Typicality Degrees to Measure Relevance of the Physiological Signals

Assessing user’s Affective States

Joseph Onderi Orero

The Faculty of Information Technology, Strathmore University, Nairobi, Kenya

Keywords:

Affective Computing, Physiological Signals, Machine Learning,Prototypes, Typicality Degrees, Gameplay.

Abstract:

Physiological measures have a key advantage as they can provide an insight into human feelings that the

subjects may not even be consciously aware of. However, modeling user affective states through pysiology

still remains with critical questions especially on the relevant physiological measures for real-life emotionally

intelligent applications. In this study, we propose the use of typicality degrees deﬁned according to cognitive

science and psychology principles to measure the relevance of the physiological features in characterizing

user affective states. Thanks to the typicality degrees, we found consistent physiological characteristics for

modeling user affective states.

1 INTRODUCTION

Analysis of physiological data during human-

computer interaction presents one of the most objec-

tive means to assess the user’s psychological states. In

addition to their ability to be measured continuously

in real-time, physiological measures grant an access

to non-conscious and non-reportable processes (Ca-

cioppo et al., 2007). As they are a record of invol-

untary autonomic nervous system processes, physi-

ological data represents information of internal psy-

chological states which is not easy to be captured in

other forms such as facial gesture or voice recognition

through video and audio recording.

In the recent past, scientiﬁc works have demon-

strated the enormous prospects in developing sys-

tems equipped with the ability to assess user emo-

tional states through the analysis of physiological

data (Fairclough, 2009; Calvo and D’Mello, 2010;

Novak et al., 2012). However, despite the advances in

this ﬁeld, there are still major difﬁculties in uniquely

mapping physiological patterns onto speciﬁc affective

states. It tends to vary considerably from one person

to another and may even display considerable differ-

ences within individuals on different occasions (Pi-

card et al., 2001). There is a need to explore more ap-

propriate approaches for design of generic real-time

emotionally intelligent applications. One of the major

challenges is to determine the relevance of the given

physiological features in characterizing the user af-

fective states.

In this study, we propose the use of typicality de-

grees deﬁned according to cognitive science and psy-

chology principles to discover generic physiological

characteristics in relation to user affective states. Typ-

icality degree is computed for each of the training ex-

ample based on its similarity to the examples in the

same class and its dissimilarity with examples belong-

ing to a different class. In our approach, the aim is to

ﬁrst discover automatically typicality of physiological

features so as to determine their relevance in charac-

terizing the user affective states.

The rest of the paper is organized as follows: in

Section 2, we give the state of the art on modeling

user affective states through physiology. In Section 3,

we give details of an approach to derive generic phys-

iological signals that characterize user affective states

through typicality degrees. Then, in Section 4, we

outline the details of the experimental data used and

analysis of the results in Section 5. Finally, we give

conclusions and future perspectives in Section 6.

2 STATE OF THE ART

2.1 Emotions and Physiology

In cognitive and psychology studies, scientiﬁc works

have proved that certain psychological processes and

states are accompanied by changes in physiological

activity (Ekman et al., 1983; Winton et al., 1984;

351

Orero J..

Typicality Degrees to Measure Relevance of the Physiological Signals - Assessing user’s Affective States.

DOI: 10.5220/0004878403510357

In Proceedings of the International Conference on Physiological Computing Systems (OASIS-2014), pages 351-357

ISBN: 978-989-758-006-2

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

Lang, 1995; Bradley et al., 1993; Detenber et al.,

1998). For example, Winton et al.’s study (Win-

ton et al., 1984) showed that pleasant and unpleasant

emotions could be differentiated through heart rate

(HR). Pleasant reaction was found to be followed by

an increase in heart rate while unpleasant images were

characterized by a heart rate deceleration. Similar re-

sults have been shown with Electromyography activ-

ity (EMG) (Cacioppo et al., 1992). Electrodermal

Activity (EDA) is considered to be the most effec-

tive correlate to arousal (Lang, 1995; Bradley et al.,

1993; Detenber et al., 1998). Likewise, respiration

pattern have been shown to discriminate emotional

dimensions related to response requirement; mainly:

calm versus excitement, relaxation versus tenseness,

and active versus passive coping (Boiten et al., 1994;

Boiten, 1998).

