Skip to main content
Kent Academic Repository

Machine learning approaches to understand the influence of urban environments on human’s physiological response

Ojha, Varun, Kumar, Griego, Danielle, Kuliga, Saskia, Bielik, Martin, Buš, Peter, Schaeben, Charlotte, Treyer, Lukas, Standfest, Matthias, Schneider, Sven, König, Reinhard, and others. (2019) Machine learning approaches to understand the influence of urban environments on human’s physiological response. Information Sciences, 474 . pp. 154-169. ISSN 0020-0255. (doi:10.1016/j.ins.2018.09.061) (KAR id:74058)

PDF (Open Access under a Creative Commons license) Publisher pdf
Language: English

Download this file
[thumbnail of Open Access under a Creative Commons license]
Request a format suitable for use with assistive technology e.g. a screenreader
Official URL:


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.

Item Type: Article
DOI/Identification number: 10.1016/j.ins.2018.09.061
Uncontrolled keywords: Signal processing; Data fusion; Features selection; Wearable devices; Physiological data
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
Q Science
Q Science > QA Mathematics (inc Computing science)
Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Arts and Humanities > Kent School of Architecture and Planning
Depositing User: Peter Bus
Date Deposited: 22 May 2019 11:34 UTC
Last Modified: 04 Mar 2024 19:37 UTC
Resource URI: (The current URI for this page, for reference purposes)

University of Kent Author Information

  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.