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

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

Building a person home behavior model based on data from smart house sensors 2015

Dissertação para a obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de EnergiaO propósito deste trabalho é construir um modelo baseado em informação captada por sensores e câmaras em uma casa onde viva somente uma pessoa. A primeira parte do trabalho consistiu em reunir informação que ajuda-se a perceber o que já tinha sido feito neste âmbito por outros engenheiros. Na realização deste trabalho, a parte que se revelou mais acessível foi a que envolveu a procura de informação sobre os diferentes métodos a usar no sistema, tais como Lógica Fuzzy, Redes Neuronais ou o método Monte Carlo via Cadeias de Markov. Em contrapartida, investigar as diferentes maneiras de guardar a informação provou ser mais desafiante. Foi praticamente impossível encontrar informação sobre os métodos usados para guardar informação recolhida pelos sensores e câmaras. A maior parte das vezes, os engenheiros não mencionavam a parte da informação relativa a este aspecto, simplesmente apresentavam os seus resultados e as suas conclusões. Além disso, quando o uso de informação era mencionada, a mesma relacionava-se com informação online, ou só informação já recolhida por sensores e câmaras, sem quaisquer detalhes. Era espectável encontrar mais informação sobre os métodos de guardar informação, mais específica, como a informação recolhida era ligada entre si e como o sistema iria interpretá-la e que resposta iria dar. Devido à falta de informação sobre como armazenar a informação recolhida, ficou decidido que se teria de assumir uma maneira para o fazer e com isso construir o modelo. Este modelo será referido neste documento. Devido a problemas relacionados com a parte da programação e com a falta de tempo, foi impossível construir o modelo usando os, considerados, melhores métodos. Não obstante, foi possível decidir as melhores opções para construir o modelo, assim como algumas formas de o fazer. Foi decidido a maneira como armazenar informação, o protocolo de informação a usar e o método que irá inferir com as actividades e comportamentos do habitante.Abstract:The purpose of this work has been to build a model based on data collected from sensors and cameras in a house with only one inhabitant. The first part of the work consisted of gathering research in order to try to understand what was already made by other engineers. One part stood out to be less complicated, as it evolved around finding information about the different methods to use with a system like Fuzzy Logic, Neural Network or Markov Chain Monte Carlo. However, investigating different ways to store information proved to be more challenging. It was pretty much impossible to get some information about the way people store the information collected from sensors and cameras. Most of the time, other engineers never mention the part related with the data, but simply presented the results and then their conclusion. Moreover, when the use of data was mentioned, it was simply related to online data, or just data which was stored after being collected from sensors and cameras, without any further detail. It was expected to find more information about the way the data was used, more specific information, covering how all the information was connected to each other, and how the system would interpret all the data and the responses. Since the lack of information related with the data, it was decided to assume a way to store the information and with that, a model was built. This model will be referred to within this paper. Due to problems with programming and lack of time, it was impossible to build a model by using the best methods. Notwithstanding, it was possible to decide all the best options to use to build the model, along with some ways to do it. It was decided the way to store information, the Communication Protocol to use and the method to infer with the inhabitant activities and behaviors

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

The recent years have witnessed the prevalence and abundance of vision sensors in various applications such as security surveillance, healthcare and Ambient Assisted Living (AAL) among others. This is so as to realize intelligent environments which are capable of detecting users’ actions and gestures so that the needed services can be provided automatically and instantly to maximize user comfort and safety as well as to minimize energy. However, it is very challenging to automatically detect important events and human behaviour from vision sensors and summarize them in real time. This is due to the massive data sizes related to video analysis applications and the high level of uncertainties associated with the real world unstructured environments occupied by various users. Machine vision based systems can help detect and summarize important information which cannot be detected by any other sensor; for example, how much water a candidate drank and whether or not they had something to eat. However, conventional non-fuzzy based methods are not robust enough to recognize the various complex types of behaviour in AAL applications. Fuzzy logic system (FLS) is an established field of research to robustly handle uncertainties in complicated real-world problems. In this thesis, we will present a general recognition and classification framework based on fuzzy logic systems which allows for behaviour recognition and event summarisation using 2D/3D video sensors in AAL applications. I started by investigating the use of 2D CCTV camera based system where I proposed and developed novel IT2FLS-based methods for silhouette extraction and 2D behaviour recognition which outperform the traditional on the publicly available Weizmann human action dataset. I will also present a novel system based on 3D RGB-D vision sensors and Interval Type-2 Fuzzy Logic based Systems (IT2FLSs) ) generated by the Big Bang Big Crunch (BB-BC) algorithm for the real time automatic detection and summarization of important events and human behaviour. I will present several real world experiments which were conducted for AAL related behaviour with various users. It will be shown that the proposed BB-BC IT2FLSs outperforms its Type-1 FLSs (T1FLSs) counterpart as well as other conventional non-fuzzy methods, and that performance improvement rises when the number of subjects increases. It will be shown that by utilizing the recognized output activity together with relevant event descriptions (such as video data, timestamp, location and user identification) detailed events are efficiently summarized and stored in our back-end SQL event database, which provides services including event searching, activity retrieval and high-definition video playback to the front-end user interfaces

Combining neural networks and fuzzy systems for human behavior understanding

The psychological overcharge issue related to human inadequacy to maintain a constant level of attention in simultaneously monitoring multiple visual information sources makes necessary to develop enhanced video surveillance systems that automatically understand human behaviors and identify dangerous situations. This paper introduces a semantic human behavioral analysis (HBA) system based on a neuro-fuzzy approach that, independently from the specific application, translates tracking kinematic data into a collection of semantic labels characterizing the behavior of different actors in a scene in order to appropriately classify the current situation. Different from other HBA approaches, the proposed system shows high level of scalability, robustness and tolerance for tracking imprecision and, for this reason, it could represent a valid choice for improving the performance of current systems. © 2012 IEEE