Towards Estimating Emotions and Satisfaction Levelof Tourist based on Eye Gaze and Head Movement
Dmitrii Fedotov∗ , Yuki Matsuda†‡ , Yuta Takahashi† , Yutaka Arakawa†§ , Keiichi Yasumoto† , Wolfgang Minker∗∗ Institute of Communications Engineering, Ulm University

Albert-Einstein-Allee 43, 89081 Ulm, Germany
Email: {dmitrii.fedotov, wolfgang.minker}@uni-ulm.de† Graduate School of Information Science, Nara Institute of Science and Technology
8916-5 Takayama-cho, Ikoma City, Nara, Japan
Email: yukimat.jp@gmail.com, {takahashi.yuta.to2, ara, yasumoto}@is.naist.jp‡ Research Fellow of Japan Society for the Promotion of Science§ JST Presto, Japan
Abstract—Following the increase in demand for “smart
tourism,” various tourist information becomes available. Current
tourist guidance systems can provide many possible routes
around the city, which are usually based on an optimal distanceand time or popularity of the place. To design more enjoyable
tourist routes, we should be aware of tourist’s perception of theurban environment. In this paper, we propose the methodology of
estimating the tourist emotions and satisfaction level by analysing
physiological features, such as head movement and eye gaze.
Features derived from raw sensor data have the correlation up to
0.58 with emotion and satisfaction labels. This study also shows
the differences in feature/label dependencies between different
touristic areas and highlights the challenges of tourist satisfaction
estimation.

I. I NTRODUCTION
Due to the ubiquity of smart devices, including smartphones
and wearable devices, various real-time urban environment
information (e.g., congestion degree, roadway traffic volume)
becomes available everywhere in the city. This information
helps to provide valuable tourist guidance for people, called“Smart Tourism” [1], [2]. Since the emotional status andsatisfaction level differ across different users and even for thesame user at a different time, the user status (e.g., emotions
and satisfaction level) needs to be taken into account to design
more context-aware tourist guidance systems.
To collect data on tourist emotions and satisfaction level, online user reviews and questionnaires, such as TripAdvisor1 , arewidely used. However, it is difficult to keep users motivated towrite the reviews, especially with medium rating values. Thus,
to provide reliable information for tourists, it is necessary tocollect quantitative data without user reviews. The aim of ourresearch is to be able to estimate context effectively with interand intra-user status information.
Many related projects try to estimate emotion/satisfactionlevel of the people with various methods [3]–[7]. However,many of those have restrictions for applying to the real-world,such as data comprehensiveness and estimation accuracy.
In this paper, we propose the method of estimating tourist
emotions and satisfaction level based on observation of the
1 https://www.tripadvisor.com/

tourist behavior during the sightseeing by using wearable
devices. To describe tourist behavior, we focused especiallyon eye gaze and head movement. The sense of sight is oneof the most important sensory systems on sightseeing, and it
can be tracked as an eye gaze using cutting-edge technologies.
Also, due to the directivity on the sense of sight and hearing,
the head movement can be affected by them. Furthermore,
intelligent eyewear devices which can measure the eye gazeand head movement will be available as a consumer productin the near future.
Thus, we conducted preliminary experiments in the realworld conditions to confirm the feasibility of estimating emotion/satisfaction level from physiological features. As the experimental field, we have selected two touristic areas: locatedin Ulm (Germany) and Nara (Japan), which have completely
different conditions. As a result, we have found a correlation
between physiological data and emotion and satisfaction leveland proved the feasibility of such research. The relationshipbetween features and label can differ, depending on the characteristics of sightseeing area.
II. R ELATED WORKIn current guidance systems, online user reviews and questionnaires are still widely used to collect tourist emotions
and satisfaction level. TripAdvisor collects 5-star rating andcomments from tourists as the user reviews about sightseeing
spots. In data collecting methods that asks tourists directly,
the motivation of posting reviews is essential to guarantee
the quantity and quality. However, it also has the risk of
skewing the evaluation due to the imbalance of reviewer’s
ratings. Many research adopt the questionnaire-based survey
for measuring the tourist satisfaction [3]. However, methods
relied on the questionnaire have problems in sustainability andspatial coverage of the survey.
To know user’s emotion status, sensing technologies areoften discussed. Resch et al. proposed the emotion collecting
system for the urban planning, called “Urban Emotions” [5].
The paper describes that wrist-type wearable devices andsocial media were used for emotion measurements. However,III. E STIMATION METHOD OF T OURIST E MOTIONS AND
S ATISFACTION L EVEL
In this study, we propose the method to estimate tourist
emotions and satisfaction level by observing the tourist behavior with wearable devices during the sightseeing continuously.
A. Workflow
Our goal is to comprehensively estimate tourist emotions
and satisfaction level in the cities. We expect that thosecan be extracted from tourist’s various behaviors appeared
unconsciously during a tour.
Fig. 1 shows the basic workflow of our proposed approach.
The details of each step is explained as follows:
Step 1 - Split the whole tour into sessions: In the tourism
domain, it is assumed that a tourist typically requests toprovide the guidance information for each sightseeing spot.
Thus, as the first step, we split the whole tour into the small
periods (sessions) which include at least one sight.
Step 2 - Sensing and ground truthing: The tourists wear
the wearable devices during a sightseeing tour. Those collecttourist behavior during the whole sightseeing continuously.
At the end of each session, the system asks tourist about
current emotions and satisfaction level and records theseinformation as the ground truth. Then, the tourist repeats thesame procedure for each session of the tour. Details of featuresand ground truth are described in the following section.
Step 3 - Building the estimating model: In our future
research, we will build on estimation model of emotions
and satisfaction level, based on tourist behavior features andground truth data.
B. Features
In this study, we used physiological features, as they proved
their usefulness in emotion recognition systems and can becollected continuously from the natural behavior [8]–[11].
Physiological features can be separated into several groups:
heart-related (electrocardiogram, heartbeat), skin- and bloodrelated (electro dermal activity, blood-pressure), brain-related

