Abstract
Identifying users frequent behaviors is considered as a key step to achieve real intelligent environments that support people in their daily lives. These patterns can be used in many different applications. An algorithm that compares current behaviors of the users with previously discovered frequent behaviors and detects shifts has been developed. In addition, it identifies the differences between both behaviors. Identified shifts can be used not only to adapt frequent behaviors, but also to detect initial signs of some disease linked to behavioral modifications, such as depression, Alzheimer’s.
This work was done while Asier Aztiria and Golnaz Farhadi were at Stanford University, CA, USA.
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Aztiria, A., Farhadi, G., Aghajan, H. (2012). User Behavior Shift Detection in Intelligent Environments. In: Bravo, J., Hervás, R., RodrĂguez, M. (eds) Ambient Assisted Living and Home Care. IWAAL 2012. Lecture Notes in Computer Science, vol 7657. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35395-6_12
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DOI: https://doi.org/10.1007/978-3-642-35395-6_12
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