Ye, Juan and Clear, Adrian and Coyle, Lorcan and Dobson, Simon (2009) On using temporal semantics to create more accurate human-activity classifiers. In: Proceedings of the 20th Irish Conference on Artificial Intelligence and Cognitive Science. .Full text not available from this repository.
Through advances in sensing technology, a huge amount of data is available to context-aware applications. A major challenge is extracting features of this data that correlate to high-level human activities. Time, while being semantically rich and an essentially free source of information, has not received sufficient attention for this task. In this paper, we examine the potential for taking temporal features inherent in human activities|into account when classifying them. Preliminary experiments using the PlaceLab dataset show that absolute time and temporal relationships between activities can improve the accuracy of activity classifiers.
|Item Type:||Contribution in Book/Report/Proceedings|
|Departments:||Faculty of Science and Technology > School of Computing & Communications|
|Deposited On:||01 Dec 2011 11:55|
|Last Modified:||09 Feb 2016 00:03|
Actions (login required)