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 978-1-4244-7315-1Full text not available from this repository.
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|
|Deposited On:||09 Mar 2012 03:33|
|Last Modified:||27 Feb 2017 01:25|
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