Virtual Concept 2003
Biarritz - France
November, 5-7
1
SUBJECTIVE EVALUATION OF FORMS IN AN IMMERSIVE
ENVIRONMENT
Jean-François Petiot, Damien Chablat
Ecole Centrale de Nantes
IRCCyN – UMR CNRS 6597 – Equipe MCM
1, rue de la Noë BP 92101
44321 NANTES Cedex 3 France
Phone : 33 2 40 37 69 59 – Fax : 33 2 40 37 69 30
E-mail : {Jean-Francois.Petiot, Damien.Chablat}@irccyn.ec-nantes.fr
Abstract : User’s perception of product, by essence subjective,
is a major topic in marketing and industrial design. Many
methods, based on users’ tests, are used so as to characterise
this perception. Methods like multidimensional scaling,
semantic differential method, and preference mapping are well
known in sensorial analysis or in the food industry. These
methods are used in order to built a perceptual space in order to
position a new product, to specify requirements by the study of
user’s preferences, to evaluate some product attributes, related
in particular to style (aesthetic). These early stages of the
design are essential for a good orientation of the project. In
parallel, virtual reality tools and interfaces are more and more
efficient for suggesting to the user complex feelings, and
creating in this way various levels of perceptions. In this
article, we present on an example an adaptation of
multidimensional scaling and preference mapping for the
subjective assessment of virtual products. These products,
which geometrical form is variable, are defined with a CAD
model and are proposed to the user with a spacemouse and
stereoscopic glasses. Advantages, uses and limitations of such
evaluation is next discussed.
Key words : subjective evaluation, multidimensional scaling,
virtual reality, preference mapping.
1- Introduction
In today’s highly competitive market, developing new products
that meet consumers’ tastes is a crucial issue in product design.
To improve attractiveness, a well-designed product should not
only satisfy requirements, but should also satisfy consumers’
psychological needs, by essence subjective. For many
products, the aesthetic properties are as important as technical
functions [1].
In order to predict the success of a product, to control and to
optimise its performances, it’s time now to take into account
esteem and aesthetics functions in the beginning of the design.
Although industrial and product designers are keenly aware of
the importance of design aesthetics [2], there is an obvious lack
of systematic, scientific, and engineering methods to help them
make aesthetic design decisions and conduct aesthetic
evaluation. For example, the perception of the shape of a
product is often nothing but a style of design, depending
much more on the designer’s taste than on real customers’
trends, as some studies clearly showed [3].
In this context, Virtual Reality (VR) seems to offer
promising functionalities for the assessment of virtual
products [4]. The Virtual reality interfaces available on the
market are now mature enough for suggesting to the user
relevant feelings and sensations [5]. The main problem is
now to learn how to use it and to define relevant methods for
their integration into the design process. How do we use
virtual products to study the aesthetics of products and to
help make them more aesthetic ? How increase the
attractiveness of the shape of a product via virtual reality
tools ?
In this article, we present the use of VR tools and user-tests
for the study of aesthetics. It is assumed that we possess an
innate ability to perceive a wide range of qualities in
products that shape our response to them [6]. But it’s often
more difficult to detect bad proportions in the design of
products, or to explain rules which lead to a feeling of
harmony and beauty. A study based on the golden section or
“divine proportion”, described in [7], shows that it is not
always clear whether the artists consciously used the golden
section or whether they intuitively approximated the
associated ratio.
Aesthetics is clearly a multidimensional characteristic, not
independent of ergonomics and usability considerations. The
concept of ergo-aesthetics, proposed in [8], refer to the
integrated design approach that is aimed at meeting both
ergonomic and aesthetic design objectives. Two main
approaches can be used for the study of aesthetics [8]:
- The top-down approach attempts to understand aesthetic
responses as a whole,
- The bottom-up approach attempts to identify the basic
pictorial features and compositional patterns that please
or displease the senses.
Virtual Concept 2002
Biarritz - France
October, 9-10
2
In this article, we propose an illustration of both approaches for
the evaluation of forms in an immersive environment. In
section 2, we present two classical methods in sensorial
analysis which can be used in product design for grasping the
subjective response of subjects. In section 3, we propose an
adaptation of these methods to the experimental study of the
“appeal” of forms. The perception of the aesthetic of forms by
a subject is carried out in a VR environment, and with
particular products: table glasses. The main results of this
experiment are discussed in section 4, and perspectives for the
use of VR for the design of forms are discussed.
2- Background
To study users’ perceptions, researchers in marketing and
sensorial analysis propose various methods [9][10][11].
Perceptual maps are commonly used to take perceptions into
account and to control the product positioning. The basic idea
is to build a multi-attribute perceptual space in which each
product is represented by a point. We propose to describe
multidimensional scaling (MDS), a classical method for
building the perceptual space, and preference mapping
techniques, which are relevant to grasp preference assessments.
An adaptation of both methods will be used in section 3.
2.1 – MDS
Multidimensional scaling uses dissimilarity assessments to
create a geometrical representation of a family of “stimuli”.
This method, developed initially for psychometric analysis
[12], is a process whereby a distance matrix among a set of
stimuli is translated into a representation of these stimuli inside
of a perceptual space. Taking all the possible pairs of stimuli
(here pairs of products) into account, the subject evaluates their
degree of similarity on a quantitative scale and fills in this way
a dissimilarity matrix. Technically, what MDS does is to find a
set of points (Xi)i=1,…,N in a K-dimensional space such that the
distances among them correspond as closely as possible to the
dissimilarity (or a function of it) in the input matrix, according
to a given optimisation criterion. This criterion can be for
example a function called stress, (equation 1) which represents
the “badness of fit”, and the distance can be the Euclidean
distance (equation 2), but several different expressions can be
used [13]. Dij is the perceptual dissimilarity between stimuli i
and j, dij the Euclidean distance, xik the coordinate of stimuli i
on dimension k, K the number of dimensions of the perceptual
space, selected by the experimenter. After computation, each
stimuli is represented in the Euclidean space by a point Xi(xi1,
…,xiK).
2
/1
2
2)
(












