A Smart Body Area Network for Diabetes Management

摘要

Smart Body Area Networks (SBAN), which utilize data fusion, machine learning and data mining, of sensor derived data, provide increased functionality and capability for more persuasive healthcare. In the area of diabetes diagnosis and treatment body area networks have been proposed for glucose monitoring while dedicated telemedicine systems for the monitoring of artificial pancreas-related clinical trials as well as home use have also been developed. An SBAN platform that allows the seamless addition of diabetes-based physiological monitoring approaches and treatment capability is proposed here for the continuous time monitoring and management of diabetes. In addition to glucose monitoring hypoglycaemia prediction as well as the influence of daily changes in the subject's insulin sensitivity on glucose regulation are also considered within the context of either self-management or automatic glucose control. Because cardiovascular disease (CVD) is a major risk factor for both type 1 and 2 diabetic subjects the inclusion of an ECG monitoring system, is also integrated within the SBAN. Though self-management of Diabetes is supported by the SBAN the ultimate aim is to provide fully automated diabetes management, and CVD monitoring via the incorporation of an artificial pancreas device and real-time ECG classification.