Personalized Monitors for Real-Time Detection of Physiological States

Abstract

The authors introduce an algorithmic framework to process real-time physiological data using nonparametric Bayesian models under the context of developing and testing personalized wellness monitors. A wearable device aggregates signals from various sensors while periodically transmitting the collected data to a backend server, which builds custom user profiles based on inferred hidden Markov states. They discuss how these user profiles can be used in various contexts as proxies for fluctuating physiological states and leveraged for various longitudinal classification tasks. Using data collected in a two-week study hosted at Jaslok Hospital, the authors show how physiological changes induced by different environments with various levels of stress can be quantified by the authors' platform. To minimize the dependence on continuous connectivity with the backend server, they introduce a heuristic to enable real-time state identification using the modest processing capabilities of the wearable device.