A Preliminary System for the Automatic Detection of Emotions based on

the Autonomic Nervous System Response

Asier Salazar-Ramirez

, Raquel Martinez

, Andoni Arruti

, Eloy Irigoyen

, J. Ignacio Martin

and Javier Muguerza

Dept. of Computer Architecture and Technology, University of the Basque Country (UPV/EHU),

Donostia-San Sebasti

an, Spain

Dept. of Automatic Control and Systems Engineering, University of the Basque Country (UPV/EHU), Bilbao, Spain

Keywords:

Emotion, Film Clip, ECG, GSR, Finite State Machine.

Abstract:

People’s life quality is being improved thanks to the advances in medicine and to the promotion of health.

One of the pillars for having a healthy life is to know and to take care of the emotions of oneself. Due to

the close relationship between the emotions and the responses of the autonomic nervous system, the aim of

this work is to study and detect the physiological patterns produced by two of the basic human emotions:

surprise and contentment. The work presents a preliminary system that processes and analyzes two non-

invasive physiological signals (the galvanic skin response and the heart rate variability) and that uses a ﬁnite

state machine for the detection of the activation of the sympathetic nervous system. The work also presents

the experimental procedure that was designed in order to elicit different emotions in laboratory conditions.

The F-score results obtained for the correlation of the analyzed emotions and the physiological patterns were

=1.00 and for surprise and F

=0.94 for contentment.

1 INTRODUCTION

Nowadays, there is an increasing concern among in-

formatics engineering research groups about applying

technical knowledge to systems that allow the im-

provement of people’s life quality. Besides, there

are medical groups that look for technological solu-

tions in order to improve the diagnostical processes

through the automatic detection of certain patholo-

gies that currently are only detected empirically. In

addition, entities from the social spheres request the

development of tools that help people with autonomy

problems recover their independence, as due to any

kind of disability they might be in danger of social

exclusion. Therefore, the ensemble of these three dis-

ciplines converges in a common objective that is to

help people that suffer from any kind of disease or

disability.

Being able to detect automatically certain psycho-

physiological patterns permits to improve human-

computer interaction (Soegaard and Dam, 2013). Mo-

reover, detecting these patterns can also be useful for

helping understand how people suffering from au-

tism or brain paralysis feel, if something pleases them

or if they are suffering any kind of pain, among ot-

her things ((Rice et al., 2015),(Johnson and Picard,

2017),(Giusiano et al., 1995),(Carcreff et al., 2018)).

The affective computing is the discipline that stu-

dies and develops systems and devices in order to re-

cognize, interpret, process and stimulate human emo-

tions (Picard, 2010). Within its ﬁelds of study, a re-

levant subject consists on identifying how people re-

act emotionally when facing certain speciﬁc events.

The intention of identifying these reactions is to help

the previously mentioned collectives to improve their

communication with the environment, their perso-

nal autonomy and their life quality. When an in-

dividual lives any positive or negative situation his

organism produces a psycho-physiological response

that produces subsequently an emotion ((Cannon,

1935),(Schachter and Singer, 1962),(Kreibig, 2010)).

Due to this reaction, monitoring the physiological va-

riables of the body is one of the methods that enable

the detection of these emotional changes.

When facing any stimuli, the physiological re-

sponse of the body is controlled by the Central

Nervous System and the coordination that this sy-

stem applies between the Autonomic Nervous Sy-

stem (ANS), the Endocrine System, the Immunolo-

gic System. . . The ANS, through its two components,

Salazar-Ramirez, A., Martinez, R., Arruti, A., Irigoyen, E., Martin, J. and Muguerza, J.

A Preliminary System for the Automatic Detection of Emotions based on the Autonomic Nervous System Response.

DOI: 10.5220/0006900600460052

In Proceedings of the 6th International Congress on Neurotechnology, Electronics and Informatics (NEUROTECHNIX 2018), pages 46-52

ISBN: 978-989-758-326-1

which are the Sympathetic Nervous System (SNS)

and the Parasympathetic Nervous System (PNS), is

the system responsible of balancing the organism.

