Implementation method of negative stiffness unit discrete distributed pressure relief insole

Abstract

A kind of negative stiffness unit discrete distributed function gradient pressure relief insole, foot bottom pressure data and foot bottom point cloud data are obtained by collecting through foot bottom pressure measurement system, the minimum enclosing rectangle of foot bottom point cloud is calculated after registration, and foot bottom pressure data is divided into regions, after obtaining the peak pressure of each sub-region, porous structure unit filling is carried out, and the personalized custom function gradient pressure relief insole model is obtained after cutting of the insole outer contour extracted from foot bottom point cloud data.The personalized pressure relief insole obtained based on foot three-dimensional scanning data and negative stiffness unit discrete distribution avoids mutual interference in the deformation process of each unit, and realizes the reasonable distribution of foot bottom pressure and the significant reduction of pressure peak.

Description

Technical Field

[0001] This invention relates to a technology in the field of insole manufacturing, specifically a method for implementing a negative stiffness unit discrete distributed pressure-reducing insole. Background Technology

[0002] The distribution characteristics of plantar pressure are an important indicator for assessing foot health. Under normal circumstances, plantar pressure is mainly concentrated in the ball of the foot and heel area. Abnormal plantar pressure, such as uneven distribution or excessive local pressure, can lead to foot pain and may further trigger other chronic foot diseases such as plantar fasciitis and diabetic foot ulcers. Summary of the Invention

[0003] To address the aforementioned shortcomings of existing technologies, this invention proposes a method for implementing a discrete distributed pressure-reducing insole with negative stiffness units. Based on three-dimensional foot scanning data and discrete distributed negative stiffness units, the resulting personalized pressure-reducing insole avoids mutual interference between units during deformation while achieving a reasonable distribution of plantar pressure and a significant reduction in pressure peaks.

[0004] This invention is achieved through the following technical solution:

[0005] This invention relates to a method for implementing a negative stiffness unit discrete distributed pressure-reducing insole. The method involves acquiring plantar pressure data and plantar point cloud data through a plantar pressure measurement system, calculating the minimum bounding rectangle of the plantar point cloud after registration, dividing the plantar pressure data into regions, obtaining the peak pressure of each sub-region, filling with negative stiffness structural units, and then cutting the insole outer contour extracted from the plantar point cloud data to obtain a personalized functional gradient pressure-reducing insole model.

[0006] The plantar pressure measurement system described herein uses, but is not limited to, XSENSOR smart insoles as the device for collecting plantar pressure data and related analysis tools.

[0007] The plantar pressure data includes: three-dimensional X, Y, Z spatial coordinates and pressure intensity value field, where Z=0.

[0008] The registration specifically includes:

[0009] 1) Convert the collected pressure data into a three-dimensional data point set. ,in: ( , ,0, ), These are spatial coordinate values. As for the pressure intensity, since the sole surface is set to the XOY plane when aligning the coordinate system, the Z coordinate value of the sole pressure is also 0.

[0010] 2) Perform interpolation processing on the data obtained in step 1. Specifically, use linear interpolation to densify the initial pressure data points, and generate intermediate interpolation points by calculating the average position and intensity values ​​of adjacent data points. This improves the resolution of the point cloud. That is: ;

[0011] 3) The interpolated 3D data point set and the plantar point cloud data obtained in step 2 are aligned in principal direction using principal component analysis (PCA) algorithm, specifically including:

[0012] i. Calculate the mean of the point cloud ,in: ( Let be any point in the point cloud, and N be the number of points in the point cloud;

[0013] ii. Calculate the covariance matrix ;

[0014] iii. Calculate the eigenvectors of the covariance matrix, specifically by performing eigenvalue decomposition on the covariance matrix. Obtain its eigenvalues ​​and corresponding eigenvectors, where: For eigenvalues, For feature vectors;

[0015] iv. Obtain the principal direction: Sort the eigenvalues ​​by size, and the eigenvector corresponding to the largest eigenvalue is the principal direction of the point cloud, i.e., the axis of the coordinate system;

[0016] v. By comparing the relative relationship between the center of mass of the whole foot and the center of mass of the sole, the positive direction of each coordinate system is determined, thereby completing the alignment of the coordinate systems.

