Individualized Modeling to Distinguish Between High and Low Arousal States Using Physiological Data.

ABSTRACT: With wearable, relatively unobtrusive health monitors and smartphone sensors, it is increasingly easy to collect continuously streaming physiological data in a passive mode without placing much burden on participants. At the same time, smartphones provide the ability to survey participants to provide "ground-truth" reporting on psychological states, although this comes at an increased cost in participant burden. In this paper, we examined how analytical approaches from the field of machine learning could allow us to distill the collected physiological data into actionable decision rules about each individual's psychological state, with the eventual goal of identifying important psychological states (e.g., risk moments) without the need for ongoing burdensome active assessment (e.g., self-r

SUBMITTER: Osotsi A 

PROVIDER: S-EPMC8982753 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature