Name
Title | ||
|---|---|---|
Saliency detection analysis of collective physiological responses of pedestrians to evaluate neighborhood built environments |
Abstract | ||
|---|---|---|
Crowdsourcing pedestrians’ physiological responses (e.g., electrodermal activity (EDA), gait patterns, and blood volume pulse) offers a unique opportunity for assessing and maintaining built environments in a neighborhood. However, raw physiological signals acquired from naturalistic ambulatory settings cannot effectively capture prominent local patterns in the data stream, since diverse technical challenges (e.g., electrode contact noise and motion artifacts) and confounding factors (e.g., heightened physiology due to the movement) make it difficult to detect significant fine-grain signal fluctuations. Motivated by this issue, this paper proposes a method to identify physical disorders that cause pedestrians physical discomfort and/or emotional distress, by using saliency detection analysis on physiological responses. A bottom-up segmentation approach was used as an unsupervised way to divide each physiological signal into homogeneous segments. A physiological saliency cue (PSC) is proposed to calculate the distinctiveness of physiological responses over each segment in contrast to the remaining segments, and the collective PSC of a physical point of interest is computed across participants. The results, obtained from physiological signals collected from wearable devices, indicate that the suggested saliency detection analysis is effectual in capturing prominent local patterns. Our statistical analysis further indicates that the proposed PSC features can be indicative of physical disorders. The outcome of this research will provide a foundation towards using physiological signals to evaluate built environments, and towards promoting neighborhood walkability, increasing feelings of safety in the urban space, and augmenting residents’ well-being. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.aei.2020.101035 | Advanced Engineering Informatics |
Keywords | Field | DocType |
Built environment assessment,Physiological response,Wearable sensing,Saliency detection,Crowdsensing,Smart city | Data mining,Gait,Pattern recognition,Data stream,Salience (neuroscience),Crowdsourcing,Segmentation,Walkability,Artificial intelligence,Engineering,Wearable technology,Optimal distinctiveness theory | Journal |
Volume | Issue | ISSN |
43 | C | 1474-0346 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| kim j kim jinwoo | 1 | 13 | 5.38 |
| Megha Yadav | 2 | 1 | 1.36 |
| Theodora Chaspari | 3 | 38 | 19.43 |
| Changbum R. Ahn | 4 | 5 | 3.99 |