A Novel Multimodal Data Analytic Scheme for Human Activity Recognition

Girija CHETTY; Mohammad Yamin

doi:10.1007/978-3-642-55355-4_47

A Novel Multimodal Data Analytic Scheme for Human Activity Recognition

Girija CHETTY, Mohammad Yamin

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

In this article, we propose a novel multimodal data analytics scheme for human activity recognition. Traditional data analysis schemes for activity recognition using heterogeneous sensor network setups for eHealth application scenarios are usually a heuristic process, involving underlying domain knowledge. Relying on such explicit knowledge is problematic when aiming to create automatic, unsupervised or semi-supervised monitoring and tracking of different activities, and detection of abnormal events. Experiments on a publicly available OPPORTUNITY activity recognition database from UCI machine learning repository demonstrates the potential of our approach to address next generation unsupervised automatic classification and detection approaches for remote activity recognition for novel, eHealth application scenarios, such as monitoring and tracking of elderly, disabled and those with special needs

Original language	English
Title of host publication	IFIP Advances in Information and Communication Technology
Editors	Changrui Yu, Kecheng Liu, Stephen R. Gulliver, Weizi Li
Place of Publication	Berlin, Germany
Publisher	Springer
Pages	449-458
Number of pages	10
Volume	426
Edition	426
ISBN (Electronic)	9783642553554
ISBN (Print)	9783642553547
DOIs	https://doi.org/10.1007/978-3-642-55355-4_47
Publication status	Published - 2014
Event	15th IFIP WG 8.1 International Conference on Informatics and Semiotics in Organisations, ICISO 2014 - Shanghai, Shanghai, China Duration: 23 May 2014 → 24 May 2014 http://www.orgsem.org/2014/

Publication series

Name	IFIP Advances in Information and Communication Technology
Volume	426

Conference

Conference	15th IFIP WG 8.1 International Conference on Informatics and Semiotics in Organisations, ICISO 2014
Country/Territory	China
City	Shanghai
Period	23/05/14 → 24/05/14
Internet address	http://www.orgsem.org/2014/

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10.1007/978-3-642-55355-4_47

Cite this

CHETTY, G., & Yamin, M. (2014). A Novel Multimodal Data Analytic Scheme for Human Activity Recognition. In C. Yu, K. Liu, S. R. Gulliver, & W. Li (Eds.), IFIP Advances in Information and Communication Technology (426 ed., Vol. 426, pp. 449-458). (IFIP Advances in Information and Communication Technology; Vol. 426). Springer. https://doi.org/10.1007/978-3-642-55355-4_47

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title = "A Novel Multimodal Data Analytic Scheme for Human Activity Recognition",

abstract = "In this article, we propose a novel multimodal data analytics scheme for human activity recognition. Traditional data analysis schemes for activity recognition using heterogeneous sensor network setups for eHealth application scenarios are usually a heuristic process, involving underlying domain knowledge. Relying on such explicit knowledge is problematic when aiming to create automatic, unsupervised or semi-supervised monitoring and tracking of different activities, and detection of abnormal events. Experiments on a publicly available OPPORTUNITY activity recognition database from UCI machine learning repository demonstrates the potential of our approach to address next generation unsupervised automatic classification and detection approaches for remote activity recognition for novel, eHealth application scenarios, such as monitoring and tracking of elderly, disabled and those with special needs",

keywords = "Multimodal, Feature-Learning, Activity-Recognition",

author = "Girija CHETTY and Mohammad Yamin",

year = "2014",

doi = "10.1007/978-3-642-55355-4_47",

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editor = "Changrui Yu and Kecheng Liu and Gulliver, {Stephen R.} and Weizi Li",

booktitle = "IFIP Advances in Information and Communication Technology",

address = "Netherlands",

edition = "426",

note = "15th IFIP WG 8.1 International Conference on Informatics and Semiotics in Organisations, ICISO 2014 ; Conference date: 23-05-2014 Through 24-05-2014",

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CHETTY, G & Yamin, M 2014, A Novel Multimodal Data Analytic Scheme for Human Activity Recognition. in C Yu, K Liu, SR Gulliver & W Li (eds), IFIP Advances in Information and Communication Technology. 426 edn, vol. 426, IFIP Advances in Information and Communication Technology, vol. 426, Springer, Berlin, Germany, pp. 449-458, 15th IFIP WG 8.1 International Conference on Informatics and Semiotics in Organisations, ICISO 2014, Shanghai, China, 23/05/14. https://doi.org/10.1007/978-3-642-55355-4_47

A Novel Multimodal Data Analytic Scheme for Human Activity Recognition. / CHETTY, Girija; Yamin, Mohammad.
IFIP Advances in Information and Communication Technology. ed. / Changrui Yu; Kecheng Liu; Stephen R. Gulliver; Weizi Li. Vol. 426 426. ed. Berlin, Germany: Springer, 2014. p. 449-458 (IFIP Advances in Information and Communication Technology; Vol. 426).

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

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T1 - A Novel Multimodal Data Analytic Scheme for Human Activity Recognition

AU - CHETTY, Girija

AU - Yamin, Mohammad

PY - 2014

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N2 - In this article, we propose a novel multimodal data analytics scheme for human activity recognition. Traditional data analysis schemes for activity recognition using heterogeneous sensor network setups for eHealth application scenarios are usually a heuristic process, involving underlying domain knowledge. Relying on such explicit knowledge is problematic when aiming to create automatic, unsupervised or semi-supervised monitoring and tracking of different activities, and detection of abnormal events. Experiments on a publicly available OPPORTUNITY activity recognition database from UCI machine learning repository demonstrates the potential of our approach to address next generation unsupervised automatic classification and detection approaches for remote activity recognition for novel, eHealth application scenarios, such as monitoring and tracking of elderly, disabled and those with special needs

AB - In this article, we propose a novel multimodal data analytics scheme for human activity recognition. Traditional data analysis schemes for activity recognition using heterogeneous sensor network setups for eHealth application scenarios are usually a heuristic process, involving underlying domain knowledge. Relying on such explicit knowledge is problematic when aiming to create automatic, unsupervised or semi-supervised monitoring and tracking of different activities, and detection of abnormal events. Experiments on a publicly available OPPORTUNITY activity recognition database from UCI machine learning repository demonstrates the potential of our approach to address next generation unsupervised automatic classification and detection approaches for remote activity recognition for novel, eHealth application scenarios, such as monitoring and tracking of elderly, disabled and those with special needs

KW - Multimodal

KW - Feature-Learning

KW - Activity-Recognition

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DO - 10.1007/978-3-642-55355-4_47

M3 - Conference contribution

SN - 9783642553547

VL - 426

T3 - IFIP Advances in Information and Communication Technology

SP - 449

EP - 458

BT - IFIP Advances in Information and Communication Technology

A2 - Yu, Changrui

A2 - Liu, Kecheng

A2 - Gulliver, Stephen R.

A2 - Li, Weizi

PB - Springer

CY - Berlin, Germany

T2 - 15th IFIP WG 8.1 International Conference on Informatics and Semiotics in Organisations, ICISO 2014

Y2 - 23 May 2014 through 24 May 2014

ER -

A Novel Multimodal Data Analytic Scheme for Human Activity Recognition

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