Step 1: Split whole tour into “session”
Place B

Place CsionSpot 2Sesthis approach relied on an assumption that posts on the social
media are written in-situ. As the other approach, emotion
recognition system based on the acoustic features via dialoguesystem on the mobile device have been proposed [6]. However, such method has not been able to achieve a realistic
accuracy yet. Shapsough et al. describes that emotions can berecognized by using typing behavior on the smartphone [7].
This approach uses machine learning technique and induced
high accuracy on emotion recognition. Yet it is not feasible
to frequently ask users to type on the smartphone during
the sightseeing tour. Ringeval et al. proposed to introduce
physiological features in addition to audio-visual and to build amultimodal system that relies on combination of them [8]. As
the physiological features, electrocardiogram and electrodermal activity were used. Physiological features have provided
lower performance and weaker correlation than audio-visualwith continuous emotional labels, but helped to increase theoverall performance of multimodal system.

Spot 3

Spot 1G

S
Place D

Place A

Place X

Step 2: Sensing & ground truthing for each session
Place A
Wearable
devices

Place B

Place C

Place X

Groundtruthing

Smartphone

time
Sensing
Ground truthing
Session 1

Session 2

Session N

Step 3: Building the estimating model of emotions/satisfactoin level
Tourist behavior features

Ground truth70%

Eye gaze

Head movement

Emotions

Satisfaction level

Building deep recurrent model etc.

Fig. 1. Workflow to estimate tourist emotions and satisfaction level(electroencephalography), eye-related (eye gaze, pupil size)
and movement-related (gestures, gyroscopic data). Some of
the features are easily covered in the real-life conditions, e.g.,
heartbeat and skin response can be collected by smart watches
and other wearable devices; other can be measured only inthe laboratory environment, e.g., electroencephalography; andsome of them can be hard to use in the real-life scenario at
the moment, but it may become much easier in the nearest
future, e.g., eye-movement with wider usage of smart glasses.
In this study, to describe the tourist behavior, we focused
especially on eye gaze and head movement. The sense of sightis one of the most important sensory systems in sightseeing,
and it can be tracked as an eye gaze using current wearable
devices and technologies. Other sensory systems: the sense ofhearing, smell, taste and touch are difficult to observe directlyfrom outside of a body. However, due to the directivity on
the sense of hearing (and sight), the head movement canbe affected by them. Thus, we used the head movement asfeatures in addition to the eye gaze.
In the context of our study, we used three devices torecord the features in real-time: Android smartphone (GPSdata, accelerometer data, gyroscope, magnetic field), mobile
eye tracking headset Pupil with two 120 Hz eye cameras

Pupil(Eye gaze)

Smartphone(Ground truth)

Senstick(Head movement)

Fig. 2. Devices used on preliminary experiments: Pupil [12], SenStick [13]and smartphone[12] (eye gaze, pupil features) and sensor board SenStick[13] mounted on an ear of eye tracking device (accelerometer,gyroscope, magnetic field, brightness, UV level, air humidity,
temperature, air pressure) shown in Fig. 2.
In near future, intelligent eyewear devices which can measure the eye gaze and head movement will be available as aconsumer product.

