Learning Health State Transition Probabilities via Wireless Body Area Networks

We consider the use of a wireless body area network (WBAN) for remote health monitoring applications. A partially observable Markov decision process is used to describe the information flow and behavior of the WBAN. We then discuss a sensor activation policy, used for optimizing the tradeoff between power consumption and probability of patient health state misclassification. In order to determine the underlying health state transition probabilities, by which a patient's health state evolves, we develop a learning algorithm which uses the data collected from a group of patients, each being monitored by a WBAN. Finally, a numerical examination demonstrates the applicability of such a system, which applies the learning process and sensor activation policy simultaneously.