EVIDENT: Extraction and Visualization
Interface of Drawing Execution
in Neuropsychological Tests
Ryukichi Sekimoto1(B) , Sachio Saiki1 , Masahide Nakamura1,2 ,Naoki Kodama3 , and Atsushi Sato4
1

Kobe University, Rokkodai-cho 1-1, Nada-ku, Kobe, Hyogo, Japan
sekimoto@ws.cs.kobe-u.ac.jp, sachio@carp.kobe-u.ac.jp,
masa-n@cs.kobe-u.ac.jp
21-4-1 Nihon-bashi, Chuo-ku, Tokyo, Japan
3
Niigata University of Health and Welfare, 1398 Shimami-cho, Kita-ku,
Niigata, Japan
kodama@nuhw.ac.jp
4
Niigata University of Rehabilitation, 2-16 Kaminoyama,
Murakami-shi, Niigata, Japana.satou@nur.ac.jp

Abstract. In order to support health professionals and clinical psychologists to understand the execution process of neuropsychologicaldrawing tests without expert knowledge of data analysis, we develop
a Web application EVIDENT (Extraction and Visualization Interface
of Drawing Execution in Neuropsychological Tests). The service provides
users with visualization result of dynamic features such as stroke speed,
stroke order, time between strokes, in the neuropsychological drawing
tests. Also, EVIDENT calculates drawing statistics, such as the stroke
average speed, the number of strokes and average time between strokes,
and display them on the screen with additional data of subject. Therefore, EVIDENT helps them to get some knowledge by dynamic featuresof drawing. In this paper, as an experiment which measures practicability of this service, we visualized dynamic features of drawing of dementia
patients, and analyzed dynamic features with clinicians. As a result, wegot two knowledges that some patients with Alzheimer’s dementia draw
in a strange order in drawing test and there is a signiﬁcantly diﬀerence
in dispersion of time between strokes, between health control (HC) andmild cognitive impairment (MCI).
Keywords: Neuropsychological Test · Drawing test · Clock Drawing
Test · Cube Drawing Test

1

Introduction

Today, in the ﬁeld of medical and psychological assessment, various simple anduseful drawing tests are used. For example, Clock Drawing Test (CDT) [3],c Springer Nature Switzerland AG 2020

V. G. Duﬀy (Ed.): HCII 2020, LNCS 12198, pp. 460–472, 2020.
https://doi.org/10.1007/978-3-030-49904-4_34

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Cube Copying Test (CCT) [6], Rey-Osterrieth complex ﬁgure (ROCF) [7] andBaumTest [8]. A drawing test is a test in which a clinical psychologist or healthprofessional hands a piece of paper to the subject and has them write or copy
the speciﬁed object freely on paper [2]. They grasp the subject’s personality
characteristics and nervous and psychological states through the drawn picturesand ﬁgures. These drawing tests are also used in screening tests for dementia,
which has become a social issue in Japan.
In the past, many of these drawing inspections were scored by experts looking
at the “ﬁnal shape of the drawn ﬁgure”. As a assessment criteria, various things
are taken into account, such as the accuracy of the drawn ﬁgure and the overallbalance [4,10]. Since these drawing inspections have traditionally been performed
on a paper basis, It was diﬃcult to use the “drawing process” of how the drawing
proceeded for evaluation. The reason is that in paper-based inspection, Fine anddynamic data such as stroke order, stroke speed, and stroke pressure of the object
could not be obtained. In recent years, by using ICT devices such as liquid crystal
pen tablets and smart pads, it is possible to acquire ﬁne-grained and dynamic
data in drawing. Therefore, the drawing process itself can be analyzed. As aresult, more reliable assessment criteria can be established and it may be possible
to obtain knowledge on neuropsychology involved in the drawing process, which
was unknown until now.
As such an approach, data-driven research of drawing test using ICT device[9] has been accelerated in recent years. For example, there are studies on automatic scoring of drawing tests [5] by machine learning and automatic discrimination of dementia. However, automated scoring with machine learning has notbeen implemented widely yet because there are various problems, for example,
interpreting the results and improvement of accuracy are related to the transactions, or interpretation of the relationship between the drawing process and the
diagnosis may be diﬃcult.
Therefore, as a method that does not rely on machine learning, it is important to develop new evaluation methods and diagnostic methods that use features of the drawing process that have not been used until now. For that purpose, it is necessary to present it to clinical psychologist or health professional(analysts) who are trying to analyze ﬁne-grained and dynamic data in an easyto-understand manner. However, since various features are mixed in the timeseries data of the drawing process, it is necessary to consider an appropriate
and intuitive visualization method suitable for each feature. Therefore, in thisresearch, we propose a web application EVIDENT (Extraction and Visualization Interface of Drawing Execution in Neuropsychological Tests) that supports
the analysis of the drawing process by humans.
EVIDENT reproduces the drawing process by plotting the point data (x, y),pen pressure, time, etc. obtained by the drawing device on the screen in real time.
In addition, from point data, it calculates the dynamic features such as change inpen pressure, stroke speed, stoke acceleration, stroke order, stroke direction, andaverage time between stroke, and, from these dynamic features, it can calculates
the statistics such as average pen pressure, dispersion of pen pressure, stroke

