Online recognition of human activities and adaptation to habit changes by means of learning automata and fuzzy temporal windows

Smart Homes are intelligent spaces that contain resources to collect information about user’s activities and ease the assisted living. Abnormal behavior detection has been remarked as one of the most challenging application fields in this research area, due to its possibilities for assisting elders or people with special needs. These systems help to maintain people’s independence, enhancing their personal comfort and safety and delaying the process of moving to a nursing home. In this paper, we describe a new approach for the behavior recognition problem based on Learning Automata and fuzzy temporal windows. Our proposal learns the normal behaviors, and uses that knowledge to recognise normal and abnormal human activities in real time. In addition, our proposal is able to adapt online to environmental variations, changes in human habits, and temporal information, defined as an interval of time when the behavior should be performed.