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Real-time recognition of human activities from wearable sensors by evolving classifiers

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Real-time recognition of human activities from wearable sensors by evolving classifiers. / Andreu, Javier; Dutta Baruah, Rashmi; Angelov, Plamen.
2011 IEEE International Conference on Fuzzy Systems (FUZZ). IEEE, 2011. p. 2786-2793.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Andreu, J, Dutta Baruah, R & Angelov, P 2011, Real-time recognition of human activities from wearable sensors by evolving classifiers. in 2011 IEEE International Conference on Fuzzy Systems (FUZZ). IEEE, pp. 2786-2793. https://doi.org/10.1109/FUZZY.2011.6007595

APA

Andreu, J., Dutta Baruah, R., & Angelov, P. (2011). Real-time recognition of human activities from wearable sensors by evolving classifiers. In 2011 IEEE International Conference on Fuzzy Systems (FUZZ) (pp. 2786-2793). IEEE. https://doi.org/10.1109/FUZZY.2011.6007595

Vancouver

Andreu J, Dutta Baruah R, Angelov P. Real-time recognition of human activities from wearable sensors by evolving classifiers. In 2011 IEEE International Conference on Fuzzy Systems (FUZZ). IEEE. 2011. p. 2786-2793 doi: 10.1109/FUZZY.2011.6007595

Author

Andreu, Javier ; Dutta Baruah, Rashmi ; Angelov, Plamen. / Real-time recognition of human activities from wearable sensors by evolving classifiers. 2011 IEEE International Conference on Fuzzy Systems (FUZZ). IEEE, 2011. pp. 2786-2793

Bibtex

@inproceedings{cbccb14ad6e54a379f38d0d35064e7f2,
title = "Real-time recognition of human activities from wearable sensors by evolving classifiers",
abstract = "A new approach to real-time human activity recognition (HAR) using evolving self-learning fuzzy rule-based classifier (eClass) will be described in this paper. A recursive version of the principle component analysis (PCA) and linear discriminant analysis (LDA) pre-processing methods is coupled with the eClass leading to a new approach for HAR which does not require computation and time consuming pre-training and data from many subjects. The proposed new method for evolving HAR (eHAR) takes into account the specifics of each user and possible evolution in time of her/his habits. Data streams from several wearable devices which make possible to develop a pervasive intelligence enabling them to personalize/tune to the specific user were used for the experimental part of the paper.",
author = "Javier Andreu and {Dutta Baruah}, Rashmi and Plamen Angelov",
year = "2011",
month = sep,
day = "1",
doi = "10.1109/FUZZY.2011.6007595",
language = "English",
isbn = "978-1-4244-7315-1 ",
pages = "2786--2793",
booktitle = "2011 IEEE International Conference on Fuzzy Systems (FUZZ)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Real-time recognition of human activities from wearable sensors by evolving classifiers

AU - Andreu, Javier

AU - Dutta Baruah, Rashmi

AU - Angelov, Plamen

PY - 2011/9/1

Y1 - 2011/9/1

N2 - A new approach to real-time human activity recognition (HAR) using evolving self-learning fuzzy rule-based classifier (eClass) will be described in this paper. A recursive version of the principle component analysis (PCA) and linear discriminant analysis (LDA) pre-processing methods is coupled with the eClass leading to a new approach for HAR which does not require computation and time consuming pre-training and data from many subjects. The proposed new method for evolving HAR (eHAR) takes into account the specifics of each user and possible evolution in time of her/his habits. Data streams from several wearable devices which make possible to develop a pervasive intelligence enabling them to personalize/tune to the specific user were used for the experimental part of the paper.

AB - A new approach to real-time human activity recognition (HAR) using evolving self-learning fuzzy rule-based classifier (eClass) will be described in this paper. A recursive version of the principle component analysis (PCA) and linear discriminant analysis (LDA) pre-processing methods is coupled with the eClass leading to a new approach for HAR which does not require computation and time consuming pre-training and data from many subjects. The proposed new method for evolving HAR (eHAR) takes into account the specifics of each user and possible evolution in time of her/his habits. Data streams from several wearable devices which make possible to develop a pervasive intelligence enabling them to personalize/tune to the specific user were used for the experimental part of the paper.

U2 - 10.1109/FUZZY.2011.6007595

DO - 10.1109/FUZZY.2011.6007595

M3 - Conference contribution/Paper

SN - 978-1-4244-7315-1

SP - 2786

EP - 2793

BT - 2011 IEEE International Conference on Fuzzy Systems (FUZZ)

PB - IEEE

ER -