=Paper=
{{Paper
|id=Vol-2092/paper5
|storemode=property
|title=A Pilot Study of Emotion Detection using Sensors in a Learning Context: Towards an Affective Learning Companion
|pdfUrl=https://ceur-ws.org/Vol-2092/paper5.pdf
|volume=Vol-2092
|authors=Haeseon Yun,Albrecht Fortenbacher,Niels Pinkwart,Tom Bisson,Fadi Moukayed
|dblpUrl=https://dblp.org/rec/conf/delfi/YunFPBM17
}}
==A Pilot Study of Emotion Detection using Sensors in a Learning Context: Towards an Affective Learning Companion==
https://ceur-ws.org/Vol-2092/paper5.pdf
Carsten Ullrich, Martin Wessner (Eds.): Proceedings of DeLFI and GMW Workshops 2017
Chemnitz, Germany, September 5, 2017
A Pilot Study of Emotion Detection using Sensors in a
Learning Context: Towards an Affective Learning
Companion
Haeseon Yun1, Albrecht Fortenbacher2, Niels Pinkwart3, Tom Bisson4 and Fadi
Moukayed5
Abstract: Emotions facilitate knowledge attainment and also affect learners on their current
behavior and future choice. Sensors which detect physiological signals have been studied and related
to emotions, and specifically electro dermal activity (EDA) and heart rate variability (HRV) have
been adopted to detect emotion. In this pilot study, we have presented visual emotional stimuli to 6
participants and attained their ratings on a picture. Their EDA and HRV values were recorded and
investigated to find any relation between the stimulated emotion, self-assessment and physiological
signals. The study explored EDA and HRV signal changes due to the visual stimuli and some signal
changes in EDA were observed, when joyful or satisfied pictures were presented. However,
limitations need to be overcome to provide clearer interpretations. A future study on providing
awareness to learners using a learning companion is suggested.
Keywords: sensor based learning, learning companion, learning analytics, adaptive learning,
emotion detection, IAPS
1 Introduction
Learning Analytics for Sensor-Based Adaptive Learning (LISA) 6 is a research project
aimed at improving learner support through the use of sensor data. Specifically, the
research to bring together user-centric learning analytics, analysis of sensor data indicating
the emotional state of a learner and adaptive feedback as a learning support is being
progressed to provide solutions for sensor-based adaptive learning. Furthermore, the
developed solutions will be integrated into learning environments and products.
In the pilot study presented in this paper, we aimed at exploring emotion detection using
sensors and tried to interpret the data attained by participants. We first discuss the role of
emotions in a learning context, classify emotions in four academic emotions, collect
1 HTW Berlin, Campus Wilhelminenhofstraße 75A, 12459 Berlin, yun@htw-berlin.de
2 HTW Berlin, Campus Wilhelminenhofstraße 75A, 12459 Berlin, forte@htw-berlin.de
3 HU Berlin, Institut für Informatik, Rudower Chaussee 25, 12489 Berlin, niels.pinkwart@hu -berlin.de
4 HTW Berlin, Campus Wilhelminenhofstraße 75A, 12459 Berlin, tom.bisson@student.htw-berlin.de
5 HTW Berlin, Campus Wilhelminenhofstraße 75A, 12459 Berlin, fadi.moukayed@student.htw-berlin.de
6 http://tel.f4.htw-berlin.de/lisa/
Haeseon Yun et al.
physiological data using sensors and interpret empirical data to relate with emotions in a
learning context.
2 Detection and Interpretation of Emotions in a Learning Context
Emotion plays a navigating role in cognitive knowledge attainment and it affects the
outcomes of learners’ behaviors and even future choice on actions [IF10]. For successful
learning, providing an environment where learners feel emotionally supported is important
so that both emotional and cognitive learning can take place [GS08]. One way to provide
an emotionally supportive environment is to provide awareness of his or her own
emotional state to a learner so that the information can encourage him or her to modify his
or her emotional state [Bu06]. For instance, if a learner is in a negative emotional state
such as frustration or boredom, knowing his or her current state can help a learner to make
a decision to break out from the state.
