PREREQUISITES FOR AFFECTIVE SIGNAL PROCESSING (ASP)

Part V - A Response to Comments and Suggestions

Egon L. van den Broek

Human-Media Interaction (HMI), Faculty of EEMCS, University of Twente

P.O. Box 217, 7500 AE Enschede, The Netherlands

Karakter University Center, Radboud University Medical Center Nijmegen

P.O. Box 9101, 6500 HB Nijmegen, The Netherlands

Joris H. Janssen

Human Technology Interaction, Eindhoven University of Technoloy, Den Dolech 2, 5600 MB Eindhoven, The Netherlands

Body, Brain & Behaviour Group, Philips Research, High Tech Campus 34, 5656 AE Eindhoven, The Netherlands

Marjolein D. van der Zwaag, Joyce H. D. M. Westerink

Body, Brain & Behaviour Group, Philips Research, High Tech Campus 34, 5656 AE Eindhoven, The Netherlands

Jennifer A. Healey

Future Technology Research, Intel Labs Santa Clara, Juliette Lane SC12-319, Santa Clara CA 95054, U.S.A.

Keywords:

Affective signal processing, Emotion, Review, Temporal aspects, Prerequisites, Guidelines.

Abstract:

In four papers, a set of eleven prerequisites for affective signal processing (ASP) were identiﬁed (van den

Broek et al., 2010): validation, triangulation, a physiology-driven approach, contributions of the signal pro-

cessing community, identiﬁcation of users, theoretical speciﬁcation, integration of biosignals, physical charac-

teristics, historical perspective, temporal construction, and real-world baselines. Additionally, a review (in two

parts) of affective computing was provided. Initiated by the reactions on these four papers, we now present: i)

an extension of the review, ii) a post-hoc analysis based on the eleven prerequisites of Picard et al.(2001), and

iii) a more detailed discussion and illustrations of temporal aspects with ASP.

1 INTRODUCTION

To align research on affective signal processing

(ASP), a set of eleven prerequisites for ASP were pro-

posed (van den Broek et al., 2010): validity, triangu-

lation, a physiology-driven approach, signal process-

ing contributions, physical characteristics, baselines,

historical perspective, integration of biosignals, user

identiﬁcation, temporal construction, and theoretical

speciﬁcation. Since the publication of these papers,

the authors have received many suggestions and com-

ments following these papers. This article provides

a response to the three most prominent reactions: i)

an extension of the review on ASP, ii) the use of the

prerequisites in practice, and iii) concerns on various

temporal aspects of ASP.

The problem with a reviewis that it is impossible to

be complete. However, one can always aim to achieve

this goal as closely as possible. Therefore, this pa-

per presents a review on ASP and affective comput-

ing (AC), complementary to the previous two (van

den Broek et al., 2010); see Table 1. In addition, to

show the use of the prerequisites in practice, we apply

the prerequisites to post-hoc analyze the seminal work

of (Picard et al., 2001); see Table 3. In Section 3,

we elaborate more on temporal aspects of ASP and,

more in general, of biosignals. Finally, we draw con-

clusions and denote the prerequisites’ implications for

applications on ASP.

301

L. van den Broek E., H. Janssen J., D. van der Zwaag M., H. D. M. Westerink J. and A. Healey J..

PREREQUISITES FOR AFFECTIVE SIGNAL PROCESSING (ASP) - Part V - A Response to Comments and Suggestions.

DOI: 10.5220/0003170703010306

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2011), pages 301-306

ISBN: 978-989-8425-35-5

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

Table 1: An overview of 18 studies on automatic classiﬁcation of emotions, using biosignals / physiological signals.

