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A Big Bang–Big Crunch Type-2 Fuzzy Logic System for Machine-Vision-Based Event Detection and Summarization in Real-World Ambient-Assisted Living

Yao, B and Hagras, H and Alghazzawi, D and Alhaddad, MJ (2016) 'A Big Bang–Big Crunch Type-2 Fuzzy Logic System for Machine-Vision-Based Event Detection and Summarization in Real-World Ambient-Assisted Living.' IEEE Transactions on Fuzzy Systems, 24 (6). pp. 1307-1319. ISSN 1063-6706

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Abstract

The area of ambient-assisted living (AAL) focuses on developing new technologies, which can improve the quality of life and care provided to elderly and disabled people. In this paper, we propose a novel system based on 3-D RGB-D vision sensors and interval type-2 fuzzy-logic-based systems (IT2FLSs) employing the big bang-big crunch algorithm for the real-time automatic detection and summarization of important events and human behaviors from the large-scale data. We will present several real-world experiments, which were conducted for AAL-related behaviors with various users. It will be shown that the proposed BB-BC IT2FLSs outperform the type-1 fuzzy logic system counterparts as well as other conventional nonfuzzy methods, and the performance improves when the number of subjects increases.

Item Type: Article
Uncontrolled Keywords: Event summarization; interval type-2 fuzzy logic systems (IT2FLS); 3-D machine vision
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 15 Mar 2016 16:36
Last Modified: 15 Jan 2022 00:44
URI: http://repository.essex.ac.uk/id/eprint/16266

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