Clustering of Physiological Signals by Emotional State, Race, and Sex

In this work, we explore the emotional responses to ten stimuli reflecting real-world experiences captured via physiological signals from 140 individuals. We employ the DBSCAN clustering algorithm to these data, and show that blood pressure and electrodermal activity may be indicative of race, and blood pressure of sex and emotional state. These findings could lead to important innovations, particularly those valuable for certain demographic groups, including, for example, culturally relevant robotics and cultural awareness in education by improving real-time measurements of stress and cognitive load.