Application of Data Mining Techniques to Efficiently Monitor Chronic Diseases Using Wireless Body Area Networks and Smartphones

摘要

This paper presents a wireless body area network platform performing daily physical activity recognition using accelerometers, biosignals and smartphones. Various classifiers have been evaluated to identify the one with the best recognition results. Functional Trees classifier provided the best performance and was used in the real time activity recognition that executed on the smartphone. Geo-location provided by the GPS receiver of the smartphone used to retrieve location based environmental data and Points of Interest via the web. Activity recognition results and environmental data were stored in a database and a cloud-hosted application performed Emerging Patterns search through the data to predict future conditions. The described framework has application in the prevention of short-term complications of metabolic diseases such as diabetes or environmental conditions related diseases such as Chronic Obtrusive Pulmonary Disease (COPD).