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Saliency Detection Analysis of Pedestrians' Physiological Responses to Assess Adverse Built Environment Features

Cited 8 time in Web of Science Cited 13 time in Scopus

Kim, Jinwoo; Yadav, Megha; Ahn, Changbum R.; Chaspari, Theodora

Issue Date
American Society of Civil Engineers (ASCE)
Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019, pp.90-97
Crowdsourced data acquired through pedestrians' physiological response [e.g., electrodermal activity (EDA), gait patterns, brain activity, and heart rate] offer a unique opportunity for diagnosing and maintaining built environment features in a neighborhood. However, using crowdsourced raw physiological responses obtained from naturalistic ambulatory settings cannot adequately capture local prominent patterns, since various technical challenges (e.g., motion artifacts, electrode contact noise) and compounding factors (e.g., heightened physiology because of movement) make it difficult to identify meaningful fine-grain signal fluctuations. Motivated by this, this study proposes a method to assess adverse built environment features that cause physical discomfort and/or emotional distress to pedestrians, by using saliency detection analysis on multiple physiological responses (i.e., EDA, heart rate, and gait patterns). Each physiological response data is segmented with a bottom-up segmentation approach in an unsupervised way. A physiological saliency cue (PSC) is proposed and used to compute the distinctiveness of physiological responses over the segment of interest in comparison to the remaining ones, and collective PSC of a physical point of interest (POI) is measured across participants. The results using physiological response data acquired from wearable devices indicates that the proposed saliency detection analysis is effective in capturing prominent local patterns. The outcome of this research will provide a foundation of using physiological response data to evaluate built environment features and eventually will promote neighborhood walkability.
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  • College of Engineering
  • Department of Architecture & Architectural Engineering
Research Area Computing in Construction, Management in Construction


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