The research in autonomic response in emotion examines various physiological signals
such as heart rate, heart rate variability, respiratory rate, electro-dermal activity to detect
positive (happiness, contentment, joy, peacefulness, and calmness) and negative (anger,
disgust, fear, sadness, surprise, fear, depression, boredom, embarrassment) emotions
[Kr10] [CM15] [Fa09]. However, applying outcomes of the studies in general emotion
may not be applicable in a learning context since the emotional states interested in learning
context are different from general emotions [Wo09]. For instance, intrinsic motivation,
state of flow [Cs90] are the emotional states that are positively related to successful
learning yet, classifying these states based on the previous studies of emotion is not simple.
Considering emotion in a learning context, Pekrun and colleagues [Pe02] focused on
academic emotion which is consisted of the positive emotions which are enjoyment, hope,
pride and relief and the negative emotions such as anger, anxiety, shame, hopelessness
and boredom.
As vast research in sensors to detect physiological changes relation to emotion has been
conducted in the field of autonomic response and the emotional state in a learning context
is investigated in education and educational psychology, the commonly used dimensions
of emotions [Wo09] were selected. Specifically, four emotions (joyful, satisfied, angry,
and bored) which are related to academic emotions were used for this study as follows:
● Positive valence and high arousal: excited and joyful
● Positive valence and low arousal: concentrated or satisfied
● Negative valence and high arousal: frustrated or angry
● Negative valence and low arousal: bored and tired
Valence refers to a range from happiness to unhappiness whereas arousal pertains to a
range from excitement to calmness. For instance, when a person is being surprised by a
A Pilot Study of Emotion Detection using Sensors in a Learning Context
nice gift, he or she can be excited (high arousal) and happy (positive valence) whereas
when one is being scared by someone, one can still be excited (high arousal) but angry or
unhappy (negative valence).
To detect emotions in an experiment setting, emotional stimuli are applied to learners and
self-reports on the emotional stimuli are used to relate the emotional stimuli with specific
emotions. For instance, Lang and colleagues [LBC08] presented a set of emotional
pictures as emotional stimuli and asked participants to indicate their levels of emotions.
Additionally, hardware sensors are also adopted to associate physiological changes during
the presence of emotional stimulus with specific emotion. For example, a camera was used
to detect head position and movement to relate to emotion [Wo09] and various researchers
advocated the feasibility of motivation detection using hardware sensors including camera
and other physiological sensors such as EEG, EMG, electro-dermal activity sensors and
heart rate sensor [MA07] [BCF08] [BCF07] [Bu06] [CM09] [Ar09] [AFR11] [DG10].
Specifically, heart rate variability is shown to tell when the given task induces stress
[YH08] and skin conductance and electro-dermal activity describes changes in emotion
[Kr10] [Mc12].
Even though there are many attempts from previous mentioned studies using sensors to
detect emotion, relating a specific emotion with sensors data in a learning context is still
in its explorative state. Therefore, in this study we have conducted a pilot study to
investigate further into sensor data in relation to emotion, specifically academic emotion.
3 A pilot study
3.1 Materials and Methods
As wearable sensors can measure physiological changes which then can elaborate
emotional state, we have replicated [LBC08] experiment in addition to using physiological
measurement of EDA and ECG. The purpose of the pilot study was to investigate
physiological signals based on the founded experiment and to further make improvements
for the future study when integrating sensors in an emotional stimuli experiment setting.
In this pilot study, we used 58 IAPS (International Affective Picture System) pictures and
each participant wore ECG and EDA sensors. The total number of six subjects participated
in this pilot study. The participants were selected based on their willingness and
availability. Participants of the studies were introduced with the purpose of the study as to
relate physiological studies with emotional pictures. Physiological signals were measured
using pre-gelled electrodes (ECG on left part of their chest or collarbone and EDA on two
fingers) and the signals were verified visually using real-time online visualization tool
before conducting the experiment. Similar to the original study, all participants were
explained to look at the picture and rate the Self-Assessment Manikin (SAM) [LBC08]
which includes three dimensions of emotions. To clarify three dimensions, example slides
Haeseon Yun et al.
were provided as figure 1. When a participant understood what to do during this
experiment, he or she began the experiment and it took around 30 minutes.