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average speed, dispersion of stroke speed, total number of strokes, total time
taken for drawing (time from the beginning to the end of drawing), the time thepen was oﬀ the paper, the time the pen was touching the paper, the average time
between strokes, and the dispersion of the time between strokes. It highlights
them on the screen in a way that depends on the features. For example, if
the speed of drawing strokes is low, it increase the thickness of the line, or it
apply color according to the time between strokes. In addition to these features,
Displays additional information such as the presence or absence of a disease
related to the cognitive function of the subject and the scores of other screening
tests, along with the visualization results.
In this paper, we focus on a method of presenting ﬁne-grained dynamic data
to analysts in an easy-to-understand manner, and we examined various visualization methods for dynamic features, and visualized actually by implementing
a prototype of EVIDENT. Using the implemented prototype, we visualized the
CDT and CCT drawing data acquired at the actual clinical site, and conducted
an experiment on the possibility of using it for analysis in collaboration withhealth professional. As a result of the experiment, it was conﬁrmed that theanalyst could grasp various dynamic features of the drawing process without
requiring specialized data analysis. Also, we discovered two knowledges, incorrect
order of drawing that was overlooked in conventional inspection and signiﬁcantdiﬀerence in dispersion in time in stroke between HC and MCI.
This research is part of a joint study with Niigata University of Health andWelfare and Niigata University of Rehabilitation, and ethical considerations havebeen approved by the Ethics Committee of Niigata University of Health andWelfare.

2

Preliminary

2.1

Neuropsychological Drawing Test

Neuropsychology is a study that clariﬁes the central nervous system such as
language, cognition, action, memory, and frontal lobe function, and deals withvarious symptoms based on the disorder. The neuropsychological drawing test
(hereinafter called drawing test) is a test that is often used in neuropsychology
to measure the state of cognitive function. This is done by a health professional
handing a piece of paper to the test subject, who draws or writes the indicatedpicture or character on the paper. As a drawing test, Clock Drawing Test (CDT)[3], which measures cognitive decline by drawing a clock as shown in the Fig. 1,
and Cube Copying Test (CCT) [6], which measures cognitive decline by drawing
a cube as shown in the Fig. 3, are famous.
In recent years, attempts have been made to save and accumulate drawing
test data by performing drawing inspection using ICT device such as pen tablets.
2.2

Dementia Test and Drawing Test

With the recent aging society, dementia has become a major social problem
in Japan. Early detection and early treatment of dementia are essential, andEVIDENT

