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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. [ABSTRACT FROM PUBLISHER]

This paper addresses the need to enhance transparency in ambient intelligent environments by developing more natural ways of interaction, which allow the users to communicate easily with the hidden networked devices rather than embedding obtrusive tablets and computing equipment throughout their surroundings. Ambient intelligence vision aims to realize digital environments that adapt to users in a responsive, transparent, and context-aware manner in order to enhance users’ comfort. It is, therefore, appropriate to employ the paradigm of “computing with words” (CWWs), which aims to mimic the ability of humans to communicate transparently and manipulate perceptions via words. One of the daily activities that would increase the comfort levels of the users (especially people with disabilities) is cooking and performing tasks in the kitchen. Existing approaches on food preparation, cooking, and recipe recommendation stress on healthy eating and balanced meal choices while providing limited personalization features through the use of intrusive user interfaces. Herein, we present an application, which transparently interacts with users based on a novel CWWs approach in order to predict the recipe's difficulty level and to recommend an appropriate recipe depending on the user's mood, appetite, and spare time. The proposed CWWs framework is based on linear general type-2 (LGT2) fuzzy sets, which linearly quantify the linguistic modifiers in the third dimension in order to better represent the user perceptions while avoiding the drawbacks of type-1 and interval type-2 fuzzy sets. The LGT2-based CWWs framework can learn from user experiences and adapt to them in order to establish more natural human–machine interaction. We have carried numerous real-world experiments with various users in the University of Essex intelligent flat. The comparison analysis between interval type-2 fuzzy sets and LGT2 fuzzy sets demonstrates up to 55.43% improvement when general type-2 fuzzy sets are used than when interval type-2 fuzzy sets are used instead. The quantitative and qualitative analysis both show the success of the system in providing a natural interaction with the users for recommending food recipes where the quantitative analysis shows the high statistical correlation between the system output and the users’ feedback; the qualitative analysis presents social science evaluation confirming the strong user acceptance of the system. [ABSTRACT FROM PUBLISHER]

Ambient Intelligence (AmI) is a multidisciplinary paradigm, which positively alters the relationship between humans and technology. Concerning home environments, the functions of AmI vision include home automation, communication, entertainment, working and learning. In the area of communication, AmI still needs better mechanisms for human-computer communication. A natural human-computer interaction necessitates having systems capable of modelling words and computing with them. For this purpose, the paradigm of Computing With Words (CWWs) can be employed to mimic human-like communication in Ambient Intelligent Environments (AIEs). This paper demonstrates the extendibility of Linear General Type-2 (LGT2) Fuzzy Logic based CWWs Framework to create an advanced real-world application, which integrates a semi-autonomous, safe and energy efficient electric hob. The motivation of this work is twofold: 1) there is a need to develop transparent human-computer communication rather than embedding obtrusive tablets and computing equipment throughout our surroundings, and 2) one of the most hazardous and energy consuming household devices, the electric hob, does not have competent levels of intelligence and energy efficiency. The proposed Ambient Intelligent Food Preparation System (AIFPS) can increase user comfort, facilitate food preparation, minimize energy consumption and be a useful tool for the elderly and people with major disabilities including vision impairment. The results of real-world experiments with various lay users in the intelligent flat (iSpace) show the success of AIFPS in providing up to 55.43% improved natural interaction (compared to Interval Type-2 based CWWs Framework) while achieving semi-autonomous, safe and energy efficient cooking that can save energy between 11.5% and 35.2%. [ABSTRACT FROM PUBLISHER]

Ambient Intelligence (AmI) is an emerging vision that aims to realize intelligent environments which are sensitive and responsive to the users' needs and behaviors. This paper presents an insight on the benefits that type-2 Fuzzy Logic Systems (FLSs) can offer towards the efforts to realize Ambient Intelligent Environments (AIEs). We will introduce research results from the Scaleup project showing different type-2 FLSs based applications in AIEs. Such applications include intelligent machine vision systems, blending real and virtual realities over dispersed geographical areas and allowing natural communication between the AIE and humans. [ABSTRACT FROM PUBLISHER]