Development of deep clustering model to stratify occurrence risk of diabetic footulcers based on foot pressure patterns and clinical indices
Xuanchen Ji", Yasuhiro Akiyarna", Hisae Hayashi", Yoji Yamada", and Shogo Okamoto""Graduate School of Engineering, Nagoya University,Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japanji. xuanchen@d.mbox.nago ya-u.ac .jp, akiyarna- yasuhiro@rnech.nagoya-u .ac.jp,
yo j i . yarna d a @rnae . na go y a - u. ac. j p , s hogo .oka rnoto @rna e. na go ya- u.a c. j p

bFaculty of Rehabilitation and Care, Seijoh University, 2-172 Fukinodai, Tokai City, 476-8588, Japanhi saehay ashi@ seij oh-u.a c.jp

Abstract
In recent years, the number ofpatients suffering from diabetes mellitus has continued to increase. When diabetes
becomes severe, ulcers may form on the feet of the patient.
In the past few years, several researchers have fo cused on
the risk fa ctors and avoidance of ulceration. One effective
method to predict the occurrence of diabetic foot ulcers isdeveloping a machine learning model. However, fe w studies combine both clinical indices and mechanical data as
the attributes of the training datasets. In this study, we developed a composite model of a convolutional neural network and K-means clustering to extract features from diabetic patients with or without ulceration as well as healthy
individuals. Using a deep clustering model, the center ofpressure (CoP) trajectory images were divided into threeclusters. Furthermore, we evaluated the performance oftheclustering by extracting the features from the CoP trajectory images in each cluster and combining them with theclinical indices ofthe patients. The results showed that patients with ulcers when walking tend to contact the groundwith a narrow area of the plantar and apply a small force.
Furthermore, it was found that patients undergoing diabetic
neuropathy or with a toe amputation have a high potentialof suffering from ulcers.

1. Introduction
The number of diabetic patients continues increasing especially in recent years. According to the International Diabetes Federation (IDF), the number of global diabetes patients reached 41.5 million in 2015 and is expected to reach
978 -1-7281 -9186-7/Z0/$31.00 @ ZOZOIEEE

64.2 million in 2040 [1]. Diabetes can cause many otherdiseases, such as cardiovascular diseases [2], blindness [3]and stroke [4]. Among these, diabetic foot ulcer (DFU)
is the most disabling complication arising from diabetes.
Approximately 40% of patients with DFU experience a recurrence within a year, and this proportion increases to approximately 60% in 3 years and 65% in 5 years [5]. Foot
deformities, duration of diabetes, and clinical treatment are
responsible for the development of ulceration [6]. Preventive measures can be adopted by both patients and doctors.
In therapeutic treatment, providing patients with preventive
medicine and well-informed nursing care can reduce theincidence of DFUs by approximately 66% [7]. However,gait variations and restrictions in subtalar and first metatarsophalangeal joint are found in persons with diabetic neuropathy even before the onset of foot deformity, which canbe regarded as the early risk marker of DFUs [8]. Therefore, regular evaluation of gait patterns is believed to be amore effective measure as patients can take the necessary
precautions to prevent the development of DFUs [9]. For
this purpose, it is important to observe the clinical and dynamical feature among DFUs patients, normal patients andhealthy people, to distinguish them from each other by creating models each of which has high correlation with thecorresponding feature.
Numerical studies on the discussion of diabetes and
DFUs feature have focused on various types of methods
and datasets. Available clinical information was commonly
combined to predict DFUs occurrence using backward stepwise elimination [10]. Borzouei and Soltanian identified
the most important demographic risk factors for Type 2 diabetes mellitus using a neural network model [11]. Botros
et al. focused on the temporal dynamic plantar pressure andestimated the occurrence risk of DFUs using a support vec-

tor machine (SVM) [12]. Bennetts et al. made an effort
on using K-means cluster analysis to determine characteristic regional peak plantar pressure distributions for diabetic patient classification that may be useful for footwear
prescription [13]. Sawacha et al. aimed to verify if threedimensional gait data obtained from a high-resolution motion capture would be able to distinguish the diabetic neuropathy(DPN) patients and healthy people [14]. However,high-resolution measurement devices always require highcost and are hard to be diffused and few researchers havefocused on the composite influence of patients' clinical indices and gait patterns on their occurrence risk of ulcer.
The objective of the present study is to develop a composite
model of convolutional neural network (CNN) and K-meansclustering to classify and distinguish the feature among diabetic patients with ulceration, normal diabetic patients andhealthy people using different types of datasets, including
their body indices, anamnesis and gait pressure distributions
obtained from four-sensor measurement.
The remainder of this paper is organized as follows. In
Section 2, we describe the materials and methods used inthis study. In Section 3, we analyze the processing and output of the center of pressure (CoP) trajectory images [15]using a deep embedded clustering model [16]. The results
and evaluation of the model are displayed in Section 4. Discussions and limitations are presented in Section 5, and we
conclude with a summary of our findings in Section 6.

