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

## TL;DR

This paper develops a framework combining signal processing and machine learning to analyze how urban environmental features influence human physiological responses, based on a field study in Zurich.

## Contribution

It introduces a comprehensive framework for multi-sensor data analysis and applies machine learning to reveal environmental impacts on physiology in urban settings.

## Key findings

- Field-of-view change correlates with increased arousal.
- Environmental conditions significantly affect physiological responses.
- High-accuracy models predict physiological changes based on environmental features.

## 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 Zurich, 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.