An Incremental Adaptive Life Long Learning Approach for Type-2 Fuzzy Embedded Agents in Ambient Intelligent Environments

Hagras, Hani; Doctor, Faiyaz; Callaghan, Vic; López, Antonio M.

doi:10.1109/tfuzz.2006.889758

Cited by 119 publications

(69 citation statements)

References 10 publications

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“…Our experiments were performed in different places such as the intelligent apartment (iSpace) [42] and intelligent Classroom iClassroom [22] [43] at the University of Essex. We tested our methods including Type-1 Fuzzy Logic System (T1FLS) and Type-2 Fuzzy Logic System (T2FLS) compared against the non-fuzzy traditional methods including Hidden Markov Models (HMM) and Dynamic Time Warping (DTW) on 22 subjects ensuring high-levels of intra-and inter-subject variation and ambiguity in behavioural characteristics.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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

Hagras

Alghazzawi

et al. 2016

IEEE Trans. Fuzzy Syst.

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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 3D RGB-D vision sensors and Interval Type-2 Fuzzy Logic based Systems (IT2FLSs) employing the Big Bang Big Crunch (BB-BC) algorithm for the real time automatic detection and summarization of important events and human behaviours from the large-scale data. We will present several real world experiments which were conducted for AAL related behaviours with various users. It will be shown that the proposed BB-BC IT2FLSs outperforms the Type-1 FLSs (T1FLSs) counterpart as well as other conventional non-fuzzy methods, and the performance improvement rises when the amount of subjects increases.Index Terms-Interval Type-2 fuzzy logic systems, 3D machine vision, event summarization.

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Section: Experiments and Resultsmentioning

confidence: 99%

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

Hagras

Alghazzawi

et al. 2016

IEEE Trans. Fuzzy Syst.

Self Cite

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“…Fuzzy rules are learned from observing resident activities [67] and are used to control select devices in the dorm room.…”

Section: Smart Homesmentioning

confidence: 99%

Ambient intelligence: Technologies, applications, and opportunities

Cook

Augusto

Jakkula

2009

Pervasive and Mobile Computing

825

443

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“…The FL-based fusion model in [35] reduces uncertainty and false-positives within the process of fault detection. In [4], a specific FL-based inference system is proposed for ambient intelligence environments. Such system learns the users' behavior in light of being adapted to the users' profiles.…”

Section: Related Workmentioning

confidence: 99%

“…4 This energy model assumes an energy of two AA batteries that approximately supply 2200 mAh with effective average voltage 3V. It consumes 20mA if running a sensing application continuously.…”

Section: Communication Cost and Computation Energy Consumptionmentioning

confidence: 99%

Predictive intelligence to the edge through approximate collaborative context reasoning

Anagnostopoulos

Kolomvatsos

2017

Appl Intell

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We focus on Internet of Things (IoT) environments where a network of sensing and computing devices are responsible to locally process contextual data, reason and collaboratively infer the appearance of a specific phenomenon (event). Pushing processing and knowledge inference to the edge of the IoT network allows the complexity of the event reasoning process to be distributed into many manageable pieces and to be physically located at the source of the contextual information. This enables a huge amount of rich data streams to be processed in real time that would be prohibitively complex and costly to deliver on a traditional centralized Cloud system. We propose a lightweight, energy-efficient, distributed, adaptive, multiple-context perspective event reasoning model under uncertainty on each IoT device (sensor/actuator). Each device senses and processes context data and infers events based on different local context perspectives: (i) expert knowledge on event representation, (ii) outliers inference, and (iii) deviation from locally predicted context. Such novel approximate reasoning paradigm is achieved through a contextualized, collaborative belief-driven clustering process, where clusters of devices are formed according to their belief on the presence of events. Our distributed and federated intelligence model efficiently identifies any localized abnormality on the contextual data in light of event reasoning through aggregating local degrees of belief, updates, and adjusts its knowledge to contextual data outliers and novelty detection. We provide comprehensive experimental and comparison assessment of our model over real contextual data with other localized and centralized event detection models and show the benefits stemmed from its adoption by achieving up to three orders of magnitude less energy consumption and high quality of inference.Keywords Collaborative event inference · Federated reasoning · Edge predictive intelligence · Optimal stopping theory · Adaptive vector quantization · Type-2 fuzzy logic inference

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An Incremental Adaptive Life Long Learning Approach for Type-2 Fuzzy Embedded Agents in Ambient Intelligent Environments

Cited by 119 publications

References 10 publications

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

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

Ambient intelligence: Technologies, applications, and opportunities

Predictive intelligence to the edge through approximate collaborative context reasoning

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