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Fig. 1. A sample of CDT

Fig. 2. A copy of CCT

Fig. 3. A sample of CCTvarious dementia tests have been performed. Dementia can be roughly divided
into four categories: Alzheimer’s disease (AD), vascular dementia (VaD), dementia with Lewy bodies (DLB), and frontotemporal lobar degeneration (FTLD).
In addition, this study discriminates cognitive dysfunction in combination withmild cognitive impairment (MCI), which is one step before becoming AD, andcognitively healthy control (HC) without abnormal cognitive function (Fig. 2).
Drawing tests such as CDT and CCT are simple screening tests that classify
only the presence or absence of cognitive problems. If abnormalities are found,a test of the question form by a doctor and brain imaging are used to identify
the type of dementia.
2.3

Scope of Research

Research on drawing inspection using ICT device and regression and classiﬁcation by machine learning has become widespread, especially in the ﬁeld of CDT.

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However, automated scoring with machine learning hasn’t been implemented
widely yet because there are various problems, for example, interpreting theresults and improvement of accuracy are related to the transactions, or interpretation of the relationship between the drawing process and the diagnosis may
be diﬃcult, and we think it will take some time to put it to practical use. The
scope of this research is to obtain new knowledge in the drawing process by amethod that does not use machine learning. The scope of this research is, bya method that does not use machine learning, to obtain new knowledge in thedrawing process or develop new evaluation and diagnostic methods that use features of the drawing process that have not been used before. n order to realize
this, it is necessary to present it to clinical psychologist or health professional(analysts) who are trying to analyze ﬁne-grained and dynamic data in an easyto-understand manner. However, since various features are mixed in the timeseries data of the drawing process, it is necessary to consider an appropriate andintuitive visualization method suitable for each feature. In this study, we propose and implement a Web application EVIDENT (Extraction and Visualization Interface of Drawing Execution in Neuropsychological Tests) that supports
the analysis of the drawing process by humans, which will be described in thefollowing chapters.

3

EVIDENT (Extraction and Visualization Interface
of Drawing Execution in Neuropsychological Tests)

3.1

System Requirement

Various information is needed for analysis through the drawing process. First, inorder to grasp the drawing process itself, it is necessary to be able to reproduce
how the subject performed drawing in time series. In addition, in order to analyze multiple drawing processes, a mechanism that can quantitatively evaluate
not only time-series features but also features of the drawing process is required.
Furthermore, what kind of cognitive function-related disease the subject himself has been diagnosed can be important information in analyzing the drawing
process. It is necessary for an analysis tool to set various conditions and present
these pieces of information in parallel. Also, the ability to constantly update andmodify information is essential for developing applications for continuous use.
Based on the above assumptions, we conclude that EVIDENT needs thefollowing ﬁve functions as a drawing process analysis support Web service.
R1:Visualization of Drawing Process
EVIDENT reproduces the drawing process, and can extract dynamic features
such as stroke, stroke order, stroke direction, stroke speed, stroke acceleration,
time between strokes, pen pressure and change in pen pressure in the drawing
process and visualize it as two-dimensional data.
R2:Calculation and Display of Statistics
EVIDENT calculates and display statistics such as time spent drawing, total
time pen touched paper, total time pen had been away from paper, number

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of strokes, average time between strokes, dispersion of time between strokes,
the stroke average speed, median of stroke speed, average of stroke speed,
dispersion of stroke speed, median of pen pressure and average of pen pressure
which is calculated from dynamic features.
R3:Display of Additional Data
EVIDENT simultaneously displays the visualization result of each subject
and various addition data such as age, sex, condition, other test scores andyears of education of that subject.
R4:Analysis Support
EVIDENT extracts and sorts visualization results based on calculated statistics and additional data.
R5:Data Maintenance
EVIDENT allows medical and psychological specialists to add, refer to,
update, and delete subjects’ drawing data and additional data without requiring specialized knowledge of ICT.
3.2