The “ﬁght or ﬂight” theory ((Cannon, 1935), (Porges,

2001)) poses that the peripheral physiological signals

that are regulated by the SNS are the ones that pro-

vide information on the arousal or activation of the

brain. On the other hand, the PNS is related to the

relaxation of the body as mention (Cacioppo et al.,

2007), (P

erez-Lloret et al., 2014) and (Benedek and

Kaernbach, 2010). The peripheral physiological sig-

nals that are regulated by the ANS are various: the

cardiac rhythm, the respiration, the sweating, the cor-

poral temperature, the diameter of the eye pupils, the

brain activation, etc.

In order to further study the relationship between

emotions and the physiological signals, the research

team designed an experiment where, in laboratory

conditions, seven of the basic emotions were indu-

ced in the participants while their physiological sig-

nals were being collected. The chosen eliciting sti-

mulus was the visualization of video clips. The phy-

siological signals that were acquired were the cardiac

activity and the sweating as they can be registered

by non-invasive means. Nevertheless, despite seven

emotions were induced during the experiment, only

two of them were taken to the study presented in this

article as they are the ones that have the clearest rela-

tionship with the SNS: surprise and contentment.

Finally, after the experimental part was ﬁnished

and the physiological signal database had been regis-

tered, the research group used the ﬁnite state machine

(FSM) developed in (Martinez et al., 2017) to detect,

classify and rate the activation of the SNS.

This work presents the study of the physiological

patterns of the SNS that are related to the emotions

of surprise and contentment. In order to do so, an

experiment was done where some of the biosignals of

the participants were collected while emotions were

being induced on them by means of projecting video

clips. Later, those biosignals would be analyzed by

the FSM in order to detect the reactions on the SNS

of the two mentioned emotions.

2 MATERIALS AND METHOD

In order to study the relationship between emotions

and the physiological changes, the research team de-

signed an experiment for inducing certain emotional

states in the participants sitting it. During the expe-

riment the electrocardiogram (ECG) and the swea-

ting of the participants, also called galvanic skin re-

sponse (GSR), were collected. The stimulus chosen

to elicit those emotions was the visualization of video

clips, which is a method validated in several research

works ((Gross and Levenson, 1995),(Martinez et al.,

2009),(Gilman et al., 2017)).

2.1 Participants

A total of 32 subjects, aged between 21 and 35

(mean=26 y SD =3.1), participated voluntarily in the

experiment (7 male and 25 female). All the partici-

pants were students or workers of the university.

2.2 Materials

The set of video clips that were used to elicit the emo-

tions in the laboratory was an adaptation to the Spa-

nish population (Martinez et al., 2009) of the audi-

ovisual database published by (Gross and Levenson,

1995). The adaptation consists on using the database

of (Gross and Levenson, 1995) updating it with more

recent video clips and adding clips showing real life

stories and situations, being all of them adapted to the

Spanish culture. The new audiovisual database, va-

lidated in different Spanish cities, proposes the use

of 20 ﬁlms that can be used to induce the following

seven basic emotions: contentment, amusement, dis-

gust, fear, surprise, sadness and anger.

Speciﬁcally, the database contained three clips for

each emotion but for contentment, that had only two:

for amusement, “When Sally met Harry”, “Bote” and

“Concejal”; for anger, “Cry Freedom”, “El bola” and

“Te doy mis ojos”; for fear, “Shining”, “The Ring”

and “The Ring 2”; for sadness, “Champ”, “Omayra”

and “Nasija”; for surprise, “Capricorn one”, “La

monja” and “El orfanato”; for disgust, “Autopsy”,

“Pink Flamingos”, “Hostel”; and ﬁnally, for content-

ment, “Dolphin”, and “BBC”. The ﬁlms have last be-

tween 32 and 214 seconds (M=256, SD=71).

