As Wear-IT begins to take shape in more detail, we’ve been looking at new ways to understand and model physiological data on an individual level. Recent work with Ame Osotsi, Zita Oravecz, and Joshua Smyth examines individual differences in physiological response in a paper called Individualized Modeling to Distinguish Between High and Low Arousal States Using Physiological Data, just published in the Journal of Healthcare Informatics Research.
In it, we illustrate the importance of individual modeling for physiological states, showing that high-arousal states have physiological signatures that can be detected by machine learners, but that those signatures differ enormously from person to person, and individualized modeling is really important.