−
=
∑∑
∑∑
i
j
ij
i
j
ij
ij
d
D
d
stress
  (1)
∑
=
−
=
K
k
jk
ik
ij
x
x
d
1
2)
(
(2)
The main advantage of this method is that the tests are based
on instinctive dissimilarity assessments, which do not impose
any criteria or predefined semantic scale. This method provides
a space for a visualisation of the relevant dimensions for
stimuli’s perception. It is used in marketing and sensorial
analysis, the stimuli can be sounds, fragrance, products,
concepts,…It is well suited to study the relationship between
products. We have used in the next section metric MDS,
based on the minimisation of the stress, and using a sparse
matrix of dissimilarity.
2.1 – Preference mapping
Preference mapping constitutes a group of statistical
techniques which are also common in sensory analysis and
marketing research. Internal (MDPREF) and external
(PREFMAP) preference mapping both generate graphic
interpretations of individual preferences [14][15]. To
construct such preference mappings, multiple regression
techniques are used. The principle is to find a correlation
between product’s preference and product’s position in the
perceptual space. Technically speaking, one must perform a
multiple linear regression of the preference on the perceptual
coordinates. 
These 
techniques 
need 
some 
strong
assumptions. The preference’s model is supposed to be linear
most of the time, it’s not possible to consider threshold
phenomena for the preference, and the method needs an
absolute evaluation of the preference as input (hedonic
scale). The use of a hedonic scale to assess the preference in
an absolute manner encounters some difficulties and
limitations which can be avoided by the use of pairwise
comparison methods [16]. In the PREFMAP model,
consumers are represented by their ideal point in the
perceptual space, which represents the most preferred
combination of attributes. Other models with infinite
preferred attribute levels are called ‘vector models’. We use
in the next section preference mapping based on the vector
model.
3- Experimental study
3.1 – Description of the VR tools
The main objective of the experiment is to show that the
study of the aesthetic properties of forms can be conducted
on virtual products, and that an immersive environment is
particularly helpful for the assessment and the improvement
of aesthetics.
To illustrate our approach, we propose to study the aesthetics
of table glasses, which are very interesting products from an
aesthetics and esteem point of view. A study on such
products (wine–glass) is proposed in [17], where the authors
present a method for form generation, and in [18], where a
methodology for a solid assessment of product semantics is
proposed.
A digital model of glasses with various forms was designed
with the CAD and VR software Catia V5R11. A texture with
the material “glass” were applied to the models of glass. A
Spacemouse and stereoscopic eyeglasses were used by the
subject for enhancing his/her perception of the environment,
and for facilitating the various assessments.
3.2 – Description of the assessment tests
Several forms of table glasses were generated with the CAD
software. All glasses are intended to the same usage, for
example wine glasses. They all have the same general form,
balloon glass, and are made off three parts: base, foot and
Virtual Concept 2002
Biarritz - France
October, 9-10
3
container. The mathematical model of the curve for the
generation of forms is given figure 1. The base, the foot, and
the container are modelled with a spline-curve with 15 control
nodes. Continuity conditions are added at points P5 and P9.
The inner shape of the container is parallel to the outer shape,
with a gap of 1 mm. The 3D solid is generated by rotation of
the curve.
P1
P2
P3
P4
P5
P6
P7
P8
P9
P10
P11
P12
P13
y
x
 