In affective computing, experimental studies have

been conducted to propose the use of such inferences

as a way to develop machines that can automati-

cally recognize and respond to these emotions (Picard

et al., 2001; Haag et al., 2004; Wagner et al., 2005;

Rainville et al., 2006; Kim and Andr

e, 2008; Bailen-

son et al., 2008; Chanel et al., 2009). A well known

example is Picard et al.’s study (Picard et al., 2001).

Their experimental study was aimed at discriminat-

ing eight emotions (anger, hate, grief, platonic love,

joy, love and no emotion) through physiological mea-

sures recorded on a trained actor who was asked to

express repeatedly these states over several days. In

addition to their impressive results (81% classiﬁca-

tion of the 8 emotions), the most striking revelation of

their experiment was the difﬁculty associated to the

variability of physiological features. Even with this

single participant, they observed a signiﬁcant day-to-

day variations. Therefore, there is a need to determine

which physiological signals and features give an opti-

mal results in discriminating between the given affec-

tive states in a real-time set-up.

2.2 Affect Recognition and Feature

Selection

In the domain of physiological computing, there have

been substantial novel models with the goal of dis-

criminating various classes of emotions from physi-

ological measures (Novak et al., 2012). In particu-

lar, such studies have made rigorous efforts to dis-

cover the most discriminant set of features relevant

for such emotionally intelligent systems. The aim is

to discover the optimal feature set for discriminating a

given set of emotions by use of a classiﬁcation method

with feature selection procedure. For example in Kim

and Andr

e’s study (Kim and Andr

e, 2008), an exper-

iment was conducted with emotions evoked through

listening to songs in which participants were asked to

listen to music of their own liking corresponding to

the four classes in each of the two dimension emo-

tional axis: anger, joy, sadness and pleasure. Then,

110 features were extracted from the four physiolog-

ical recordings (electromyogram, electrocardiogram,

respiration and skin conductance) and extended linear

discriminant analysis was used as the machine learn-

ing method. Feature selection procedures were em-

ployed to extract the most relevant features for the

four emotions. Similar models have also been ex-

ploited in gameplay psychophysiological studies such

as in (Yannakakis and Hallam, 2008).

However, the optimal set of features tend to dif-

fer depending on the method used. For example, Pi-

card et al.’s study (Picard et al., 2001), depending on

whether Sequential Floating Forward search (SFFS)

or SFFS Followed by Fisher Projection or SFFS Fol-

lowed by Fisher Projection Using the Day Matrix was

followed, the optimal features were different for the

same set of emotions. It also becomes much more

difﬁcult when comparing between different experi-

ments so as to develop generic psychophysiological

user models.

Currently, modeling of emotions from physiolog-

ical signals has mainly relied on classiﬁcation meth-

ods such as linear discriminant methods, neural net-

works, k-nearest neighbors and decision trees. Al-

though these methods have been proved to be very ef-

ﬁcient in classiﬁcation, the goal is not only to discrim-

inate affective experiences (recognition) but also dis-

cover meaningful relations between physiology and

affective states (characterization psychophysiological

relations).

Therefore, there is a need for an develop mod-

els that can be used to measure the characterization

power of physiological features in relation to user af-

fective states. In this study, we propose an approach

based on typicality degrees to determine the most rel-

evant physiological signals. Typicality degrees are

computed depending on the similarity between exam-

ples in the same class and dissimilarities with exam-

ples in other classes. The use of typicality degrees has

not been applied in this domain before. Yet, as we

elaborate in the subsequent sections, the use of typi-

cality degrees is a systematic approach to characterize

physiological features.

In our context, the goal is to discover the most typ-

ical physiological patterns that best describe a given

affective state. This can be achieved by considering

several temporal segments during a given affective

state and computing the similarity to the other seg-

ments within that state and the dissimilarity with the

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352

physiological segments during other affective states.

Indeed, as we are dealing with temporal data, it is

natural that the physiological patterns will not be con-

stant over time within a given affective states but we

can extract some consistent patterns across the partic-

ipants. The segment with the highest typicality will

then be taken as the prototype for that state. Thanks

to these prototypes, we can study the cardinal physio-

logical characteristics in relation to the affective states

of interest.

