CN111127446A - Gait analysis-oriented plantar pressure image partitioning method - Google Patents

Gait analysis-oriented plantar pressure image partitioning method Download PDF

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CN111127446A
CN111127446A CN201911366465.8A CN201911366465A CN111127446A CN 111127446 A CN111127446 A CN 111127446A CN 201911366465 A CN201911366465 A CN 201911366465A CN 111127446 A CN111127446 A CN 111127446A
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姚志明
李波陈
杨先军
王辉
王涛
张晓翟
李红军
孙怡宁
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Hefei Institutes of Physical Science of CAS
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Abstract

The invention relates to a gait analysis-oriented plantar pressure image partitioning method, which comprises the following steps: obtaining plantar pressure data and carrying out early-stage processing; clustering the sole pressure data after the early-stage processing to obtain a sole pressure image of a footprint area and performing integral projection to obtain pressure centers of a heel and a sole; establishing a plantar pressure characteristic point positioning model, and positioning characteristic points of a plantar pressure image; partitioning the plantar pressure image according to the relative position relation between each area of the foot and the plantar pressure characteristic points, wherein the plantar pressure characteristic point positioning comprises two stages of model training and characteristic point searching; in the model training stage, a sufficient number of plantar pressure images are selected as a training set, the accurate positions of the characteristic points are marked manually, and a plantar pressure characteristic point positioning model is established; and in the characteristic point searching stage, a plantar pressure characteristic point positioning model is initialized according to the pressure centers of the heel and the sole, and then the accurate position of the plantar pressure image characteristic point is found through model searching.

Description

Gait analysis-oriented plantar pressure image partitioning method
Technical Field
The invention relates to the field of gait analysis, in particular to a method for partitioning a plantar pressure image for gait analysis.
Background
When a human body stands still or walks dynamically, the sole of a foot can be subjected to the reaction force of the human body on the ground, wherein the reaction force vertical to the contact plane of the sole and the ground is the most obvious force, namely the pressure of the sole. When the foot structure of the human body is diseased or dysfunctional, the distribution of the plantar pressure can also be greatly changed. The modern medical means can assist doctors to diagnose the disease cause of sick feet of patients and appoint a rehabilitation treatment scheme by dividing the sole pressure according to anatomy and comparing the sole pressure distribution conditions of healthy feet and pathological feet with the pressure distribution conditions of different sole pressure areas of the same patient.
The existing plantar pressure subareas are semi-manual subareas which are manually divided by doctors or scientific researchers through subjective experiences or manually adjusted by software users after a fixed template is mechanically used for preliminarily dividing plantar pressure data by data acquisition software.
Therefore, there is a need for an efficient, low-cost method for plantar pressure partition that is easy to code.
Disclosure of Invention
The invention solves the problems that: the method overcomes the defects of the prior art, provides a gait analysis-oriented plantar pressure image partition method, automatically selects a footprint area from a frame in a plantar pressure image, partitions the plantar pressure image according to a characteristic point positioning method, and has the advantages of high efficiency, low cost and quick realization.
The technical scheme adopted by the invention is as follows: obtaining plantar pressure data and carrying out early-stage processing; clustering the sole pressure data after the early-stage processing to obtain a sole pressure image of a footprint area; performing integral projection on the pressure image of the sole to obtain the pressure centers of the heel and the sole; establishing a plantar pressure characteristic point positioning model, and positioning characteristic points of a plantar pressure image; and partitioning the plantar pressure image according to the relative position relation between each area of the foot and the plantar pressure characteristic points. The method comprises the following steps of positioning plantar pressure characteristic points, wherein the plantar pressure characteristic point positioning comprises two stages of model training and characteristic point searching; in the model training stage, selecting a sufficient number of plantar pressure images as a training set, manually marking the accurate positions of the characteristic points in the training samples, establishing a plantar pressure characteristic point positioning model, and only performing the model training stage once; in the characteristic point searching stage, a plantar pressure characteristic point positioning model is initialized according to the pressure centers of the heel and the sole, and then the accurate position of the plantar pressure image characteristic point is found through model searching.
