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  • An Evolving Machine Learning Method for Human Activity Recognition Systems

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An evolving machine learning method for human activity recognition systems

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An evolving machine learning method for human activity recognition systems. / Andreu, Javier; Angelov, Plamen.
In: Journal of Ambient Intelligence and Humanized Computing, Vol. 4, No. 2, 2013, p. 195-206.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Andreu, J & Angelov, P 2013, 'An evolving machine learning method for human activity recognition systems', Journal of Ambient Intelligence and Humanized Computing, vol. 4, no. 2, pp. 195-206. https://doi.org/10.1007/s12652-011-0068-9

APA

Andreu, J., & Angelov, P. (2013). An evolving machine learning method for human activity recognition systems. Journal of Ambient Intelligence and Humanized Computing, 4(2), 195-206. https://doi.org/10.1007/s12652-011-0068-9

Vancouver

Andreu J, Angelov P. An evolving machine learning method for human activity recognition systems. Journal of Ambient Intelligence and Humanized Computing. 2013;4(2):195-206. Epub 2011 Oct 5. doi: 10.1007/s12652-011-0068-9

Author

Andreu, Javier ; Angelov, Plamen. / An evolving machine learning method for human activity recognition systems. In: Journal of Ambient Intelligence and Humanized Computing. 2013 ; Vol. 4, No. 2. pp. 195-206.

Bibtex

@article{ee7cbf4d75834a3db78e580fdce75950,
title = "An evolving machine learning method for human activity recognition systems",
abstract = "In this paper is presented a novel approach for human activity recognition (HAR) through complex data provided from wearable sensors. This approach considers the development of a more realistic system which takes into account the diversity of the population. It aims to define a general HAR model for any type of individuals. To achieve this much-needed processing capacity, this novel approach makes use of customizable, self-adaptive, self-development capacities of the so-called machine learning technique named evolving intelligent systems. An online pre-processing model to suit real-time capacities has been developed and is also explained in detail in this paper. Additionally, this paper provides valuable information on sensor analysis, online feature extraction, and evolving classifiers used for the attainment of this purpose. ",
keywords = "Human activity recognition , machine learning, Fuzzy classifiers, Evolving systems, Wearable sensors",
author = "Javier Andreu and Plamen Angelov",
note = "Online first",
year = "2013",
doi = "10.1007/s12652-011-0068-9",
language = "English",
volume = "4",
pages = "195--206",
journal = "Journal of Ambient Intelligence and Humanized Computing",
issn = "1868-5137",
publisher = "Springer Verlag",
number = "2",

}

RIS

TY - JOUR

T1 - An evolving machine learning method for human activity recognition systems

AU - Andreu, Javier

AU - Angelov, Plamen

N1 - Online first

PY - 2013

Y1 - 2013

N2 - In this paper is presented a novel approach for human activity recognition (HAR) through complex data provided from wearable sensors. This approach considers the development of a more realistic system which takes into account the diversity of the population. It aims to define a general HAR model for any type of individuals. To achieve this much-needed processing capacity, this novel approach makes use of customizable, self-adaptive, self-development capacities of the so-called machine learning technique named evolving intelligent systems. An online pre-processing model to suit real-time capacities has been developed and is also explained in detail in this paper. Additionally, this paper provides valuable information on sensor analysis, online feature extraction, and evolving classifiers used for the attainment of this purpose.

AB - In this paper is presented a novel approach for human activity recognition (HAR) through complex data provided from wearable sensors. This approach considers the development of a more realistic system which takes into account the diversity of the population. It aims to define a general HAR model for any type of individuals. To achieve this much-needed processing capacity, this novel approach makes use of customizable, self-adaptive, self-development capacities of the so-called machine learning technique named evolving intelligent systems. An online pre-processing model to suit real-time capacities has been developed and is also explained in detail in this paper. Additionally, this paper provides valuable information on sensor analysis, online feature extraction, and evolving classifiers used for the attainment of this purpose.

KW - Human activity recognition

KW - machine learning

KW - Fuzzy classifiers

KW - Evolving systems

KW - Wearable sensors

U2 - 10.1007/s12652-011-0068-9

DO - 10.1007/s12652-011-0068-9

M3 - Journal article

VL - 4

SP - 195

EP - 206

JO - Journal of Ambient Intelligence and Humanized Computing

JF - Journal of Ambient Intelligence and Humanized Computing

SN - 1868-5137

IS - 2

ER -