[0017] 4) Based on the alignment in step 3, the centroids of the interpolated 3D data point set and the foot point cloud data are further aligned using a translation transformation algorithm. Specifically, after calculating the centroids of the two point clouds respectively, the translation vector T is set to the difference between the centroids of the foot point cloud and the pressure point cloud. Each point P in the pressure point cloud is translated according to the translation vector T to obtain a new coordinate P', thus completing the position alignment. The alignment of the point cloud attitude is achieved by aligning the main direction of the pressure point cloud with the main direction of the foot point cloud.

[0018] The alignment ensures that each pressure data point in the three-dimensional data point set corresponds to the three-dimensional coordinates of each point cloud in the plantar point cloud data.

[0019] The minimum bounding rectangle of the plantar point cloud is calculated as follows: traverse all points in the plantar point cloud, extract the maximum and minimum values ​​on the X and Y axes, and take (Xmin, Ymin) as the lower left vertex and (Xmax, Ymax) as the upper right vertex. The resulting rectangle is the minimum bounding rectangle of the plantar point cloud, which can completely contain all plantar point cloud data.

[0020] The personalized, customized functional gradient pressure-reducing insole model is manufactured using, but is not limited to, 3D printing technology.

[0021] Technical effect

[0022] This invention achieves coordinate system alignment and automatic feature point localization based on a 3D point cloud model of the foot in any standing posture. It designs eight gradient structural units with different elastic moduli, changing the elastic modulus by modifying structural parameters. A mapping relationship between plantar pressure and gradient structural units is established, and corresponding structural units are allocated to fill the insole according to the pressure value. The insole contour is automatically cut using 3D foot scanning model data. Compared to traditional measurement methods, this method eliminates the need for manual marker addition, provides more comprehensive measurement parameters, and yields accurate and reliable results. It significantly simplifies the design and manufacturing process of pressure-reducing insoles, improving design efficiency. The designed insole offers excellent pressure reduction, effectively improving foot pressure distribution and reducing the risk of foot injury. Attached Figure Description

[0023] Figure 1 This is a flowchart of the present invention;

[0024] Figure 2 For the registration flowchart;

[0025] Figure 3 Schematic diagram of a porous negative stiffness structural unit;

[0026] Figure 4 This is a schematic diagram of a basic filling element;

[0027] Figure 5 This is a schematic diagram of pressure point cloud densification.

[0028] Figure 6 A schematic diagram illustrating the registration of plantar pressure with a 3D model of the foot;

[0029] Figure 7 This is a graph showing the peak pressure as a function of time during a complete gait cycle. Detailed Implementation

[0030] like Figure 1 As shown, this embodiment relates to a method for implementing a discrete distributed pressure-reducing insole with negative stiffness elements, including:

[0031] Step 1: Obtain 3D model data of the foot using a UPOD laser scanner.

[0032] Step 2: Collect plantar pressure data during the complete gait cycle under natural gait using a plantar pressure measurement system.

[0033] The aforementioned plantar pressure measurement system refers to the equipment and supporting analysis tools used to collect plantar pressure data, primarily using XSENSOR smart insoles as the plantar pressure measurement device.

[0034] The plantar pressure data includes: pressure distribution data when standing still and pressure distribution variation data during natural gait.

[0035] Step 3: Export the foot model STL file from the foot model data obtained in Step 1, convert it to point cloud data, and use the Random Sample Consensus (RANSAC) algorithm to extract the plantar plane from the foot point cloud.

[0036] Step 4: Use a registration algorithm to accurately register and couple the pressure data obtained in Step 2 and the plantar point cloud data obtained in Step 3. This specifically includes:

[0037] 4.1 Convert the pressure data collected in step 2 into a three-dimensional planar data point set. Specifically, convert the collected pressure data into a three-dimensional planar data point set. ,in ( , ,0, ), These are spatial coordinate values. As for the pressure intensity, since the plantar surface was set to the XOY plane when aligning the coordinate system in Chapter 2, the Z coordinate value of the plantar pressure is also 0.