The concrete implementation is as follows:
step (1): obtaining plantar pressure data and carrying out early-stage processing, and specifically comprising the following substeps:
step (11): acquiring a plurality of adjacent frames (at least 3 frames) of plantar pressure data containing complete footprints in a time domain by adopting an array type pressure sensor;
step (12): performing time domain mean filtering on the middle frame by using the multiple frames of plantar pressure data obtained in the step (11);
step (13): sequentially carrying out maximum filtering processing and interpolation processing on the intermediate frame data obtained in the step (12) to obtain sole pressure data with uniform size and equal row-column spacing;
step (2): clustering the sole pressure data after the preprocessing in the step (1), locating the position of the footprint in the sole pressure data, and obtaining a sole pressure image of the footprint area, specifically comprising the following substeps:
step (21): clustering the sole pressure data obtained in the step (1) after the pre-treatment by using a DBSCAN clustering algorithm to obtain a plurality of pressure area blocks, wherein a footprint area may be composed of a plurality of pressure area blocks;
step (22): taking the minimum external moment central point of each pressure area block obtained in the step (21) as the center of the area block, and using a K-means clustering algorithm to aggregate all pressure area blocks contained in each footprint into a large sole pressure area block;
step (23): extracting the pressure data of the footprint area obtained in the step (22) to obtain a sole pressure image containing the complete footprint;
and (3): performing integral projection on the sole pressure image obtained in the step (2) to obtain the pressure centers of the heel and the sole, and specifically comprising the following substeps:
step (31): taking the long side direction of the minimum external torque of the plantar pressure image obtained in the step (2) as a longitudinal axis and the short side direction as a transverse axis, and performing integral projection on the plantar pressure image along the longitudinal axis to obtain a gray level histogram;
step (32): if the gray level histogram obtained in the step (31) contains a plurality of similar peak points, performing mean filtering on the gray level histogram by adopting a sliding window with the length of N;
step (33): because the sole is wider than the heel, the highest peak point of the filtered gray level histogram is the sole center line, and the next highest peak point is the heel center line;
step (34): 1/6 rows of the total row number of the sole pressure images are respectively taken at the upper side and the lower side of the longitudinal middle row of the sole to obtain a sole area, the sole area images are subjected to integral projection along a transverse axis to obtain a sole gray histogram, the highest peak point of the sole gray histogram is a sole central row, the next highest peak point is a heel central row, the intersection point of the sole central row and the sole central row is a sole central point, and the intersection point of the heel central row and the heel central row is a heel central point;
and (4): establishing a plantar pressure characteristic point positioning model, and positioning characteristic points of a plantar pressure image; the method specifically comprises two stages of model training and feature point searching:
step (41): in the model training stage, selecting a sufficient number of plantar pressure images as a training set, manually marking the accurate positions of the characteristic points in the training samples, and establishing a plantar pressure characteristic point positioning model, wherein the model training stage is only executed once;
step (42): in the characteristic point searching stage, a plantar pressure characteristic point positioning model is initialized according to the pressure centers of the heel and the sole, and then the accurate position of the plantar pressure image characteristic point is found through an iterative model;
further, the model training phase processing method comprises: selecting N sheets (N)>200) The plantar pressure image obtained in the step (3) is used as a training set; manually marking n accurate plantar pressure images (n)>6) the position of the characteristic point (the characteristic point is selected to comprise the heel, the sole central point and the inflection point of the sole pressure outline, the characteristic point and each area of the foot have obvious relative position relation), the sequenced coordinates of the characteristic points are sequentially connected in series to form a shape vector, and the shape vector set obtained by N sole pressure images is marked as XuWherein the shape vector obtained from the ith plantar pressure image is recorded as Xui，Xui＝(xi0,…,xi(n-1),yi0,…,yi(n-1))TI-0, …, N-1, wherein xik、yikRespectively representing the horizontal and vertical coordinates of a characteristic point k in the ith plantar pressure training image, wherein k is more than or equal to 0 and less than n, and n is the number of the characteristic points; set of shape vectors XuAs input for establishing the part of the plantar pressure characteristic point positioning model, the X set is formed according to the shape vectorsuEstablishing a plantar pressure characteristic point positioning model for searching plantar pressure characteristic points; the accurate position of the characteristic points in the manual marking training sample and the establishment of the plantar pressure characteristic point positioning model part are only executed once when the movable shape model is established;
furthermore, the plantar pressure feature point positioning model is a template-based feature point positioning method and can be an ASM model or an AAM model; first, the model can be normalized by ProcrustesThe method is that the shape vectors are collected X on the basis of not changing the point distribution model through translation, rotation and scaling transformation operationsuAligning to the same shape vector X, wherein the shape vector X is a feature point template; then, constructing local features for each feature point; at this point, the feature point positioning model is constructed;
further, the shape vector is collected into XuAligning to the same shape vector X comprises the steps of:
step (411): with a certain training sample XjAs a shape reference
Figure BDA0002338559340000031
For other training samples XiPerforming translation, rotation and scaling to make all samples as close to the reference shape as possible; training sample XiAnd a shape reference
Figure BDA0002338559340000032
The degree of proximity between them is defined using Euclidean distance
Figure BDA0002338559340000033
Obtaining a transformed shape vector of X'i＝M(s,θ)[Xi]-t, where s is the scaling scale, θ is the rotation angle, t is the translation vector;
step (412): calculating all transformed training samples X'uAs a new shape reference
Figure BDA0002338559340000034
And calculating a current shape reference
Figure BDA0002338559340000035
And last time shape reference
Figure BDA0002338559340000036
Translation, rotation and zoom deviations between;
step (413): iteration steps (411) and (412), if the deviation is smaller than a specified threshold or the iteration exceeds a specified maximum iteration number, stopping the iteration; and taking the shape reference obtained at the last time as an average shape vector X after all the training samples are aligned.