2. Materials and methods
The gait data and clinical indices of patients and healthy
individuals were collected from several hospitals and preprocessed for further clustering. An unsupervised method
can distinguish between areas that may not be obvious tothe human eye. To investigate the features of the gait patterns of the subjects, a composite model of a CNN and Kmeans clustering was developed as an unsupervised clustering model. After classifying the dataset, we extracted several parameters from the gait data and clinical indices andconducted the Kruskal-Wallis test and chi-squared test toinvestigate the features and differences among the clusters.

2.1. Data acquisition
Since March 2019, we have been collecting the clinical indices and gait data of diabetic patients from thedatasheets of a prospective trial conducted at the Nagoya
Kyoritsu Hospital, Masuko Memorial Hospital, Matsunami
General Hospital, and Seijoh University. The data is in ananonymized manner and will not be made public. 73 patients have enrolled in this experiment: 26 females (age,71.0 ± 10.3 years; height, 150.6± 6.7 cm; weight, 51.1 ±10.3 kg), and 47 males (age, 71.5 ± 9.7 years; height, 165.9± 6.9 cm; weight, 62.9 ± 11.9 kg). Among these, 23 individuals were found to have ulcerations at the timing of mea-

Left

Sensor 2

Right

Sensor 1

Sen sor 3+--~~·~.

Sensor 4
Figure 1: Sensor distribution .

surement while the others did not. Among them, 2 individuals developed ulceration on both two feet. Therefore, 25 feetwere found to get the ulceration. Moreover, we also conducted the same measurement of gait data and body indiceson 25 healthy persons since August 2018. Among them,
20 females (age, 74.0±6.4 years; height, 151.3±7.1 cm;
weight, 50.7±5.4 kg), and 5 males (age, 77.8±4.7 years;
height, 164.6±4.1 cm; weight, 61.4±7.8 kg). We processed
gait data of healthy persons and patients at the same epoch
to investigate the feature of foot patterns
The device used to measure the gait data was a pair offorce-measuring shoes (Balance Aid, Leimac Ltd., Japan).
The sole of each shoe was divided into four parts. A force
sensor was installed on each part to obtain the ground reaction force (GRF) data (Figure 1). The subjects were required to wear these force-measuring shoes and walk for 10
m in a straight line, with and without a walking aid. The
sensors on the soles recorded the GRF data of the subjects
as they walked. However, the data were obtained only inthe vertical direction to ensure realistic data collection.

2.2. Architecture of deep clustering
After collecting the gait and clinical information of thesubjects, we transformed the gait patterns to images. For
further investigation of the gait features, deep clustering,which was constructed using K-means clustering and a
CNN, was used to divide the image dataset into several
clusters by an unsupervised method. This is because it can
fit small datasets well and provide spectrally distinct areas,
which may not be notable to a human eye. Because three
types of subjects were included in the dataset, we defined
the number of clusters as three.

2.2.1 Convolutionalautoencoders
The CNN is an improvised variant of a mu1tilayer perceptron. The most extensively used neural network indeep clustering algorithms is a convolutional autoencoder(CAB); it can be trained in an end-to-end manner and was
designed for learning features from unlabe1ed images [17].
The designed CAB is superior to stacked autoencoders ow-

ing to the incorporation of the spatial relationships between
the pixels in the images.
A CAE is generally composed of two type s of layers,
corresponding to encoder fw (.) and decoder gu (.) respectively. It aims to identify a code for each input sample byminimizing the mean squared error (MSE) between the input and the output over all the samples. For a fully connected autoencoder,O"(Wx) == hgu (h) = O"(Uh)fW (Xi) =x

X' iii'
o

()

:::J

~

2

co0..

