.png)
In Emotion Analysis, a representative method for interpreting biometric data is by defining emotions using two dimensions: Arousal and Valence.
This approach is particularly useful for clearly visualizing and understanding the complex emotional states of users.
a. Arousal
Arousal refers to the intensity or activation level of emotions and can be assessed through physiological responses.
High Arousal:
Reflected in high EDA/GSR values.
Indicates intense emotional states such as excitement, fear, or anger.
Example: Increased heart rate, sweating.
Low Arousal:
Reflected in low or stable EDA/GSR values.
Indicates calmness, drowsiness, or indifference.
Example: A state of relaxation or focused attention.
b. Valence
Valence describes the positive (Positive) or negative (Negative) nature of emotions, often inferred from HRV or respiratory data.
Positive Valence:
Reflected in high HRV and slower breathing rates.
Indicates positive emotions such as happiness, interest, or satisfaction.
Negative Valence:
Reflected in low HRV and faster breathing rates.
Indicates negative emotions such as stress, anger, or sadness.
c. Emotional State Mapping
Visualizing emotional states based on the Arousal-Valence Model allows intuitive interpretation of emotion data.
X-axis (Valence): Negative (left) → Positive (right).
Y-axis (Arousal): Low (bottom) → High (top).
Example of Emotional State Mapping:
High Arousal & High Valence: Happiness, interest.
High Arousal & Low Valence: Stress, anger.
Low Arousal & High Valence: Calmness, satisfaction.
Low Arousal & Low Valence: Lethargy, depression.
Practical Application Process
Data Collection:
Measure arousal levels based on EDA/GSR data.
Infer valence through HRV and respiratory data.
Data Visualization:
Map data onto the Arousal-Valence Model to represent emotional states in an emotion map format.
Example: Identify high arousal and low valence after a specific screen transition → potential stress trigger.
Utilizing in UX Design:
Improve negative valence areas by designing for positive valence and low arousal.
Example: Simplify stress-inducing elements and provide positive feedback.
Conclusion
Interpreting emotions using the Arousal-Valence Model enables biometric data to go beyond mere numerical values and be directly applied to UX improvements. This model is a powerful tool for combining data-driven UX design with emotional empathy.
UXR player leverages wearable devices to measure biometrics, enhancing the reliability of UX research.
Comments