System Architecture

Figure 4 shows the architecture of EVIDENT. The user prepares and pass to
EVIDENT drawing data including the (x, y) coordinates, pen pressure, and time
information, and converts the drawing data to the plot data format handled by
EVIDENT. However, it is assumed that the pen pressure of the drawing data
handled by EVIDENT is expressed as a percentage with the maximum measurable pen pressure of the ICT device used to acquire the drawing data being
100%. Each data passed to EVIDENT is automatically stored in the database.
The transfer of the databases on the EVIDENT-service side and the EVIDENTUI side is all performed through the EVIDENT-API, and an external application
can cooperate with the EVIDENT-service to provide another service. R5 is realized by “data maintenance” at EVIDENT-UI, R1 is realized by “playback” and“image creation”, R2 is rasterized by “display statistics”, R3 is realized by “display additional information” and R4 is realized by “sort and extraction”.
3.3

Visualization of Dynamic Features

Visualization is achieved by reproducing the original ﬁnal shape with white or
black plots, and then performing highlighting based on the dynamic featuresusing the color and line thickness.
I visualize in 15 diﬀerent ways such as “a method of highlighting by lowering
the opacity of the plot as the pen pressure is lower and the opacity is higher as thepen pressure is lower”, “a method that reproduces the ﬁnal shape of the drawing
with a black plot, and highlights it by overlaying a thin cyan plot whose radiusincreases with increasing pen pressure in plot”, “a method that changes the colorof the stroke from cold to warm as you follow the stroke order”, in addition thismethod, “the method of writing the stroke number near the stroke in the samecolor as the corresponding stroke”, “A method in which the ﬁnal shape of thedrawing is reproduced in black and emphasized by overlaying a purple plot whose466

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Fig. 4. System architectureradius decreases as the stroke speed increases”, “A method of emphasizing byplotting with the color changed from cool to warm in the order of blue, green,
and red as the stroke speed increases”, “A method in which the radius decreases
as the stroke speed increases, and is emphasized by plotting so that the color
approaches a warm color”, “a method for expressing the direction of a strokewith an arrow”, “a method that draws a red plot for a part where a constantpositive change is seen in the pen pressure change and stroke acceleration, anddraws a blue plot if a negative change is seen”, “At the time between strokes,
the corresponding strokes are connected by arrows, and the color of the arrows
changes from cool to warm according to the time”, in addition it, “a methodthat draws the order next to the arrow” and “a method that draws the time
next to the arrow” as concrete methods.
3.4

Visualization Interface

Figure 5 shows the screen design of the EVIDENT visualization interface. Selectthe type of drawing inspection to be visualized in “Select test”, and selectwhich feature is to be visualized by which visualization method in “visualization method” next to it. To select and visualize a single test (called a session inthis paper) for one subject, check the “a session” checkbox, select the subject by“subject ID”, and select the date and time of the session to draw by “date time”.
Also, you can check the “multiple sessions” checkbox and use the “classiﬁcation”
select tab to select, for example, only sessions for subjects with a speciﬁc disease.

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By specifying the sort order on the “sort” tab, the corresponding sessions canbe displayed in a speciﬁc order.

Fig. 5. Screen design of EVIDENT-UI (Color ﬁgure online)

4

Prototype Implementation

In the previous chapter, we deﬁned the requirements for the drawing process
analysis support Web service EVIDENT, and proposed the required mechanisms and various visualization methods. In this chapter, in order to verify theeﬀectiveness of EVIDENT, we ﬁrst focus on realizing the functions required foruse in veriﬁcation, and develop a prototype system.
4.1

Prototype System Overview

The prototype realizes data visualization and additional information display
functions in EVIDENT-UI, data shaping and feature value calculation of
EVIDENT-service, and match with additional information. In this prototype,drawing data is obtained by directly reading data output from the smart pad
via a browser. Calculation of dynamic features from drawing data is performedon the client side at the time of data loading, and can be visualized by all
visualization methods. At the same time, statistics can be calculated from the468

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dynamic features, and by passing the subject’s supplementary information to
the EVIDENT prototype in advance, the statistics in the drawing data and the
additional information of the drawn subject are output with the visualization
result.
4.2

