Saliency detection analysis of collective physiological responses of pedestrians to evaluate neighborhood built environments Abstract   Crowdsourcing pedestriansxe2x80x99 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 finegrain signal fluctuations. Motivated by this issue, this paper proposes a method to identify physical disorders that cause pedestrians physical discomfort andor emotional distress, by using saliency detection analysis on physiological responses. A bottomup 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 residentsxe2x80x99 wellbeing.

Research on Generating an Indoor Landmark Salience Model for SelfLocation and Spatial Orientation from EyeTracking Data Landmarks play an essential role in wayfinding and are closely related to cognitive processes. Eyetracking data contain massive amounts of information that can be applied to discover the cognitive behaviors during wayfinding; however, little attention has been paid to applying such data to calculating landmark salience models. This study proposes a method for constructing an indoor landmark salience model based on eyetracking data. First, eyetracking data are taken to calculate landmark salience for selflocation and spatial orientation tasks through partial least squares regression (PLSR). Then, indoor landmark salience attractiveness (visual, semantic and structural) is selected and trained by landmark salience based on the eyetracking data. Lastly, the indoor landmark salience model is generated by landmark salience attractiveness. Recruiting 32 participants, we designed a laboratory eyetracking experiment to construct and test the model. Finding 1 proves that our eyetracking databased modelling method is more accurate than current weighting methods. Finding 2 shows that significant differences in landmark salience occur between two tasks; thus, it is necessary to generate a landmark salience model for different tasks. Our results can contribute to providing indoor maps for different tasks.