Optimal Health Monitoring via Wireless Body Area Networks

David, Yair Bar; Geller, Tal; Khmelnitsky, Eugene; Ben‐Gal, Irad; Ward, Andrew; Miller, Delbert C.; Bambos, Nicholas

doi:10.1109/cdc.2018.8619446

Cited by 4 publications

(2 citation statements)

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“…WBANs enable the estimation of the physical and health condition of a patient with unprecedented precision as well as the ability to monitor the evolution of these heath conditions over time [ 1 , 2 , 3 , 4 , 5 ]. The control of WBANs involves activating a set of sensors that are located on or near a patient (e.g., wearable) while collecting data related to their physiological and mental activities.…”

Section: Introductionmentioning

confidence: 99%

“…Recent research and practical developments in sensors and WBAN technology allow efficient implementations of WBANs in modern real-world applications [ 3 , 4 , 6 ]. One of the main factors that impacts the performance of such systems in the context of health monitoring is the inherent trade-off between minimizing the energy consumption of operating the sensors and maximizing the accuracy of the health state estimation based on the measured information [ 1 , 2 ]. In this paper, we focus on this trade-off between energy costs and accuracy and propose a tractable model that allows designing efficient algorithms that balance this trade-off.…”

Section: Introductionmentioning

confidence: 99%

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

David

Geller

Bistritz

et al. 2021

Sensors

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Wireless body area networks (WBANs) have strong potential in the field of health monitoring. However, the energy consumption required for accurate monitoring determines 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. Then, we 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) dataset 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.

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