Evidential fusion of sensor data for activity recognition in smart homes
Advances in technology have provided the ability to equip the home environment with a layer of technology to provide a truly Smart Home. These homes offer improved living conditions and levels of independence for the population who require support with both physical and cognitive functions. At the core of the Smart Home is a collection of sensing technology which is used to monitor the behaviour of the inhabitant and their interactions with the environment. A variety of different sensors measuring light, sound, contact and motion provide sufficient multi-dimensional information about the inhabitant to support the inference of activity determination. A problem which impinges upon the success of any information analysis is the fact that sensors may not always provide reliable information due to either faults, operational tolerance levels or corrupted data. In this paper we address the fusion process of contextual information derived from uncertain sensor data. Based on a series of information handling techniques, most notably the DempsterShafer theory of evidence and the Equally Weighted Sum operator, evidential contextual information is represented, analysed and merged to achieve a consensus in automatically inferring activities of daily living for inhabitants in Smart Homes. Within the paper we introduce the framework within which uncertainty can be managed and demonstrate the effects that the number of sensors in conjunction with the reliability level of each sensor can have on the overall decision making process.
activity recognition smart homes uncertainty context aware sensor fusion evidential reasoning