Further, the specific operation of constructing the local feature for each feature point is as follows: calculating the average local texture of the characteristic points i (i is 0,1, …, n-1) in the training sample
Figure BDA0002338559340000037
Sum variance Si(ii) a Firstly, on both sides of the ith characteristic point of the jth (j is 0,1, …, N-1) training sample, respectively selecting N pressure points along a direction perpendicular to a connecting line of the two characteristic points before and after the point to form a vector with the length of 2N +1, and obtaining a local texture g by differentiating pressure values contained in the vectorijThe same operation is carried out on all samples in the training set to obtain N local textures of the ith characteristic point, and the mean value is calculated
Figure BDA0002338559340000041
Sum variance
Figure BDA0002338559340000042
Further, the characteristic point searching stage comprises the steps of initializing a model according to the pressure centers of the heel and the sole, and finding out the accurate position of the characteristic point of the sole pressure image through an iterative model; the method specifically comprises the following substeps:
step (421): calculating the pressure centers (x) of heel and sole in the target plantar pressure image by taking the pressure centers of heel and sole as the reference1,y1) And (x)2,y2) And a shape reference
Figure BDA0002338559340000043
Center of pressure of middle heel and sole (x'1,y'1) And (x'2,y'2) Translation, rotation, scaling deviation between, to, shape reference
Figure BDA0002338559340000044
Transforming to obtain the initial position X of the modelc；
Step (422): searching a new position of each feature point in the plantar pressure image; first, the initial position X of the active shape model is setcCovering on the plantar pressure image, for the ith characteristic point in the model, taking the ith characteristic point as the center in the direction vertical to the connecting line of the front characteristic point and the rear characteristic point, and respectively selecting m (m) at two sides>n) pressure points and the characteristic point i form a search neighborhood with the length of 2m + 1; sliding windows with the length of n pressure points in the search neighborhood, and calculating the local texture g of each windowiAnd calculating the local texture and average texture
Figure BDA0002338559340000045
The mahalanobis distance between them, such that the center point of the window with the smallest mahalanobis distance serves as the new position of the feature point i.
Step (423): calculating translation, rotation and scaling parameters of the shape vector of the initial position of the model and the shape vector after updating the position;
step (424): repeating steps (422) and (423) to calculate a new shape vector XnewAnd the original shape vector XcDistance D ofx，
Figure BDA0002338559340000046
Wherein x isnewi、ynewiAre respectively a new shape vector XnewThe abscissa and ordinate, x, of the ith feature point of (1)ci、yciAre respectively the original shape vector XcThe abscissa and the ordinate of the ith feature point of (1). If it is
Figure BDA0002338559340000047
Or the circulation reaches the maximum times, the search is completed, and the accurate position of each feature point in the plantar pressure image is obtained;
and (5): and (4) according to the relative position relation between each area of the foot and the characteristic points of the sole pressure, combining the characteristic points of the sole pressure image obtained in the step (4), and partitioning the sole pressure image.
The invention has the following beneficial effects:
(1) the plantar pressure partition method can remove contact noise, adhesion noise, network signal noise and acquisition circuit noise of original plantar pressure data, and automatically extracts frames from a plantar pressure image to select a footprint area through twice clustering and a minimum external moment algorithm;
(2) the invention adopts a multiple integral projection method to detect the sole central point and the heel central point, ensures the reliable positioning of the heel center and the sole central point, and simultaneously improves the accuracy of model initialization in the plantar pressure characteristic point searching stage, thereby improving the accuracy of the positioning of the plantar pressure characteristic points and the accuracy of plantar pressure image partition;
(3) the plantar pressure partitioning method selects points with distinct characteristics such as a heel central point, a sole central point, an inflection point of a plantar pressure contour and the like as characteristic points of a plantar pressure image, is favorable for building a plantar pressure characteristic point positioning model according to a training sample set, has obvious relative position relation between the selected characteristic points and each area of the foot, and is also favorable for partitioning the plantar pressure image according to the characteristic points;
(4) the plantar pressure partitioning method can obtain accurate plantar pressure partitioning results by accurately positioning the characteristic points of the plantar pressure image and partitioning the plantar pressure image according to the relative position relation between the characteristic points and the areas of the foot;
(5) the model training stage of the plantar pressure characteristic point positioning model adopted by the invention only needs to be executed once, and the trained model can be directly used in the plantar pressure characteristic point searching stage, thereby ensuring that the plantar pressure partitioning method can be executed efficiently.