The experiment was consisted of 58 sessions. Each session was consisted of a prompt
(“Get ready for the next slide”), one of 58 emotional pictures and a SAM rating scale for
self-report. The participant was prompted to prepare for the next picture for a duration of
five seconds. Then the picture was displayed for six seconds followed by the screen with
the 9-point scale SAM ratings. The rating process was accomplished by clicking on radio
buttons associated with the three SAM scales for valence, arousal and dominance. A
countdown timer was located on the bottom of the rating page to inform the remaining
time to encourage a participant to make his or her selection.
Fig. 1: One session with a prompt, a visual stimulus and a rating
The hardware used for the experiment was the Bitalino Plugged Kit with various pluggable
sensors (EMG, EDA, ECG and EEG), a status LED and Bluetooth communication
channel. For the purpose of our experiment, only ECG and EDA sensors were utilized and
the kit was enclosed by the 3D printed casing also manufactured by Bitalino as shown in
the figure 2. The digitized signal data had a resolution of 10 bits and was transmitted to
the receiver via Bluetooth at a sampling rate of 1000 Hz.
Fig. 2: Bitalino Kit with ECG and EDA sensors plugged in
In order to acquire the subject’s ECG activity, sticky pre-gelled electrodes were attached
to the participant’s chest or collarbone and waist. Similarly, two other electrodes were
positioned on the participant’s index and middle fingers in order to detect his/her EDA
activity. The ECG/EDA signals were transferred via Bluetooth 2.0 + EDR to a PC running
A Pilot Study of Emotion Detection using Sensors in a Learning Context
the OpenSignals (r)evolution signal acquisition and recording software (Fig. 2, left), which
is used for capturing and storing the signals. The recorded ECG/EDA signal data was then
post-processed using the Python-based signal processing toolkit BioSPPy in order to
supplement it with the heart rate variability (HRV) data and more accurate timing
information. Specifically, P. S. Hamilton’s QRS detection algorithm [Ha02] was used to
compute the HRV from the ECG signal. The visualization of the ECG and EDA time-
series, as well as the RR-Tachogram of the HRV data were performed by a custom
visualization utility written for this purpose (Fig. 3 right). The timestamps of each prompt-
picture-SAM loop with picture number and marked SAM raiting was stored in a CSV file.
Fig. 3: OpenSignals recording session (left), ECG/EDA/HRV visualization utility (right)
3.2 Results
Three physiological signals (ECG, EDA and HRV) along with self-report response value
were collected from the six participants. The analysis of the collected data took two
parallel approaches. First, participants’ self-report response on presented pictures were
classified into 4 dimensions of emotion (joyful, satisfied, angry and bored) and compared
with the value from the original study we have replicated. We were able to attain all six
participant’s response on their perceptions on presented pictures.
To classify pictures into four emotions, self-report values that the subjects made were
taken into consideration. Out of 9 scale, when the self-report value for a given picture was
higher than 6, it is considered high and when the value was lower than 4, it was considered
low. When the value falls between 4 to 6, it was marked with multiple emotions. For
instance, when a picture was marked as 8 in arousal and 2 in valence, the picture was
classified as angry and when the picture was perceived as 4 in arousal and 8 in valence, it
was marked as satisfied/joyful based on the dimension described in section 2. As pictures
with intermediate value is difficult to interpret at this stage, we have focused on the
pictures with extreme values (high valence with low arousal, high valence with high
arousal, how valence with high arousal and how valence with low arousal) as in the Table
1.
Haeseon Yun et al.
