Assessing the Impact of Street Visual Environment on the Emotional Well-Being of Young Adults through Physiological Feedback and Deep Learning Technologies
Abstract
:1. Introduction
1.1. Research Background
1.2. Technology Status
2. Literature Review
2.1. Measurement of Emotion
2.2. Exploring the Impact of Street Visual Environment on Emotions
2.3. Research Goals
- To reveal how specific aspects of the street visual environment impact emotions, providing new theoretical insights into the interaction between street design and emotions.
- To develop and validate a technical framework that combines physiological feedback technology and deep learning methods, aimed at enhancing the accuracy and reliability of research on the impact of emotions through large-scale street sample data.
- Based on the analysis of data, to propose recommendations for street design improvements that promote residents’ emotional well-being and the development of psychologically friendly environments.
3. Data and Methodology
3.1. Research Design
3.2. Study Area and Sample Selection
3.2.1. Study Area
3.2.2. Stimulus Materials
3.3. Experimental Design
3.3.1. Volunteer Recruitment
3.3.2. Equipment
3.3.3. Experimental Process and Data Acquisition
3.4. Data Processing
3.4.1. Physiological Data Processing
3.4.2. Emotion Signal Recognition
3.4.3. Semantic Segmentation of Visual Images
3.5. Statistical Analysis
3.5.1. Multiple Linear Regression Model
3.5.2. Multinomial Logistic Regression Model
4. Results
4.1. Descriptive Analysis
4.2. Multiple Linear Regression Model Results
4.3. Multinomial Logistic Regression Model Results
5. Discussion
5.1. The Impact of Street Visual Environment on Emotions
5.2. Advantages and Challenges of the Research Method
5.3. Implications for Street Space Design
5.4. Limitations and Future Directions
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variable | Features | Formula |
---|---|---|
Green view factor (GVF) | Percentage of green plant pixels of the street view image | GVF = |
Sky view factor (Sky VF) | Percentage of sky pixels of the street view image | Sky VF = |
Visual enclosure (VE) | Percentage of vertical terrain features pixels in the street view image | VE = |
Vehicles view factor (VVF) | Percentage of non-motorized and motorized vehicle pixels in the street view image | VVF = |
Road view factor (RVF) | Percentage of road pixels of the street view image | RVF = |
Sidewalk view factor (SVF) | Percentage of sidewalk pixels of the street view image | SVF = |
Person view factor (PVF) | Percentage of person pixels of the street view image | PVF = |
Building view factor (BVF) | Percentage of building pixels of the street view image | BVF = |
Characteristics of Participants | Variant | N (%) |
---|---|---|
Gender | Male | 27 (54%) |
Female | 23 (46%) | |
Age | 18–22 years | 4 (8%) |
23–25 years | 20 (40%) | |
Over 25 years | 26 (52%) | |
Educational Background | Bachelor Student | 20 (40%) |
Master Student | 28 (56%) | |
Doctoral Student | 2 (4%) |
Variables | Mean | SD | Min | Max |
---|---|---|---|---|
Street visual environmental characteristics | ||||
GVF | 0.326 | 0.167 | 0.00005 | 0.759 |
Sky VF | 0.08 | 0.07 | 0.00006 | 0.364 |
VE | 0.516 | 0.105 | 0.19 | 0.825 |
VVF | 0.04 | 0.045 | 0 | 0.257 |
RVF | 0.086 | 0.062 | 0 | 0.35 |
SVF | 0.129 | 0.068 | 0 | 0.379 |
PVF | 0.011 | 0.02 | 0 | 0.179 |
BVF | 0.214 | 0.129 | 0 | 0.582 |
participants’ emotional characteristics | ||||
Arousal | 0.077 | 0.6 | −1 | 1 |