()

I
I
I

y
Encoder

3Qj

I

:0
I
I

:

!..___(1)

Decoder

_

~

{

Otl
(D

Cluste r 1
Cluster 2

Cluster 3

K-means clust ering

where an m-dimensional vector h, which is regarded as embedded code of layers, serves as the new representation of
input sample x, and 0" is activation function . The activation
functions used in our study are ReLU and sigmoid. After
training, the embedded code h is regarded as the new representation of input image samples.

Figure 2: Structure of deep embedded clustering model. CAB
contain encoder and decoder layers. The original image X is embedded using encoder layers to obtain the feature image, which
can be reconstructed and recovered as X' .

y112

wrl , wr2

2.2.2 K-means clustering
K-means clustering is an unsupervised model and a methodof vector quantization. Given a set of data samples Xi (i =1, . . . ,N) where Xi E ~M, the aim of this method is topartition N observations into K clusters, with each observation belonging to the cluster with the nearest mean . Kmeans clustering achieves this by optimizing the following
cost function :N~ Il xi - QSi l12
Q EIRM XK ,Si E IRK ~
2
min

(2)

s.t. Si, j E {O, I}, 1 T Si = 1 Vi , j ,where 1 is the all-one column vector, Si is the assignment
vector of data point i which has only one non-zero element,
Si,j denotes the jth element of Si (1 :S j :S k) , and the kth
column of Q, i.e., qk, denotes the centroid of the kth cluster.

2.2.3 Overview of deep clustering model
The proposed deep convolutional embedded clusteringmodel iteratively groups visual features using a standard
clustering algorithm. Moreover, it uses subsequent assignments as supervi sions to update the weights of the network.
Its structure is composed of a CAE and a clustering layer, as
shown in Figure 2. The clustering model in this study contains three convolutional layers in the encoder layers . We
input 400 x 400 original images to the model , and finally,
obtain an output from the encoder layers of size 100 x 100,
which are regarded as the features of the images and inputof the K-means clustering part . Subsequently, the K-meansclustering model is used to classify the images into several
clusters according to their features.

Figure 3: Position and pressure parameters of sensors. wr p and
hrp are distances of right sensor p from the vertical and horizontal
central axes, respectively, while frp represents pressure of sensor
p . Parameters of the left foot are similarly labeled.

3. Analysis ofGRF data
The GRF data of the patients obtained from the experiment constitute a significant factor reflecting their ulcer
condition. To confirm the ulcer position in the sole of apatient, the CoP trajectory was used to explain the plantarpressure distribution. In this study, we obtained the CoP
trajectory of each patient from the GRF data and clustered
the trajectory images into three groups using the deep clustering model. The CoP trajectory was determined based on
the position and pressure value of each sensor. Figure 3
illustrates the position and pressure parameters of the sensors. The coord inates of CoP are defined according to thefollowing formulae:_ fr2 ·wr2+ fr4·wr4- fr1·wrl- f r3·wr3
Rx 4
L: p= l frp_ fr1·hr1 + fr2·hr2 - fr3·hr3 - fr4 ·hr4
R y4
L: p= l frp(3)

Where, R x and Ry represent the CoP coordinates of theright foot, wrp and hrp represent the distances of sensor

Category

Cluster 1
n=1l1

Cluster 2
n=56

Cluster 3
n=29

Patients' feet with ulcer
Patients' feet without ulcer
Healthy persons' feet

4 feet65 feet42 feet7 feet42 feet7 feet14 feet14 feet1 feetTable l : Clustering resultp on the right foot from the vertical and horizontal cen-tral axes, respectively. f rp which is normalized by patient's
weight, represents the pressure value of sensor p . The parameters of the left foot are labelled similarly by replacing
Rand r with L and I respectively in formula (4) and (5).
The CoP trajectory images were drawn with reference
to the CoP coordinates obtained from the GRF data. They
were prepared as follows: (1) Four steps each from the startand end of the walking duration were selected. The CoP
coordinates of each step were then calculated, (2) The CoP
trajectory figure of each phase was drawn and placed into a3 x 3 panel picture using MATLAB, (3) The plot size waschanged to express the magnitude of pressure.
Figure 4 shows samples of the CoP trajectories of two
different types of subjects, which reflect their different gait
conditions. The subjects started to walk at to and stopped
at te' The number of plots shows the time period of the current step while the size of each plot reflects the magnitudeof pressure. Large plots represent high pressure. The pressure values were standardized with reference to the weightof each subject. A comparison between the left foot trajectories of a healthy person and a patient is shown in Figure
4. The gait of the healthy person presents a more regularshape, from which the parts of the foot from heel to toe
can be discerned, while that of the patient is too irregular topresent any recognizable part of the foot. Before clustering,
all the CoP trajectory images were pre-processed to obtainthe following second moment of area about the geometriccenter, Cxy, of CoP trajectory in each image.
i;Vrx = 2)Rx (t ) - C xy )2t = toi;(4)