Fig. 1  : Model of the outer curve of the glass.
#1
#2
#3
#4
#5
#6
#7
#8
#9
#10
#11
#12
#13
#14
#15
#16
#17
#18
 
Fig. 2  : Pictures of the 18 glasses.
All the glasses used for the experimental study are an
“instanciation” of this model. 18 glasses were generated
(figure 2), which constitute the stimuli for the assessment
tests.
VR tools were next used for the evaluation of the aesthetics
qualities of the glasses The assessment performed by the
subject was made of several stages, presented in the
following sections.
3.2.1 –Stage 1: Building of the perceptual space
The aim of this stage is to find how many dimensions can
describe the aesthetic, or the “appeal” of the glasses, and to
find the location of the glasses according to these
dimensions. For that, the subject had to quantify the
perceived differences between pairs of products from an
aesthetic point of view. This stage belongs to the “top-down”
approach.
The subject was asked to say to what degree the products
were different. He/she chose between “identical”, ”a little
different”, ”different” and “very different”. The answer was
coded on an integer scale from 0 (identical) to 3 (very
different). In order to reduce the assessment task, the subject
was not forced to fill all the dissimilarity matrix. He only had
to chose the more obvious comparisons. He had the complete
choice of the comparisons to fill in the N.(N-1)/2=153
potential comparisons of the superior half of the dissimilarity
matrix. Nevertheless, so as to have computable data, and to
reveal the multidimensionality of aesthetics, each product
should be involved in at least three comparisons.
The pairwise comparison were performed by the subject with
the VR software and easy “click and drag” operations. The
(sparse) dissimilarity matrix provided by the subject is given
table 1.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
1
0
2
*
1
*
*
0
*
*
*
1
*
*
*
2
*
*
*
2
2
0
1
3
2
1
2
0
1
3
2
1
1
0
1
3
2
1
3
*
1
0
2
2
*
*
*
0
2
2
*
*
2
0
*
3
*
4
1
3
2
0
2
2
1
3
2
0
1
3
2
3
2
1
2
2
5
*
2
2
2
0
*
1
*
*
*
0
*
*
*
2
*
*
*
6
*
1
*
2
*
0
*
*
*
*
*
*
*
*
2
*
*
*
7
0
2
*
1
1
*
0
2
*
*
3
2
0
2
0
*
*
*
8
*
0
*
3
*
*
2
0
*
*
3
*
*
*
1
*
*
*
9
*
1
0
2
*
*
*
*
0
*
3
*
*
*
0
*
*
*
10
*
3
2
0
*
*
*
*
*
0
1
*
*
*
2
*
*
*
11
1
2
2
1
0
*
3
3
3
1
0
1
2
3
3
1
0
3
12
*
1
*
3
*
*
2
*
*
*
1
0
*
*
2
*
*
*
13
*
1
*
2
*
*
0
*
*
*
2
*
0
*
2
*
*
*
14
*
0
2
3
*
*
2
*
*
*
3
*
*
0
2
*
*
*
15
2
1
0
2
2
2
0
1
0
2
3
2
2
2
0
2
1
1
16
*
3
*
1
*
*
*
*
*
*
1
*
*
*
2
0
*
*
17
*
2
3
2
*
*
*
*
*
*
0
*
*
*
1
*
0
*
18
*
1
*
2
*
*
*
*
*
*
3
*
*
*
1
*
*
0
 