Subject/ Original
A B C D E F
Emotion Study
5450,5910 1750,2190 5950,7430 1710,1920 2780,5950 5480, 8620,8490
8260,8490 7330,7090 7390,5450 8620,8510 1710 1750,5950
8510 5480,8620 2070,7400 2810,5450 5450
2660,1710 2650,1920 1710,8490
Joyful 2070,7170 1920,5910
8260,5450 7510
2320,1920
1300
5020, 7410, None 7510,
7410, 2660, 7510
5030 7430, 5450
2650, 1750,
7510 2190, 7390,
2660, 5020,
7430, 7250
Satisfied 2120,
7150,
5020,
5030,
1750
3530,6230 3100,2130 3100,3500 3100,9480 3530,1300 3100,3230 3100,6230
3100 6370,2280 9421,6300 7380,6300 1120,6230 6300,9480 7380,3230
6230,3500 3530,6230 6370,3230 7380,3280 9050,6370
3230,2120 1280,2890 7380,6230
Angry 3280,7380 3500,1300
9050,1930 1040
2650,3500
6300
3230 - - 2780,5950 3100,3230 1920,2280 1300,2120
1040 7080,9421 3280,2660
Bored 6300,7390 7040,2070
7040 7030
Tab. 1: Classification of IAPS picture by ID number in 4 emotions (joyful, satisfied, angry and
bored)
The overall results show that there were some similar evaluations of the pictures especially
for pictures classified in angry and joyful dimensions between the original group and our
experiment subjects. For example, 3100 (burned victim) were claimed to stimulate anger
by 5 out of 6 participants. Picture 5450 (liftoff) was perceived as joyful picture by 4 out
of 6 participants which corresponds to the normative study result. However, for satisfied
and bored, only one participant indicated bored in one picture (3230: dying man) which
was coincided with the study that we have replicated. Even though similarity in evaluation
of pictures in satisfied or bored dimensions were observed, the relation between our
subjects and the group from the original study was more obvious for the pictures with high
arousal (angry or joyful).
A Pilot Study of Emotion Detection using Sensors in a Learning Context
The physiological data (HRV and EDA) successfully retrieved in this pilot study resulted
in 3 data set out of 6 subject data due to technical problems. Therefore, the analysis of the
HRV and EDA will focus on 3 data set. For the EDA measured during the experiment, we
could find the individual difference as shown in the figure 3. Three recorded signals
showed that individual started at the different point (9-13 micro Siemens) and their range
of changes was different by person. This difference could be due to the random order of
picture presented or individual difference in perceiving emotional pictures or this may be
due to the individual difference in EDA detection. The skin conductance is known to
widely different between subjects [Br16].
Fig.4: EDA graphs of 3 participants (left: subject A, middle: subject B, right: subject C)
Overall, we have observed some high peaks in EDA value when angry or joyful pictures
were shown to the participants which was also shown in the previous study [Mc01]. For
instance, subject A rated the picture 3100 (burned victim) as the picture that induced angry
emotion and the signal had slight peak from 5 to 5.2 micro Siemens. 8620 (Circus Horse)
was presented afterward and it was marked as joyful and the EDA signal shows upward
trend to 5.5 micro Siemens. When 2070 (baby) was shown to the subject A, the subject
had a dynamic change in EDA (6.4 to 7 micro Siemens) and this picture was reported
joyful. EDA data of subject B also showed highest peak of 7250 (b-day cake) with 11.2
micro Siemens and the picture was perceived as joyful. From subject C, low value in EDA
was also recorded when 2280 (Neut. Boy), 5480 (Fireworks), 7510 (skyscraper), 2780
(actor makeup) and 1930 (shark) were presented and these pictures were considered low
or undefined in arousal level (satisfied or bored) by the subject. Similar to subject A and
B, EDA value of subject C was high when positive emotion was induced. For instance,
highest EDA was observed when 1920 (dolphins) was presented and furthermore, when
5910 (fireworks), 2070 (baby), 7330 (ice cream), 5020(flowers) and 7410 (M&M) were
presented, the subject C had high EDA signal and the pictures were marked as joyful and
satisfied.
The heart rate variability (HRV) recorded for this experiment was analyzed to check if the
irregularity in heart beat is observed. According to [HH79], the HRV is suppressed
(regular heart beat) when the task is demanding, which can be also translated as stressful.