Vry = 2)Ry (t ) - C Xy )2
t=to

Where Vrx and Vry are the second moment of area about
C xy , Rx(t) and Ry(t) represent n; and n; in formula (4)and (5) at time t, respectively, similar manner of labelingholds for Vix and Vi y •4. Results and feature analysis
The CoP trajectory images obtained from the gait data
were divided into three clusters using deep clusteringotoHealthy people

Patients with ulcer

Figure 4: Comparison between CoP trajectories of healthy subjects and patients with ulcer. to is the time at which the subjects start walking. t; is the time when the subjects stop walking.
t m = 1/2 * (to + t e)model. To determine the trajectory features of each cluster, trajectory images of 25 healthy persons were processedin the same period. Thus, the dataset used for clustering
contains information on 73 patients and 25 healthy persons.
Right and left feet of all the participants subjects are processed as dataset.

4.1. Clustering results
We input 196 CoP trajectory images into the deep clustering model, and the clustering results are summarized inTable 1. It is noteworthy that there exists a possibility thatthe right and left feet were divided into two different clusters, even though they belong to one subject category. As
summarized in Table 2, Cluster 1 contains 4 ulcer feet ofpatients, 65 no-ulcer feet of patients, and 42 feet of healthy
persons. Cluster 2 contains 7 ulcer feet, 42 no-ulcer feet,and 7 healthy feet. Compared with Clusters I and 2, Cluster 3 has 14 ulcer cases, which is the most among the threeclusters, 14 no-ulcer feet, and no healthy foot.

4.2. Feature extraction
Each of the CoP trajectory images contains eight steps intotal, and the gait patterns may differ from each other. To
investigate the concrete differences among the three clusters more accurately, several parameters of the patients representing the cluster features were extracted from the gait
data for each step.

6.00
4.00
2.00

E

$ outeranterior

r·•

C2

-1.50

6.00

anterior

4.00

C3

0.00
-0.50

x

•Cl
inner

0.50

2.00

E
$ outerx-1. 501.50-2 .00Cl•-4 .00

•

•

0.50

1.50

-2 .00C2 -4.00-6 .00 Y(c m)-6 .00 Yfcm )posterior
Figure5: X-Ycoordinates of Pc: Pc is the CoP plot at the timing
when opposite foot contacts the ground. Cl represents Cluster
1 (0.55***, 5.81***), C2 represents Cluster2 (0.12, 5.55*)while
C3representsCluster3 (0.03, 3.55). *=p-value < 0.05, **=p-value< 0.01, ***=p-value <0.001 vs Cluster 3 Kruskal-Wallis test

inner

0.00

-0.50 C3

posterior
Figure6: X-Ycoordinates of P] : Pf is the CoP plot at the timing
whenthe opposite foot starts swinging. Cl representsCluster 1 (1.14***, -3.42**), C2 represents Cluster2 (-0.55,-2.98)while C3
represents Cluster3 (-0.28, -1.43). *=p-value <0.05, **=p-value< 0.01, ***=p-value<0.001 vs Cluster3 Kruskal-Wallis test

4.2.1 X-Y coordinates of CoP plot
The x-y coordinates of the CoP plot at a specific timing can
be a typical feature of gait patterns . This parameter shows
the position of the CoP that reflects the pattern of applying
the body mass on foot. In this study, we extracted two typesof coordinates at different times to investigate the features
among clusters. The first is defined as the x-y coordinatesof the CoP plot when opposite foot contacts the ground in
One gait cycle. We denote this plot as Pc. The second isdefined as the x-y coordinates of the CoP plot at the time
the opposite foot starts swinging, and this point is denotedas P]. We computed the average value of the coordinatesin each cluster shown in Figure s 5 and 6, and p-value wascomputed using Kruskal-Wallis test.

