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On using temporal semantics to create more accurate human-activity classifiers

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Publication date08/2009
Host publicationProceedings of the 20th Irish Conference on Artificial Intelligence and Cognitive Science
Number of pages0
<mark>Original language</mark>English


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.