Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Wireless Body Area Network Control Policies for Energy-Efficient Health Monitoring

Version 1 : Received: 18 April 2021 / Approved: 19 April 2021 / Online: 19 April 2021 (12:08:56 CEST)

How to cite: David, Y.B.; Geller, T.; Bistritz, I.; Ben-Gal, I.; Bambos, N.; Khmelnitsky, E. Wireless Body Area Network Control Policies for Energy-Efficient Health Monitoring. Preprints 2021, 2021040470 (doi: 10.20944/preprints202104.0470.v1). David, Y.B.; Geller, T.; Bistritz, I.; Ben-Gal, I.; Bambos, N.; Khmelnitsky, E. Wireless Body Area Network Control Policies for Energy-Efficient Health Monitoring. Preprints 2021, 2021040470 (doi: 10.20944/preprints202104.0470.v1).

Abstract

Abstract: Wireless body area networks (WBANs) have strong potential in the field of health monitoring. However, the energy consumption required for accurate monitoring limits the time between battery charges of the wearable sensors, which is a key performance factor (and can be critical in the case of implantable devices). In this paper, we study the inherent trade-off between the power consumption of the sensors and the probability of misclassifying a patient’s health state. We formulate this trade-off as a dynamic problem, in which at each step we can choose to activate a subset of sensors that provide noisy measurements of the patient’s health state. We assume that the (unknown) health state follows a Markov chain, so our problem is formulated as a partially observable Markov decision problem (POMDP). We show that all the past measurements can be summarized as a belief state on the true health state of the patient, which allows tackling the POMDP problem as an MDP on the belief state. We then empirically study the performance of a greedy one-step look-ahead policy compared to the optimal policy obtained by solving the dynamic program. For that purpose, we use an open-source Continuous Glucose Monitoring (CGM) data set of 232 patients over six months and extract the transition matrix and sensor accuracies from the data. We find that the greedy policy saves ~50% of the energy costs while reducing the misclassification costs by less than 2% compared to the most accurate policy possible that always activates all sensors. Our sensitivity analysis reveals that the greedy policy remains nearly optimal across different cost parameters and a varying number of sensors. The results also have practical importance, because while the optimal policy is too complicated, a greedy one-step look-ahead policy can be easily implemented in WBAN systems.

Subject Areas

wireless body area networks; controlled sensing; energy efficiency; partially observable Markov decision processes (POMDPs); remote health monitoring

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