A smart architecture design for health remote monitoring systems and heterogeneous wireless sensor network technologies: a machine learning breathlessness prediction prototype

In this paper, we propose a remote patient monitoring architecture based on WBAN wireless body sensor network for breathlessness prediction using machine learning mechanism, we develop a new gateway architecture able to interconnect heterogeneous sensor networks not equipped with the HTTP/TCP/UDP stack. To ensure interoperability and facilitate seamless access to data from different types of body sensors that communicates via multiple technologies. We have designed an application-layer approach for a web service gateway to interact with heterogeneous WSN. The gateway manages the service consumption and communicates with the server via the SOAP protocol. The proposed platform targeted to monitor and process patient health data. An improved machine learning algorithm is used for patient health status prediction to perform patient self-training models based on k-means algorithm. For our platform evaluation we study the gateway power consumption then we investigate the communication delay between the gateway and the server over three communication scenarios (3G, ADSL, LOCAL).