Tracking of Activities in A Smart Environment Using Fuzzy-State Qlearning Algorithm
The machine learning and pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. In order to monitor the functional health of smart home residents, the authors need to design technologies that recognize and track activities that people normally perform as part of their daily routines. Although approaches do exist for recognizing activities, the approaches are applied to activities that have been preselected and for which labeled training data are available. In contrast, they introduce an automated approach to activity tracking that identifies frequent activities that naturally occur in an individual's routine.