[0038] 4.2 Linear interpolation was used to perform the following steps on the initial pressure data points: Figure 5 The densification process shown is as follows: .

[0039] 4.3 After aligning the principal directions of the pressure point cloud and the plantar point cloud obtained by Principal Component Analysis (PCA) through rotation transformation, the centroids of the pressure point cloud and the plantar point cloud are then aligned by translation transformation to achieve positional alignment. This completes the alignment of the pressure point cloud and the plantar point cloud, resulting in the following: Figure 6 The registered pressure and 3D model are shown.

[0040] Step 5: After registering the pressure point cloud and the plantar point cloud, traverse the registered plantar point cloud and extract the maximum and minimum values ​​of its X-axis and Y-axis. Construct a rectangle with these four values ​​as the boundary, which is the minimum bounding rectangle covering the entire plantar area.

[0041] Step 6: Based on the minimum bounding rectangle, the rectangle is divided into multiple grid cells of size 8mm×8mm (consistent with the size of the negative Poisson's ratio structural cell) using a uniform meshing method. Each grid cell serves as a sub-region for subsequent matching of gradient negative stiffness structural cells by region.

[0042] Step 7: In the registered mesh generation, for each sub-region (mesh cell), extract the maximum pressure value in that region as the peak pressure of that sub-region, which is used to match the corresponding gradient negative stiffness structural element.

[0043] Step 8: Construct eight gradient negative stiffness structural elements with different elastic moduli and negative stiffness characteristics. Let the set of structural elements be M = {m0, m1, ..., m8}. Divide the pressure range evenly into eight intervals. Assign corresponding structural elements according to the interval to which the pressure value belongs. The region with higher pressure uses elements with higher elastic moduli, i.e., elements with smaller rod radii. For a specific pressure value... The corresponding structural unit is determined by the following formula: ,in .

[0044] like Figure 3 As shown, the porous negative stiffness structural unit includes: an upper seat 1, a rod-shaped component 2, and a lower seat 3, wherein: the upper seat 1 and the lower seat 3 are parallel and are connected by several intersecting rod-shaped components 4.

[0045] The upper seat 1 has a ventilation hole at its center, preferably with a diameter of 2mm, to enhance the ventilation performance of the insole and ensure that the insole provides necessary functional support while maintaining foot comfort and health.

[0046] like Figure 4 As shown, in this embodiment, several 8mm×8mm×10mm negative Poisson's ratio structural units are constructed and arranged in an array as the basic filling elements of the insole. Different elastic moduli are achieved by adjusting the diameter of the rod-shaped components in the structural units. Considering the accuracy and cost of 3D printing technology, eight gradient structural units with different elastic moduli are designed, with the radius of the rod-shaped components 2 ranging from 0.5mm to 0.85mm, and the radius increment between each specification being 0.05mm. This division method can cover a wide range of elastic moduli to adapt to different application requirements.

[0047] Step 9: Map and fill the negative stiffness structural unit obtained in Step 8 onto the sole plane. By translating the centroid of the filling unit to the center of the corresponding mesh, eight gradient negative stiffness structural units with different elastic moduli are constructed to achieve precise position matching of the structural units and form the insole model.

[0048] Preferably, to avoid interference between the deformations of the units, this embodiment uses, as follows: Figure 3 The negative stiffness structural unit shown serves as the basic filling element for the insole, and different elastic moduli are achieved by adjusting the diameter of the rod-shaped components in the structural unit.

[0049] Step 10: Extract the point cloud data 5mm away from the sole of the foot from the foot point cloud data, then extract the boundary of the point cloud as the outer contour of the insole, and use Boolean operation to cut the filled insole obtained in step 6 to finally generate the decompression insole.

[0050] Through specific practical experiments, XSENSOR pressure insoles were used to measure plantar pressure during static standing and dynamic walking. Plantar pressure was measured without insoles, with ordinary flat insoles, and with functionally graded pressure-reducing insoles. The plantar pressure without insoles served as the basis for designing the functionally graded pressure-reducing insoles. Initial zero-value calibration was performed according to the insole manual before each measurement and evaluation. After familiarizing themselves with the testing conditions, subjects walked at their chosen pace.

