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

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3 Author(s)
Andreu, J. ; Infolab21, Lancaster Univ., Lancaster, UK ; Baruah, R.D. ; Angelov, P.

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.

Published in:

Fuzzy Systems (FUZZ), 2011 IEEE International Conference on

Date of Conference:

27-30 June 2011