Tab. 1 : Sparse dissimilarity matrix.
Next, we used MDS for determining how many dimensions
can represent the dissimilarities, and for building a
perceptual space which expresses these differences.
With this sparse matrix as input, an own implementation of
metric MDS has been used to calculate the perceptual
coordinates 
of 
the 
glasses 
[19]. 
A 
2-dimensional
configuration, with a stress value equal to 0.12 (considered
as a correct “badness of fit”) has been retained (figure 3).
Virtual Concept 2002
Biarritz - France
October, 9-10
4
-1
-0.5
0
0.5
1
1.5
2
2.5
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
#1 
#2 
#3 
#4 
#5 
#6 
#7 
#8 
#9 
#10
#11
#12
#13
#14
#15
#16
#17
#18
axe 1
axe 2
2 D-perceptual space
 
Fig. 3  : 2D Perceptual space of the glasses
These results reveal the two main dimensions (axe1, axe2)
which are relevant for describing the differences between the
products from an “appeal” point of view.
3.2.2 –Stage 2: assessment of the appeal
The aim of this stage is to get an absolute assessment of the
appeal, on a continuous scale. The task is the same as previous
stage, but the subject is now forced to provide a mono-
dimensional answer. In order to reveal the multidimensionality
of aesthetic during stage 1, it’s important that the subject do
not carry out stage 1 from the results of stage 2. In other words,
stage 1 must be performed before stage 2.
The subject was asked to rank the products in the order of
increasing appeal, or into piles of similar appeal. The subject
associated to each product (or each pile) an absolute value of
the appeal, on a continuous scale from 0 to 10. This scale is
similar to the “hedonic” evaluation used in the food industry
for preference assessments. The assessment of the glasses,
provided by the subject, is given table 2.
Appeal
P
0
1
2
3
4
5
6
7
8
9
10
Glass #
2
8
14
12
18
9
15
6
3
17 13
7
1
11
5
16
4
10
 
Tab. 2 : Appeal scores of the glasses.
3.2.3 –Stage 3: a model of appeal
The main objective is to find how sensitive is the subject in
detecting small variations in aesthetics, and to find his abilities
to perceive and judge values, changes, and variations in design
parameters. This stage belongs now to the “bottom-up”
approach [20].
For that, three shape regulating rules were proposed to the
subject. These rules correspond to a modification of the three
determining design parameters of a glass, proposed in [21].
They  are expressed as following (figure 4):
- R1: increase the total height of the container, d1,
- R2: increase the height of the foot, d2,
- R3: increase the diameter of the container, d3.
R1
R2
R3
d1
d2
d3
 
Fig. 4  : Description of the shape regulating rules
For each glass Gj, the subject has to indicate the effect of the
proposed shape regulating rule on his/her assessment of the
appeal:
- If rule “Ri” increases the appeal, ∆Pi(Gj) = 1
- If rule “Ri” does not change the appeal, ∆Pi(Gj) = 0
- If rule “Ri” decreases the appeal, ∆Pi(Gj) = -1
This is equivalent to an assessment of the “derivative” of the
appeal according to the design parameters.
The virtual reality model of the glass is particularly suitable
for the required assessments. Indeed, the subject can even
make the modification on the virtual model to visualise and
compare the differences, if the assessment of the appeal
change is not obvious.
The values of the dimensions d1, d2, d3 for each glass and
the assessments proposed by the subject are given in the
following table 3. For each shape regulating rule, these
values ∆Pi(Gj) are proportional to the partial derivative
dj
P
∂
∂
, with a positive multiplicative factor kj.
Glass
#
d1
cm
d2
cm
d3
cm
R1
1
1 d
P
k ∂
∂
R2
2
2 d
P
k
∂
∂
R3
3
3 d
P
k ∂
∂
G1
8
7
8
-1
0
-1
G2
8
7
9,5
-1
1
-1
G3
8
7
9,5
-1
0
-1
G4
8
7
6
-1
-1
0
G5
8
7
7
-1
-1
-1
G6
8
7
9
-1
0
-1
G7
8
5
8
-1
1
-1
G8
8
5
9,5
-1
1
-1
G9
8
5
9,5
-1
1
-1
G10
8
5
6
-1
1
0
G11
8
5
7
-1
1
-1
G12
8
5
9
-1
1
-1
G13
8
3
8
-1
0
-1
G14
8
3
9,5
-1
1
-1
G15
8
3
9,5
-1
1
-1
G16
8
3
6
-1
1
-1
G17
8
3
7
-1
1
-1
G18
8
3
9
-1
1
-1
 