Fig. 3. Android application for collecting ground truth data of Satisfaction
Level (left) and Emotion Status (right)

AROUSED
ACTIVE / AROUS

Angry

We conducted preliminary experiments in real-world conditions to find the dependencies between features and labels.
As the experimental field, we selected the two touristic areas
depicted in Fig. 5, which have completely different conditions.
The first one is the center of Ulm, Germany. The sights inthis area include particular buildings as well as the walking
routes with high touristic value (e.g., Fisherman’s Quarter). As
it is located in the city center, the sights are surrounded by
the common city buildings and may be crowded, dependingon the time. The approximate length of the route is 1.5 km
divided into eight sessions. The second area is the Nara Park,

ExcitedAnnoyed⓪

ArousalFrustrated⑤

Delighted

Happy
Valence

Distrustful

NEGATIVE

IV. P RELIMINARY E XPERIMENTS AND R ESULTS

AlarmedAfraidArousedDistressed

C. Ground truth
We collect ground truth data by using the Android application shown in Fig. 3. Tourist can manually enter the ratingsof the session (labels) using two scales: the emotional status
and satisfaction level.
Satisfaction level: The tourists can choose the current
satisfaction level between 0 (fully unsatisfied) and 100 (fully
satisfied). A neutral satisfaction level is 50 and it should
approximately represent the state of the participant at the
beginning of the experiment.
Emotion status: To represent emotion status of tourists, we
adopt two-dimensional map defined on Russell’s circumplex
space model [14]. Fig. 4 shows the representation of theemotion status. We divided this map into nine categories
as follows: excited (0), happy/pleased (1), calm/relaxed (2),sleepy/tired (3), bored/depressed (4), disappointed (5), distressed/frustrated (6), afraid/alarmed (7) and neutral (8).

⑦⑥PleasedDissatisfied④

POSITIVE

NeutralDisappointed⑧①

Depressed
RelaxedRelaxed
Calm
Calm

Bored
③

Tired Sleepy②

PASSIVE / CALM

Fig. 4. Two-dimensional emotion status model. Figure taken from [14], [15]the historic outskirts of Nara, Japan. The route through thearea includes many temples and shrines, it is located in natureand has no distraction from the sights included in the sessions.
The approximate length of the route is 2 km divided into sevensessions.
Participants were asked to follow the described routes andtake as much time as they need to see the sights. During thesessions, we recorded the features described in Section III-B
in real time. At the end of each session, participants rated itusing emotion and satisfaction scales.
In this experiments, nine participants were asked to make ashort sightseeing tour. The meta information about participantsis the following: six participants are in Ulm (five male, one
female) and three participants are in Nara (three male). For
some participants, one or several sets of features can be
missing due to technical problems.

Session 2

Session 4

Ulmer Münster

Session 1

Bell Tower

Ulmer Weihnachtsmarkt(Christmas Market)

Session 5

Session 3

Hokke-doArea

Todaiji temple(Great Buddha Hall)

START

Session 2

GOAL

Kagami-ike Pond

Session 3(Mirror Pond)

Ulmer Rathaus(City Hall)

Session 4

Session 1

Stadtbibliothek Ulm

Nandaimon(City Library)

Session 6(Great South Gate)

Nara Park
Area

Session 7

Session 8

Fischerviertel

Session 6(Fisherman’s Quater)

Stadtmauer(City Wall)

Sansha-ike
Pond

Session 5

Metzgerturm(Butcher’s Tower)

START
GOAL

Session 7

Schiefes Haus
(Crooked House)

