Real-Time Sensor-Based and Self-Reported Emotional Perceptions of Urban Green-Blue Spaces: Exploring Gender Differences with FER and SAM
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View abstract on PubMed
Summary
This summary is machine-generated.This study reveals gender-specific emotional responses to urban green-blue spaces (UGBS). Facial expression recognition technology offers new insights into how men and women experience these environments differently, aiding inclusive urban planning.
Area Of Science
- Environmental Psychology
- Urban Planning
- Human-Computer Interaction
Background
- Urban green-blue spaces (UGBS) are vital for well-being.
- Limited research exists on real-time, gender-specific emotional responses to UGBS.
- Understanding these differences is crucial for equitable urban design.
Purpose Of The Study
- To investigate gender differences in real-time emotional evaluations of UGBS.
- To explore subjective and objective emotional responses using a dual-method approach.
- To assess the utility of facial expression recognition (FER) technology in this context.
Main Methods
- A dual-method approach combining self-reported measures and facial expression recognition (FER).
- Static images (Google Street View) and 360-degree videos were used as stimuli.
- Experiments involved 108 participants, generating over two million data points with FER.
Main Results
- Distinct gender-specific emotional patterns were identified.
- Women reported stronger pleasant emotions and preferred higher arousal scenes.
- Men showed sharper responses and favored peak valence scenes; grass evoked relaxation in women and arousal in men, while blue spaces induced calmness in both.
Conclusions
- Facial expression recognition (FER) technology shows potential for assessing real-time emotional responses to urban environments.
- Findings provide actionable insights for inclusive urban planning to enhance emotional well-being.
- Integrating advanced tools can help create more livable cities catering to diverse user needs.
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