Tab. 3 : Dimensions of the glasses and assessment of the
appeal changes according to the shape regulating  rules.
Virtual Concept 2002
Biarritz - France
October, 9-10
5
3.2.3 –Building of an model of appeal
A quadratic model of the appeal P is proposed. The predicted
value, denoted P , is given by:
10
3
.2
.9
3
.1
.8
2
.1
.7
3
.6
2
.5
1
4
3
.3
2
.2
1
.1
2
2
2
a
d
d
a
d
d
a
d
d
a
d
a
d
a
d
a
d
a
d
a
d
a
P
+
+
+
+
+
+
+
+
+
=
(3)
The predicted partial derivatives of P are given by:
3
).
2
.
9
1
.
8
6
.
2
(
3
3
2
).
3
.
9
1
.
7
5
.
2
(
2
2
1
).
3
.
8
2
.
7
4
.
2
(
1
1
d
d
a
d
a
a
a
d
P
d
d
a
d
a
a
a
d
P
d
d
a
d
a
a
a
d
P
+
+
+
=
∂
∂
+
+
+
=
∂
∂
+
+
+
=
∂
∂
(4)
The set of coefficients {a1, a2,…, a10} is determined with an
optimisation procedure, similar to the least square regression.
It’s a generalisation of the linear regression of the appeal on
the design parameters, taking into account more data
concerning the partial derivative.
With all observations 
)
,
(
dj
P
P
i
i ∂
∂
, the vector of variables
X= (a1, a2,…, a10, k1, k2, k3) is determined by minimising
the following function F :
)
,
,
,
,...,
,
(
.
.
.
)
(
)
(
)
(
min
3
2
1
10
2
1
2
1
3
1
2
1
k
k
k
a
a
a
X
t
r
w
d
P
k
d
P
P
P
X
F
N
i
j
i
j
j
j
i
N
i
i
i
=
∂
∂
−
∂
∂
+
−
=
∑∑
∑
=
=
=
(5)
iP is the appeal, 
dj
Pi
∂
∂
 the partial derivative, provided by the
subject, for glass Gi,
i
P is the appeal, 
dj
P i
∂
∂
 the partial derivative, provided by the
model, for glass Gi,
The result of the optimisation procedure is given table 2.
a1
a2
a3
a4
a5
a6
a7
a8
a9
a10
3.6
0.12
13.6
-1.52
0.02
-0.13
-0.01
-1.71
0.01
82.36
k1
k2
k3
-0.01
14.15
0.01
 
Tab. 4 : Coefficients of the model of appeal.
The response surface, corresponding to this model, is plotted in
the next section.
4- Results and discussions
4.1 – Preference mapping 1
The first analysis which can be done is to try to explain the
appeal of the glasses by the perceptual dimensions given by
MDS.
Like for the PREFMAP model, this is done by a multiple
regression using the perceptual axes (axe1, axe2) as
independent variables and the appeal P as the dependent
variable (equation 4).
c
axe
b
axe
a
P
+
+
=
2
.
1
.
(6)
The results of the regression is given table 5.
a
b
c
R2
F
Significant1 (p=0.01)
-2
-1,8
7
0,91
80
yes
 
