Comparative analysis of artificial hydrocarbon networks and data-driven approaches for human activity recognition

Hiram Ponce; María de Lourdes Martínez-Villaseñor; Luis Miralles-Pechúan

doi:10.1007/978-3-319-26401-1_15

Comparative analysis of artificial hydrocarbon networks and data-driven approaches for human activity recognition

Hiram Ponce
, María de Lourdes Martínez-Villaseñor
, Luis Miralles-Pechúan

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

Abstract

In recent years computing and sensing technologies advances contribute to develop effective human activity recognition systems. In contextaware and ambient assistive living applications, classification of body postures and movements, aids in the development of health systems that improve the quality of life of the disabled and the elderly. In this paper we describe a comparative analysis of data-driven activity recognition techniques against a novel supervised learning technique called artificial hydrocarbon networks (AHN). We prove that artificial hydrocarbon networks are suitable for efficient body postures and movements classification, providing a comparison between its performance and other well-known supervised learning methods.

Original language	English
Title of host publication	Ubiquitous Computing and Ambient Intelligence
Subtitle of host publication	Sensing, Processing, and Using Environmental Information - 9th International Conference, UCAmI 2015, Proceedings
Editors	Juan M. García-Chamizo, Giancarlo Fortino, Sergio F. Ochoa
Publisher	Springer Verlag
Pages	150-161
Number of pages	12
ISBN (Print)	9783319264004
DOIs	https://doi.org/10.1007/978-3-319-26401-1_15
Publication status	Published - 2015
Externally published	Yes
Event	9th International Conference on Ubiquitous Computing and Ambient Intelligence, UCAmI 2015 - Puerto Varas, Chile Duration: 1 Dec 2015 → 4 Dec 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9454
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Conference on Ubiquitous Computing and Ambient Intelligence, UCAmI 2015
Country/Territory	Chile
City	Puerto Varas
Period	1/12/15 → 4/12/15

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Artificial hydrocarbon networks
Artificial organic networks
Classification
Human activity recognition
Supervised learning
Wearable sensors

Access to Document

10.1007/978-3-319-26401-1_15

Cite this

Ponce, H., de Lourdes Martínez-Villaseñor, M., & Miralles-Pechúan, L. (2015). Comparative analysis of artificial hydrocarbon networks and data-driven approaches for human activity recognition. In J. M. García-Chamizo, G. Fortino, & S. F. Ochoa (Eds.), Ubiquitous Computing and Ambient Intelligence: Sensing, Processing, and Using Environmental Information - 9th International Conference, UCAmI 2015, Proceedings (pp. 150-161). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9454). Springer Verlag. https://doi.org/10.1007/978-3-319-26401-1_15

Ponce, Hiram ; de Lourdes Martínez-Villaseñor, María ; Miralles-Pechúan, Luis. / Comparative analysis of artificial hydrocarbon networks and data-driven approaches for human activity recognition. Ubiquitous Computing and Ambient Intelligence: Sensing, Processing, and Using Environmental Information - 9th International Conference, UCAmI 2015, Proceedings. editor / Juan M. García-Chamizo ; Giancarlo Fortino ; Sergio F. Ochoa. Springer Verlag, 2015. pp. 150-161 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{cff60469ea0d47a1be9bc62740f5398b,

title = "Comparative analysis of artificial hydrocarbon networks and data-driven approaches for human activity recognition",

abstract = "In recent years computing and sensing technologies advances contribute to develop effective human activity recognition systems. In contextaware and ambient assistive living applications, classification of body postures and movements, aids in the development of health systems that improve the quality of life of the disabled and the elderly. In this paper we describe a comparative analysis of data-driven activity recognition techniques against a novel supervised learning technique called artificial hydrocarbon networks (AHN). We prove that artificial hydrocarbon networks are suitable for efficient body postures and movements classification, providing a comparison between its performance and other well-known supervised learning methods.",

keywords = "Artificial hydrocarbon networks, Artificial organic networks, Classification, Human activity recognition, Supervised learning, Wearable sensors",

author = "Hiram Ponce and \{de Lourdes Mart{\'i}nez-Villase{\~n}or\}, Mar{\'i}a and Luis Miralles-Pech{\'u}an",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 9th International Conference on Ubiquitous Computing and Ambient Intelligence, UCAmI 2015 ; Conference date: 01-12-2015 Through 04-12-2015",

