Human activity recognition on mobile devices using artificial hydrocarbon networks

Hiram Ponce; Guillermo González; Luis Miralles-Pechuán; Ma Lourdes Martínez-Villaseñor

doi:10.1007/978-3-030-02837-4_2

Human activity recognition on mobile devices using artificial hydrocarbon networks

Hiram Ponce, Guillermo González, Luis Miralles-Pechuán, Ma Lourdes Martínez-Villaseñor

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

Abstract

Human activity recognition (HAR) aims to classify and identify activities based on data-driven from different devices, such as sensors or cameras. Particularly, mobile devices have been used for this recognition task. However, versatility of users, location of smartphones, battery, processing and storage limitations, among other issues have been identified. In that sense, this paper presents a human activity recognition system based on artificial hydrocarbon networks. This technique have been proved to be very effective on HAR systems using wearable sensors, so the present work proposes to use this learning method with the information provided by the in-sensors of mobile devices. Preliminary results proved that artificial hydrocarbon networks might be used as an alternative for human activity recognition on mobile devices. In addition, a real dataset created for this work has been published.

Original language	English
Title of host publication	Advances in Soft Computing - 16th Mexican International Conference on Artificial Intelligence, MICAI 2017, Proceedings
Editors	Sabino Miranda-Jiménez, Félix Castro, Miguel González-Mendoza
Publisher	Springer Verlag
Pages	17-29
Number of pages	13
ISBN (Print)	9783030028367
DOIs	https://doi.org/10.1007/978-3-030-02837-4_2
Publication status	Published - 2018
Externally published	Yes
Event	16th Mexican International Conference on Artificial Intelligence, MICAI 2017 - Enseneda, Mexico Duration: 23 Oct 2017 → 28 Oct 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10632 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th Mexican International Conference on Artificial Intelligence, MICAI 2017
Country/Territory	Mexico
City	Enseneda
Period	23/10/17 → 28/10/17

Keywords

Artificial organic networks
Classification
Human activity recognition
Machine learning
Mobile
Sensors

Access to Document

10.1007/978-3-030-02837-4_2

Cite this

Ponce, H., González, G., Miralles-Pechuán, L., & Martínez-Villaseñor, M. L. (2018). Human activity recognition on mobile devices using artificial hydrocarbon networks. In S. Miranda-Jiménez, F. Castro, & M. González-Mendoza (Eds.), Advances in Soft Computing - 16th Mexican International Conference on Artificial Intelligence, MICAI 2017, Proceedings (pp. 17-29). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10632 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-02837-4_2

Ponce, Hiram ; González, Guillermo ; Miralles-Pechuán, Luis et al. / Human activity recognition on mobile devices using artificial hydrocarbon networks. Advances in Soft Computing - 16th Mexican International Conference on Artificial Intelligence, MICAI 2017, Proceedings. editor / Sabino Miranda-Jiménez ; Félix Castro ; Miguel González-Mendoza. Springer Verlag, 2018. pp. 17-29 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Human activity recognition (HAR) aims to classify and identify activities based on data-driven from different devices, such as sensors or cameras. Particularly, mobile devices have been used for this recognition task. However, versatility of users, location of smartphones, battery, processing and storage limitations, among other issues have been identified. In that sense, this paper presents a human activity recognition system based on artificial hydrocarbon networks. This technique have been proved to be very effective on HAR systems using wearable sensors, so the present work proposes to use this learning method with the information provided by the in-sensors of mobile devices. Preliminary results proved that artificial hydrocarbon networks might be used as an alternative for human activity recognition on mobile devices. In addition, a real dataset created for this work has been published.",

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author = "Hiram Ponce and Guillermo Gonz{\'a}lez and Luis Miralles-Pechu{\'a}n and Mart{\'i}nez-Villase{\~n}or, \{Ma Lourdes\}",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 16th Mexican International Conference on Artificial Intelligence, MICAI 2017 ; Conference date: 23-10-2017 Through 28-10-2017",

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doi = "10.1007/978-3-030-02837-4\_2",

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Ponce, H, González, G, Miralles-Pechuán, L & Martínez-Villaseñor, ML 2018, Human activity recognition on mobile devices using artificial hydrocarbon networks. in S Miranda-Jiménez, F Castro & M González-Mendoza (eds), Advances in Soft Computing - 16th Mexican International Conference on Artificial Intelligence, MICAI 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10632 LNAI, Springer Verlag, pp. 17-29, 16th Mexican International Conference on Artificial Intelligence, MICAI 2017, Enseneda, Mexico, 23/10/17. https://doi.org/10.1007/978-3-030-02837-4_2

Human activity recognition on mobile devices using artificial hydrocarbon networks. / Ponce, Hiram; González, Guillermo; Miralles-Pechuán, Luis et al.
Advances in Soft Computing - 16th Mexican International Conference on Artificial Intelligence, MICAI 2017, Proceedings. ed. / Sabino Miranda-Jiménez; Félix Castro; Miguel González-Mendoza. Springer Verlag, 2018. p. 17-29 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10632 LNAI).

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

TY - GEN

T1 - Human activity recognition on mobile devices using artificial hydrocarbon networks

AU - Ponce, Hiram

AU - González, Guillermo

AU - Miralles-Pechuán, Luis

AU - Martínez-Villaseñor, Ma Lourdes

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2018.

PY - 2018

Y1 - 2018

N2 - Human activity recognition (HAR) aims to classify and identify activities based on data-driven from different devices, such as sensors or cameras. Particularly, mobile devices have been used for this recognition task. However, versatility of users, location of smartphones, battery, processing and storage limitations, among other issues have been identified. In that sense, this paper presents a human activity recognition system based on artificial hydrocarbon networks. This technique have been proved to be very effective on HAR systems using wearable sensors, so the present work proposes to use this learning method with the information provided by the in-sensors of mobile devices. Preliminary results proved that artificial hydrocarbon networks might be used as an alternative for human activity recognition on mobile devices. In addition, a real dataset created for this work has been published.

AB - Human activity recognition (HAR) aims to classify and identify activities based on data-driven from different devices, such as sensors or cameras. Particularly, mobile devices have been used for this recognition task. However, versatility of users, location of smartphones, battery, processing and storage limitations, among other issues have been identified. In that sense, this paper presents a human activity recognition system based on artificial hydrocarbon networks. This technique have been proved to be very effective on HAR systems using wearable sensors, so the present work proposes to use this learning method with the information provided by the in-sensors of mobile devices. Preliminary results proved that artificial hydrocarbon networks might be used as an alternative for human activity recognition on mobile devices. In addition, a real dataset created for this work has been published.

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

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BT - Advances in Soft Computing - 16th Mexican International Conference on Artificial Intelligence, MICAI 2017, Proceedings

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PB - Springer Verlag

T2 - 16th Mexican International Conference on Artificial Intelligence, MICAI 2017

Y2 - 23 October 2017 through 28 October 2017

ER -

Ponce H, González G, Miralles-Pechuán L, Martínez-Villaseñor ML. Human activity recognition on mobile devices using artificial hydrocarbon networks. In Miranda-Jiménez S, Castro F, González-Mendoza M, editors, Advances in Soft Computing - 16th Mexican International Conference on Artificial Intelligence, MICAI 2017, Proceedings. Springer Verlag. 2018. p. 17-29. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-02837-4_2

Human activity recognition on mobile devices using artificial hydrocarbon networks

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Keywords

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