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

Juan Ye; Adrian K. Clear; Lorcan Coyle; Simon Dobson

doi:10.1007/978-3-642-17080-5_29

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

Juan Ye
, Adrian K. Clear
, Lorcan Coyle
, Simon Dobson

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

9 Citations (Scopus)

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.

Original language	English
Title of host publication	Artificial Intelligence and Cognitive Science - 20th Irish Conference, AICS 2009, Revised Selected Papers
Pages	273-282
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-17080-5_29
Publication status	Published - 2010
Externally published	Yes
Event	20th Annual Irish Conference on Artificial Intelligence and Cognitive Science, AICS 2009 - Dublin, Ireland Duration: 19 Aug 2009 → 21 Aug 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6206 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th Annual Irish Conference on Artificial Intelligence and Cognitive Science, AICS 2009
Country/Territory	Ireland
City	Dublin
Period	19/08/09 → 21/08/09

Access to Document

10.1007/978-3-642-17080-5_29

Cite this

Ye, J., Clear, A. K., Coyle, L., & Dobson, S. (2010). On using temporal features to create more accurate human-activity classifiers. In Artificial Intelligence and Cognitive Science - 20th Irish Conference, AICS 2009, Revised Selected Papers (pp. 273-282). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6206 LNAI). https://doi.org/10.1007/978-3-642-17080-5_29

Ye, Juan ; Clear, Adrian K. ; Coyle, Lorcan et al. / On using temporal features to create more accurate human-activity classifiers. Artificial Intelligence and Cognitive Science - 20th Irish Conference, AICS 2009, Revised Selected Papers. 2010. pp. 273-282 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{1fe53a8dbc354c14bce88346d9935d77,

title = "On using temporal features 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 Clear, \{Adrian K.\} and Lorcan Coyle and Simon Dobson",

year = "2010",

doi = "10.1007/978-3-642-17080-5\_29",

language = "English",

isbn = "364217079X",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "273--282",

booktitle = "Artificial Intelligence and Cognitive Science - 20th Irish Conference, AICS 2009, Revised Selected Papers",

note = "20th Annual Irish Conference on Artificial Intelligence and Cognitive Science, AICS 2009 ; Conference date: 19-08-2009 Through 21-08-2009",

}

Ye, J, Clear, AK, Coyle, L & Dobson, S 2010, On using temporal features to create more accurate human-activity classifiers. in Artificial Intelligence and Cognitive Science - 20th Irish Conference, AICS 2009, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6206 LNAI, pp. 273-282, 20th Annual Irish Conference on Artificial Intelligence and Cognitive Science, AICS 2009, Dublin, Ireland, 19/08/09. https://doi.org/10.1007/978-3-642-17080-5_29

On using temporal features to create more accurate human-activity classifiers. / Ye, Juan; Clear, Adrian K.; Coyle, Lorcan et al.
Artificial Intelligence and Cognitive Science - 20th Irish Conference, AICS 2009, Revised Selected Papers. 2010. p. 273-282 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6206 LNAI).

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

TY - GEN

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

AU - Ye, Juan

AU - Clear, Adrian K.

AU - Coyle, Lorcan

AU - Dobson, Simon

PY - 2010

Y1 - 2010

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.

UR - https://www.scopus.com/pages/publications/78650123230

U2 - 10.1007/978-3-642-17080-5_29

DO - 10.1007/978-3-642-17080-5_29

M3 - Conference Publication

AN - SCOPUS:78650123230

SN - 364217079X

SN - 9783642170799

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 273

EP - 282

BT - Artificial Intelligence and Cognitive Science - 20th Irish Conference, AICS 2009, Revised Selected Papers

T2 - 20th Annual Irish Conference on Artificial Intelligence and Cognitive Science, AICS 2009

Y2 - 19 August 2009 through 21 August 2009

ER -

Ye J, Clear AK, Coyle L, Dobson S. On using temporal features to create more accurate human-activity classifiers. In Artificial Intelligence and Cognitive Science - 20th Irish Conference, AICS 2009, Revised Selected Papers. 2010. p. 273-282. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-17080-5_29

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

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this