Ground truthing point© OpenStreetMap contributors

100 m(a) Experimental field in Ulm, Germany© OpenStreetMap contributors

Ground truthing point

100 m(b) Experimental field in Nara, Japan

Fig. 5. Experimental field

A. Eye gaze features

B. Head movement features
To collect head movement, we used sensor data of accelerometer and gyroscope from SenStick [13]. Both sensors
have three axes: X-axis, Y-axis and Z-axis. In the experiment,
the sampling frequency of both sensors is set to 50Hz to detecteven slight head motion. Accelerometer and gyroscope ranges
were set to 2G and 250dps respectively.
From sensor data, we can get two types of features: headtilt and state. Head tilt feature is the number of head tilt suchphi [rad]To find dependencies between eye gaze features and labels,
we use several features derived from the raw output data,
extracted directly by Pupil Labs eye tracker. We used thetaand phi values, which represent the normal of the pupil as 3D
circle in spherical coordinates. Hence we can use only twovariables to describe the position of pupils and thus eye gaze.
The mean values of these variables differ across users anddepend on the physical setting of a camera and eye peculiarity.
Therefore, they should not be generalized to different users.
Eye gaze data was analyzed using two methodologies: (1)mean, minimum and maximum values for theta and phi were
calculated for each participant; four levels (20, 40, 60, 80%)
were set for the range [mean, min(max)] as shown at Fig. 6
and then used to count percentage of time outside each level
per session (feature F1 in Table I); (2) standard deviation oftheta and phi was calculated for small window of recorded
data and the values corresponding to the same session wereaveraged (feature F2 in Table I). The following window sizes
were used: 1, 5, 10, 20, 60, 120, 200, 300 seconds with theoffset of 31 of the window size.

Up

Lv.4
Lv.3
Lv.2
Right

Lv.1
Left

Down
theta [rad]Fig. 6. Eye gaze in spherical coordinates

as up/down or left/right and it can be detected from gyroscope
due to its rotation motion. First, we calculated average µ andstandard deviation σ of gyroscope values of each axis i andset the upper and lower thresholds with following equations:ψupper,i = µi + 2σi(1)ψlower,i = µi − 2σi(2)Then, we checked windows with the size of one second andcounted the number of gyroscope value of which axis is aboveψupper,i or below ψlower,i as a head tilt. In our condition, Yaxis indicates looking up/down motion and Z-axis indicateslooking left/right motion.
Head state feature is the time while keeping head to lookup, down or front. It can be easily distinguished from anaccelerometer. Features of looking left/right can be extracted

TABLE I
R ESULT OF CORRELATION ANALYSIS BETWEEN SATISFACTION LEVEL AND FEATURES

Category

Feature ID

F1
Eye gaze1
F2
Head movement2
1

F3

FeatureUlm, Germany

Nara, JapanAllR

p-valueR

p-valueR

p-value

Lv.1

0.294

0.077

0.171

0.558

0.315

0.025

Lv.2

0.344

0.037

0.243

0.403

0.284

0.043

Lv.3

0.378

0.021

0.086

0.771

0.384

0.005

Lv.4

0.206

0.221

0.116

0.894

0.228

0.107

theta

0.376 (60 sec)

0.020

-0.584 (10 sec)

0.028

0.259 (10 sec)

0.064

phi

0.290 (10 sec)

0.077

-0.258 (5 sec)

0.372

0.235 (10 sec)

0.094

left/right

0.470

0.007

0.159

0.491

0.098

0.486

up/down-0.4710.007-0.5030.020-0.5280.000

38 sessions, 5 participants (Germany) and 14 sessions, 3 participants (Japan)

in the case of head tilt, whereas it is difficult to extract it in thecase of the head state. To distinguish head state, we used theaxis parallel to the face direction (Y-axis). Similarly to head tilt
feature process, first, we checked the one second windows andcalculated the average of values on Y-axis. Then we used thevalues of Y-axis to detect looking up (above ψupper,Y ), down
(below ψlower,Y ) or front (none of the conditions) actions.
These thresholds are experimentally determined as follows:ψupper,Y = 0.4 and ψlower,Y = −0.4. The threshold values
indicate the head tilts about 24 degrees.
C. Results of preliminary experiments
At first, we analyzed the relationship between tourist satisfaction level and features by using Pearson correlation coefficient. Table I shows the results of correlation analysis.
The significant results are highlighted with bold font. The
number in brackets for feature F2 indicates window size,
which provides the corresponding result, as the best among
other options. Due to the technical issues as detailed in next
section, we could not use several sets of features for someparticipants.
Interestingly, we confirmed that some of the features correlate to tourist satisfaction level regardless of sightseeing
area. The most correlated features are: F1–Lv.3 (R = 0.384,
p < 0.05) and F3–up/down (R = −0.528, p < 0.05).
In contrast, we found that some features are strongly
affected by differences of sightseeing areas. For example,
F2–theta represents the standard deviation of horizontal eye
movements and it has the positive correlation with satisfactionlevel in the case of city center sightseeing in Ulm, Germany(R = 0.376, p < 0.05), but the negative correlation in the caseof historic outskirts sightseeing in Nara, Japan (R = −0.584,
p < 0.05). F3–left/right in Germany has the correlation withsatisfaction level (R = 0.470, p < 0.05), however thiscorrelation cannot be observed when we use features of botharea (R = 0.098, p > 0.05).
We also analyzed the relationship between tourist emotions
and features. Since we did not have much data, we categorized
all emotion labels into 2 emotion groups: positive and negative