4.2.2 Gait parameters
To investigate the differences among the clusters, we extracted several gait parameters that reflect the value of the
GRF and walking speed, including peak GRF in one gait
cycle, and the period of One gait cycle. The peak GRF isnormalized by each subject's weight and is defined by thefollowing equation:
P eak = Ground R eaction Forcem a x
W eight(5)Moreover, we tried to investigate the variance of GRF
distribution by computing the second central moment of it,which is defined as 'MomenL2' according to the equation
below:nMomenL2 = 2)(Xp - Xc )2 + (Yp - Yc )2) . Fp (6)p =lWhere, X c and Ye are the geometric center of the whole
CoP trajectory, X p and Yp are the X-Y coordinates of each
CoP while Fp is the GRF of it. Noted that GRF is normalized by patient's weight. This parameter describes GRF
distribution.
The gait parameters of each cluster are provided in Table 2. Moreover, Kruskal-Wallis test was utilized to checkthe significant differences of these parameters among threeclusters

4.2.3

Clinical indices

In addition to gait parameters, clinical indices of subjects

in each cluster also have a significant influence on gait

patterns. After evaluating several clinical indices , we selected 5 parameters including age, Body Mass Index (BMI),
whether use walking aids or not, condition of Diabetic Peripheral Neuropathy (DPN), toe amputat ion and whether
feel a pain in feet or not. Table 3 displays the clinical indices of subjects in three clusters, we computed the meanand standard deviation of each index. Kruskal-Wallis test
was utilized to check the significant differences of age and
BMI among three clusters; As for walking aids, DPN, toe
amputation and pain, Chi-Squared Test was conducted to
examine the p -value two clusters by two clusters .

5. Discussion and limitation
Based On this clustering result in Table I, we hypothe size that the samples in Cluster 1 has the lowest potentialof suffering from DFUs, whereas those in Cluster 3 havethe highest potential for this. In this section, we discuss theCategory

Cluster I
ne ll lCluster 2
n=56

Cluster 3
n=29

p-valuePeak Ground Reaction Force
Period(s)11oment-2(crn 2 )0.99±0.20*
1.16±0.20**
430.43±233.82***

0.95±0.18
1.22±0.22
385.47±132.78***0.90±0.18
1.30±0.25
209.80±150.03< 0.05< 0.05< 0.001

Table 2: Gait Parameters: *=p-value < 0.05, **=p-value < 0.01, ***=p-value < 0.001 vs Cluster 3 Kruskal-Wallis test

Category
Age(years)
B11I(kg/rn 2 )Walking Aids(Yes)
Diabetic peripheral
neuropathy(Yes)
Toe Amputation(Yes)
Pain(Yes)

Cluster 1
n=111

Cluster 2
n=56

Cluster 3
n=29

72.39±8.74
22.83±3.46
23 cases (20.7%)

71.66±9.01
22.80±2.57
11 cases (19.6%)

72.52± 11.40
20.99±3.84
10 cases (34.5%)

2.856

0.795
< 0.050.240

31 cases (44.9%)*27 cases (55.1%)

20 cases (83.3%) t

10.6100<0.01

4 cases (5.7%)*
4 cases (3.6%)*

4 cases (7.7%)*
3 cases (5.4%)

15 cases (53.5%)t
6 cases (20.6%)t38.18238.182< 0.001< 0.01X

2

p-value

Table 3: Clinical indices: Applied Kruskal-Wallis test on age and BMI; Applied Chi-Squared Test on walking aids, DPN, toe amputationand pain; Adjusted residuals *::S -1.96, t: 1.962

features of the three clusters based on this hypothesis fromthe perspective of the x-y coordinates of the CoP plot, gaitparameters, and clinical indices, respectively.

5.1. X-Y coordinates of CoP plot
Under the condition of Pc, the position of Cluster 3 isquite different from others especially the y coordinates ofCluster 3, which is lower than other two clusters, as shownin Figure 5. This distribution indicates that when the opposite foot contacts the ground, the CoPs of healthy persons and low-risk patients are already reached the toe area,
whereas the CoP of a patient with ulcers is still in the centerarea of the soles. Furthermore, p-value of x-y coordinatesof Pc shows that x-y coordinate s of Cluster 3 have a significant difference from others, which can support the hypothesis that subjects in Cluster 3 are quite different from other2 clusters in terms of gait patterns
Under the second condition, when we extracted x-y coordinates of Pf, all the plots are distributed in the posterior of
the sole. The position of Cluster 3 is distributed in the center
part of sole, while in Cluster 1 and Cluster 2 are distributedon the outer side. This phenomenon indicates that patientswith ulcers are more likely to apply their body weight tothe center area of the sole but not the heel part like healthy
persons and low-risk patients when the opposite foot starts
swinging, similar to that in the previous condition. Similar
results that stride length of diabetic foot ulcer patients is reduced are proposed by Drerup et al. [18]. The p-value of
x-y coordinates of Pf indicates this parameter has a significant difference among the three clusters.

