The influence of built environment features on crowdsourced physiological responses of pedestrians in neighborhoods

Kim, Jinwoo; Nam, Yunwoo

doi:10.1016/j.compenvurbsys.2019.02.003

Cited by 34 publications

(38 citation statements)

References 53 publications

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“…The physiological responses of older adult pedestrians can be reflective of human experience toward a surrounding environment, providing us unique insights into the elements of the urban built environments (e.g., neighborhood disorders and environmental barriers) ( 19 – 23 ). Various types of physiological response data including gait patterns, electrodermal activity (EDA), heart rate and brain activity ( 22 , 24 – 26 ) have been have been investigated from collected physiological signals in virtual environments ( 27 – 29 ) naturalistic ambulatory settings and daily life locations, such as neighborhoods, downtown, urban parks, and university campuses ( 18 , 20 , 22 , 23 , 30 – 35 ). See Table 1 for a full list of references ( 18 – 32 , 36 – 55 ).…”

Section: Resultsmentioning

confidence: 99%

Use of Connected Technologies to Assess Barriers and Stressors for Age and Disability-Friendly Communities

Zanwar

Kim

et al. 2021

Front. Public Health

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Background: The benefits of engaging in outdoor physical activity are numerous for older adults. However, previous work on outdoor monitoring of physical activities did not sufficiently identify how older adults characterize and respond to diverse elements of urban built environments, including structural characteristics, safety attributes, and aesthetics.Objective: To synthesize emerging multidisciplinary trends on the use of connected technologies to assess environmental barriers and stressors among older adults and for persons with disability.Methods: A multidisciplinary overview and literature synthesis.Results: First, we review measurement and monitoring of outdoor physical activity in community environments and during transport using wearable sensing technologies, their contextualization and using smartphone-based applications. We describe physiological responses (e.g., gait patterns, electrodermal activity, brain activity, and heart rate), stressors and physical barriers during outdoor physical activity. Second, we review the use of visual data (e.g., Google street images, Street score) and machine learning algorithms to assess physical (e.g., walkability) and emotional stressors (e.g., stress) in community environments and their impact on human perception. Third, we synthesize the challenges and limitations of using real-time smartphone-based data on driving behavior, incompatibility with software data platforms, and the potential for such data to be confounded by environmental signals in older adults. Lastly, we summarize alternative modes of transport for older adults and for persons with disability.Conclusion: Environmental design for connected technologies, interventions to promote independence and mobility, and to reduce barriers and stressors, likely requires smart connected age and disability-friendly communities and cities.

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Section: Resultsmentioning

confidence: 99%

Use of Connected Technologies to Assess Barriers and Stressors for Age and Disability-Friendly Communities

Zanwar

Kim

et al. 2021

Front. Public Health

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“…They found variability in accordance with physiological responses in high-demand environments (e.g., absence of traffic signals and sidewalk defects). Also, our previous study [6] highlighted that collective levels of gait stability and relative heart rates captured from pedestrians in naturalistic ambulatory settings can be indicative of adverse built environmental features that harm neighborhood walkability.…”

Section: Urban Sensing Data and Environmental Distressmentioning

confidence: 99%

“…One promising approach is to exploit new sources of urban data emerging from technological, social, and business innovations. For example, wearable sensing data crowdsourced from urban residents' wearable devices could provide direct observations on how residents feel and respond to the environmental demand of urban communities [6]. In addition, visual sensing data on urban built environment (e.g., Google Street view, crowdsourced photos) could contain key information on environmental stressors in urban communities.…”

Section: Introductionmentioning

confidence: 99%

“…The mobility of an older adult is mainly affected by his/her physical capacity and surrounding environmental conditions including distance, ambient conditions (e.g., weather conditions), terrain characteristics (e.g., slope, stairs, and uneven surfaces), physical disorders (e.g., vandalized building and litter in the streets), objects or people density in walking path, and speeding cars [3,5,6,7]. Various attempts have been made to assess environmental demands associated with older adults' mobility.…”

Section: Introductionmentioning

confidence: 99%

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A Digital Twin City Model for Age-Friendly Communities: Capturing Environmental Distress from Multimodal Sensory Data

Ham¹,

Kim²,

Kim³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…This data was successfully used to examine the associations between physical activity behaviors in previous research. For instance, Jansen et al ( 34 ) used a combination of a GPS sensor and accelerometer to determine the influence of the geographical environment on physical activity for different intensity levels, and Kim et al ( 35 ) applied wearable devices to collect crowdsourced physiological data from pedestrians and analyze the features of walkable environment.…”

Section: Introductionmentioning

confidence: 99%

What Are Good Situations for Running? A Machine Learning Study Using Mobile and Geographical Data

Wang

Scheider

Sporrel

et al. 2021

Front. Public Health

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Running is a popular form of physical activity. Personal, social, and environmental determinants influence the engagement of the individual. To get insight in the relation between running behavior and external situations for different types of users, we carried out an extensive data mining study on large-scale datasets. We combined 4 years of historical running data (collected by a mobile exercise application from over 10K participants) with weather, topographical and demographical datasets. We introduce weighted frequent item mining for the analysis of the data. In this way, we capture temporal and environmental situations that frequently associate with different running performances. The results show that specific temporal and environmental situations (hour in a day, day in a week, temperature, distance to residential areas, and population density) influence the running performance of users more than other situational features. Hierarchical agglomerative clustering on the running data is used to split runners in two clusters (with sustained and less sustained running behavior). We compared the two groups of runners and found that runners with less sustained behavior are more sensitive to the environmental situations (especially several weather and location related features, such as temperature, weather type, distance to the nearest park) than regular runners. Further analysis focused on the situational features for the less sustained runners. Results show that specific feature values correspond to a better or worse running distance. Not only the influence of individual features was examined but also the interplay between features. Our findings provide important empirical evidence that the role of external situations in the running behavior of individuals can be derived from analysis of the combined historical datasets. This opens up a large potential to take those situations specifically into consideration when supporting individuals which show less sustained behavior.

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The influence of built environment features on crowdsourced physiological responses of pedestrians in neighborhoods

Cited by 34 publications

References 53 publications

Use of Connected Technologies to Assess Barriers and Stressors for Age and Disability-Friendly Communities

Use of Connected Technologies to Assess Barriers and Stressors for Age and Disability-Friendly Communities

A Digital Twin City Model for Age-Friendly Communities: Capturing Environmental Distress from Multimodal Sensory Data

What Are Good Situations for Running? A Machine Learning Study Using Mobile and Geographical Data

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