Tab. 5 : Coefficients of the model of preference mapping 1
The regression is significant and the determination
coefficient R2 is close to 1. So the linear model is well
adapted for describing the data provided by the subject. This
good correlation shows a good coherency of the subject: the
data provided in stage 1 are quite compatible with the data
provided in stage 2.
Furthermore, the VR interfaces proposed to the subject do
not introduce incoherence or “noise” in the assessment of the
shape. In other words, the good coherence of the data is a
sign for the perfect adaptation of the interface to the task
proposed to the subject.
So as to give a graphical interpretation of the regression, the
vector model of the appeal is plotted in the perceptual space.
The origin of the vector is located arbitrarily in the origin of
the frame, the values of the regression coefficients (a, b) give
the orientation of the arrow, the arrowhead points in the
direction of increasing appeal (figure 5). It can be shown that
this vector is parallel to the steepest slope line of the plane
(equation (6)). The perpendiculars to the vector are the iso-
appeal curves.
-1
0
1
2
-1
-0,5
0
0,5
1
1,5
2
Appeal
2D-perceptual
space
16
11
6
15
14
1
2
3
4
5
13
7
8
9
10
12
17
18
 
Fig. 5  : vector model of appeal in the perceptual space
                                                
1 according to a Fisher-Snedecor table
Virtual Concept 2002
Biarritz - France
October, 9-10
6
The interest of this graph lies in its ability to study the
multidimensionality of aesthetics. What differentiate glass #13
and #17, which have about the same appeal ? May a particular
attribute, or a particular design parameter, explain the
difference ? Further studies are needed for answering these
questions.
4.2 – Preference mapping 2
The second analysis which can be done is to try to explain the
appeal of the glasses by certain design parameters of the forms.
The response surface of the appeal P , obtained in section 3,
can be plotted, according to the design variables (d2, d3)
(equation 7)2:
d3
d2
d3
d2
d3
d2
.
.9
.6
.5
).
8
.8
3
(
).
7
.8
2
(
4
.
64
10
1
.8
2
2
a
a
a
a
a
a
a
a
a
a
P
+
+
+
+
+
+
+
+
+
=
(7)
The colormap of this surface is presented figure 6.
 
Fig. 6  Colormap of the appeal in the design parameter space
(d2, d3).
The structure of the appeal seems to be very simple and uni-
dimensional. Increasing d2 and decreasing d3 lead to an
increase of the appeal. The perpendicular to the steepest
descent line, given by the relation d3=d2/2+c, is about an iso-
appeal line (figure 7).
                                                
2 for all glasses, the dimension d1=8cm
5
6
7
8
9
2
3
4
5
6
7
8
d2
d3
16
11
6
15 14
1
2
4
5
13
7
8
9
10
12
17
18
3
Appeal
Iso appeal line
 
Fig. 7  : Iso-appeal line in the design parameter space
The relation d3=d2/2+c is important for the study of the
aesthetic of a glass. It’s a characteristic of the subject, who
has provided data which satisfy this relation. The strong
point of our study is that the methodology presented allows
the discovery of such relation. The subject is in most of the
cases unconscious of this relation and is not able to formulate
it explicitly.
Both preference mappings are interesting for improving the
design of forms, or seeking “good” products because they
use two complementary approaches.
5- Conclusions
We have presented in this paper an experimental study of the
aesthetic of table glasses. This study of the “appeal” of the
shapes has been carried out on virtual models, generated by a
CAD/VR software, and with classical VR interface: a
spacemouse and stereoscopic eyeglasses. With this tools, the
study was based on the assessment by a subject of various
characteristics of the glass. Two ways of investigations have
been used: (1) a top-down approach, using  multidimensional
scaling and linear regression of the “appeal” of the shapes ;
(2) a bottom-up approach, with the building of a model of the
appeal, based on the assessment of “shape regulating rules “.
A new method for establishing the model of appeal, based on
an optimisation process, has been proposed.
Firstly, the good coherence of the data proposed by the
subject shows the perfect adaptation of the interfaces to the
proposed task. Furthermore, the subject does not encounter
difficulties in the assessment tasks.
Secondly, the model of appeal, based on two design
parameters of the shape of the glass, reveals how these
parameters are linked for the subject’s assessments.
In perspective, we will conduct a similar study using more
design parameters, and based on principal component
analysis. The next step will be to use a model of subjective
evaluation for forms generation and product synthesis,
approach used for example in Kansei engineering type 3
Virtual Concept 2002
Biarritz - France
October, 9-10
7
[22].
In future works, haptic devices will be introduced for the
assessment of weight, stability and more generally ergonomic
attributes.
5- Acknowledgements
We acknowledge the help of Hélène Compain for performing
the tests and for various assessments of aesthetics.
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