Jung-Mi Park & Philipp Leppert

USING IMPLICIT RESEARCH
TO TRACK IN-VEHICLE USER EXPERIENCE
& FUEL DESIGN INNOVATION
2015. 06. 25
Jung-Mi Park, Hyundai Motor Company
Philipp Leppert, Eye Square GmbH
Copyright ⓒ Hyundai Motor Group & Eye Square GmbH All rights reserved

BACKGROUND : Hyundai
To Inform In-house Designers Deliver Better HMIs
(Human-Machine Interaction)
• To help what they really do : Pinpointing what to improve
• Not just a moment : Capturing the ‘CHANGES’ of experiences
http://victorzambrano.com/projects/patch-perks-ios-
app/attachment/desktop_flows_v4/
User-Centered Design for Experience
• Insights from consumers are essential not just for selling & marketing cars,
but also for shaping in-vehicle user experience.
• Conflicts of different interests : ‘Being connected’ vs. ‘Being safe’
https://www.edgarsnyder.com/car-accident/cause-of-
accident/cell-phone/cell-phone-statistics.html
https://www.digitalmethods.net/MoM/Interview/
By Implicit Research
• People cannot speak up something directly
• ‘While Driving’ condition : Real-time tracking vs. Zero-interruption

AGENDA
3. STUDY EXAMPLES
• Capturing Scattered Eyes
• Remembering Numbers Is Hard
• Fingers Are Tensed
2. METHODS
• Eye Tracking
• EEG
• Electrodermal activity
• Facial emotion coding
1. BACKGROUND
4. CLOSING REMARKS

How Usability impacts
Expectations
satisfied and even
exceeded
System can help
users in complicated
situations
Users have better
control
Little details that
add to the
experience
Safety while driving
can be improved
Users enjoy their
Car moreHigher acceptance
and recommendation
Great usability
can lead to…
Joy-of-Use
Image
Acceptance
Re-use probability
rises
Safety
Error tolerance
Ergonomics
Ease of Use
Engaging
Higher trust into
product and brand
Modern and innov
ative interfaces ge
nerate halo for the
brand
Effective
Efficient

WHY IMPLICIT
What are implicit measures?
How can implicit methods help to
better understand your customers?

Driving Experience is more than Usability
PRAGMATIC
Error tolerance
Ergonomics
HEDONIC
Joy of Use
Identification
USABILITY
USER EXPERIENCE
PRODUCT FEATURES
Functionality
Design
Material
Interaction
ACCEPTANCE
Preference
Joy
Satisfaction
Trust
Aesthetic
quality
Ease of Use
Control
Hassenzahl, M. (2003). The thing and I: understanding the relationship between user and product. In M. Blythe, C. Overbeeke, A. F. Mon
k, & P. C. Wright (Eds.), Funology: From Usability to Enjoyment (pp. 31-42). Dordrecht: Kluwer Academic Publishers.

Attention as Implicit Bottleneck
AUTO PILOT
11 million bits
0,0004 %
conscious
PILOT
40 bits
10 million bits are dedicated visual
The Nature and Function of Intuitive Thought and Decision Making (2015) - Springer

Implicit Measures & Explicit Measures
Explicit MethodsImplicit Methods
Questionnaire
Semantic differential
Interviews
Focus Groups
EyeTracking
Physiological measurement (skin
conductance, activation, EEG)
Emotional analysis
Reaction time measurement
Creative methods
Ethnographic studies
Thinking aloud
Rating scales

Implicit Measures
IMPLICIT
MEASURES
EYE-TRACKING
EEG
EDA
FACIAL EMOTIONS

Methods
Facial emotion
tracking
EEG
Indicator: Facial expression
Facial emotion tracking shows the emotional
expressions of a viewer, recorded by a cam.
Indicator: Arousal
The electrodermal activity
(skin conductance) indicates
the state of physiological in
volvement and activity of
the participant.
Indicator:
Workload & emotional engagement
The brain activity is measured. Workload
and engagement indicate the emotional
state of the user.
Indicator: Eye Movement
Lightest mobile eye-tracking system
Automatic analysis with marker
tracking
EDA

Fieldwork – Video Voice Command Task
Single Subject - Eye-Tracking, EEG, EDA

Eye Tracking - Visit counts
• The visit count describes the average
number of visits across all subjects.
• A visit is defined as the entrance and
exit of fovea attention to the AOI.
One visits consists of a minimum of
1 Fixation.
Implications:
• How many times is the ad frequented on average?
• What areas of the page are being most frequented and may thus
operate as visual pivot points

Eye Tracking - Dwell Time Share
• The orange bubble describes the
average share of dwell time across
all subjects.
• The percentile of time spent on a
designated area.
• All areas sum up to 100%.

