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

Authors:
Juan Ye;Adrian K. Clear;Lorcan Coyle;Simon Dobson
Affiliations:
Centre for Sensor Web Technologies, University College Dublin;Centre for Sensor Web Technologies, University College Dublin;Lero, The Irish Software Engineering Research Centre, University of Limerick;Centre for Sensor Web Technologies, University College Dublin and Lero, The Irish Software Engineering Research Centre, University of Limerick
Venue:
AICS'09 Proceedings of the 20th Irish conference on Artificial intelligence and cognitive science
Year:
2009

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Abstract

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.