3 TYPICALITY DEGREES BASED

APPROACH

3.1 Typicality and Prototypes from

Cognitive Science Perspective

The concept of typicality and prototypes has been

studied in the ﬁeld cognitive science and psychology,

initially by Rosch and Mervis (Rosch and Mervis,

1975). According to their study, typicality relies on

the notion that some members of the same category

are more characteristic of the category they belong

to than others. This is contrary to the traditional

thoughts that have treated category membership of

items as possessing a full and equal degree of mem-

bership. Some members are more characteristic (typ-

ical) of the category they belong i.e have features that

can be said to be most descriptive of that category.

Thus, subjects can belong to the same category but

differing in their level of typicality. Typicality degree

of an item depends on two factors (Rosch and Mervis,

1975):

(i) internal resemblance: an object’s resemblance

to the other members of the same category, and

(ii) external dissimilarity: its dissimilarity to the

members of the other categories.

The concept of typicality can be used to deﬁne

prototypes for a given group or category as an object

that summarizes the characteristics of the group. In

this case, a prototype of a given category is the object

with the highest typicality in that category i.e closely

resembles the other members of the same class (in-

ternal resemblance) and signiﬁcantly differs from the

members of the other classes (external dissimilarity).

3.2 Typicality Degrees

The aim is to discover pertinent psychophysiologi-

cal characteristics based on the concept of typical-

ity. Since the typicality degree of an object indicates

the extent to which it resembles the members of the

same group and differs from the members not in the

same group, we can measure its power to character-

ize i.e its ability to summarize the cardinal proper-

ties of a group. Speciﬁcally, we consider Rifqi’s for-

malism (Rifqi, 1996) that computes the typicality de-

grees of objects to automatically construct fuzzy pro-

totypes.

Formally, let X be a data set composed of m in-

stances in n dimensional space and labeled to be-

long to a particular state or class, k ∈ C and C =

{1, . . . k, . . . c} where c is the number of all possible

states or classes.

It computes for each example, x ∈ X, belonging to

a given class, k, its internal resemblance, R(x), the ag-

gregate of similarity to the members in the same class

and its external dissimilarity, D(x), the aggregate of

dissimilarity to members not in the same class. The

typicality degree, T (x), of x is then computed as the

aggregate of these two quantities given as:

R(x) =

|k|

∑

y∈k

r(x, y) (1)

D(x) =

|X\k|

∑

z6∈k

d(x, z) (2)

T (x) = t(R(x), D(x)) (3)

Where r is a similarity measure for computing inter-

nal resemblance, d is a dissimilarity measure for com-

puting external dissimilarity, and t is an aggregation

operator for aggregating resemblance and dissimilar-

ity. y is used to designate examples belonging to the

same class while z designates examples not belonging

to the same class as the given example x.

The choice of similarity measures, dissimilarity

measures and aggregation operators depends on the

nature of the desired properties and have been studied

in detail (Bouchon-Meunier et al., 1996; Detyniecki,

2001; Lesot et al., 2006; Lesot et al., 2008). In this

study, we choose to use the normalized euclidian dis-

tance as dissimilarity measure in Equation 2 and its

complement as a similarity measure in Equation 1.

This ensures that both the internal resemblance and

external dissimilarity on a comparative scale. Then,

to compute typicality degrees, as an aggregation of in-

ternal resemblance and external dissimilarity in Equa-

tion 3, we chose to use the symmetric sum. The sym-

metric sum has a reinforcement property (Detyniecki,

2001). In such a case, if both the similarity and the

dissimilarity are high, the aggregated value becomes

higher than any of the two and if both are low, the

aggregate becomes lower than any of the two values.

This ensures that the aggregation is high only if both

the similarity and the dissimilarity are high and vise

TypicalityDegreestoMeasureRelevanceofthePhysiologicalSignals-Assessinguser'sAffectiveStates

353

versa. Thus, the aggregation is not just a simple mean

which can be misleading when the example has high

similarity but lower dissimilarity and vise versa.

