Personalized Monitors for Real-Time Detection of Physiological States

Objectives

The primary objective of the current work is to investigate how a generative model can be implemented in a wearable wellness monitoring system to make meaningful predictions in real-time. Specifically, we will build physiological profiles derived from hidden variables discovered through Bayesian data analysis, and then evaluate these profiles based on their effectiveness in detecting changes in physiological states. We emphasize the difference between the profiles, which are features extracted from the time series data under the assumption of a hidden variable model, and real physiological states such as stress. A successful profile would indicate a quantifiable connection between the hidden variables and the physiological state. Although workplace stress was the focus of the Jaslok study, it is impractical to collect accurate and objective measurements of stress in real-time. Therefore, as a proxy for physiological states we will use each subject’s “current activity” (working/relaxing) to validate the profiles. We will explore the prediction performance based on our platform by showing how the learned physiological profiles can be used to make predictions.

A secondary objective is to mitigate the computational difficulties involved in learning a hidden variable model in real-time. The class of generative models is flexible enough to capture a large variety of dynamics, but it has been unclear how the system can be trained and tested on wearable devices with limited capabilities. We will examine how personalized models can be pre-built from a training sample, then used to detect physiological states in real-time. We will see that the performance of real-time classifiers is comparable to that of offline classifiers, which require constant connectivity with the backend processor.