![]() Moreover, negative environmental stimuli can limit the mobility, especially walking, of disadvantaged and vulnerable populations (e.g., children, older adults, and people with mobility, cognitive, vision, and hearing impairments) 4. Both prolonged and short-term exposure to negative environmental stimuli (e.g., litter, partially demolished houses, graffiti, abandoned vehicles, unattended dogs, and so forth) is shown to trigger physiological stress symptomatology that increases the risk of chronic illness 1. This method is expected to advance our ability to sense and react to not only built environmental issues but also urban dynamics and emergent events, which together will open valuable new opportunities to integrate human biological and physiological data streams into future built environments and/or walkability assessment applications.īuilt environments, from neighborhoods to cities, have been connected to various physical and mental health outcomes and risk factors such as hypertension, respiratory disease, asthma, and obesity 1– 3. Results show that the machine learning algorithm predicted location-based collective distress of pedestrians with 80% accuracy, showing statistical associations between biosignals and the self-reported stimuli. Data was analyzed using spatial methods with statistical and machine learning models. We collected and analyzed geocoded biosignals and self-reported stimuli information in real-life settings. This research examines the usability of biosignals (electrodermal activity, gait patterns, and heart rate) acquired from real-life settings to capture the environmental distress experienced by pedestrians. However, to date, most prior work has been conducted in a controlled setting and there has been little investigation into utilizing biosignals captured in real-life settings. This physiological monitoring approach in an ambulatory setting can mitigate the subjectivity and reliability concerns of traditional self-reported surveys and field audits. ![]() ![]() Biosignals from wearable sensors have shown great potential for capturing environmental distress that pedestrians experience from negative stimuli (e.g., abandoned houses, poorly maintained sidewalks, graffiti, and so forth). ![]()
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