2.3 Assessment Instruments

The assessment of the emotional responses of the

participants was done by collecting their impressions

through two questionnaires already validated by the

psychological community in the existing literature.

The ﬁrst questionnaire, designed by (Gross and Le-

venson, 1995), is the Post ﬁlm Questionnaire (PFQ).

This survey is used to assess the capacity of each clip

for eliciting each of the emotions. In addition, the

PFQ also collects information on whether the subjects

had previously seen the clip or not. The second que-

stionnaire is the Self-Assessment Manikin and it is

used to assess the emotions from a dimensional point

of view or paradigm (Lang, 1980).

A Preliminary System for the Automatic Detection of Emotions based on the Autonomic Nervous System Response

2.4 Procedure

All the sessions of the experiment took place in the

audiovisual projection room of the university, asses-

sing the emotional responses and collecting the phy-

siological signals to be studied from two participants

per sessions. Prior to starting the test the participants

received an explanation on the objective and the pro-

cedure of the experiment, the documentation they had

to fulﬁll, the questionnaires and the physiological sig-

nals that were going to be collected. In addition, the

participants were explained that the experiment had

passed all the requirements of the ethical committee

of the university and that all their privacy rights were

preserved and that all the laws related to these experi-

mental procedures were being respected.

Despite being 20 clips, not all of them were used

in every experiment; the clips were divided in 6 diffe-

rent projection sets, using 5 of clips in each of those

sets. At the beginning of all the projections a wel-

come message was displayed on the screen (15 se-

conds). After the welcome video the same sequence

was repeated ﬁve times, being this sequence compo-

sed of a neutral video (60 seconds), the emotion elici-

ting clip and a message asking the participants to ful-

ﬁll the corresponding questionnaires in paper format

(80 seconds). The sequence followed in the experi-

ment is shown graphically in Figure 1.

Figure 1: Temporal sequence of the projections of the clips

during the experiment.

The data acquisition system used to collect the

physiological signal was the BIOPAC MP150. The

sweating was measured through the galvanic skin re-

sponse (GSR) and SS3LA gel electrodes were used

on the participants non-dominant hand to collect the

signal. In addition, the cardiac activity was studied by

analyzing the electrocardiogram (ECG) and to collect

it the three terminals of the SS2LB electrodes were

placed on the chest of the participants.

2.5 Psycho-physiological Analysis

The design of the experiment was to aiming to eli-

cit anger, sadness, contentment, fear, surprise, amu-

sement and disgust. Due to the complexity of identi-

fying certain emotions from the physiological signals,

this work has only studied those emotions that present

a clear and speciﬁc physiological pattern, which are:

contentment, related to the sympathetic inhibition sta-

tes, and surprise, related to activation states.

The physiological patterns associated to the acti-

vation of the SNS correspond to the acceleration of

the cardiac rhythm and to an increase of the sweating.

On the other hand, those changes related to its inhibi-

tion are the relaxation of the cardiac rhythm and the

decrease if the sweating.

As previously mentioned, the signals collected du-

ring the experimentation were the ECG and GSR.

Anyway, as the only indicator of heart activity to be

used was the cardiac rhythm, during the phase of ana-

lysis the researchers decided to use the Heart Rate

Variability (HRV) instead of using the ECG itself.

The HRV is the signal that provides information of

the time changes between each heartbeat of the ECG

(Thayer et al., 2010). Thus, when the cardiac activity

accelerates the time between heartbeats decreases and

so does the HRV. On the contrary, if the heart beats

relaxed then the time intervals between heartbeats get

greater and it produces bigger HRV values.

On the one hand, Figure 2 depicts the evolution

of the physiological variables when, during the pro-

jection of one of the clips, a participant in the experi-

ment gets eventually shocked (marked with SS). On

the other hand, Figure 3 shows how the physiological

variables tend to relax during the projection of a clip

corresponding to contentment.

