Classifying human activities with temporal extension of random forest

Shih Yin Ooi; Shing Chiang Tan; Wooi Ping Cheah

doi:10.1007/978-3-319-46681-1_1

Classifying human activities with temporal extension of random forest

Shih Yin Ooi, Shing Chiang Tan, Wooi Ping Cheah

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

Sensor-Based Human Activity Recognition (HAR) is a study of recognizing the human’s activities by using the data captured from wearable sensors. Avail the temporal information from the sensors, a modified version of random forest is proposed to preserve the temporal information, and harness them in classifying the human activities. The proposed algorithm is tested on 7 public HAR datasets. Promising results are reported, with an average classification accuracy of ~ 98%.

Original language	English
Title of host publication	Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings
Editors	Kazushi Ikeda, Minho Lee, Akira Hirose, Seiichi Ozawa, Kenji Doya, Derong Liu
Publisher	Springer Verlag
Pages	3-10
Number of pages	8
ISBN (Print)	9783319466804
DOIs	https://doi.org/10.1007/978-3-319-46681-1_1
Publication status	Published - 2016
Externally published	Yes
Event	23rd International Conference on Neural Information Processing, ICONIP 2016 - Kyoto, Japan Duration: 16 Oct 2016 → 21 Oct 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9950 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	23rd International Conference on Neural Information Processing, ICONIP 2016
Country/Territory	Japan
City	Kyoto
Period	16/10/16 → 21/10/16

Keywords

Classification
Human activity
Machine learning
Random forest
Temporal sequences

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science (all)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-319-46681-1_1

Cite this

Ooi, S. Y., Tan, S. C., & Cheah, W. P. (2016). Classifying human activities with temporal extension of random forest. In K. Ikeda, M. Lee, A. Hirose, S. Ozawa, K. Doya, & D. Liu (Eds.), Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings (pp. 3-10). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9950 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46681-1_1

Ooi, Shih Yin ; Tan, Shing Chiang ; Cheah, Wooi Ping. / Classifying human activities with temporal extension of random forest. Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings. editor / Kazushi Ikeda ; Minho Lee ; Akira Hirose ; Seiichi Ozawa ; Kenji Doya ; Derong Liu. Springer Verlag, 2016. pp. 3-10 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Classifying human activities with temporal extension of random forest",

abstract = "Sensor-Based Human Activity Recognition (HAR) is a study of recognizing the human{\textquoteright}s activities by using the data captured from wearable sensors. Avail the temporal information from the sensors, a modified version of random forest is proposed to preserve the temporal information, and harness them in classifying the human activities. The proposed algorithm is tested on 7 public HAR datasets. Promising results are reported, with an average classification accuracy of ~ 98%.",

keywords = "Classification, Human activity, Machine learning, Random forest, Temporal sequences",

author = "Ooi, {Shih Yin} and Tan, {Shing Chiang} and Cheah, {Wooi Ping}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 23rd International Conference on Neural Information Processing, ICONIP 2016 ; Conference date: 16-10-2016 Through 21-10-2016",

year = "2016",

doi = "10.1007/978-3-319-46681-1_1",

language = "English",

isbn = "9783319466804",

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

publisher = "Springer Verlag",

pages = "3--10",

editor = "Kazushi Ikeda and Minho Lee and Akira Hirose and Seiichi Ozawa and Kenji Doya and Derong Liu",

booktitle = "Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings",

address = "Germany",

}

Ooi, SY, Tan, SC & Cheah, WP 2016, Classifying human activities with temporal extension of random forest. in K Ikeda, M Lee, A Hirose, S Ozawa, K Doya & D Liu (eds), Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9950 LNCS, Springer Verlag, pp. 3-10, 23rd International Conference on Neural Information Processing, ICONIP 2016, Kyoto, Japan, 16/10/16. https://doi.org/10.1007/978-3-319-46681-1_1

Classifying human activities with temporal extension of random forest. / Ooi, Shih Yin; Tan, Shing Chiang; Cheah, Wooi Ping.
Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings. ed. / Kazushi Ikeda; Minho Lee; Akira Hirose; Seiichi Ozawa; Kenji Doya; Derong Liu. Springer Verlag, 2016. p. 3-10 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9950 LNCS).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Classifying human activities with temporal extension of random forest

AU - Ooi, Shih Yin

AU - Tan, Shing Chiang

AU - Cheah, Wooi Ping

N1 - Publisher Copyright: © Springer International Publishing AG 2016.

PY - 2016

Y1 - 2016

N2 - Sensor-Based Human Activity Recognition (HAR) is a study of recognizing the human’s activities by using the data captured from wearable sensors. Avail the temporal information from the sensors, a modified version of random forest is proposed to preserve the temporal information, and harness them in classifying the human activities. The proposed algorithm is tested on 7 public HAR datasets. Promising results are reported, with an average classification accuracy of ~ 98%.

AB - Sensor-Based Human Activity Recognition (HAR) is a study of recognizing the human’s activities by using the data captured from wearable sensors. Avail the temporal information from the sensors, a modified version of random forest is proposed to preserve the temporal information, and harness them in classifying the human activities. The proposed algorithm is tested on 7 public HAR datasets. Promising results are reported, with an average classification accuracy of ~ 98%.

KW - Classification

KW - Human activity

KW - Machine learning

KW - Random forest

KW - Temporal sequences

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U2 - 10.1007/978-3-319-46681-1_1

DO - 10.1007/978-3-319-46681-1_1

M3 - Conference contribution

AN - SCOPUS:84992754528

SN - 9783319466804

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

SP - 3

EP - 10

BT - Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings

A2 - Ikeda, Kazushi

A2 - Lee, Minho

A2 - Hirose, Akira

A2 - Ozawa, Seiichi

A2 - Doya, Kenji

A2 - Liu, Derong

PB - Springer Verlag

T2 - 23rd International Conference on Neural Information Processing, ICONIP 2016

Y2 - 16 October 2016 through 21 October 2016

ER -

Ooi SY, Tan SC, Cheah WP. Classifying human activities with temporal extension of random forest. In Ikeda K, Lee M, Hirose A, Ozawa S, Doya K, Liu D, editors, Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings. Springer Verlag. 2016. p. 3-10. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-46681-1_1

Classifying human activities with temporal extension of random forest

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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