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Development of Emotion-Sensitive Design for and Games

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Claudia Schrader

on 25 September 2016

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Transcript of Development of Emotion-Sensitive Design for and Games

Part 2: Methods to Detect Emotions in Runtime
Which methods do we know and how effective are these in accurately measuring emotions in terms of

What is the current state-of-the art in (game) research on emotions and learning?

What are the 5 big questions that should be adressed in further research?
Part 1: State-of-the-art
Part 1: Overview current state-of-the-art
Part 2: Methods to detect emotions
Part 3: Emotion-sensitive design interventions
Part 4: Summary and next steps
So what ...
Write down ...
problems in current research
big questions for further research

Physiological Measure
Autonomous nervous system
Somatic nervous system
Central nervous system
Cardiovascular signals: HR, HRV
Electrodermal activity (EDA):
Skin response
Electroencephalogram (EEG): brain activity
Electromyography (EMG) and visual data: Facial behavior
Body behavior

Self reports of emotions
limitation in measurement of runtime emotions
interrupting learning / gameplay
Unobstrusively integration of self-report as solution?
Frommel, Rogers, Brich, Riemer, Schrader, & Weber (2015). Integrated questionnaires: Maintaining presence in game environments for self-reported data acquisition.CHI play, London, UK.
Inconclusive findings according to emotional valence, e.g.
increased EDA= linked with higher levels of enjoyment but also with higher levels of frustration
(Drachen et al., 2010; Mandryk et al., 2006)

Inconclusive findings according to correlation with subjective measured
emotions, e.g. for heart rate variability = repressive coping
(Mandryk et al., 2010; Schrader, in press; Tognetti et al., 2010)

Time-related dismatch between design interventions, subjectively rated emotions and physiological reaction

So what ...
What would a multi-method approach look like?
Part 3: Emotion-Sensitive Design Interventions
What specific design features exist in various
digital media that can be leveraged to regulate
negative emotions?
Representational features

Visual aesthetics
Method of delivery

Interactive elements

Mechanical elements

Levels of difficulty
Outcomes, e.g.
reward and badges
Emotion-Sensitive Design of Games for Learning
September 26th 2016

Subjective Measures
Objective Measures
Learners' Behavior
Frommel, Rogers, Brich, Riemer, Schrader, & Weber (2015).Integrated Questionnaires: Maintaining presense in game-environments for self-reported data acquistion. CHI play. London, UK.
So what ...
What create the emotional challenge in these games?
Identify design responses when a student encounters negative emotions

Time: 30 min.
Bodily Expression as Indicator of Experienced Emotions
Research Aims

Testing the validity of Kinect body data for inferring naturally occurring emotions in a serious game setting

Identifying specific body posture and activity parameters that predict specific emotions

The Game
Cure Runners (Three Coins, 2013)
Platform game to foster practical money skills

Sample, Design & Procedure
N = 44 (29 female. 13 male)
Mean age: 22.55 years

Kinect Data
Kinect Data &
Assessment of emotions
at certain evens
Kinect Data &
Assessment of emotions
at certain evens
Kinect Data &
Assessment of emotions
at certain evens
Data Analyses

Baseline-corrected Means of coordinates for each game phase separately

Baseline-corrected Means of coordinate-differences in consecutive frames in each game phase separately

In Total: 5 (Coordinates) x 2 (Parameters) x 5 (Game Phases) Kinect variables

Head rotation:
Riemer, V., Frommel,J., Layher, G., Neumann, H., & Schrader, C. (in prep). Bodily expressions as indicators of experienced emotions in serious games.

For heart rate data see, Schrader, C. (2016). Towards adaptive challenge: subjective and objective assessment of frustration during gameplay.
Results – Relation self reported joy, neck & head rotation

Kinect Setup – Neck Joints & Head Rotation
Positive values = Joint displaced to the right
Negative values= Joint displaced to the left

Positive values = Joint displaced upward
Negative values= Joint displaced downward

Lower values = Joint closer to Kinect
Higher values = Joint further away from Kinect

X-Axis („Pitch“)
Positive values = Face turned upward
Negative values = Face turned downward

Y-Axis ("Yaw")
Positive values = Face turned towards the left shoulder
Negative values = Face turned towards the right shoulder

Z-Axis („Roll“)
Positive values = Face bent to the left shoulder
Negative values = Face bent to the right shoulder
Full transcript