Implementation

Creating Drawing Data. In this prototype, it is assumed that Bamboo Slate[1], a smart pad of wacom, is used as an ICT device. In Bamboo Slate, thecoordinates (x, y) of the pen tip of the ballpoint pen and the pen pressure canbe acquired only at the moment when the dedicated ballpoint pen is in contact
with the paper surface. Here, the range of the coordinates of the pen tip (x, y)that Bamboo Slate can obtain is approximately 0 ≤ x ≤ 29700, 0 ≤ y ≤21600. A size 1 in coordinates corresponds to one thousandth of a centimeter on
paper. The pen pressure of this Bamboo Slate is output in the range of 0.05% ≤pen pressure ≤ 99.95%, with the maximum pressure that can be measured by
the Bamboo Slate body as 99.95% and the minimum pressure as 0.05%. To
obtain drawing data from Bamboo Slate, use a communication application thatconnects a personal computer and a device to perform data communication suchas coordinates and pen pressure, and an output application that attaches a timestamp to the obtained data and outputs it as csv. These applications are provided
by wacom, the selling agency of Bamboo Slate. The csv data obtained by thisapplication is as follows.
pres:21.04%, x:14955, y:17839, time:22:43:44.769
pres:22.61%, x:14937, y:17841, time:22:43:44.777
pres:26.03%, x:14883, y:17849, time:22:43:44.784
The csv data consists of the pen pressure, x-coordinate, y-coordinate, andtime stamp of a point.
Loading and Shaping of Drawing Data. In the prototype, the visualization interface is implemented using HTML5, CSS, and JavaScript, and the csv
obtained from the output application is read on the browser. In this prototype,
instead of using EVIDENT-API, the read drawing data is replaced with an arrayin the plot data format using JavaScript.
The prototype uses this array instead of the plot database, and calculates
dynamic features and statistics from this array using JavaScript. The dynamic
features and statistics calculated here are not stored in the database but are
stored in an array using JavaScript.
Creating a Visualization Interface. Visualization presented in 3.3 is performed using HTML5 and JavaScript. Visualization is performed by drawing
points on Canvas, which is a speciﬁcation for drawing ﬁgures on JavaScript andEVIDENT469

HTML5 browsers. Also, arrows necessary for visualizing some dynamic features
are drawn using canvas-arrow.js, a library for drawing arrows on Canvas.
In addition, HTML5, css, and JavaScript were used to directly write the additional information of the subjects in a JavaScript ﬁle instead of the additional
information database, and to output the statistics and the additional information
to the screen simultaneously with the visualization results.

5

Experimental Evaluation

Various visualization of drawing test can be realized by using EVIDENT prototype. In this section, we verify how the visualization method implemented using
the EVIDENT prototype can be used in drawing test analysis, using actual test
results.
5.1

Experimental Purpose and Method

EVIDENT’s main function is to realize various analyses by presenting dynamic
features of results of drawing test to clinicians and medical professionals who
do not have specialized knowledge of data analysis. It is diﬃcult to directly
evaluate the visualization method itself. However, considering the original purpose of EVIDENT, if health professional can visualize dynamic features using
EVIDENT, and if they can conﬁrm any trends from these results, at least, it
is possible to evaluate the eﬀectiveness as a support tool for analyzing dynamic
features without knowledge of data analysis.
Therefore, in this experiment, using the EVIDENT prototype created in theprevious section, we analyze the visualization results of CDT and CCT drawing
data for a total of 60 people, including about 50 people with abnormal cognitive
function and about 10 suspected HC.
In order to focus on the analysis of the visualization result itself, all operation
were performed by the authors, and the analysis of the visualization result was
performed in the presence of health professional.
The drawing data used in the experiment uses the results of drawing tests
performed by healthcare workers at medical institutions as part of dementia
tests, and results of tests performed to HC under similar conditions for thisexperiment.
5.2

What EVIDENT Prototype Revealed

Case1: Abnormality of Drawing Strategy in CCT. Figure 6 shows theresults of CCT for a patient with a deﬁnitive diagnosis of AD, and Fig. 7 shows
the visualization of the drawing order in the same drawing data. This is a methodthat changes the color of the stroke from cold to warm as you follow the strokeorder and write the stroke number near the stroke in the same color. When the
CCT of this patient was scored using only the conventional ﬁnal form, there wasno tendency for AD. However, if you look at Fig. 7, you can see that the line

