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

Andreu, Javier and Dutta Baruah, Rashmi and Angelov, Plamen (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. ISBN 978-1-4244-7315-1

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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.

Item Type: Contribution in Book/Report/Proceedings
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 53151
Deposited By: ep_importer_pure
Deposited On: 09 Mar 2012 03:33
Refereed?: No
Published?: Published
Last Modified: 17 Mar 2018 02:27
Identification Number:

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