[0051] Comparative analyses of plantar pressure during static standing and dynamic walking were conducted, using peak pressure (a crucial indicator of plantar pressure distribution, representing the maximum pressure at a specific moment, whose magnitude and location significantly influence foot injury), peak pressure-time integral (which considers not only pressure magnitude but also duration, aiding in assessing the potential long-term effects of pressure on the foot and possessing significant application value for long-term pressure management), and plantar contact area (pressure is the ratio of force to area; increasing the contact area allows the same force to be distributed over a larger area, leading to reduced pressure at individual points and decreasing pressure concentration. An effective insole increases the total contact area between the foot and the insole, reducing high-pressure areas on the sole). The results showed that under conditions of wearing a standard flat insole, the average static peak plantar pressure decreased by 8.58%, while under conditions of wearing personalized gradient pressure-reducing insoles, the average static peak plantar pressure decreased by 30.98%. In addition, wearing gradient pressure-reducing insoles can increase the contact area between the foot and the insole by 55%, which can distribute pressure more evenly in different areas of the foot, reduce friction and slippage between the foot and the insole, thereby reducing the risk of foot pain and injury.

[0052] To verify the pressure-reducing effect of gradient pressure-reducing insoles during dynamic walking, plantar pressure images from three gait cycles of the subjects were superimposed, and the average pressure values ​​in the same area were calculated. The results showed that compared to not wearing insoles, subjects wearing ordinary flat insoles and gradient pressure-reducing insoles experienced a 11.25% and 20.57% reduction in average peak plantar pressure, respectively, during dynamic walking. The peak pressure variation over time during the gait cycle is shown below. Figure 7 As shown, the results indicate that the peak pressure decreased by 11.38% and 25.97% over time when wearing ordinary flat insoles and gradient pressure-reducing insoles, respectively.

[0053] The experimental results above show that, regardless of whether standing or walking, wearing ordinary flat insoles and gradient pressure-reducing insoles reduces peak plantar pressure compared to not wearing insoles. However, customized gradient pressure-reducing insoles perform better in terms of pressure reduction. By adjusting the insole stiffness to increase the contact area, the plantar pressure distribution is more even. The proposed automated rapid design method for functional gradient pressure-reducing insoles is not only effective and feasible, but also significantly improves foot pressure distribution and reduces the risk of foot injury.

[0054] Whether standing statically or walking dynamically, wearing ordinary flat insoles and pressure-reducing insoles can reduce peak pressure on the soles of the feet compared to not wearing insoles. However, the negative stiffness element discrete distributed personalized pressure-reducing insole performs better in terms of pressure reduction and has a more reasonable distribution of sole pressure. The proposed method for constructing and manufacturing the negative stiffness element discrete distributed personalized pressure-reducing insole is not only feasible but also significantly improves foot pressure distribution and reduces the risk of foot injury.

[0055] Compared to existing technologies, this invention couples plantar pressure information with foot morphology information to ensure a perfect match between the insole and the individual's foot shape. Based on this, it constructs negative stiffness structural units with different elastic moduli, selecting units to fill the insole according to the relative pressure distribution to achieve effective pressure dispersion. The basic filling element of the insole uses a negative Poisson's ratio structural unit, which exhibits contraction characteristics in the vertical direction under axial pressure. This structure has good impact resistance and shock absorption effects, offering significant advantages. Existing insole generation research mostly involves complex processes completed in additional specialized construction software. The negative stiffness unit discrete distributed personalized pressure-reducing insole performs superiorly in terms of pressure reduction, increasing the contact area by adjusting the insole stiffness, thus distributing plantar pressure more evenly.

[0056] The above-described specific implementations can be partially adjusted by those skilled in the art in different ways without departing from the principles and purpose of the present invention. The scope of protection of the present invention is defined by the claims and is not limited to the above-described specific implementations. All implementation schemes within the scope of the claims are bound by the present invention.