Adjusted Arousal | 0.016 | 0.616 | −1 | 1 |
Valence | 0.163 | 0.432 | −0.999 | 0.996 |
Variables | Model 1 (Arousal) | Model 2 (Adjusted Arousal) | Model 3 (Valence) | VIF |
---|---|---|---|---|
Standardized Coefficients | Standardized Coefficients | Standardized Coefficients | ||
GVF | −0.069 *** | 0.248 *** | 0.295 *** | 2.123 |
Sky VF | −0.065 *** | 0.195 *** | 0.274 *** | 3.339 |
VE | −0.003 | −0.059 *** | −0.109 *** | 3.184 |
VVF | 0.008 | −0.065 *** | −0.156 *** | 1.461 |
RVF | 0.011 | −0.048 *** | −0.119 *** | 1.448 |
SVF | −0.047 *** | 0.090 *** | 0.210 *** | 1.284 |
PVF | 0.019 * | −0.074 *** | −0.141 *** | 1.418 |
BVF | −0.003 | −0.066 *** | −0.007 | 1.796 |
R-squared | 0.012 | 0.144 | 0.269 |
Emotional State | Variables | β | SE | Wald | Exp (β) |
---|---|---|---|---|---|
LAPV | GVF *** | 0.668 | 0.027 | 615.566 | 1.950 |
Sky VF *** | 0.682 | 0.036 | 362.300 | 1.977 | |
VE *** | −0.163 | 0.032 | 25.584 | 0.850 | |
VVF *** | −0.297 | 0.021 | 192.398 | 0.743 | |
RVF *** | −0.233 | 0.021 | 117.830 | 0.792 | |
SVF *** | 0.431 | 0.021 | 421.793 | 1.539 | |
PVF *** | −0.331 | 0.023 | 215.981 | 0.718 | |
BVF | 0.010 | 0.023 | 0.190 | 1.010 | |
HAPV | GVF *** | 0.692 | 0.028 | 623.493 | 1.997 |
Sky VF *** | 0.685 | 0.036 | 352.755 | 1.983 | |
VE *** | −0.212 | 0.033 | 41.223 | 0.809 | |
VVF *** | −0.370 | 0.022 | 271.004 | 0.691 | |
RVF *** | −0.263 | 0.022 | 140.296 | 0.769 | |
SVF *** | 0.452 | 0.022 | 442.022 | 1.572 | |
PVF *** | −0.319 | 0.023 | 193.756 | 0.727 | |
BVF | −0.007 | 0.024 | 0.085 | 0.993 | |
LANV | GVF | 0.001 | 0.035 | 0.001 | 1.001 |
Sky VF | −0.005 | 0.048 | 0.011 | 0.995 | |
VE | 0.028 | 0.041 | 0.480 | 1.029 | |
VVF | 0.040 | 0.026 | 2.396 | 1.041 | |
RVF | 0.016 | 0.026 | 0.377 | 1.016 | |
SVF * | −0.063 | 0.027 | 5.298 | 0.939 | |
PVF * | 0.045 | 0.022 | 4.313 | 1.046 | |
BVF | −0.026 | 0.029 | 0.819 | 0.974 |
Street Type | Emotional Goals | Emotional Needs | Optional Design Strategies |
---|---|---|---|
Residential Streets | LAPV | Promote positive emotions and well-being | 1. Enhance natural connections and increase street greenery 2. Improve walking experience and prioritize pedestrian traffic 3. Optimize street scale to create comfortable spaces. |
Commercial Streets | HAPV | ||
Landscape Streets | LAPV\HAPV | ||
Specialized Streets | LAPV\HAPV | ||
Industrial Streets | LAPV | Alleviate negative emotions and stress | 1. Reduce traffic interference and ensure pedestrian needs 2. Improve spatial design to reduce visual distractions 3. Ensure green travel and maintain smooth pedestrian flow |
Traffic Streets | LAPV |
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Share and Cite
Zhao, W.; Tan, L.; Niu, S.; Qing, L. Assessing the Impact of Street Visual Environment on the Emotional Well-Being of Young Adults through Physiological Feedback and Deep Learning Technologies. Buildings 2024, 14, 1730. https://doi.org/10.3390/buildings14061730
Zhao W, Tan L, Niu S, Qing L. Assessing the Impact of Street Visual Environment on the Emotional Well-Being of Young Adults through Physiological Feedback and Deep Learning Technologies. Buildings. 2024; 14(6):1730. https://doi.org/10.3390/buildings14061730
Chicago/Turabian StyleZhao, Wei, Liang Tan, Shaofei Niu, and Linbo Qing. 2024. "Assessing the Impact of Street Visual Environment on the Emotional Well-Being of Young Adults through Physiological Feedback and Deep Learning Technologies" Buildings 14, no. 6: 1730. https://doi.org/10.3390/buildings14061730