year = "2015",

doi = "10.1007/978-3-319-26401-1\_15",

language = "English",

isbn = "9783319264004",

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

publisher = "Springer Verlag",

pages = "150--161",

editor = "Garc{\'i}a-Chamizo, \{Juan M.\} and Giancarlo Fortino and Ochoa, \{Sergio F.\}",

booktitle = "Ubiquitous Computing and Ambient Intelligence",

address = "Germany",

}

Ponce, H, de Lourdes Martínez-Villaseñor, M & Miralles-Pechúan, L 2015, Comparative analysis of artificial hydrocarbon networks and data-driven approaches for human activity recognition. in JM García-Chamizo, G Fortino & SF Ochoa (eds), Ubiquitous Computing and Ambient Intelligence: Sensing, Processing, and Using Environmental Information - 9th International Conference, UCAmI 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9454, Springer Verlag, pp. 150-161, 9th International Conference on Ubiquitous Computing and Ambient Intelligence, UCAmI 2015, Puerto Varas, Chile, 1/12/15. https://doi.org/10.1007/978-3-319-26401-1_15

Comparative analysis of artificial hydrocarbon networks and data-driven approaches for human activity recognition. / Ponce, Hiram; de Lourdes Martínez-Villaseñor, María; Miralles-Pechúan, Luis.
Ubiquitous Computing and Ambient Intelligence: Sensing, Processing, and Using Environmental Information - 9th International Conference, UCAmI 2015, Proceedings. ed. / Juan M. García-Chamizo; Giancarlo Fortino; Sergio F. Ochoa. Springer Verlag, 2015. p. 150-161 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9454).

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

TY - GEN

T1 - Comparative analysis of artificial hydrocarbon networks and data-driven approaches for human activity recognition

AU - Ponce, Hiram

AU - de Lourdes Martínez-Villaseñor, María

AU - Miralles-Pechúan, Luis

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - In recent years computing and sensing technologies advances contribute to develop effective human activity recognition systems. In contextaware and ambient assistive living applications, classification of body postures and movements, aids in the development of health systems that improve the quality of life of the disabled and the elderly. In this paper we describe a comparative analysis of data-driven activity recognition techniques against a novel supervised learning technique called artificial hydrocarbon networks (AHN). We prove that artificial hydrocarbon networks are suitable for efficient body postures and movements classification, providing a comparison between its performance and other well-known supervised learning methods.

AB - In recent years computing and sensing technologies advances contribute to develop effective human activity recognition systems. In contextaware and ambient assistive living applications, classification of body postures and movements, aids in the development of health systems that improve the quality of life of the disabled and the elderly. In this paper we describe a comparative analysis of data-driven activity recognition techniques against a novel supervised learning technique called artificial hydrocarbon networks (AHN). We prove that artificial hydrocarbon networks are suitable for efficient body postures and movements classification, providing a comparison between its performance and other well-known supervised learning methods.

KW - Artificial hydrocarbon networks

KW - Artificial organic networks

KW - Classification

KW - Human activity recognition

KW - Supervised learning

KW - Wearable sensors

UR - https://www.scopus.com/pages/publications/84952333273

U2 - 10.1007/978-3-319-26401-1_15

DO - 10.1007/978-3-319-26401-1_15

M3 - Conference contribution

AN - SCOPUS:84952333273

SN - 9783319264004

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

SP - 150

EP - 161

BT - Ubiquitous Computing and Ambient Intelligence

A2 - García-Chamizo, Juan M.

A2 - Fortino, Giancarlo

A2 - Ochoa, Sergio F.

PB - Springer Verlag

T2 - 9th International Conference on Ubiquitous Computing and Ambient Intelligence, UCAmI 2015

Y2 - 1 December 2015 through 4 December 2015

ER -

Ponce H, de Lourdes Martínez-Villaseñor M, Miralles-Pechúan L. Comparative analysis of artificial hydrocarbon networks and data-driven approaches for human activity recognition. In García-Chamizo JM, Fortino G, Ochoa SF, editors, Ubiquitous Computing and Ambient Intelligence: Sensing, Processing, and Using Environmental Information - 9th International Conference, UCAmI 2015, Proceedings. Springer Verlag. 2015. p. 150-161. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-26401-1_15

Comparative analysis of artificial hydrocarbon networks and data-driven approaches for human activity recognition

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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Cite this