Recognition of Activities of Daily Living from Topic Model
Ihianle, I., Naeem, U. and Tawil, A. 2016. Recognition of Activities of Daily Living from Topic Model. Procedia Computer Science. 98, pp. 24-31.
|Authors||Ihianle, I., Naeem, U. and Tawil, A.|
Research in ubiquitous and pervasive technologies have made it possible to recognise activities of daily living through non-intrusive sensors. The data captured from these sensors are required to be classified using various machine learning or knowledge driven techniques to infer and recognise activities. The process of discovering the activities and activity-object patterns from the sensors tagged to objects as they are used is critical to recognising the activities. In this paper, we propose a topic model process of discovering activities and activity-object patterns from the interactions of low level state-change sensors. We also develop a recognition and segmentation algorithm to recognise activities and recognise activity boundaries. Experimental results we present validates our framework and shows it is comparable to existing approaches.
|Journal||Procedia Computer Science|
|Journal citation||98, pp. 24-31|
|Digital Object Identifier (DOI)||doi:10.1016/j.procs.2016.09.007|
|21 Sep 2016|
|Publication process dates|
|Deposited||23 Sep 2016|
|Accepted||20 Jun 2016|
|Copyright information||© 2016 The Authors|
Published in Procedia Computer Science (Volume 98, 2016) special issue:
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