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In order to more clearly illustrate the technical solution of the method of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without inventive efforts.
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic view of an integral projection of a plantar pressure image;
FIG. 3 is a block diagram illustrating a sub-process of the plantar pressure feature point locating portion according to an embodiment of the present invention;
FIG. 4 is a schematic view of the selection of plantar pressure feature points in an embodiment of the present invention;
FIG. 5 shows an initial position X of the active shape model in an embodiment of the present inventioncAn effect map overlaid on the pressure image of the sole of the foot;
fig. 6 is a schematic diagram of the result of partitioning the pressure image of the sole of a foot according to the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
Example 1
As shown in fig. 1, the core of the present invention is to provide a method for determining a pressure image of a sole facing gait analysis, comprising the following steps:
step (1): obtaining plantar pressure data and carrying out early-stage processing, and specifically comprising the following substeps:
step (11): acquiring a plurality of adjacent frames (at least 3 frames) of plantar pressure data containing complete footprints in a time domain by adopting an array type pressure sensor;
step (12): performing time domain mean filtering on the middle frame by using the multiple frames of plantar pressure data obtained in the step (11);
step (13): sequentially carrying out maximum filtering processing and interpolation processing on the intermediate frame data obtained in the step (12) to obtain sole pressure data with uniform size and equal row-column spacing;
step (2): clustering the sole pressure data after the preprocessing in the step (1), locating the position of the footprint in the sole pressure data, and obtaining a sole pressure image of the footprint area, specifically comprising the following substeps:
step (21): clustering the sole pressure data obtained in the step (1) after the pre-treatment by using a DBSCAN clustering algorithm to obtain a plurality of pressure area blocks, wherein a footprint area may be composed of a plurality of pressure area blocks;
step (22): using the minimum external moment center point of each pressure area block obtained in the step (21) as the center of the area block, and gathering the pressure area blocks into a footprint area by using a K-means clustering algorithm;
step (23): extracting the pressure data of the footprint area obtained in the step (22) to obtain a sole pressure image containing the complete footprint;
and (3): according to the method shown in fig. 2, the integrated projection is performed on the plantar pressure image obtained in the step (2) to obtain the pressure centers of the heel and the sole, and the method specifically comprises the following substeps:
step (31): taking the long side direction of the minimum external torque of the plantar pressure image obtained in the step (2) as a longitudinal axis and the short side direction as a transverse axis, and performing integral projection on the plantar pressure image along the longitudinal axis to obtain a gray level histogram;
step (32): if the gray level histogram obtained in the step (31) contains a plurality of similar peak points, performing mean filtering on the gray level histogram by adopting a sliding window with the length of N;
step (33): because the sole is wider than the heel, the highest peak point of the filtered gray level histogram is the sole center line, and the next highest peak point is the heel center line;
step (34): 1/6 rows of the total row number of the sole pressure images are respectively taken at the upper side and the lower side of the longitudinal middle row of the sole to obtain a sole area, the sole area images are subjected to integral projection along a transverse axis to obtain a sole gray histogram, the highest peak point of the sole gray histogram is a sole central row, the next highest peak point is a heel central row, the intersection point of the sole central row and the sole central row is a sole central point, and the intersection point of the heel central row and the heel central row is a heel central point;
and (4): establishing a plantar pressure characteristic point positioning model as shown in fig. 3, and positioning characteristic points of a plantar pressure image; the method specifically comprises two stages of model training and feature point searching;
step (41): in the model training stage, selecting N (N >200) plantar pressure images as a training set, manually marking the accurate positions of characteristic points in a training sample, and establishing a plantar pressure characteristic point positioning model, wherein the model training stage is only executed once;
step (42): in the characteristic point searching stage, a plantar pressure characteristic point positioning model is initialized according to the pressure centers of the heel and the sole, and then the accurate position of the plantar pressure image characteristic point is found through an iterative model;