2

32 sessions, 4 participants (Germany) and 21 sessions, 3 participants (Japan)(in the context of touristic application). According to Fig.
4, the emotion labels 0–2 belong to positive and emotion
label 3–8 belong to negative emotion group. Fig. 7 shows
the distribution of feature values among emotion groups fordifferent sightseeing areas. The statistical significance wascalculated using Student’s t-test analysis at the 10% confidence
level.
We found significant differences in feature values between
two emotion groups (indicated as red color at Fig. 7). In mostof the cases, we can use the derived features to distinguishemotion group of each session. However, we haven’t founddifferences in data of Japanese sessions (Fig. 7–(a) and (c)).
The most probable reason is the lack of data; therefore, weshould collect more data and confirm the relationship in the
future research. Also, we found the same effect of the F2–theta on the emotional label as on satisfaction level. The
dissimilarity in the behavior during the sightseeing session indifferent areas makes it necessary to distinguish the area typewhen eye gaze features are used.
To understand the emotional state of tourist more precise,
we need to recognize emotions in the positive group. Thus, weinvestigated the differences between emotions which belong
to the positive group. Fig. 8 shows F1–Lv.3 (eye gaze) andF3–up/down (head movement) features for each emotion in
the positive group. We found significant differences between
exciting (0), happy (1) and calm (2) indicated as red color.
This result indicates the potential possibility that eye gaze andhead movement features can be used for emotion recognition
during the sightseeing.
V. C ONCLUSION
Following the increasing usage of smart devices, the intelligent tourist guidance is required as well. To design moreenjoyable tourist routes, we must consider emotions and satisfaction level in a viewpoint of tourists. In this study, we havedeveloped a methodology to estimate emotions and satisfactionlevel based on continuous observation of the tourist behavior
with wearable devices during the sightseeing. Preliminary
experiments have proved the feasibility of such research asp=0.0011.1

p=0.3564
3
2
1
positive

negative

Germany

positive

negative

positiveJapan

negativeAll(a) Relationship between emotion groups (eye gaze feature F1 - Lv.3)

F3 - up/down [count/sec]

p=0.132

50

p=0.036p=0.008

p=0.001F1 - Lv.3 [%/session]

F1 - Lv.3 [%/session]766
p=0.0024

20

p=0.063p=0.121F2 - theta [rad]0.2

p=0.307

0.5

Happy
(1)

Excited
(0)

calm
(2)

All

Happy
(1)

calm
(2)

All

(b) Head movement feature
F3 - up/down

p=0.011R EFERENCES

0.15
0.1

positive

negative

Germany

positive

negative

positiveJapan

negativeAll(b) Relationship between emotion groups (eye gaze feature F2 - theta)1.2
p=0.162p=0.017

F3 - up/down [count/sec]

0.7

Fig. 8. Relationship between emotions in the positive group

0.25

0.05

0.9

0.3
Excited(0)(a) Eye gaze feature F1 - Lv.3
0.3

p=0.020

p=0.012

1
0.8
0.6
0.4
0.2
positive

negative

Germany

positive

negative

positiveJapan

negativeAll(c) Relationship between emotion groups (head movement feature F3 - up/down)

Fig. 7. Relationship between two emotion groups

well as revealed the challenges, that have to be faced whilesolving a task of tourist satisfaction estimation. As a result,
we have found a moderate correlation between physiologicalfeatures and emotion and satisfaction level of a tourist. This
correlation may be positive as well as negative, dependingon the type of sightseeing area. In our future research, wewill conduct more experiments to collect additional data andwill build a prototype of tourist satisfaction estimation system
based on machine learning techniques.
ACKNOWLEDGMENT
This work was supported by JSPS KAKENHI Grant Numbers 16J09670 and 16H01721.

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