Current approaches to networked robot systems(or ecology of robots and sensors) in ambient assisted livingapplications (AAL) rely on pre-programmed models of theenvironment and do not evolve to address novel states of theenvironment. Envisaged as part of a robotic ecology in an AALenvironment to provide different services based on the eventsand user activities, a Markov based approach to establishing auser behavioural model through the use of a cognitive memorymodule is presented in this paper. Upon detecting changes inthe normal user behavioural pattern, the ecology tries to adaptits response to these changes in an intelligent manner. Theapproach is evaluated with physical robots and anexperimental evaluation is presented in this paper. A majorchallenge associated with data storage in a sensor richenvironment is the expanding memory requirements. In orderto address this, a bio-inspired data retention strategy is alsoproposed. These contributions can enable a robotic ecology toadapt to evolving environmental states while efficientlymanaging the memory footprint.
|Title of host publication||Unknown Host Publication|
|Number of pages||6|
|Publication status||Published - 5 Dec 2014|
|Event||IEEE International Conference on Robotics and Biometrics - Bali, Indonesia|
Duration: 5 Dec 2014 → …
|Conference||IEEE International Conference on Robotics and Biometrics|
|Period||5/12/14 → …|
Vance, P., Das, G., McGinnity, TM., Coleman, SA., & Maguire, LP. (2014). Novelty Detection in User Behavioural Models within Ambient Assisted Living Applications: An Experimental Evaluation. In Unknown Host Publication (pp. 1868-1873). IEEE.