information source year signals parti- number ofselection / classiﬁers target classiﬁcation

cipantsfeatures reduction result

Fernandez & Picard 1997 C ,E 24 5 B-W HMM,Viterbi frustration / not 63%

Healey & Picard 1998 C ,E ,R ,M 1 11 Fisher QuadC,LinC 3 emotions 87%-75%

anger / peacefulness 99%

2 arousal levels 84%

2 valence levels 66%

Healey & Picard 2000 C ,E ,R ,M 1 12 SFS kNN 4 stress levels 87%

Takahashi & Tsukaguchi2003 C ,B 10 12 NN,SVM 2 valence levels 62%

Rani et al. 2003 C ,E ,M ,S 1 18 FL, RT 3 anxiety levels 59%-91%

Herbelin et al. 2004 C ,E ,R ,M ,S 1 30 reg,LDA kNN 5 emotions 24%

Takahashi 2004 C ,E ,B 12 18 SVM 5 emotions 42%

12 18 SVM 3 emotions 67%

Zhou & Wang 2005 C ,E ,S , and oth-

ers

32 ? kNN,NN 2 fear levels 92%

Rainville et al. 2006 C ,R 15 18 ANOVA,PCA LDA 4 emotions 65%

15 18 ANOVA,PCA LDA 2 emotions 72%-83%

Liu et al. 2007 C ,E ,M 3 54 SVM 3× 2 levels 85%/80%/84%

Villon et al. 2007 C ,E 40 28 regression model 5 emotions 63%-64%

Rani et al. 2007 C ,E ,M ,S 5 18 FL, RT anxiety scale? 57%-95%

H¨onig et al. 2007 C ,E ,R ,M ,S 24 4×50 LDA,GMM 2 levels of stress 94%-89%

Kreibig et al. 2007 C ,E ,R ,M ,O 34 23 ANOVA PDA fear, sadness, neutral 69%-85%

Liu et al. 2008 C ,E ,M 6 54 SVM 3×2 levels 81%

C ,E ,M 6 54 SVM 3 levels 72%

Conn et al. 2008 C ,E ,M ,S 6 ? SVM,QV-learning3× 2 levels 83%

6 ? SVM 3 behaviors 81%

Cheng et al. 2008 M 1 12 DWT NN,TM 4 emotions 75%

Benovoy et al. 2008 C ,E ,R ,S 1 225 SFS,Fisher LDA,kNN,NN 4 emotions 90%

Signals: C : cardiovascular activity; E : electrodermal activity; R : respiration; M : electromyogram; S : skin temperature; O : Expiratory pCO

Classiﬁers: HMM: Hidden Markov Model; RT: Regression Tree; NN: Artiﬁcial Neural Network; SVM: Support Vector Machine; LDA: (Fisher) Linear Discrim-

inant Analysis; kNN: k-Nearest Neighbors; FL: Fuzzy Logic System; TM: Template Matching classiﬁer; QuadC: Quadratic classiﬁer; LinC; Linear classiﬁer;

Viterbi: Viterbi decoder

Selection: B-W: Baum-Welch re-estimation algorithm; PCA: Principal Component Analysis; SFS: Sequential Forward Selection; ANOVA: Analysis of Variance;

DWT: Discrete Wavelet Transform; Fisher: Fisher projection; PDA: Predictive Discriminant Analysis.

2 REVIEWING AFFECTIVE

SIGNAL PROCESSING (ASP)

ASP is mainly employed in four specialized areas

of signal processing: movement analysis, computer

vision techniques, speech processing, and biosignal

processing (van den Broek et al., 2010). This article

focusses on the last category, which has received very

little attention compared to the other three.

Although studies on AC are sometimes claimed to

be successful, their results are hardly brought to the

market (cf. van den Broek, 2010b). The burden on

ASP no longer lies in the recording and processing of

biosignals. Nowadays, high ﬁdelity, cheap, and unob-

trusive biosignal recordings are easy to obtain and can

even be easily integrated into various products. The

problem lies in the lack of in depth understanding of

the relation between biosignals and our emotions (Pi-

card, 2010; van den Broek, 2010a; van den Broek,

2010b).(Picard, 2010; van den Broek, 2010a; van den

Broek, 2010b).

The review in Table 1 illustrates both the differ-

ences and the similarities between studies on AC. As

this table shows, most studies recorded people’s car-

diovascular and electrodermal activity. However, dif-

ferences between the studies prevail over the simi-

larities. The number of participants varies from 1

to 40, with studies including > 15 participants being

rare, see Table 1. The number of features extracted

from the biosignals also varies considerably: from 5

to 225. Only half of the studies applied feature selec-

tion/reduction, where this would be advisable in gen-

eral.