further, the model training phase processing method comprises: selecting N plantar pressure images obtained in the step (3) as a training set; manually marking n accurate plantar pressure images (n)>6) (the selection of the feature points includes the heel, the center point of the sole of the foot, and the inflection point of the pressure profile of the sole of the foot, the feature points should have obvious relative position relationship with each area of the foot), as shown in fig. 4, as an example, 17 feature points are selected in this embodiment, that is: f1, F2, …, F16, F17, including heel center point F2 and ball center point F1; the coordinates after the characteristic points are sequenced are sequentially connected in series to form a shape vector, and a shape vector set obtained by N plantar pressure images is recorded as XuWherein the shape vector obtained from the ith plantar pressure image is recorded as Xui，Xui＝(xi0,…,xi(n-1),yi0,…,yi(n-1))TI-0, …, N-1, wherein xik、yikRespectively representing the kth characteristic point F in the ith plantar pressure training imagek(xik,yik) K is more than or equal to 0 and less than n; set of shape vectors XuAs input for establishing the part of the plantar pressure characteristic point positioning model, according to the shapeSet of vectors XuEstablishing a plantar pressure characteristic point positioning model for searching plantar pressure characteristic points; the accurate position of the characteristic points in the manual marking training sample and the establishment of the plantar pressure characteristic point positioning model part are only executed once when the movable shape model is established;
furthermore, the plantar pressure feature point positioning model is a feature point positioning method based on a template, and preferably, a movable shape model is adopted in the embodiment; firstly, the model can be formed by a Procrustes normalization method, namely, a translation, rotation and scaling transformation operation, and a shape vector set X can be collected on the basis of not changing a point distribution modeluAligning to the same shape vector X, wherein the shape vector X is a feature point template; then, constructing local features for each feature point; at this point, the feature point positioning model is constructed;
further, the shape vector is collected into XuThe specific steps of aligning to the same shape vector X are as follows:
step (ii) of
Figure BDA0002338559340000071
Selecting XuThe first shape vector as the initial average shape vector
Figure BDA0002338559340000072
Step (ii) of
Figure BDA0002338559340000073
Each shape vector XuiVector to average shape
Figure BDA0002338559340000074
And (2) aligning, wherein a transformation vector in the aligning process is recorded as (scos theta, ssin theta, T)x,ty)TWhere s is the scaling, θ is the rotation angle, txIs the x-axis translation vector, tyIs a y-axis translation vector, and the aligned shape vector is denoted as X'ui；
Operation of alignment is X'ui＝XuiAnd T, wherein the calculation method of the transformation vector T comprises the following steps:
Figure BDA0002338559340000075
wherein the content of the first and second substances,
Figure BDA0002338559340000081
w is a weight matrix, and the calculation method comprises the following steps:
first, a feature point k (x) in the ith shape is calculatedik,yik)、l(xik,yik) The distance between:
Figure BDA0002338559340000082
then, a weighted value of the feature point k is calculated
Figure BDA0002338559340000083
Wherein the variance VDklFor all N characteristic points k (x) in the plantar pressure imageik,yik)、l(xik,yik) Distance Dikl(i ═ 0, …, N-1) variance; weighting, value wkRepresenting the stability degree of the characteristic point k;
finally, with wkMaking a diagonal matrix W for the diagonal, wherein the diagonal matrix is a weight matrix;
Figure BDA0002338559340000084
step (ii) of
Figure BDA0002338559340000085
Updating all shape vectors X 'after alignment'uiIs given as the average shape vector of
Figure BDA0002338559340000086
Figure BDA0002338559340000087
Step (ii) of
Figure BDA0002338559340000088
Repeating the steps
Figure BDA0002338559340000089
Outputting the aligned shape vector to be marked as X after convergence or maximum iteration times;
the convergence determination condition is: calculating a transformation vector T between the average shape vectors of the two times before and after, and converging if the conditions of | s-1| < 0.001, | theta | < 0.001 pi/180, and | T | < 0.01 are simultaneously met;
further, the specific operation of constructing the local feature for each feature point is as follows: calculating the average local texture of the characteristic points i (i is 0,1, …, n-1) in the training sample
Figure BDA00023385593400000810
Sum variance Si(ii) a Firstly, h pressure points are respectively selected on two sides of the ith characteristic point of the jth (j is 0,1, …, N-1) training sample along the direction vertical to the connecting line of the two characteristic points before and after the point to form a vector with the length of 2h +1, and a local texture g is obtained by differentiating the pressure values contained in the vectorijThe same operation is carried out on all samples in the training set to obtain N local textures of the ith characteristic point, and the mean value is calculated
Figure BDA00023385593400000811
Sum variance
Figure BDA00023385593400000812
Further, the establishing the active shape model includes PCA analysis, which includes the following steps:
step (PCA-1): calculating an average shape vector of the aligned N shape vectors
Figure BDA0002338559340000091
Step (PCA-2): computing covariance moments of N shape vectorsMatrix of
Figure BDA0002338559340000092
Step (PCA-3): calculating an eigenvalue λ of a covariance matrixiAnd sorting from big to small, and marking the corresponding eigenvector as pi,i＝0,1,…,2n-1；
Step (PCA-4): selecting the first k maximum eigenvalues, and forming a principal component analysis matrix P (P) by the corresponding eigenvectors0,p1,…,pk-1)；