Based on the results shown in Figure 5 and Figure 6, it
can be inferred that patients with ulcers apply body weights
to a limited area mainly in the center part of soles while
healthy persons and low-risk patients tend to use the whole
sole when walking.

5.2. Gait parameters
From Tables 1 and 2, it is clear that the peak GRF of
Cluster 1 is larger than those of the other two clusters,
whereas its period is the shortest. This indicates that thefeet of healthy persons or affected patients having a lower
risk of suffering DFUs tend to apply a larger force on theirfoot and walk faster.
Cluster 3 has the lowest peak GRF and longest period, indicating that people with serious DFUs tend to walkslowly. This phenomenon agrees with Youdas' s study [19].
One possible reason for this phenomenon is that healthy
persons and patients without ulcers do not have difficulties
in walking; therefore, they tend to apply more force on theirfeet.
As for the 'Moment.Z' , the values of Clusters I and 2 are
far larger than those of Cluster 3, and there is a significant
difference among the three clusters in this parameter. The
results of this parameter show that the GRF distributions
along the CoP trajectories of Clusters I and 2 are wider
than that of Cluster 3. This indicates that the feet of patients having a high risk of developing ulcers tend to apply
force on a narrow area around the geometric center of theplantar. In comparison , generally, healthy feet and low-riskfeet will apply a force from the heel to the toe when walk-ing [13]. This result also supports the findings regarding the

X-Y coordinate distribution presented in Section 4.2.1.

5.3. Clinical indices
It can be inferred through Table 3 that Cluster 3 has themost subjects who have DPN and got toe amputation . Besides, 20.6% of them felt a pain in their feet. The result
corresponds to the hypothesis that the subjects in Cluster 3
have the highest risk of suffering from DFUs. These indicates that the DPN, toe amputation, and pain significantly
influence the risk of contracting DFUs [10] [20] [21]. However, as for age and walking aids, it seems no significant
difference based on this dataset.

5.4. Feature conclusion
We can concluded the feature of each cluster according
to the results in section 4.2.1-4.2.3 that:
1. Cluster 1 contains the most healthy persons and least
feet with ulcer samples which is shown in Table 1.
Thus, we can consider that subjects in Cluster 1 isunder the lowest risk of contracting DFUs. Basedon this hypothesis, Table 2 shows that most patientstend to apply larger force on their foot and walk faster.
The GRF distributions of feet in this cluster are longer
than those in other clusters in y direction , showing that
healthy feet and low-risk feet apply force more widely
than feet with ulcer. Moreover, according to the resultsin Table 3, the subjects in this cluster have the lowest
percentage of having DPN, getting toe amputation or
feeling a pain in feet. This indicates they are in a low
risk of getting DFUs in clinical explanation [10].
2. According to Table 1, most samples in Cluster 2 are patients' feet without ulcer, with the percentage of 75%.
As for the gait parameters and clinical indices in Table
2 and Table 3, the values of Cluster 2 are always placed
between Cluster 1 and Cluster 3. Among the no-ulcer
feet in Cluster 2, the opposite feet of 26.2% of them areclassified to Cluster 3, which is regarded as a high-riskcluster. It indicates that if opposite foot is a high-risk
foot, it will influence gait pattern of no-ulcer foot [22].
Based on this phenomenon, we consider that Cluster
2 is a preliminary group of Cluster 3. However, since
right and left feet of 1 individual are processed separately, the laterality of them becomes meaningless . To
deal with the problem, the laterality study of subjects'
gait and utilization of supervised model will be conducted as the plan of next stage.
3. Based on the clustering results shown in Table 1. We
proposed the hypothesis that Cluster 3 is consideredas the group with the highest potential of having ulcers. Through Figure 5",6, it is known that subjects