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connecting the lower left back vertex to the lower right back vertex is drawn in
two strokes instead of one.
Normally, when copying a cube, it is rare that HC describes a straight line
connecting vertices into two lines, which is considered to be a lack of planningin cube copying. In this way, EVIDENT was able to express abnormalities that
could not be determined only by the ﬁnal shape of the drawing.
Case2: Dispersion of Time Between Strokes. The time between strokes
was visualized with arrows and their colors for each case and compared. As
a result, the visualization results of HC showed mostly blue arrows, and thevisualization results of MCI tended to have many arrows of various colors fromblue to red. In addition, as for the visualization results of AD, similar to thevisualization results of MCI, there were some that had various colors from blue
to red, and some visualization results were only blue arrows. From these facts,
we thought that there was something to do with the vividness of the colors andthe cases.
Therefore, in this experiment, it is assumed that the chromaticity of the arrow
and the dispersion of the time between strokes are equivalent (the dispersion islarge if the color is vivid, and the dispersion is small if the color is not vivid),
and The average of the dispersion of the time between strokes for each painter’scase (HC, MCI, AD) was 2.726106 for AD, 4.384947 for MCI, and 1.687476
for HC. However, in calculating this average, two data with a dispersion of
Q3 + (Q3 − Q1) ∗ 1.5 (Q1:ﬁrst quartile, Q3:third quartile) or more were excluded
as outliers.
Therefore, when the diﬀerence of the mean of the obtained variances was
subjected to t-test, no signiﬁcant diﬀerence was found between HC and AD,
but there was a signiﬁcant diﬀerence between HC and MC as t(21) = 2.209
and p = 0.0192. The reason is that HC can draw numbers and hands with aconstant rhythm at a good tempo because the complete drawing of the clock isin the head from the beginning, while many MCI think about what to draw next
immediately after drawing a stroke. The reason why the diﬀerence between AD
and HC was smaller than HC and MCI is probably because there were a certain
number of subjects who roughly and quickly draw clocks in AD to deceive facts
that cannot be drawn accurately. Figure 8 and 9 show the results of CDT drawn
by such AD and the time between strokes visualized by arrows and their colors.
5.3

ProblemIn this study, the following two issues are raised. One is about a method to add
arrows and visualize them.
As you can see in Fig. 9, it is very diﬃcult to grasp the features where thearrows are displayed overlapping. In the future, we plan to consider some othermethods, such as adding a function in which EVIDENT users display arrows one
by one on the screen by clicking on them one after another, and plan to improvethem.

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Fig. 6. Drawing result in CCT of an
AD

Fig. 7. Stroke order in CCT of an AD(Color ﬁgure online)

Fig. 8. Drawing result in CDT of an
AD

Fig. 9. Time in air in CDT of an AD(Color ﬁgure online)

The other is about dynamic features to be visualized. In this experiment,
we visualized various dynamic features with the advice of two health professionals. However, besides the dynamic features visualized this time, there may be
dynamic features that are useful for discovering new knowledge. In the future,
EVIDENT will be also used by health professionals and clinical psychologists
other than these two, and we will continue to develop new systems while receiving feedback on new dynamic features and feedback on visualization results.

6

Conclusion

In this paper, we proposed EVIDENT, a support tool to analyze dynamic features in drawing test for health professionals and clinical psychologists who do
not have specialized knowledge such as data mining.

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In experiments conducted to show the usefulness of this service, In an experiment conducted to show the usefulness of this service, it was possible to express
abnormalities in the drawing strategy of AD, which were diﬃcult to grasp fromthe ﬁnal result of drawing, Also, between HC and MCI, we could ﬁnd the signiﬁcant diﬀerence in dispersion of time between strokes. However, there may be
useful dynamic features other than the dynamic features visualized this time,
and we would like to develop the whole proposed system while exploring such
dynamic features.

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