Activity recognition in the home using a hierarchal framework with object usage data

Abstract

Smart environments are emerging as platforms that can be used to help recognise activities and hence provide context sensitive services and assistance, e.g. switching on the music while the person being monitored is having an evening meal. The ability to monitor everyday activities in a smart environment is seen as a key approach for tracking functional decline among elderly people. The motivation is to allow patients with early Alzheimer's disease to have additional years of independent living before the disease reaches the latter stages (moderate and severe). This paper describes an approach to detecting the goals of the individual subjects from sensor data that are generated by objects that are used when performing everyday activities around the home. To limit intrusion into personal privacy cameras and visual surveillance equipment are not used, as the activities are monitored using simple RFID sensors. Identification of the intentions of subjects is based on interpretation of the sensor data exploiting known structures of typical behaviours.