Learning Health State Transition Probabilities via Wireless Body Area Networks
| Autor: | Daniel Miller, Evgeni Khmelnitsky, Andrew Ward, Yair Bar David, Nicholas Bambos, Tal Geller, Irad Ben-Gal |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
business.industry
Computer science 05 social sciences Real-time computing Body area Process (computing) Partially observable Markov decision process 050801 communication & media studies 0508 media and communications 0502 economics and business Body area network Wireless 050211 marketing State (computer science) business |
| Zdroj: | ICC |
| DOI: | 10.1109/icc.2019.8761425 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|