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Real-time human activity recognition from wireless sensors using evolving fuzzy systems.

Andreu, Javier and Angelov, Plamen (2010) Real-time human activity recognition from wireless sensors using evolving fuzzy systems. In: IEEE International Conference on Fuzzy Systems (FUZZ), 2010. IEEE, pp. 2652-2659. ISBN 978-1-4244-6919-2

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Abstract

A new approach to real-time knowledge extraction from streaming data generated by wearable wireless accelerometers based on self-learning evolving fuzzy rule-based classifier is proposed and evaluated in this paper. Based on experiments with real subjects we collected data from 18 different classifieds activities. After preprocessing and classifying data depending on the sequence of activities regarding time, we achieved up to 99.81% of accuracy in recognizing a sequence of activities. This technique allows re-training the system as long as the application is running on the wearable intelligent/smart sensor, getting a better classification rate throughout the time without an increase of the delay in performance. (c) IEEE Press

Item Type: Contribution in Book/Report/Proceedings
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Uncontrolled Keywords: activity recognition ; evolving fuzzy classifier
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 33927
Deposited By: Dr. Plamen Angelov
Deposited On: 29 Jul 2010 14:56
Refereed?: No
Published?: Published
Last Modified: 10 Apr 2014 01:03
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/33927

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