Abstract :
This paper describes user activity recognition for lifestyle disease patients at home: ways to define data mining system for sensing, logging, analyzing, mining, measuring and recognizing user´s daily activities. Lifestyle disease patients spend most of the time at home. There are lots of sensing data that can be based on home devices with home networking (sensors, gadgets, appliances, cameras, smart phones and some software applications running on computers). Main problem is interoperability, there is no standard framework for logging, analyzing and utilizing the available data sources. In this paper, we will introduce our layered architecture to do data mining for user´s activity recognition. Understand user´s life pattern can help medical services to cure and prevent diseases from developing.
Keywords :
data analysis; data mining; home computing; medical computing; patient diagnosis; pattern recognition; sensor fusion; data analysis; data logging; data measurement; data mining; data recognition; data sensing; data utilization; home device; home networking; home sensor; interoperability; lifestyle disease patient; patient monitoring; user activity recognition; Data mining; Diseases; Intelligent sensors; Monitoring; Noise; Smart phones;
