User activities outliers detection; integration of statistical and computational intelligence techniques

Mahmoud, S., Lotfi, A. ORCID: 0000-0002-5139-6565 and Langensiepen, C. ORCID: 0000-0002-0165-9048, 2016. User activities outliers detection; integration of statistical and computational intelligence techniques. Computational Intelligence, 32 (1), pp. 49-71. ISSN 0824-7935

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Abstract

In this paper, a hybrid technique for user activities outliers detection is introduced. The hybrid technique consists of a two-stage integration of Principal Component Analysis (PCA) and Fuzzy Rule-Based Systems (FRBS). In the first stage, the Hamming distance is used to measure the differences between different activities. PCA is then applied to the distance measures to find two indices of Hotelling's T2 and Squared Prediction Error. In the second
stage of the process, the calculated indices are provided as inputs to the FRBSs to model them heuristically. The model is used to identify the outliers and classify them. The proposed system is tested in real home environments, equipped with appropriate sensory devices, to identify outliers in the activities of daily living of the user. Three case studies are reported to demonstrate the effectiveness of the proposed system. The proposed system successfully identifies the outliers in activities distinguishing between the normal and abnormal behavioural patterns.

Item Type:	Journal article
Publication Title:	Computational Intelligence
Creators:	Mahmoud, S., Lotfi, A. and Langensiepen, C.
Publisher:	Wiley Periodicals
Date:	February 2016
Volume:	32
Number:	1
ISSN:	0824-7935
Identifiers:	Number Type 10.1111/coin.12045 DOI
Divisions:	Schools > School of Science and Technology
Record created by:	EPrints Services
Date Added:	09 Oct 2015 10:52
Last Modified:	09 Jun 2017 13:42
URI:	https://irep.ntu.ac.uk/id/eprint/19341

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