Also, our aim is to extract prototypes that best de-

scribe the cardinal characteristics of a particular class.

In this case, prototypes are deﬁned based on the com-

puted typicality degrees for each example. Prototypes

are taken as the examples with the highest typicality

degree. We consider a prototype formulated by com-

puting typicality degrees attribute by attribute. For

each attribute, A, the typicality degree of each value

of A for each of the objects in the class is computed.

In our context, we exploit this concept of typical-

ity to determine the characterization power of a given

physiological feature from the typicality degree of its

prototypes. As the typicality degree of an example

indicates the extent to which it resembles the mem-

bers of the same group and differs from the members

not in the same group, we can measure an attribute’s

power to characterize. If an attribute typicality degree

is high, then it follows that the attribute is relevant in

characterizing the given state. On the contrary, if the

typicality is low, then the attribute alone, can not be

used as reference for characterizing the given states.

4 DATA

4.1 Experiment

In this study, we use data from two experimental

study in which physiological measures were recorded

on players involved in an action aimed at discover-

ing typical physiological signatures associated with

various gaming experiences (Levillain et al., 2010).

During this experiment, participants played succes-

sively four game sequences. The game session always

started with an introductory sequence (Sequence 1)

corresponding to the very ﬁrst minutes of the game.

After having played Sequence 1, participants were

asked to complete three other sequences (Sequence 2,

Sequence 3 and Sequence 4), the order of presentation

of these sequences was counterbalanced.

The selected four sequences vary both in terms

of difﬁculty and in terms of the gameplay they pro-

pose. Sequence 4, which was the most difﬁcult game

sequence. This reﬂects the fact that participants felt

their skills exceeded in this episode, with a feeling of

frustration as a consequence. On the opposite side,

Sequence 1, which was the introduction of the game,

was the least challenging sequence. In this case, the

lack of challenge is likely to lead to boredom. In be-

tween, Sequence 2 being the favorite of most partici-

pants.

In particular, Sequence 1, Sequence 2 and Se-

quence 4 were distinguished in terms of level of chal-

lenge and user’s affective states we classify the play-

ers’ experiences in relation to appraisal of challenge

in three distinct categories as follows:

i) boredom (due to an insufﬁcient challenge i.e Se-

quence 1),

ii) ﬂow/comfort (due to comfortable level of chal-

lenge i.e Sequence 2) and

iii) frustration (due to very high challenge i.e Se-

quence 4).

4.2 Physiological Measures and Data

Set Construction

Thirty (30) of participants physiological data from the

following signals was used:

i) Electrodermal activity measure (EDA)

ii) Heart Rate (HR)

iii) Respiration Rate (RR)

First, due to the fact that the length of the game

sequences varied from participant to participant and

also to minimize the effect of the transition periods,

only the physiological recordings of the last two min-

utes of each game sequence was used.

Secondly, to account for variations between par-

ticipants, each participant’s signal normalized value,

, from the raw value, S

, was calculated using the

signal’s standard deviation, S

sdv

, and its mean, S

mean

as shown in Equation 4.

− S

mean

sdv

(4)

To validate the homogeneity of the physiological

signatures throughout a given affective state session,

these game sequences were subdivided into 10 sec-

onds (2000 data points) segments, with a total of 12

segments for each game sequence. Thus, we have a

total of 1080 samples or segments.

Regarding the extraction of features from the

physiological signals, for each segment, the features

shown in Table 1 were calculated for each of the three

signals. The aim is to determine the most relevant

physiological feature from each of the physiological

signals based on typicality degrees.

5 RESULTS

As discussed before, typicality degrees for each seg-

ment/sample was computed for each signal. Based on

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Table 1: Features from physiological measures.

