Smart House Environment Research Articles

Design of Unlicensed Dual Band Quasi-Yagi Antenna Using Semi-Bowtie for Indoor Wireless Power Transfer Application

This paper focuses on antenna for harvesting energy from a dedicated transmitter. The potential novel quasi-Yagi antenna with semi-bowtie driven element can be used as part of rectenna due to its characteristic of having directional properties and considerably wide bandwidth covering the Industrial, Scientific, and Medical (ISM) band consists of 863-870 MHz band and 902-928 MHz band. The modified quasi-Yagi is designed on a low-cost FR4 substrate with a physical size of 130 x 100 mm2 equivalent to 0.35λo x 0.27λo. The antenna has a peak directivity of 2.7 dBi and peak gain of 2.2 dBi in the targeted unlicensed bands with bandwidth of 14.4% for the range between 0.820-0.944 GHz. The shift in the resonant frequency is achieved by varying the phase shifter length and maintaining the same width for consistency. The antenna's operating frequency range varies between 0.8 GHz until 1 GHz which is less than 1 GHz by using semi-bowtie as driven element with a specific 7.22⸰ flare angle. The phase shift arm length of the antenna has been studied and simulated by using Computer Simulation Technology (CST) software and verified by using analytical equations. The simulated results are in accordance with the results obtained using analytical method. The same antenna geometry except variation in phase shifter arm length has been used throughout the study for consistency. The proposed antenna is promising to be used as part of rectenna for powering power management chip such as BQ25570 in a smart house environment operating at the indicated ISM bands.

Human behaviour modelling for welfare technology using hidden Markov models

Abstract Human behaviour modelling for welfare technology is the task of recognizing a person's behaviour patterns in order to construct a safe environment for that person. It is useful in building environments for older adults or to help any person in his or her daily life. The aim of this study is to model the behaviour of a person living in a smart house environment in order to detect abnormal behaviour and assist the person if help is needed. Hidden Markov models, location of the person in the house, posture of the person, and time frame rules are implemented using a real-world, open-source dataset for training and testing. The proposed model presented in this study models the normal behaviour of a person and detects anomalies in the usual pattern. The model shows good results in the identification of abnormal behaviour when tested.

Decision Trees for Human Activity Recognition Modelling in Smart House Environments

Decision Trees for Human Activity Recognition Modelling in Smart House Environments

INTELLIGENT AND ADAPTIVE FUZZY CONTROL SYSTEM FOR ENERGY EFFICIENT HOMES

“Smart houses” have widely established their position as a research field during the last decade. Nowadays the technical solutions related to energy resource management are being rapidly developed and integrated into the daily lives of people. The energy resource management systems use sensor networks for receiving and processing information during the realia time. Smart house adaptive and intelligent solutions has advanced towards common environment, which can take care of the inhabitants’ well-being in numerous ways. This paper propose to use a context sensitive and proactive fuzzy control system for controlling the automation processes in smart house environment. The designed monitoring system has adaptive and intelligent options, and it can operate using real time information received from sensors. The system is designed to operate fully in the background and can be installed to any exiting working system. This paper describes a central heating boiler control system implemented using the fuzzy control system designed. Author concentrates on the basic operation of such systems and present findings from the design process and initial tests.

Sensor Network Information Flow Control Method with Static Coordinator within Internet of Things in Smart House Environment

Nowadays the technical solutions related to energy resource management are being rapidly developed and integrated into the daily lives of people. The energy resource management systems use sensor networks for receiving and processing information during the realia time. Despite the large number of research related to increasing the life expectancy of a network, node positioning, network clustering as well as the optimization of the data-processing model, issues related to the data flow management and classification of the information become more and more topical; they allow to reduce the amount of transmitted information within the network. The network load of the unstructured information flow is the most important aspect that impacts the service quality of the network as well as the life expectancy of a node; as a result, the methods of network performance optimization and increase in life expectancy in the networks with a high information transmission rate become ineffective. Nowadays the amount of transmitted information in the Internet of Things networks is increased and becomes chaotic which results in the reduction of the overall network efficiency and life expectancy. The article discusses the management method of the network data flow that can respond to a data flow programming task whilst taking into account the balancing of the node energy in the network.

