  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.