For AC, a plethora of classiﬁers are used. The

characteristics of the categories among which has to

be discriminated is different from those in most other

classiﬁcation problems. The emotion classes used are

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Table 2: Physiological processes and delay in recording

them through biosignals.

Physiological process delay

Cardiovascular activity (except HR) 30 sec.

Heart Rate (HR) 1 sec.

Electrodermal Activity (EDA) > 2-4 sec.

Skin temperature (ST) > 10 sec.

Respiration 5 sec.

Muscle activity through EMG instantly

Movements / Posture instantly

typically ill deﬁned, which makes it hard to compare

studies. Also, the number of emotion categories (i.e.,

the classes) to be discriminated is small: from 2 to 5

(see Table 1) up to (sometimes) 8 (Picard et al., 2001;

Picard, 2010). Although these are small numbers in

terms of pattern recognition and machine learning, the

results lie behind those of other classiﬁcation prob-

lems. Moreover, it is unlikely that human’s affect can

be described via discrete states. With AC, a large va-

riety of recognition rates is present: 42%–94%; see

also Table 1. In other pattern recognition problems,

only recognition rates of > 90% are reported. Taken

together, this all illustrates the complex nature of AC

and the need to consider prerequisites for ASP.

To bring the prerequisites from theory to practice,

we conducted a post-hoc analysis of the most inﬂu-

ential article on affective computing, and with that on

ASP, so far: (Picard et al., 2001). As is shown in Ta-

ble 3, this revealed pros and cons of this study and

provides valuable directives for future research.

3 TEMPORAL CONSTRUCTION

Among the questions the authors received on their

prerequisites for ASP, a signiﬁcant body concerned

temporal aspects in ASP. In processing biosignals,

temporal aspects are (indeed) of crucial importance.

This importance exceeds the domain of affective com-

puting and holds, in general, for biosignal processing.

Therefore, in this section, we will elaborate on tem-

poral aspects in biosignals and explain and illustrate

why they should be taken into account with ASP.

First of all, people habituate; this is something

ASP has to deal with. For this, it is necessary to track

the number of stimuli that could trigger changes in

emotional state. However,outside controlled (lab) en-

vironments, this requires a true understanding of both

context and user, which is beyond science’s current

state-of-the art (vanden Broek, 2010a; van den Broek,

2010b). An initial approach could be to add a variable

to current models that represents the time or amount

of stimuli that the participants have received so far.

This should help to model a part of the habituation

noise.

Second, another issue is the use of different time

windows. For instance, we can look at parts of > 30

minutes but also at 5, 10, or 60 seconds, see also Fig-

ure 1. Figure 1a provides an EDA signal under in-

vestigation. On the right, Figure 1 presents three time

windows of this signal that surround the event indi-

cated in Figure 1 on the right. The distinct shapes of

the signal within these three time windows perfectly

illustrates the signiﬁcant impact window length has

on calculating a signal’s features, which is conﬁrmed

by their statistics as shown in Table 4. The challenge

lies in the fact that we must estimate when the ac-

tual emotional event occurred and how long it per-

sisted. This is problematic because there is always

a lag between the changes in biosignals and user re-

sponses on the onset of the event; see also Table 2.

In practice, time window selection can be done em-

pirically; either manually or automatically; for exam-

ple, by ﬁnding the nearest signiﬁcant local minima or

making assumptions about the start time and duration

of the emotion.

Third, different psychological processes develop

over different time scales. So, the time window to

be used should depend on the psychological construct

studied. Furthermore, the lag between the occurrence

of an emotion and the accompanying physiological

change differs per signal. For example, heart rate

changes almost immediately, skin conductance takes

2 − 5 seconds, and skin temperature can take even

longer to change; see Table 2. Hence, time windows

should depend on the used signal.

