Optimal Health Monitoring via Wireless Body Area Networks

Yair Bar David, Tal Geller, Evgeni Khmelnitsky, Irad Ben-Gal, Andrew Ward, Daniel Miller, Nicholas Bambos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations


We consider the use of a wireless body area network for remote patient health monitoring applications. Our proposed network consists of a controller and multiple sensors, whose signals provide information on the health state of a patient. We model this patient-sensor network as a partially observable Markov decision process. The sensor outputs are used by the controller to update the patient's health-state belief probabilities and select a subset of sensors to be activated at the next decision epoch. We propose two operational algorithms that allow accurate monitoring of a patient's health state while minimizing operational and misclassification costs: i) a greedy algorithm, which applies a one-step look-ahead approach, and ii) a dynamic programming-based algorithm which yields the optimal policy. We provide a numerical example which demonstrates the applicability of the suggested methods and provides insights.

Original languageEnglish
Title of host publication2018 IEEE Conference on Decision and Control, CDC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781538613955
StatePublished - 2 Jul 2018
Event57th IEEE Conference on Decision and Control, CDC 2018 - Miami, United States
Duration: 17 Dec 201819 Dec 2018

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Conference57th IEEE Conference on Decision and Control, CDC 2018
Country/TerritoryUnited States


FundersFunder number
Koret Foundation


    • Wireless body area networks
    • controlled sensing
    • dynamic programming
    • dynamic sensor selection
    • optimal control
    • partially observable Markov decision processes (POMDP)


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