Resident activity recognition in smart homes by using artificial neural networks

Homay Danaei Mehr; Huseyin  Polat; Aydin Cetin

doi:10.1109/SGCF.2016.7492428

Resident activity recognition in smart homes by using artificial neural networks

Homay Danaei Mehr, Huseyin Polat, Aydin Cetin

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

66 Citations (Scopus)

Abstract

Recognition and detection of human activity is one of the challenges in smart home technologies. In this paper, three algorithms of artificial neural networks, namely Quick Propagation (QP), Levenberg Marquardt (LM) and Batch Back Propagation (BBP), have been used for human activity recognition and compared according to performance on Massachusetts Institute of Technology (MIT) smart home dataset. The achieved results demonstrated that Levenberg Marquardt algorithm has better human activity recognition performance (by 92.81% accuracy) than Quick Propagation and Batch Back Propagation algorithms.

Original language	English
Title of host publication	2016 4th International Istanbul Smart Grid Congress and Fair (ICSG 2016)
Place of Publication	Istanbul
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Number of pages	5
ISBN (Electronic)	9781509008667
ISBN (Print)	9781509008674
DOIs	https://doi.org/10.1109/SGCF.2016.7492428
Publication status	Published - 20 Apr 2016
Externally published	Yes
Event	4th International Istanbul Smart Grid Congress and Fair (ICSG 2016) - Istanbul, Turkey Duration: 20 Apr 2016 → 21 Apr 2016

Conference

Conference	4th International Istanbul Smart Grid Congress and Fair (ICSG 2016)
Country/Territory	Turkey
City	Istanbul
Period	20/04/16 → 21/04/16

Keywords

smart home
artificial neural network
human activity recognition

Access to Document

10.1109/SGCF.2016.7492428

Cite this

@inproceedings{3373470692bc4e618cadf231827e07f7,

title = "Resident activity recognition in smart homes by using artificial neural networks",

abstract = "Recognition and detection of human activity is one of the challenges in smart home technologies. In this paper, three algorithms of artificial neural networks, namely Quick Propagation (QP), Levenberg Marquardt (LM) and Batch Back Propagation (BBP), have been used for human activity recognition and compared according to performance on Massachusetts Institute of Technology (MIT) smart home dataset. The achieved results demonstrated that Levenberg Marquardt algorithm has better human activity recognition performance (by 92.81% accuracy) than Quick Propagation and Batch Back Propagation algorithms.",

keywords = "smart home, artificial neural network, human activity recognition",

author = "{Danaei Mehr}, Homay and Huseyin Polat and Aydin Cetin",

year = "2016",

month = apr,

day = "20",

doi = "10.1109/SGCF.2016.7492428",

language = "English",

isbn = "9781509008674",

booktitle = "2016 4th International Istanbul Smart Grid Congress and Fair (ICSG 2016)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "4th International Istanbul Smart Grid Congress and Fair (ICSG 2016) ; Conference date: 20-04-2016 Through 21-04-2016",

}

Danaei Mehr, H, Polat, H & Cetin, A 2016, Resident activity recognition in smart homes by using artificial neural networks. in 2016 4th International Istanbul Smart Grid Congress and Fair (ICSG 2016) . Institute of Electrical and Electronics Engineers (IEEE), Istanbul, 4th International Istanbul Smart Grid Congress and Fair (ICSG 2016) , Istanbul, Turkey, 20/04/16. https://doi.org/10.1109/SGCF.2016.7492428

Resident activity recognition in smart homes by using artificial neural networks. / Danaei Mehr, Homay; Polat, Huseyin ; Cetin, Aydin.
2016 4th International Istanbul Smart Grid Congress and Fair (ICSG 2016) . Istanbul: Institute of Electrical and Electronics Engineers (IEEE), 2016.

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

TY - GEN

T1 - Resident activity recognition in smart homes by using artificial neural networks

AU - Danaei Mehr, Homay

AU - Polat, Huseyin

AU - Cetin, Aydin

PY - 2016/4/20

Y1 - 2016/4/20

N2 - Recognition and detection of human activity is one of the challenges in smart home technologies. In this paper, three algorithms of artificial neural networks, namely Quick Propagation (QP), Levenberg Marquardt (LM) and Batch Back Propagation (BBP), have been used for human activity recognition and compared according to performance on Massachusetts Institute of Technology (MIT) smart home dataset. The achieved results demonstrated that Levenberg Marquardt algorithm has better human activity recognition performance (by 92.81% accuracy) than Quick Propagation and Batch Back Propagation algorithms.

AB - Recognition and detection of human activity is one of the challenges in smart home technologies. In this paper, three algorithms of artificial neural networks, namely Quick Propagation (QP), Levenberg Marquardt (LM) and Batch Back Propagation (BBP), have been used for human activity recognition and compared according to performance on Massachusetts Institute of Technology (MIT) smart home dataset. The achieved results demonstrated that Levenberg Marquardt algorithm has better human activity recognition performance (by 92.81% accuracy) than Quick Propagation and Batch Back Propagation algorithms.

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KW - artificial neural network

KW - human activity recognition

U2 - 10.1109/SGCF.2016.7492428

DO - 10.1109/SGCF.2016.7492428

M3 - Conference proceeding contribution

SN - 9781509008674

BT - 2016 4th International Istanbul Smart Grid Congress and Fair (ICSG 2016)

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T2 - 4th International Istanbul Smart Grid Congress and Fair (ICSG 2016)

Y2 - 20 April 2016 through 21 April 2016

ER -

Resident activity recognition in smart homes by using artificial neural networks

Abstract

Conference

Keywords

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