality of critical heel heights, and the plurality of pressure sensors respectively obtain multiple soles of the sole of the foot. pressure value; obtaining, by a processor, a plurality of key sole pressure values of the sole of the foot from at least one key pressure sensor, wherein the at least one key pressure sensor belongs to one of the plurality of pressure sensors, The at least one critical pressure sensor obtains the multiple critical sole pressure values of the sole of the foot, wherein the storage medium stores a regression model; the processor determines the multiple critical heel heights according to the regression model , wherein the plurality of critical sole pressure values include a first critical sole pressure value and a second critical sole pressure value, the plurality of critical heel heights include a first critical heel height and a second critical heel height, The first critical heel height is different from the second critical heel height, and the first critical sole pressure value is when the height of the heel of the sole of the foot from the horizontal plane is the first critical heel height. The second critical sole pressure value is measured when the height of the heel of the sole of the foot from the horizontal plane is the second critical heel height. Obtain; and the processor inputs the plurality of key plantar pressure values into the regression model, and evaluates the height range of high heels suitable for the user according to the output results of the regression model. The method for evaluating the suitable height range of high heels as described in request 5, further includes: The processor inputs key sole data corresponding to the sole of the foot into the regression model, and evaluates the height range of the high heels suitable for wearing by the user based on the output of the regression model. The high-heeled shoes fit height range assessment method of claim 5, further comprising: training the regression model by the processor using a training data set, wherein the training data set includes data from each of a plurality of training data providers. Training sole data, a plurality of training sole pressure values respectively obtained by the plurality of pressure sensors at a plurality of rear heel heights, and a training high heel suitable height range, wherein each of the plurality of rear heel heights indicates a rear heel height. The height of the heel from the horizontal surface. The method for evaluating the suitable height range of high heels according to claim 5 further includes: the processor determines the at least one key pressure sensor corresponding to the plurality of key heel heights according to the regression model.

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