780 790 800 810 820 830 840

0.5

1.5

HRV

SURPRISE

780 790 800 810 820 830 840

Time (s)

GSR

Figure 2: Physiological signals for surprise.

NEUROTECHNIX 2018 - 6th International Congress on Neurotechnology, Electronics and Informatics

290 300 310 320 330 340 350

0.2

0.4

0.6

0.8

1.2

HRV

CONTENTMENT

290 300 310 320 330 340 350

3.4

3.6

3.8

Time (s)

GSR

Figure 3: Physiological signals for contentment.

Analyzing Figure 2 it can be seen that there is an

increase in the GSR and a big decrease in HRV in

the moment of the shock happens. Looking at Figure

3 it can be seen that the reaction to the contentment

clips is opposite to what happened in Figure 2, there

is a clear decrease of the GSR and the cardiac rhythm

remains stable.

2.6 Automatic Detection of the

Emotional States

The identiﬁcation of emotions by the automatic de-

tection of physiological change patterns was done

using the system designed by (Martinez et al., 2017).

The detection system is a ﬁnite state machine (FSM)

that detects, rates and classiﬁes the arousal of the

SNS. When the FSM detects a SNS arousal it rates it

depending on its intensity: Low Alert, Medium Alert,

High Alert. In addition, if the activation lasts for lon-

ger than 3 iterations, then the system changes its clas-

siﬁcation from alert to stress: Low Stress, Medium

Stress or High Stress. Therefore, the output of the

FSM can vary within seven states, one for the absence

of arousal and six for activation states. The numerical

outputs of the FSM for the classiﬁed states labels are

the following: 0= No Arousal, 1=Low Alert, 2=Low

Stress, 3=Medium Alert, 4=Medium Stress, 5=High

Alert and 6=High Stress.

In order to analyze the signal throughout the

whole experiment the researchers chose a sliding win-

dow approach where the window had a width of 20

seconds and slided using 5 second steps. The charac-

teristics of the physiological signals that were used as

inputs for the FSM were the slopes of both GSR and

HRV signals, the multiplication of the two slopes be-

tween them and the amount of consecutive windows

computed by the machine for the detected state.

Despite the FSM is further explained by (Martinez

et al., 2017), Figure 4 provides a graphic explanation

on how it works. Each of the circles of Figure 4 stand

for one of the seven classiﬁed levels of stress, being

0 the default state. If the subject eventually suffers a

SNS arousal, then a transition condition is triggered

and it makes the FSM change its state. For instance,

if the activation was not very intense, Activation Re-

sponse 1 (AR1) would trigger taking the FSM from

state 0 to state 1 (Low Alert). In the same way, if the

activations were medium or high then AR2 and AR3

would trigger and the states would change to state 3

and 5 respectively.

Once one of the alarm states has been reached, the

FSM starts checking whether the values of physiolo-

gical signals are maintained for the following itera-

tions: (S AR condition). On the one hand, if S AR

is not fulﬁlled then the machine goes back to its de-

fault 0 state. On the other hand, if after three win-

dow sliding iterations (n=3, i.e., 15 seconds) S AR is

still true, then n=3 condition is triggered and the FSM

changes its output from alarm state 1, 3 or 5 to the

corresponding stress state 2, 4 or 6 respectively. Af-

ter that happened, in the next iteration the machine

would go back to the default 0 state and would wait

there until a new activation happened. Anyway, if the

machine was in an alarm or stress state and an acti-

vation of greater intensity took place, then the ma-

chine would move from the current state to a higher

intensity alarm state. For example, if the current state

was 1 or 2 and AR2 happened, then the FSM would

change to state 3.

(AR1,AR2,AR3)

AR1

AR2

AR3

S_AR

n=3

AR2

S_AR

Figure 4: State transition diagram of the FSM.

