Recognition of interleaved activities within Smart Environments
As the needs and demands of the population constantly change there is now a growing amount of pressure to identify solutions which will provide a successful means of independent living. The deployment of technology within the home environment and its remote monitoring is one possible way in which this can be achieved [1]. At a general level we can decompose the construct of a smart environment into three main components. In the first instance we have the core sensing technology which has the ability to record the interactions with the environment. These may be in the form of for example video, contact sensors or motion sensors. A data processing module has the task to infer decisions based on the information gleaned from the sensing technology and with the third and final component providing the feedback to those within the environment via a suite of multi-modal interfaces. It is the aim of this Project to focus specifically on the data processing module, specifically on the notion of activity recognition. Within the domain of smart environments some may have the view that the process of activity recognition forms the critical path in providing a truly automated environment [2]. This process is tasked with extracting and establishing meaningful activities from a myriad of sensor activations. Although work in this area is still deemed to be emerging, the initial results achieved have been more than impressive. The key research focus of the Project will be directed towards the identification and management of interleaved activities. This will require investigations into appropriate approaches for the processing of sensor based information to firstly identify the instances when interleaved activities occur and secondly to decompose the interleaved activities into a number of individual activities. It is anticipated that the developed models will be evaluated within the smart environment located within the School of Computing and Mathematics (http://serg.ulster.ac.uk/). [1] S. Helal, W. Mann, H. El-Zabadani, J. King, Y. Kaddoura, E. Jansen, “The Gator Tech Smart House: a programmable pervasive space,” Computer, Vol 38, 3, pp. 50 – 60, 2005. [2] L. Chen, C. Nugent, J. Biswas, J. Hoey, Activity Recognition in Pervasive Intelligent Environments, Atlantis Press, 2011.
Personnel Involved
First Supervisor: Nugent, CD Professor
Second Supervisor: Chen, L Dr
Collaboration: This project does not involve collaboration with another establishment