The HRV signal data collected by the subjects had a different range. For instance,
participant A and 3’s HRV was from 520 to 850 millisecond whereas participant B’s range
was from 550 to 1100 millisecond as shown in the figure 2.
Haeseon Yun et al.
Fig. 5: Heart Rate Variability graphs of 3 participants (left: subject A, middle: subject B, right:
subject C)
The participant A showed highest HRV value when marking 1710 (3 puppies), 5950
(lightning) and 2070 (baby) and these pictures were marked as all joyful. The lowest HRV
was marked during 7010 (basket) and 7380 (roach pizza) which were defined as angry by
the participant. Participant B also had high peak, close to 1100 millisecond, when viewing
1710 (3 puppies) and the picture was marked as joyful picture by the subject. The lowest
peak was observed when 2130 (angry woman) was marked neutral but somewhat aroused,
which tends toward angry. Participant C showed high peaks during 7400 (candy), 7100
(fire hydrant) and 7510 (skyscraper) and low peaks during 7030 (iron), 7380 (roach pizza),
7010 (basket). The high HRV value seems to correlate with positive emotion with rather
low arousal which can be defined as joyful and low HRV explains bored or angry.
Distinguishing between anger and boredom from the data was not clear.
4 Discussion & Outlook
Awareness of one’s cognitive and emotional state (self-awareness) is the first step to
improve learning process (self-control) and the lack of self-awareness attributes to
learning deficiencies [Zi02]. As emotion plays an important role in learning, our pilot
study strived to integrate objective means to detect emotions using physiological sensors
and relate to previous findings to further investigate the ways to provide awareness back
to learners. Specifically, the paper focused on measuring two physiological signals (EDA
and HRV) during emotional picture experiment and aimed to take a step closer to detection
and interpretation of physiological data in emotion. Clear statement between physiological
data and emotions could not be made due to the small sample size, yet the measured signals
such as changes in EDA data were observed when joyful and satisfactory pictures were
presented. Furthermore, from HRV values, we observed a distinction between positive and
negative emotions, even though distinguishing within positive emotions (joyful or
satisfied) and negative emotions (angry or bored) was not possible at this stage. The
limitations of the study, namely the small sample size and some technical problems,
resulted in a small data set for viable data analysis. Further improvement in hardware and
software for accurate detection should be realized along with an increase in sample size as
A Pilot Study of Emotion Detection using Sensors in a Learning Context
a next step.
In addition to emotion detection and interpretation using sensors, as a next step in LISA
project, we are investigating the ideal way to provide awareness of one’s academic
emotion and recommendation. As the primary findings, based on the users’ input (focus
group and workshop), heuristic evaluation on the prototypes and consideration of suitable
pedagogical approach, we have suggested further steps in [Yu17]. Furthermore, the
pedagogical approach, a learning companion, to design a device to provide awareness to
learners in a friendly and intuitive way was explained in the study.
A learning companion investigated in [Yu17] in connection with this pilot study implies
the need for a further systematic consideration in four aspects: 1) user experience, 2)
pedagogical/ instructional support, 3) technical realization and 4) data privacy. The user
experience should focus on how to design a learning companion to be trusted and
respected by a user [JL16]. The pedagogical/instructional support will focus on emotional
support which plays a guiding role in knowledge attainment [IF10], creativity and problem
solving [Ah13]. The technical realization should consider feasibility of the concept
considering the finance, hardware and software development and data privacy will follow
a general rule of thumb to entrust all data control over to the user without transferring to
any other medium or cloud without user’s knowledge.
Research conducted in the LISA project requires a comprehensive investigation on not
only the detection and interpretation of sensor data for emotion but also the means to
provide information back to learners in a meaningful, intuitive way. Our current findings
in this paper focused on the former part of the overall project and presented an initial work
in emotional detection and interpretation using sensor device. The findings showed
promising results as a work in progress as the physiological sensor data were able to
distinguish between negative and positive emotions even with the discussed limitations.
5 Acknowledgement
This work has been funded by the BMBF project LISA (16SV7534K).
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