Towards a Mobile Health Context Prediction: Sequential Pattern Mining in Multiple Streams
Context prediction is an emerging topic in the fields of data mining and information management which is both promising and challenging. Predicting the location of mobile objects was a frequently tackled subtask of mobile context prediction in recent researches. For scenarios of managing health information of mobile persons, the prediction of near future health status of persons is at least equally important to predicting their location. We introduce in this paper, to the best of our knowledge, a first method for predicting the next health context of mobile persons equipped with body sensors and a mobile device. The suggested PrefixSpan-based method searches for sequential patterns within multiple streaming inputs from the body sensors as well as other contextual streams that influence the health context. We discuss additionally the implementation of our method in an energy aware mobile-server environment.
|Authors:||Hassani M., Seidl T.|
|Published in:||Proc. of the international workshop on managing Health Information in Mobile Applications (HIMoA'11) in conjunction with the IEEE International conference on Mobile Data Management (MDM 2011), Luleå, Sweden.|
|Publisher:||IEEE Computer Society - Washington, DC, USA|