Finally, physiological activity tends to move to a

stable neutral state. So, the more extreme a physiolog-

ical state is, the smaller reactions towards this extreme

become given the same stimulus. Therefore, the pre-

stimulus physiological level should always be taken

into account. This is also known as the principle of

initial values (van den Broek et al., 2010). The prin-

ciple of initial values has shown to be a linear rela-

tionship; hence, it can be conveniently modeled (e.g.,

using linear regression).

4 CONCLUSIONS

This paper provides a response to the three main com-

ments the authors received on their four prerequisites

papers (van den Broek et al., 2010). First, follow-

ing the comments that our review was limited, a re-

view on ASP, complementaryto the previous two (van

den Broek et al., 2010), has been presented; see Ta-

PREREQUISITES FOR AFFECTIVE SIGNAL PROCESSING (ASP) - Part V - A Response to Comments and

Suggestions

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Table 3: Post-hoc analysis of (Picard et al., 2001), using the complete set of eleven prerequisites (van den Broek et al., 2010).

Introduction One of the ﬁrst cases of the application of ASP to the extended monitoring of an individual’s emotional patterns

was (Picard et al., 2001). This was a laboratory study in which a single subject used method acting (i.e., acting and feeling)

to portray eight emotions every morning for approximately one month.

Validity The Clynes (1977) protocol for emotion generation of was adopted. This provided a detailed speciﬁcation of eight

emotions. The subjects kept a record describing the particular aspects of each emotion during each day’s session. However,

only an aggregate label was used to analyze each of the emotions as a group, which decreased the construct validity of the

data, because the actual signal for each day is less accurately described. The construct validity of the expressed emotions

would have been increased if there had been sufﬁcient data to analyze different types of expression of each emotion. The

construct validity is limited by the fact that the emotions were always expressed in the same order and that the length of each

expression was three minutes. Because of this it is unclear to what extent steady state anger is being measured as opposed to

the transition between “no emotion” and “anger”. Ecological validity for this study is conﬁned to lab conditions with seated

subjects. Moreover, there was no automatic way to capture context that was not part of the experiment.

Triangulation Multiple biosignals as well as observation and introspection were used to measure the construct under inves-

tigation. This enabled a rich set of data.

Physiology driven approach The goal of this project was to develop a physiological self-monitoring system that was tailored

to the individual. This paper shows how unique patterns can develop for each of the emotion states. However, these patterns

may be individual-speciﬁc and as such can potentially achieve a higher degree of speciﬁcity than is generally the case.

Signal Processing Contributions This paper puts forward several unique signal processing contributions, including the idea

of using features of the spectrum of respiration for identifying individual emotion patterns and exhaustively combining sets

of features to ﬁnd the optimal differentiating set for this individual. The Sequential Floating Forward Search (SFFS) method

used in the paper is a standard feature selection method and could be applied to the same data set for a different individual,

perhaps allowing the creation of different optimal subsets of sensors for different individuals.

Identiﬁcation of Users This study used data from only one person. So, the models ﬁt very well for this person but are

probably over ﬁtted with respect to other people. Moreover, the authors did not record any speciﬁcs about this user so no

generalization to groups can be made. Therefore, it is impossible to determine which of the features are valid for only this

user as opposed to which might be valid for all users or a group of users. Thus, this study poorly fulﬁlls this prerequisite and

would deﬁnitely have beneﬁted from including more users.

Theoretical speciﬁcation The theoretical speciﬁcation of this study is limited. Although there are references to literature

specifying the theoretical relationship between the signals chosen and various emotions, there is neither discussion on the

reason for choosing particular features, nor an explanation for the success of certain subsets of features in differentiating

between these emotional states, where other subsets fail.

Integration of biosignals Although this study uses advanced signal processing techniques to ﬁnd patterns and evaluate them

across multiple physiological features, it fails to take advantage of known relationships between biosignals and integrate

them at the feature level. The models used had generic feature selection algorithms and do not represent a framework that

theorizes a relationship between multiple biosignals, an appropriate model, data gathering, and model training. Instead,

data was ﬁrst gathered with no particular hypothesis about the relationship between features and an exhaustive search was

conducted, using randomly selected groups of features to ﬁnd the best result. Incorporating features combining respiration

and heart rate variability, or accelerometer and EDA data might have resulted in a higher performance.