Step (PCA-5): constructing a movable shape model as
Figure BDA0002338559340000093
Wherein, b is a k-dimensional shape parameter for controlling the shape change of the feature point; here b is constrained to
Figure BDA0002338559340000094
Further, the feature point searching stage includes the following steps: initializing the movable shape model according to the pressure centers of the heel and the sole, and finding out the accurate position of the characteristic point of the sole pressure image through the iterative model, wherein the specific steps are as follows:
step (421): initializing the movable shape model through affine transformation; the coordinates of the pressure centers of the heel and the sole in the target plantar pressure image are respectively marked as (x)1,y1) And (x)2,y2) The coordinates of the pressure centers of the heel and sole in the active shape model are respectively expressed as (x'1,y'1) And (x'2,y'2) First, calculate the scaling s and the rotation angle θ to make the translation vector txAnd tyTo be 0, the movable shape model is zoomed and rotated to obtain a temporary shape, and the pressure center coordinates of the heel and the sole of the foot which are recorded with the temporary shape are respectively (x ″)1,y″1) And calculating a translation vector tx,tyThen, let s be 0 and θ be 0, and translate the temporary shape to obtain the initial position X of the modelc；
Step (422):searching a new position of each feature point in the plantar pressure image; first, the initial position X of the active shape model is setcOverlaid on the plantar pressure image, as shown in particular in FIG. 5, wherein point Fc1，Fc2，…,Fc16，Fc17, are all initial positions XcThe characteristic point of (1). For the ith characteristic point in the model, taking the ith characteristic point as the center in the direction vertical to the connecting line of the two characteristic points in front of the ith characteristic point, and selecting m (m) from two sides of the ith characteristic point>h) The pressure points and the characteristic point i form a search neighborhood with the length of 2m + 1; sliding windows with the length of h pressure points in the search neighborhood, and calculating the local texture g of each windowiCalculating the Mahalanobis distance between the local texture and the average texture, and taking the central point of the window with the minimum Mahalanobis distance as a new position of the characteristic point i;
step (423): updating the attitude parameters; calculating a shape vector X of an initial position of the active shape modelcAnd the updated shape vector XnewThe transformation vector T and the transformation parameter b;
step (424): repeating steps (422) and (423) to calculate a new shape vector XnewAnd the original shape vector XcDistance D ofx，
Figure BDA0002338559340000101
Wherein x isnewi、ynewiAre respectively a new shape vector XnewThe abscissa and ordinate, x, of the ith feature point of (1)ci、yciAre respectively the original shape vector XcThe abscissa and the ordinate of the ith feature point of (1). If it is
Figure BDA0002338559340000102
Or the circulation reaches the maximum times, the search is completed, and the accurate position of each feature point in the plantar pressure image is obtained;
and (5): as shown in fig. 6, wherein point Fo1，Fo2，…,Fo16，Fo17, the exact location of each feature point F1, F2, …, F16, F17 in the plantar pressure image. According to the characteristics of various areas of the foot and the pressure of the solePartitioning the plantar pressure image by combining the relative position relation between the points and the characteristic points of the plantar pressure image obtained in the step (4); in this embodiment, the plantar pressure image is divided into 6 areas, namely, toe area, ball area, medial foot, lateral foot, medial heel and lateral heel; the method comprises the following specific steps: fo3-FoThe envelope surface of the No. 17 characteristic point is the whole footprint area; fo3-Fo4-Fo5-Fo16-FoThe envelope surface of the No. 17 characteristic point is a heel area, FoNo. 3 and FoThe No. 2 characteristic point connecting line can divide the heel area into a heel inner side area and a heel outer side area; fo5-Fo6-Fo15-FoThe envelope surface of the No. 16 characteristic point is a midfoot area, FoNo. 1 and FoThe connecting line of the No. 2 characteristic points can further divide the midfoot area into a medial midfoot area and a lateral midfoot area; fo7-Fo8-Fo9-Fo13-Fo14-FoThe envelope surface of the No. 15 characteristic point is a sole area; fo9-Fo10-Fo11-Fo12-FoThe envelope surface of the feature point No. 13 is a toe region.

Claims (10)

1. A gait analysis-oriented plantar pressure image partitioning method is characterized by comprising the following steps:
step (1): obtaining plantar pressure data and carrying out early-stage processing;
step (2): clustering the foot sole pressure data processed in the early stage in the step (1), and positioning the position of the foot print in the foot sole pressure data to obtain a foot sole pressure image of a foot print area;
and (3): performing integral projection on the plantar pressure image obtained in the step (2) to obtain a heel and sole pressure center;
and (4): establishing a plantar pressure characteristic point positioning model, and positioning characteristic points of a plantar pressure image;
and (5): and (4) according to the relative position relation between each area of the foot and the characteristic points of the sole pressure, combining the characteristic points of the sole pressure image obtained in the step (4), and partitioning the sole pressure image.