Claims

1. A method for implementing a discrete distributed pressure-reducing insole with negative stiffness elements, characterized in that, Plantar pressure data and plantar point cloud data are collected by a plantar pressure measurement system. After registration, the minimum bounding rectangle of the plantar point cloud is calculated and the plantar pressure data is divided into regions. After obtaining the peak pressure of each sub-region, negative stiffness structural units are filled. Then, the insole outer contour extracted from the plantar point cloud data is cut to obtain a personalized functional gradient decompression insole model. The plantar pressure data includes: three-dimensional X, Y, Z spatial coordinates and pressure intensity value field, where Z=0; The registration specifically includes: 1) Convert the collected pressure data into a three-dimensional data point set. ,in: ( , ,0, ), These are spatial coordinate values. As for the pressure intensity, since the sole surface is set to the XOY plane when aligning the coordinate system, the Z coordinate value of the sole pressure is also 0. 2) Perform interpolation processing on the data obtained in step 1. Specifically, use linear interpolation to densify the initial pressure data points, and generate intermediate interpolation points by calculating the average position and intensity values ​​of adjacent data points. This improves the resolution of the point cloud, i.e.: ; 3) Align the principal directions of the interpolated 3D data point set and the plantar point cloud data obtained in step 2 using the principal component analysis (PCA) algorithm; 4) Based on the alignment in step 3, the centroids of the interpolated 3D data point set and the plantar point cloud data are further aligned using a translation transformation algorithm. Specifically, after calculating the centroids of the two point clouds respectively, let the translation vector T be the difference between the centroids of the plantar point cloud and the pressure point cloud. Each point P in the pressure point cloud is translated according to the translation vector T to obtain the new coordinates P', thus completing the position alignment. The alignment of the point cloud posture is achieved by aligning the main direction of the pressure point cloud with the main direction of the plantar point cloud, so that each pressure data point in the 3D data point set corresponds to the 3D coordinates of each point cloud in the plantar point cloud data.

2. The method for implementing the negative stiffness element discrete distributed pressure-reducing insole according to claim 1, characterized in that, The aforementioned main direction alignment specifically includes: i. Calculate the mean of the point cloud ,in: ( Let be any point in the point cloud, and N be the number of points in the point cloud; ii. Calculate the covariance matrix ; iii. Calculate the eigenvectors of the covariance matrix, specifically by performing eigenvalue decomposition on the covariance matrix. Obtain its eigenvalues ​​and corresponding eigenvectors, where: For eigenvalues, For feature vectors; iv. Obtain the principal direction: Sort the eigenvalues ​​by size, and the eigenvector corresponding to the largest eigenvalue is the principal direction of the point cloud, i.e., the axis of the coordinate system; v. By comparing the relative relationship between the center of mass of the whole foot and the center of mass of the sole, the positive direction of each coordinate system is determined, thereby completing the alignment of the coordinate systems.

3. The method for implementing the negative stiffness element discrete distributed pressure-reducing insole according to claim 1, characterized in that, The minimum bounding rectangle of the plantar point cloud is calculated as follows: traverse all points in the plantar point cloud, extract the maximum and minimum values ​​on the X and Y axes, and take (Xmin, Ymin) as the lower left vertex and (Xmax, Ymax) as the upper right vertex. The resulting rectangle is the minimum bounding rectangle of the plantar point cloud, which can completely contain all plantar point cloud data.

4. The negative stiffness unit discrete distributed pressure-reducing insole according to claim 1, characterized in that, The negative stiffness structural unit includes: an upper seat, a rod-shaped component, and a lower seat, wherein: the upper seat and the lower seat are parallel and both are connected by several intersecting rod-shaped components; The upper seat has a ventilation hole at its center.

5. The method for implementing the negative stiffness element discrete distributed pressure-reducing insole according to claim 1 or 4, characterized in that, The filling is achieved by constructing several negative Poisson's ratio structural units and arranging them in an array as the basic filling elements of the insole, and by adjusting the diameter of the rod-shaped components in the structural units to achieve different elastic moduli.

6. A pressure-reducing insole obtained by the method according to any one of claims 1-5, characterized in that, It is composed of several negative stiffness structural units with different elastic moduli arranged in an array.

Images