Features Description

µEDA,µHR, µRR mean amplitude

δEDA,δHR, δRR standard deviation

EDA,

HR,

RR mean of absolute ﬁrst derivative

EDA,

HR,

RR max gradient

EDA,Φ

HR, Φ

RR PSD 0.0. . . 0.2 frequency range

EDA,Φ

HR, Φ

RR PSD 0.2. . . 0.4 frequency range

EDA,Φ

HR, Φ

RR PSD 0.4. . . 0.6 frequency range

EDA,Φ

HR, Φ

RR PSD 0.6. . . 0.8 frequency range

minEDA,minHR, minRR min signal amplitude

maxEDA,maxHR, maxRR max signal amplitude

Total 30

HR: Heart Rate

EDA: Electrodermal Activity

RR: Respiration Rate

PSD: Power Spectrum Density

the typicality degrees, the three most typical features

from each of the three signals were: µEDA, minHR

and µRR, the average signal amplitude of electroder-

mal activity (EDA), the minimum heart rate (HR)

and average signal amplitude of respiration rate (RR)

respectively. We obtained typicality degree curves

shown in Figure 1, Figure 2 and Figure 3, for µEDA,

minHR and µRR, respectively.

µEDA was the most relevant feature for character-

izing these three states: its typicality degree curves

are clearly distinct for the three states. The boredom

state can be easily characterized by low µEDA (most

typical value of about −1.18 with typicality degree of

0.79). Similar behavior is seen in the case of frus-

tration state. The comfort state is less distinctive (the

most typical value of about 0.11 with typicality de-

gree of 0.63). These results reﬂect that there were dis-

tinct µEDA patterns for the three states which varied

in challenge level and thus inducing different arousal

levels. Therefore, our typicality results are consistent

with previous works in which EDA has been found to

correlate well with arousal (Lang, 1995).

On the other hand, the ability of HR to charac-

terize the three states was lower than EDA (Figure 2).

We found out that comfort state is better characterized

by HR (most typical value of about −0.43 with typ-

icality degree of 0.60) than boredom and frustration

states (with typicality degrees of 0.54 and 0.51, re-

spectively). Thus, when the participants are in a com-

fort state, the physiological characteristics are typical

across participants, but not for the other states. This

is consistent with the studies that have shown heart

rate to correlate with positive user experiences (Win-

ton et al., 1984).

However, although respiration rate has been pro-

posed as a measure to differentiate calm and excite-

ment (Lang, 1995; Bradley et al., 1993; Detenber

et al., 1998), our results did not show clearly this kind

of preposition. As shown on Figure 3, µRR ’s the char-

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

-3 -2 -1 0 1 2 3 4

Electrodermal Activity (Average)

Typicality Degree

Boredom Comfort Frustration

Figure 1: Flow states typical values for average electroder-

mal activity (µEDA).

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

-3 -2 -1 0 1 2 3

Heart Rate (min)

Typicality Degree

Boredom Comfort Frustration

Figure 2: Flow states typical values for minimum heart rate

(minHR).

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

-4 -3 -2 -1 0 1 2 3

Respiration Rate (Average)

Typicality Degree

Boredom Comfort Frustration

Figure 3: Flow states typical values for average respiration

rate (µRR).

acterization power is low for all the three states i.e:

the prototype typicality degree is less than 0.6 for all

the three states and their curves are almost horizontal.

This may be due to the signal noise associated with

the respiration measure and in this case is not appro-

priate for characterizing these three affective states.

TypicalityDegreestoMeasureRelevanceofthePhysiologicalSignals-Assessinguser'sAffectiveStates

355

6 CONCLUSIONS

In this study, we have tested the use of typicality de-

grees measure the relevance of physiological signals

to model users’ affective states. We considered typ-

icality as per cognitive and psychology principles of

categorization to discover pertinent psychophysiolog-

ical relations. We showed how this framework is a

powerful characterization tool.

Regarding characterization task, we were able to

extract key psychophysiological characteristics for

modeling real-life affective systems. Our experimen-

tal results revealed that Electrodermal activity (EDA)

measure is very powerful in characterizing all the

users’ states. When considering a player’s affective

states, we found that heart rate is less relevant than

EDA, but is critical to distinguish a state of comfort

from a state of frustration. On the contrary, the char-

acterization power of respiration recordings (RR) was

low. Thus, in relation to affective gaming, our results

show that it is possible to gain information from phys-

iological signals considering the optimal state of sat-

isfaction of a player.

However, still much is to be done before getting

access to the structure of the player’s emotional pro-

cesses. In particular, to consider multi-modal fusion

of measures such as audio-visual and various physio-

logical measures.

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