Description

Despite the potential for stress and emotion recognition outside the lab environment, very little work has been reported that is feasible for use in the real world and much less for activities involving physical activity. In this project, I first explored how stress responses and physilogical signals change with demographics and exertion level. Then, using the data collected I developed a method that uses clustering to separate the data into physical exertion levels and later performs stress classification over the discovered clusters. This approach was validated on physiological stress dataset from 20 participants who performed 3 different activities of varying intensity under 3 different types of stimuli intended to cause stress.