A Preliminary System for the Automatic Detection of Emotions based on the Autonomic Nervous System Response

3 RESULTS

This section presents the results obtained from the use

of the FSM on the physiological signals collected in

the experiments as inputs. As previously mentioned,

the emotions that were studied were surprise and con-

tentment. For the clips targeting surprise, in the in-

stant of the shock, the physiological signals should

experiment an increase of the sweating and a decrease

of the HRV, being them correlated to a medium-high

level of arousal. The adaptive function of the surprise

is to warn the body that something not expected is ta-

king place. Because of that, it is the shortest of all the

emotions and it is immediately followed by the emo-

tion produced after the assimilation of what is happe-

ning in the new event (Verduyn and Lavrijsen, 2015)

. As it is a short emotion that does not necessarily re-

main during the time, the output of the FSM for this

emotion can be either alert or stress depending on its

duration. On the other hand, when the subject visuali-

zes the clip for contentment tends to relax and there is

no SNS activation. Thus, looking to the physiological

signals, the GSR decreases and the HRV remains at

the same values or increases. This reaction gives evi-

dence that there is no sympathetic activation, hence

the output of the FSM is equal 0.

Figure 5 and Figure 6 illustrate both the physio-

logical signals of a participant of the experiment and

the output of the FSM for the signals collected in the

projections. In the case of Figure 5 the signals de-

picted correspond to surprise. On the other hand, Fi-

gure 6 depicts those signals collected during the clip

for contentment.

780 790 800 810 820 830 840

0.5

1.5

HRV

SURPRISE

780 790 800 810 820 830 840

Time (s)

FSM

AROUSAL

780 790 800 810 820 830 840

GSR

Figure 5: The physiological signals and the output of the

FSM during the clip of surprise.

In order to determine the degree of success of the

290 300 310 320 330 340 350

0.2

0.4

0.6

0.8

1.2

HRV

CONTENTMENT

290 300 310 320 330 340 350

3.4

3.6

3.8

GSR

290 300 310 320 330 340 350

Time (s)

FSM

AROUSAL

Figure 6: The physiological signals and the output of the

FSM during the clip of contentment.

detection system, the researchers have analyzed the

outputs of the detection system during the projections

of the clips targeting surprise and contentment. For

surprise, it is considered as a positive result if in the

moment of the shocking images the system’s output

was 3, 4, 5 or 6 corresponding to Medium Alert, Me-

dium Stress, High Alert or High Stress respectively.

Consequently, a positive result for contentment would

be when the system gives an output of no activation,

i.e., the output equals 0.

Table 1 shows the results that have been obtai-

ned in the research. The columns reﬂect the results

obtained in the following way: Manual Label stands

for the amount of clips labeled by the researchers for

each emotion; True Positive (TP) stands for the states

correctly detected by the FSM; False Negative (FN)

stands for the cases in which the algorithm should

have detected a state but did not do it; False Posi-

tive (FP) represents the cases where the algorithm has

detected certain states without them being previously

labeled for that state. The performance of the system

has been estimated using Precision (P), Recall (R) and

the F-Score (F

), whose calculation formulas are pre-

sented in equations (1), (2) and (3) respectively. The

best possible score for these indicators is 1 and the

worst is 0.

P =

T P

T P + FP

(1)

R =

T P

T P + FN

(2)

2 · P · R

P + R

(3)

NEUROTECHNIX 2018 - 6th International Congress on Neurotechnology, Electronics and Informatics

Table 1: Statistical results of the automatic detection.

Emotion

Manual

Label

FSM

TP FN FP Precision Recall F

Contentment 19 17 2 0 1.00 0.89 0.94

Surprise 9 9 0 0 1.00 1.00 1.00

As shown in Table 1, the results for surprise achie-

ved a Precision of 1.00, a Recall of 1.00 and a F

score

of 1.00. For contentment the results obtained were

1.00 for Precision, 0.89 for the Recall value and 0.94

for the F

. Therefore, considering the performance

rates obtained by the algorithm, it can be said that the

combination of the chosen signal characteristics and

the FSM results in a valid system for detecting the

emotional states of surprise and contentment.

