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 usabilitycan 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 innovative interfaces generate 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 EXPERIENCEPRODUCT FEATURES

Functionality

Design

Material

Interaction

ACCEPTANCE

Preference

Joy

SatisfactionTrust

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. Monk, & P. C. Wright (Eds.), Funology: From Usability to Enjoyment (pp. 31-42). Dordrecht: Kluwer Academic Publishers.


Attention as Implicit Bottleneck

AUTO PILOT11 million bits

0,0004 %

conscious

PILOT40 bits

10 million bits are dedicated visual

The Nature and Function of Intuitive Thought and Decision Making (2015) - Springer


AGENDA



1. BACKGROUND

4. CLOSING REMARKS


Implicit Measures & Explicit Measures

Explicit MethodsImplicit Methods

Questionnaire

Semantic differential

Interviews

Focus Groups

Eye Tracking

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 expressionFacial emotion tracking shows the emotional expressions of a viewer, recorded by a cam.

Indicator: ArousalThe electrodermal activity(skin conductance) indicates the state of physiological involvement and activity of the participant.

Indicator: Workload & emotional engagementThe brain activity is measured. Workload and engagement indicate the emotional state of the user.

Indicator: Eye MovementLightest mobile eye-tracking systemAutomatic 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 electricresistance on the skin):

indicates the level of emotional tension &stress

9.93

7.598.96

EDA

Hyundai Chrysler BuickCar 3

Peak count:

Evaluation for non-synchronous stimuli (e.g. cardriving) is done by counting peaks per minute.

The higher the level of peaks per minute, thehigher 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.


AGENDA



1. BACKGROUND

4. CLOSING REMARKS


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 ARE TENSED

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


AGENDA



1. BACKGROUND

4. CLOSING REMARKS


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/

http://www.jpatrick.com/our-blog/bid/208201/What-It-Takes-to-Be-a-Sales-Engineer-in-Today-s-Competitive-Market

http://www.jpatrick.com/our-blog/bid/208201/What-It-Takes-to-Be-a-Sales-Engineer-in-Today-s-Competitive-Market


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


Date post:	02-Aug-2015
Category:	Documents
Upload:	mrs
View:	197 times
Download:	0 times

Jung-Mi Park & Philipp Leppert

Documents