2. The plantar pressure image partition method for gait analysis according to claim 1, wherein: the specific implementation process of the step (1) comprises the following steps:
step (11): acquiring a plurality of adjacent frames (at least 3 frames) of plantar pressure data containing complete footprints in a time domain by adopting an array type pressure sensor;
step (12): performing time domain mean filtering on the middle frame plantar pressure data by using the multiple frames of plantar pressure data obtained in the step (11);
step (13): and (4) sequentially carrying out maximum value filtering processing and interpolation processing on the mid-frame plantar pressure data obtained in the step (12) to obtain pre-processed plantar pressure data with uniform size and equal row-column spacing.
3. The plantar pressure image partition method for gait analysis according to claim 1, wherein: the specific process of the step (2) comprises the following steps:
step (21): clustering the sole pressure data obtained in the step (1) after the pre-treatment by using a DBSCAN clustering algorithm to obtain a plurality of pressure area blocks, wherein the footprint area is composed of a plurality of pressure area blocks;
step (22): using the minimum external moment center point of each pressure area block in the plurality of pressure area blocks obtained in the step (21) as the center of the pressure area block, and gathering the pressure area blocks into a footprint area by adopting a K-means clustering algorithm;
step (23): and (4) extracting the pressure data of the footprint area obtained in the step (22) to obtain a sole pressure image containing the complete footprint.
4. The plantar pressure image partition method for gait analysis according to claim 1, wherein: the specific process of the step (3) comprises the following steps:
step (31): taking the long side direction of the minimum external torque of the plantar pressure image obtained in the step (2) as a longitudinal axis and the short side direction as a transverse axis, and performing integral projection on the plantar pressure image along the longitudinal axis to obtain a gray level histogram;
step (32): if the gray level histogram obtained in the step (31) contains a plurality of similar peak points, performing mean filtering on the gray level histogram by adopting a sliding window with the length of N;
step (33): based on the fact that the sole is wider than the heel, the highest peak point of the filtered gray level histogram is the sole center line, and the next highest peak point is the heel center line;
step (34): 1/6 rows of the total row number of the sole pressure images are respectively taken at the upper side and the lower side of the longitudinal central row of the sole to obtain a sole area, the images of the sole area are subjected to integral projection along a transverse axis to obtain a sole gray histogram, the highest peak point of the sole gray histogram is a sole central row, the next highest peak point is a heel central row, the intersection point of the sole central row and the sole central row is a sole central point, and the intersection point of the heel central row and the heel central row is a heel central point.
5. The plantar pressure image partition method for gait analysis according to claim 1, wherein: the step (4) realizes the following processes: the method comprises two stages of model training and feature point searching:
step (41): in the model training stage, selecting N plantar pressure images as a training set, wherein N is more than 200; manually marking the accurate positions of the characteristic points in the training sample, establishing a plantar pressure characteristic point positioning model, and executing the model training stage only once;
step (42): in the characteristic point searching stage, a plantar pressure characteristic point positioning model is initialized according to the pressure centers of the heel and the sole, and then the accurate position of the plantar pressure image characteristic point is found through an iterative model.
6. The plantar pressure image partition method for gait analysis according to claim 5, wherein: the processing method of the model training stage comprises the following steps: selecting N plantar pressure images obtained in the step (3) as a training set; the accurate n characteristic point positions of each sole pressure image are marked manually, n is the number of the characteristic points, n is>Selection package of feature points 6Including the heel, the sole central point and the inflection point of the sole pressure outline, the characteristic points should have an obvious relative position relation with each area of the foot, the sequenced coordinates of the characteristic points are sequentially connected in series to form a shape vector, and the shape vector set obtained by N sole pressure images is marked as XuWherein the shape vector obtained from the ith plantar pressure image is recorded as Xui，Xui＝(xi0,…,xi(n-1),yi0,…,yi(n-1))TI-0, …, N-1, wherein xik、yikRespectively representing the kth characteristic point F in the ith plantar pressure imagek(xik,yik) K is more than or equal to 0 and less than n; set of shape vectors XuAs input for establishing the part of the plantar pressure characteristic point positioning model, the X set is formed according to the shape vectorsuAnd establishing a sole pressure characteristic point positioning model for searching sole pressure characteristic points, wherein the accurate positions of the characteristic points in the manual marking training sample and the establishment of the sole pressure characteristic point positioning model part are only executed once when the movable shape model is established.