in Cluster 3 tend to apply weight to the center part ofthe plantar at the timing when the opposite foot starts
swinging and contact ground. Comparing gait parameters with the results for other clusters in Table 2, subjects are more likely to apply lower force on their footand walk more slowly. 'MomenL2' indicates patients'
feet with a high potential of having an ulcer tend to apply force to a narrow area around geometric center onplantar, as they cannot use ulcer area to contact groundet al. [13] [18]. The result of 'Moment.Z' supports the
distribution results shown in Figure 5-6. According toclinical indices displayed in Table 3, 53.5% of subjects
got toe amputation, 83.3% of them have trouble with
DPN and 20.6% of them felt a pain in their feet. These
percentages are the highest compared with other clusters. This result supports the hypothesis that cluster 3
has the highest potential of having DFUs, proposed at
the beginning of this paragraph.
However, the clustering results show that the proposedmodel has several limitations such as the feature and difference of age, BMI and whether use walking aids or notamong the three clusters can't be reflected and three typesof samples: ulcer cases, no-ulcer cases and healthy persons
are not classified into three clusters completely. One possible reason is that the resolution of device used in the experiment is not high enough and there are only 25 feet withulcers among the 73 participants at the time of measurement. The distribution of the dataset is irregular. Thus, toimprove the dataset and test the stability of the model, weplan to enlarge it in the future by conducting more experiments and collecting more information in both clinical anddynamical field.

6. Conclusion
This study primarily aims to develop a machine learningmodel to investigate the differences among DFU patients,
diabetic patients without DFUs, and healthy persons using
information such as their clinical indices and gait patterns.
To achieve this objective, we proposed a composite modelof a CNN and K-means clustering to classify the CoP trajectory images of the subjects and subsequently extracted thegait and clinical parameters to discuss the clustering features. The clustering results show that patients with DFUs
are more likely to apply smaller forces on their feet andwalk slowly. The areas of the plantar used to contact the
ground is narrow during walking. They also tend to apply weight on the inner sides of the plantar when the pressure attains the peak value. In clinical explanation, patients
who underwent toe amputations, have difficulty with DPN,
or feel a pain in their feet have a higher potential of suffering from DFUs. However, some clinical indices remaininconclusive as they are unexplainable from the clusteringresults. Hence, we aim to conduct additional experiments
to improve the dataset and collect more information to indentify more influencing factors .

7. Acknowledgments
We gratefully acknowledge the support of Nagoya Kyoritsu Hospital, Masuko Memorial Hospital and Matsunami
General Hospital and Mr. Takayasu Koike with the donationof the patients' data used for this research and the supportof Mr. Takaaki Namba for his technical contribution aboutmachine learning. A part of research was supported by The
Japan Health Foundation, Grant-in-Aid for Scientific Research (C)(19K12915)

References[I] K. Ogurtsova, J. da Rocha Femandes, Y. Huang, U. Linnenkamp, L. Guariguata, N. H. Cho, D. Cavan, J. Shaw, and
L. Makaroff, "IDF Diabetes Atlas : Global estimates for theprevalence of diabetes for 2015 and 2040," Diabete s researchand clinical practice, vo!. 128, pp. 40-50, 2017 .
[2] N. J. Tripolt, S. H. Narath, M. Eder, T. R. Pieber, T. C.
Wascher, and H. Sourij , "Multiple risk factor intervention
reduces carotid athero sclerosis in patients with type 2 diabetes ," Cardiovascular diabetology, vo!. 13, no. I , p. 95,
2014 .
[3] World Health Organization, "Prevention of blindness fromdiabetes mellitus: Report of a who consultation in gene va,
switzerland, 11 november, 2005 ," Switzerland: World HealthOrganization, 2006.
[4] A. Tuttolomondo, C. Maida, R. Maugeri, G. Iacopino,and A. Pinto , "Relationship between diabetes and ischemic
stroke: analysis of diabetes-related risk factors for stroke andof specific patterns of stroke associated with diabetes mellitus ,' Journal of Diabetes and Metabolism , vo!. 6, no. 5,
pp .544-551,2015.
[5] T. del Pino-Sedefio, M. M. Trujillo-Martin, I. Andia,
J. Aragon-Sanchez, E. Herrera-Ramos, F. J. Iruzubieta Barragan, and P. Serrano-Aguilar, "Platelet-rich plasma for thetreatment of diabetic foot ulcers : A meta-analysis,' Wound
Repair and Regeneration , vo!. 27, no. 2, pp. 170-182,2019.
[6] Z.-H. Huang, S.-Q. Li, Y. Kou, L. Huang, T. Yu, and A. Hu,"Risk factor s for the recurrence of diabetic foot ulcer s among
diabetic patients: a meta-analysis,' International Wound
Journal , vo!. 16,no.6,pp. 1373-1382,2019.
[7] J. M. Malone, M. Snyder, G. Anderson, V. M. Bernhard,
G. A. Holloway Jr, and T. J. Bunt, "Prevention of amputation
by diabetic education," The American journal of surgery,vo!. 158,no. 6,pp. 520-524, 1989.
[8] S. Gnanasundaram, P. Ramalingam, B. N. Das , and
V. Viswanathan, "Gait changes in per sons with diabetes:
Early risk marker for diabetic foot ulcer," Foot and Ankle
Surgery, vo!. 26, no. 2, pp. 163-168,2020.
[9] W. J. Jeffcoate and K. G. Harding, "Di abetic foot ulcers ,"The lancet, vo!. 361, no. 9368, pp. 1545-1551,2003.

