Activity recognition using temporal evidence theory

The ability to identify the behavior of people in a home is at the core of Smart Home functionality. Such environments are equipped with sensors that unobtrusively capture information about the occupants. Reasoning mechanisms transform the technical, frequently noisy data of sensors into meaningful interpretations of occupant activities. Time is a natural human way to reason place at distinct times throughout the day and last for predicable lengths of time. However, the inclusion of temporal information is still limited in the domain of activity recognition. Evidence theory is gaining increasing interest in the field of activity recognition, and is suited to the incorporation of time related domain knowledge into the reasoning process. In this paper, an evidential reasoning framework that incorporates temporal knowledge is presented. We evaluate the effectiveness of the framework using a third party published smart home dataset. An improvement in activity recognition of 70% is achieved...

• Type: Journal
• Year: 2010
• Where: JAISE
• Authors: Susan McKeever, Juan Ye, Lorcan Coyle, Chris Bleakley, Simon Dobson
• Keywords: Activity Recognition, Algorithms, Evidence Theory, JAISE 2010, Smart Home