Machine learning approaches to understand the influence of urban environments on human’s physiological response
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Author / Producer
Date
2019-02
Publication Type
Journal Article
ETH Bibliography
yes
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OPEN ACCESS
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Abstract
This research proposes a framework for signal processing and information fusion of spatial-temporal multi-sensor data pertaining to understanding patterns of humans physiological changes in an urban environment. The framework includes signal frequency unification, signal pairing, signal filtering, signal quantification, and data labeling. Furthermore, this paper contributes to human-environment interaction research, where a field study to understand the influence of environmental features such as varying sound level, illuminance, field-of-view, or environmental conditions on humans’ perception was proposed. In the study, participants of various demographic backgrounds walked through an urban environment in Zürich, Switzerland while wearing physiological and environmental sensors. Apart from signal processing, four machine learning techniques, classification, fuzzy rule-based inference, feature selection, and clustering, were applied to discover relevant patterns and relationship between the participants’ physiological responses and environmental conditions. The predictive models with high accuracies indicate that the change in the field-of-view corresponds to increased participant arousal. Among all features, the participants’ physiological responses were primarily affected by the change in environmental conditions and field-of-view.
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Publication status
published
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Editor
Book title
Journal / series
Volume
474
Pages / Article No.
154 - 169
Publisher
Elsevier
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
machine learning; Data science; Urban design; urbanism; Environmental change; climate information; Physiology; perception
Organisational unit
03276 - Schmitt, Gerhard (emeritus) / Schmitt, Gerhard (emeritus)
Notes
Funding
149552 - Untersuchung der Zusammenhänge zwischen der energetischen und sozialen Performanz städtebaulicher Formen (SNF)