[10] E. J. Boyko, J. H. Ahroni, V. Cohen, K. M. Nelson, and P. J.
Heagerty, "Prediction of diabetic foot ulcer occurrence using
commonly available clinical information: the seattle diabetic
foot study," Diabetes care, vo!. 29, no. 6, pp. 1202-1207,
2006.
[11] S. Borzouei and A. R. Soltanian, "Application of an artificial
neural network model for diagnosing type 2 diabetes mellitus and determining the relative importan ce of risk factors ,"Epidemiology and health , vo!. 40, 2018 .
[12] F. S. Botros, M. F. Taher, N. M. ElSayed, and A . S. Fahmy,"Prediction of diabetic foot ulceration using spatial and temporal dynamic plantar pressure," in 2016 8th Cairo International Biomedical Engineering Conference (CIBEC), pp. 4347 , IEEE, 2016 .
[13] C. J. Bennetts, T. M. Owings, A. Erdemir, G. Botek, and
P. R. Cavanagh, "Clustering and classification of regional
peak plantar pressures of diabetic feet," Journal of biomechanics, vo!. 46, no. I , pp. 19-25,2013.
[14] Z. Sawacha, G. Guameri, A. Avogaro, and C. Cobelli, "A
new classification of diabetic gait pattern based on cluster
analysis of biomechanical data ," Journal ofDiabetes Scienceand Technology, vo!. 4, no. 5, pp. 1127-1138,2010.
[15] X. Li, H. Huang, J. Wang, Y. Yu, and Y. Ao, "The analysis ofplantar pressure data based on multimodel method in patientswith anterior cruciate ligament deficiency during walking,"
BioMed research international, vo!. 2016, p. 7891407, 2016.
[16] M. Caron, P. Bojanowski, A. Joulin, and M. Douze, "Deepclustering for unsupervised leaming of visual features," inProceedings of the European Conference on Computer Vision (ECCV) , pp . 132-149,2018.
[17] S. K. Kumaran, D. P. Dogra, P. P. Roy, and A. Mitra, "Video
trajectory classification and anomaly detection using hybrid
CNN-VAE," CoRR, vo!. abs/1812.0n03, 2018 .
[18] B. Drerup, K. Ch , A. Koller, and H. Wetz, "Reduction ofplantar peak pressure by limiting stride length in diabetic patients," Der Orthopade, vo!. 33, no. 9, pp . 1013-1019,2004.
[19] J. W. Youdas, B. J. Kotajarvi, D. J. Padgett, and K. R. Kaufman , "Partial weight-bearing gait using conventional assistive devices," Archives of physical medicine and rehabilitation, vo!. 86, no. 3, pp. 394-398, 2005 .
[20] A. Hartemann-Heurtier, J. Robert, S. Jacqueminet,
G. Ha Van, J. Golmard, V. Jarlier, and A. Grimaldi,"Diabetic foot ulcer and multidrug-resistant organisms:
risk factors and impact," Diabetic Medicine, vo!. 21, no. 7,
pp .710-715,2004.
[21] D. J. Margolis, J. Kantor, J. Santanna, B. L. Strom, and J. A.
Berlin, "Risk factors for delayed healing of neuropathic diabetic foot ulcers : a pooled analysis," Archives of dermatology, vo!. 136,no. 12,pp. 1531-1535,2000.
[22] B. Iraj, F. Khorvash, A. Ebneshahidi, and G. Askari, "Prevention of diabetic foot ulcer," International journal ofpreventive medicine, vo!. 4, no. 3, p. 373, 2013 .