Mitigation of Channel Diversity and Indoor Channel Modeling for Remote Patient Monitoring Systems

The continuously increasing demand for better healthcare services has added another application to the sphere of remote monitoring services. This extension to e-Health and Telecare services is known as Remote Patient Monitoring (RPM). The direct impact of this application on human life has made it a mission critical application. The randomness and diversity in location and movement profile of patients and Doctors further complicates the modeling and analysis of this system. This paper concentrates on the mitigation of the diversity in communication links involved in the entire system and a decision rule based adaptive indoor channel model. This paper highlights the diversity modeling of wireless channels due to variations in location phenomenon along with movement of the user through a four state Markov Model. From this model, one state corresponding to indoor channel (representing pre or post hospital monitoring) has been analyzed. It suggests the use of an adaptive model with corresponds to a decision rule as per the local conditions of the user and the best suited value representing a model is selected to capture the randomness of the received signal. It is established that the proposed techniques predicts more real values of QoS parameters such as Bit Error Rate (BER) and Outage probability, leading to a higher reliability of wireless channels. It is also shown that the proposed model can be a best fit for this mission critical application remote patient monitoring through wireless communication