What is it about?

This study investigates the impact of visual features and other en- environmental factors in a city on physiological markers of stress. Forty-four participants walked a predefined urban route while wearable sensors recorded street-level noise, air pollutants, and video footage. Stress responses were measured via heart rate, heart rate variability, and electrodermal activity. Urban scenes were categorized using an image segmentation model. A neural-network-based model was applied to predict stress markers and a functional data analysis was used for inferential statistics. Data collection and analyses are ongoing.

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Why is it important?

Urbanisation and urban living have been associated with stress-related disorders. However, measuring urban stress has been notoriously challenging due to the complexity of factors in cities. Recent studies have also used image segmentation to link objects in urban scenes with perceived psychological stress. Yet, to our knowledge, no study has used wearable sensors and image segmentation to study the association between the visual perception of urban environments and physiological markers of stress. Findings could reveal what urban aspects increase or reduce stress, contributing to healthier and more liveable cities.

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This page is a summary of: How We Perceive Places: Measuring Biomarkers of Stress in Urban Environments Using Personal Exposure Sensors, Deep Learning and Functional Data Analysis, August 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3675231.3678876.
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