Physical characteristics Equipment and materials used are summarized. However, information on the type of electrodes

used (wet or dry), the size of the EMG electrodes, and the position of the location of the EDA electrodes are omitted. In

addition, the temperature and humidity of the environment are not reported, although it must be noted that the authors deal

with this by normalization of the data per session per participant. This makes it hard to judge whether or not these results

can be generalized to different situations, and how the models would work with small wearable sensor devices.

Historical perspective Previous work on signal processing and pattern recognition is discussed, in particular that in relation

to affective computing. In contrast, little attention was given to the rich history of both psychophysiology and emotion

research. However, in more recent work, (Picard, 2010) stresses the importance of this prerequisite.

Temporal Construction The subject was trained in acting and visualization techniques and could probably continue to

evoke the emotion consistently for the three minute period. Considering the fact that emotions are generally much shorter

than three minutes, the actor had to re-express each emotion several times within the three minutes. This will deﬁnitely

have led to habituation and created noise in the biosignals. Moreover, there was no gap to allow the subject to fully recover

between emotion states. With the analysis, the authors tried to minimize this effect by taking only the later part of each

emotion period, but EDA recovery times can be quite long (>15 minutes); so, this may still represent a temporal transition.

Instead, adjusting for the initial value with regression would probably have been a more successful solution.

Baselining Because this work studied only one user, baselining over different users was not necessary. However, they did

ﬁnd large inter-day differences and used baselining to normalize for those. Speciﬁcally, they tried two approaches that were

both successful, which shows how important baselining is for ASP.

Conclusion By applying the prerequisites to a post-hoc analysis of the seminal work of Picard et al. (2001), we have

illustrated their use. Some of the prerequisites were already applied successfully by the authors, while others were neglected.

By taking into account all of the prerequisites, we are sure that, even post-hoc, the results can be improved.

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3 3.1 3.2 3.3 3.4 3.5

500

1000

1500

2000

2500

3000

3500

Event in a 30 Minute Window

Hours

GSR

Event

0 10 20

3280

3290

3300

3310

3320

3330

3340

5 Seconds

GSR

0 20 40

3280

3290

3300

3310

3320

3330

3340

3350

3360

3370

3380

10 Seconds

Looking Through Different Windows

0 100 200

2600

2700

2800

2900

3000

3100

3200

3300

3400

3500

1 Minute

Figure 1: A 30 minutes time window of an EDA signal with three close-ups surrounding an ‘event’ (denoted as such).

ble 1. Second, a post-hoc analysis was conducted

of the seminal work of (Picard et al., 2001), as pro-

vided in Table 3. This analysis illustrated the use of

all eleven prerequisites in practice. Third, a more de-

tailed discussion on temporal aspects of ASP was ini-

tiated, since the authors received many comments on

this prerequisite. This extended elaboration on this

prerequisite includes a concise overview of the lag of

biosignals; see Table 2. In addition, the impact of

choosing the time window was illustrated both by Fig-

ure 1 and by the accompanying statistics as provided

in Table 4.

The additional review, the post-hoc analysis of Pi-

card et al. (2001), and the additional discussion on

temporal aspects of ASP all illustrate both the com-

plexity and lack of success of AC. This makes one

wonder whether or not affective computing can pay

off its promises. The bottleneck is not the technique

but our lack of understanding of affective signals (Pi-

card, 2010; van den Broek, 2010a; van den Broek,

2010b). We hope that these prerequisites can initiate

a ﬁrst step towards rethinking ASP.

Table 4: Standard statistics on the three time windows of an

EDA signal, as presented in Figure 1 (right).

Statistic Time window

5 sec. 10 sec. 60 sec.

mean 3314 3312 3083

standard deviation 19 23 217

slope 43 -69 697

ACKNOWLEDGEMENTS

Egon L. van den Broek acknowledges the support of

the BrainGain Smart Mix Programme of the Nether-

lands Ministry of Economic Affairs and the Nether-

lands Ministry of Education, Culture and Science.

Further, we thank the three reviewers for their con-

structive suggestions on and Lynn Packwood for

proof reading of a previous version of this article.

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