Body responses towards a morning walk in a tropical city

The purpose of this study is to present an exploratory analysis of the relationship between body responses, immediate environmental factors and stress-related events. Using an experimental setup for data collection and information fusion from wearable sensors, this work tests three Machine Learning Algorithms for supervised classification of stress detection. Body skin temperature and electrodermal activity are processed to identify patterns of stress reaction while walking. Immediate environmental features from continuous sensor data are found to be useful in identifying stress-related events. The experiment was carried out in Singapore, a city-state with hot tropical weather where the climate conditions of the city encourage urban planners to meet walkability needs of the residents as well as to ensure short walking trips.