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

Varun Kumar Ojha, Danielle M. Griego, Saskia Kuliga, Martin Bielik, Peter Bus, Charlotte Schaeben, Lukas Treyer, Matthias Standfest, Sven Schneider, Reinhard König, Dirk Donath, Gerhard Schmitt

Research output: Contribution to journalArticlepeer-review


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 un- derstand 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 environ- ment 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 condi- tions. 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.
Original languageEnglish
Pages (from-to)154-169
Number of pages16
JournalInformation Sciences
Publication statusPublished - 2019

Research Field

  • Former Research Field - Energy


  • Signal processing Data fusion Features selection Wearable devices Physiological data


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