7. The plantar pressure image partition method for gait analysis according to claim 6, wherein: the plantar pressure characteristic point positioning model is a characteristic point positioning based on a template and is established by adopting an ASM (automatic sequence model) or AAM (architecture analysis model), and the establishing method comprises the following steps: firstly, a shape vector set X is collected on the basis of not changing a point distribution model through a Procrustes normalization method, namely through translation, rotation and scaling transformation operationuAligning to the same shape vector X, wherein the shape vector X is a feature point template; and then, constructing local features for each feature point, so far, completing the construction of the feature point positioning model.
8. The plantar pressure image partition method for gait analysis according to claim 7, wherein: the shape vectors are collected into XuAligning to the same shape vector X comprises the steps of:
step (411): with a certain training sample XjAs a shape reference
Figure FDA0002338559330000031
For other training samples XiPerforming translation, rotation and scaling to make all samples approximate to the reference shape; training sample XiAnd a shape reference
Figure FDA0002338559330000032
The degree of proximity between them is defined using Euclidean distance
Figure FDA0002338559330000033
Obtaining a transformed shape vector of X'i＝M(s,θ)[Xi]-t, where s is the scaling scale, θ is the rotation angle, t is the translation vector;
step (412): calculating all transformed training samples X'uAs a new shape reference
Figure FDA0002338559330000034
And calculating a current shape reference
Figure FDA0002338559330000035
And last time shape reference
Figure FDA0002338559330000036
Translation, rotation and zoom deviations between;
step (413): iteration steps (411) and (412), if the deviation is smaller than a specified threshold value or the iteration exceeds a specified maximum iteration number, stopping the iteration; and taking the shape reference obtained at the last time as an average shape vector X after all the training samples are aligned.
9. The plantar pressure image partition method for gait analysis according to claim 7, wherein: the specific operation of constructing the local feature for each feature point is as follows: calculating average local texture of characteristic point i in training sample
Figure FDA0002338559330000037
Sum variance SiI ═ 0,1, …, n-1; firstly, on two sides of the ith characteristic point of the jth training sample, j is 0,1, … and N-1, h pressure points are respectively selected along the direction vertical to the connecting line of the two characteristic points before and after the point to form a vector with the length of 2h +1, and a local texture g is obtained by derivation of pressure values contained in the vectorijThe same operation is carried out on all samples in the training set to obtain N local textures of the ith characteristic point, and the mean value is calculated
Figure FDA0002338559330000038
Sum variance
Figure FDA0002338559330000039
10. The plantar pressure image partition method for gait analysis according to claim 5, wherein: the characteristic point searching stage comprises the following steps: initializing the model according to the pressure centers of the heel and the sole, and finding out the accurate positions of the characteristic points of the plantar pressure image through an iterative model; the method comprises the following specific steps:
step (421): calculating the pressure centers (x) of heel and sole in the target plantar pressure image by taking the pressure centers of heel and sole as the reference1,y1) And (x)2,y2) And a shape reference
Figure FDA00023385593300000310
Center of pressure of middle heel and sole (x'1,y'1) And (x'2,y'2) Translation, rotation, scaling deviation between, to, shape reference
Figure FDA00023385593300000311
Transforming to obtain the initial position X of the modelc；
Step (422): searching a new position of each feature point in the plantar pressure image; first, the initial position X of the active shape model is setcPressure chart covered on soleAs above, for the ith characteristic point in the model, the ith characteristic point is centered on the ith characteristic point in the direction perpendicular to the connecting line of the two characteristic points before and after the ith characteristic point, and m pressure points are respectively selected at two sides of the ith characteristic point>h, a search neighborhood with the length of 2m +1 is formed by adding the characteristic points i; sliding windows with the length of h pressure points in the search neighborhood, and calculating the local texture g of each windowiAnd calculating the local texture and average texture
Figure FDA0002338559330000041
The central point of the window with the minimum Mahalanobis distance is used as the new position of the characteristic point i;
step (423): calculating model initial position XcThe shape vector of (1) and the translation, rotation and scaling parameters of the shape vector after the position is updated;
step (424): repeating steps (422) and (423) to calculate a new shape vector XnewAnd the original shape vector XcDistance D ofx，
Figure FDA0002338559330000042
Wherein x isnewi、ynewiAre respectively a new shape vector XnewThe abscissa and ordinate, x, of the ith feature point of (1)ci、yciAre respectively the original shape vector XcThe abscissa and ordinate of the ith feature point of (1), if
Figure FDA0002338559330000043
Or when the circulation reaches the maximum times, the search is finished, and the search is finished, so that the accurate position of each feature point in the plantar pressure image is obtained.
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