Remote patient monitoring service for sleeping human postures in a WBAN

Abstract

Wireless Body Area Networks (WBANs) provide wireless remote patient monitoring services where doctors get patients' health records without physically visiting them. In WBANs, biosensors are placed on the patient's body that sense and transmit physiological data to the paired medical personnel. Such medical setups are appropriate for COVID-19 patient monitoring, where the patient remains isolated for an extended period. Sometimes, human body parts impede the signals transmitted by biosensors to the coordinator and this type of occlusion lasts for a longer duration during sleeping human postures. In such circumstances, an intermediate biosensor forwards the signals of the occluded biosensor node. The forwarding of messages results in quick depletion of energy resources at the intermediate biosensor, affecting the overall WBAN services. To resolve this, first, we propose an adaptive Relay-Node Centric (RNC) relay-based communication protocol for WBANs, which reduces energy used in relaying and improves the stability period of the network. Second, we design a novel simulation model using an existing real-life experimental dataset to simulate a WBAN placed on the sleeping patient's body. We derive a Discrete Markov Chain (DTMC) model from real-life data and use human biomechanisms to simulate biosensors' connectivity status in four human sleeping positions. Lastly, we evaluate the performance of RNC against the existing cost-function-based and Analytical Hierarchical Process (AHP) based relay selection protocols. Results obtained on the real-life dataset and designed simulation model show that RNC outperforms the existing methods in terms of network stability period and packet success ratio.