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

Computing and Communications

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Published

Standard

On using temporal semantics to create more accurate human-activity classifiers. / Ye, Juan; Clear, Adrian; Coyle, Lorcan et al.
Proceedings of the 20th Irish Conference on Artificial Intelligence and Cognitive Science. 2009.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Ye, J, Clear, A, Coyle, L & Dobson, S 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. <http://www.adrianclear.com/aics2009.pdf>

APA

Ye, J., Clear, A., Coyle, L., & Dobson, S. (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 http://www.adrianclear.com/aics2009.pdf

Vancouver

Ye J, Clear A, Coyle L, Dobson S. On using temporal semantics to create more accurate human-activity classifiers. In Proceedings of the 20th Irish Conference on Artificial Intelligence and Cognitive Science. 2009

Author

Ye, Juan ; Clear, Adrian ; Coyle, Lorcan et al. / On using temporal semantics to create more accurate human-activity classifiers. Proceedings of the 20th Irish Conference on Artificial Intelligence and Cognitive Science. 2009.

Bibtex

@inproceedings{b382af370a21482595eccddceceb9e2c,

title = "On using temporal semantics to create more accurate human-activity classifiers",

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.",

author = "Juan Ye and Adrian Clear and Lorcan Coyle and Simon Dobson",

year = "2009",

month = aug,

language = "English",

booktitle = "Proceedings of the 20th Irish Conference on Artificial Intelligence and Cognitive Science",

}

RIS

TY - GEN

T1 - On using temporal semantics to create more accurate human-activity classifiers

AU - Ye, Juan

AU - Clear, Adrian

AU - Coyle, Lorcan

AU - Dobson, Simon

PY - 2009/8

Y1 - 2009/8

N2 - 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.

AB - 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.

M3 - Conference contribution/Paper

BT - Proceedings of the 20th Irish Conference on Artificial Intelligence and Cognitive Science

ER -

Research

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