Electroencephalography (EEG)
• Eye square‘s EEG solution records a
continuous stream of analog electrical
brainwaves and digitizes this data.
• Different electrical frequencies can be
linked to actions and different stages
of consciousness; To do so the raw
signals are translated into meaningful
constructs like workload and
engagement.
• These constructs can then be
visualized over time or, in cases where
the presentation of stimuli (in this
case the cars UI) is of different
duration for every respondent, as an
aggregate over time.
0.42
0.43
0.45
0.44
0.45
0.455
0.465
0.41
0.41
0.40
0.42
0.42
0.443
0.447
0.47
0.46
0.46
0.44
0.43
0.456
0.449
A1 A2 B1 B2 C1 C2 C3
EEG – avgWorkload task/car
0.46
0.44
0.44
0.45
0.43
0.45
0.438
0.45
0.47
0.49
0.49
0.49
0.48
0.477
0.44
0.44
0.44
0.44
0.43
0.46
0.443
A1 A2 B1 B2 C1 C2 C3
EEG – avgEngagement, per task/car

Electrodermal activity (EDA)
Electrodermal Activity (average electric
resistance on the skin):
indicates the level of emotional tension &
stress
9.93
7.59
8.96
EDA
Hyundai Chrysler BuickCar 3
Peak count:
Evaluation for non-synchronous stimuli (e.g. car
driving) is done by counting peaks per minute.
The higher the level of peaks per minute, the
higher the arousal of the respondent.
(N=30)
Car 1 Car 2

Facial emotion coding
• Ekman et al. found that facial expressions –
indepent of the respondents culture- are
closely linked to human emotional states.
These universal facial expressions reflect the
so called basic emotions: anger, disgust, fear,
happiness, sadness and surprise.
• Since computer-aided facial expression
detection is still on the verge of having a 100%
success rate, eye square combines manual FET
coding with computer assisted recognition. In
manual coding a trained expert on FET
watches a user cam video frame-by-frame and
codes his findings on a timeline.
• Facial expressions are then converted into a
binary scheme of positive/negative emotions
and can now be evaluated.
18.5%
41.4%
15.4%
FET: Share of drivers with negative
emotions
Hyundai Chrysler BuickCar 1 Car 2 Car 3
Ekman, P., Sorenson. E. R., & Friesen, W. V. (1969). Pan Cultural Elements In Facial Display Of Emotion. Science.164(3875), 86-88.

Facial emotion coding
• The result of the measuring with implicit methods shows us more detail hints and
evidences, which allow us to understand the behavior of user better, but meanwhile
are difficult to discover with the explicit measurement.
• These details findings also provide us a deeper insight into the automatic and
unconscious part of interacting process. They indicate the users’ preference of the
interface and their difficulties during the interaction.

Stimuli – 3 cars (interior)
Car 3
Car 1 Car 2

STUDY EXAMPLES
CAPTURING SCATTERED EYES
(left) Cluster Screen- operating with voice control
(right) Main Display- selecting the address with touch screen
Task: Navigation-Find the address and start the guide
by voice
Eye Tracking-Dwell Share,
(left) Phone Task, (right) Navigation Task
Starting and ending of a task with different media(inputs,
displays) provoke danger on the road.
Phone Task: Please dial the number by voice
Navigation Task: Please find the address by voice and
start the guidance.
(N=30 San Francisco and Chicago, 10 minutes driving in real traffic)

STUDY EXAMPLES
CAR CCAR H
REMEMBERING NUMBERS IS HARD
Drivers are eager to be confirmed by system when they
speak numbers.
Task: Please dial this number by voice. Eye Tracking- Visit count

STUDY EXAMPLES
FINGERS ARETENSED
Touchscreen interaction while driving makes drivers nervous
and stressed.
EDA Data, Highlight
Highlight Task : Turn on the radio by touchscreen
All Tasks: Radio / Phone / Navigation
Eye Tracking- Dwell Share Data, (left) 70.2%, (right) 40.8%
Radio Task: Turn on the radio by voice / by touchscreen

CLOSING REMARKS : Hyundai
Bridging people with shared understanding
• Users vs. internal stakeholders
• ‘Why they use’ >‘What they use’ > ‘How they use’
http://telecomnewsroom.com/2014/10/28/what-it-takes-to-be-a-
sales-engineer-in-todays-competitive-market-part-i/
http://www.globalone.tv/profiles/blogs/six-blind-men-
and-an-elephant
Keeping your eyes open with diverse toolboxes
• ‘Blind men are touching an elephant.’
• Implicit research can help reveal another facet of people
Facing with automotive contexts
• Cognitive Distractions vs. Real-time information
• Multi-tasking issue : ‘Driving’ first, ‘Informing’ later
http://www.makingdifferent.com/car-driving-tips-
beginners/

CLOSING REMARKS : Eyesquare
Combine explicit and implicit
• Both sides of consumer insight are equally important
• Research shows equal explanation of variance (50%/50%)
Measuring implicit means in-context
• Implicit measures have to be natural by definition
• In context means while driving, sitting interacting
• This means high data load and complicated time-series data
Combination of explicit and implicit data
• Path modelling can integrate implicit and explicit data
Technology Acceptance Model (TAM)
Usability Evaluation for in-vehicle systems (2014) CRC Press

Thank you for your attention!
Eye Square GmbH
Hyundai Motor Company

Jung-Mi Park & Philipp Leppert

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