Anyway, it is not a trivial issue to recognize and

to classify an speciﬁc emotion. Due to this reason

several authors prefer to classify grouped by clus-

ters. For example, (Canento et al., 2011) achieved

80%, 70% and 70% success rates using the leave one

out cross validation methodology with a k-NN clas-

siﬁer in order to distinguish between positive and ne-

gative, positive and neutral and neutral and negative

emotions respectively. Other authors prefer to clas-

sify emotions in a dimensional manner paying at-

tention to valence and arousal. This is the case of

(Kim and Andr, 2008) who presented a novel scheme

of emotion speciﬁc multilevel dichotomous classiﬁ-

cation that achieved success rates of 95% and 70%

for subject-dependent and subject-independent emo-

tion classiﬁcation respectively. Finally, (Soleymani

et al., 2015) presented an speciﬁc emotion classiﬁca-

tion distinguishing between fear, sadness, frustration,

happiness, please and satisfaction. The classiﬁcation

was done using an artiﬁcial neural network and due to

the complexity of the classiﬁcation the network achie-

ved an accuracy of 55.8%.

Thus, it seems clear that the success rates decre-

ase quickly as the classiﬁcation gets more emotion-

speciﬁc. The FSM presented in this work has only

been used to classify between two speciﬁc emotions

and so, despite it has been proved to be effective for

this certain problem, it would be recommendable to

expand the study to other emotions and see how it per-

forms as the classiﬁcation gets more complex.

4 CONCLUSIONS

The aim of this work was to study the response of the

SNS during the elicitation of emotions through the

interpretation of physiological signals. Speciﬁcally,

two of the seven basic emotions have been taken to

analysis, surprise and contentment for being directly

related to ANS activation and inhibition respectively.

The research team developed an experiment to induce

the studied emotions in laboratory conditions while,

at the same time, the sweating and the cardiac activity

of the subjects was collected. The stimulus chosen

to elicit the emotions was the visualization of audio-

visual clips. The chosen clips elicited both content-

ment and surprise to the participants. Anyway, the

clips only elicited surprise as long as the subjects had

not previously seen the clip, as pointed by (Martinez

et al., 2009).

The physiological signals collected during the ex-

periment were used as inputs of an algorithm that

detects and classiﬁes the activation of the SNS. The

signals conﬁrmed that the SNS got active when the

participants were surprised by the clip and as a con-

sequence the output of the FSM gave values of me-

dium or high alert or stress. When the subjects felt

that the abnormal or hazardous sensation was over the

activation of the SNS stopped, giving conﬁrmation to

what posed by (Schachter and Singer, 1962) . On the

contrary, during the visualization of the clips of con-

tentment the physiological signals responded accor-

ding to the pattern of inhibition of the ANS (Cacioppo

et al., 2007) and, because of it, the output of the FSM

was 0 which corresponds to the ANS not being active.

The results obtained from the performance analy-

sis (F-score) of the FSM were F

=1.00 and F

=0.94

for surprise and contentment respectively, and so the

algorithm gets validated as a useful tool for the study

of the activation of the SNS.

As a future approach the researchers propose the

study of other emotions in order to see their relations-

hip with the ANS. To do so, it would be necessary

to expand the amount of used physiological signals,

including to the study signals as the respiration, the

photoplethysmography or the encephalography.

ACKNOWLEDGEMENTS

This work was partially supported by the Depart-

ment of Education, Universities and Research of the

Basque Government under Grant IT980-16 and by

the Ministry of Economy and Competitiveness of

A Preliminary System for the Automatic Detection of Emotions based on the Autonomic Nervous System Response

the Spanish Government and the European Regio-

nal Development fund- ERFD (PhysComp project,

TIN2017-85409-P).

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NEUROTECHNIX 2018 - 6th International Congress on Neurotechnology, Electronics and Informatics