A Robust Building Energy Management Algorithm Validated in a Smart House Environment

A building energy management (BEM) system is a core hardware/software platform that enables demand response applications for building operators in the smart grid environment. This paper presents the BEM algorithm that is designed to be robust against communication failures and data errors. It has been implemented in the smart building located at Yildiz Technical University in Istanbul, Turkey, and results are reported herein. Appliance usage profiles and customer preferences used in the BEM demonstration were derived from a survey of Turkish customers. Both summer and winter usage profiles were used to validate the efficacy of the proposed algorithm in a real-world smart building environment. The paper also discusses lessons learned from field implementation.

Dynamic privacy assessment in a smart house environment using multimodal sensing

Surveillance applications in private environments such as smart houses require a privacy management policy if such systems are to be accepted by the occupants of the environment. This is due to the invasive nature of surveillance, and the private nature of the home. In this article, we propose a framework for dynamically altering the privacy policy applied to the monitoring of a smart house based on the situation within the environment. Initially the situation, or context, within the environment is determined; we identify several factors for determining environmental context, and propose methods to quantify the context using audio and binary sensor data. The context is then mapped to an appropriate privacy policy, which is implemented by applying data hiding techniques to control access to data gathered from various information sources. The significance of this work lies in the examination of privacy issues related to assisted-living smart house environments. A single privacy policy in such applications would be either too restrictive for an observer, for example, a carer, or too invasive for the occupants. We address this by proposing a dynamic method, with the aim of decreasing the invasiveness of the technology, while retaining the purpose of the system.

Intelligent Interaction for Human-Friendly Service Robot in Smart House Environment

The smart house under consideration is a service-integrated complex system to assist older persons and/or people with disabilities. The primary goal of the system is to achieve independent living by various robotic devices and systems. Such a system is treated as a human-in-the loop system in which humanrobot interaction takes place intensely and frequently. Ba sed on our experiences of having designed and implemented a smart house environment, called Intelligent Sweet Home (ISH), we present a framework of realizing human-friendly HRI (human-robot interaction) module with various effective techniques of computational intelligence. More specifically, we partiti on the robotic tasks of HRI module into three groups in consideration of the level of specificity, fuzzine ss or uncertainty of the context of the system, and present effective interaction method for each case. We fi rst show a task planning algorithm and its architecture to deal with well-structured tasks autonomously by a simplified set of commands of the user instead of inconvenient manual operations. To provide with capability of interacting in a human-friendly way in a fuzzy context, it is proposed that the robot should make use of human bio-signals as input of the HRI module as shown in a hand gesture recognition system, called a soft remote control system. Finally we discuss a probabilistic fuzzy rule-based life-l ong learning system, equipped with intention reading capability by learning human behavioral patterns, which is introduced as a solution in uncertain and time-varying situations.

A Steward Robot for Human-Friendly Human-Machine Interaction in a Smart House Environment

The independence of people who need help with daily activities will become of vital importance to all societies in the future. This paper addresses the problem of controlling the assistive home environment and emphasizes human-friendly human-machine interactions in an approach designed to achieve independence. To provide residents with an accessible, convenient, and cost-effective environment for independent living, we introduce a new service robot, categorized as a steward robot, as an intermediate agent between residents and their complex smart house environment. The learning capability and emotional interaction of the robot can make it more human-friendly in various tasks. A learning system enables the robot to provide customized services by accumulating knowledge of the user's behavioral patterns in daily activities. An emotional interaction system generates facial expressions to communicate with the user in a human-friendly manner. We have developed two types of a steward robot: a software type, which can be used everywhere via personal computing devices such as a PDA and a cellular phone, and a hardware type, which provides tangible services with physical interaction via two robotic arms and a mobile base.

Intelligent Interaction for Human-Friendly Service Robot in Smart House Environment

Intelligent Interaction for Human-Friendly Service Robot in Smart House Environment

Intelligent Interaction For Human-Friendly Service Robot in Smart House Environment

Intelligent Interaction For Human-Friendly Service Robot in Smart House Environment

A Steward Robot to Help Daily Activities in a Smart House Environment

Independence of the people in need in their activities of daily living becomes a matter of vital importance to any society in the years to come. As an approach to achieve independence, this paper addresses the problem of controlling assistive home environment emphasizing human-friendly human-machine interaction. To provide inhabitants with easily accessible, convenient, and cost-effective environment for independent living, we introduce a new service robot categorized into a steward robot as an intermediate agent between inhabitants and complex smart house environment. The learning capability and emotional interaction of the robot can enhance human-friendliness in various tasks. A learning system enables the robot to provide customized services by accumulating knowledge of the user's behavior patterns in daily activities. An emotional interaction system generates facial expressions to communicate with the user in a human-friendly manner. We have developed two types of steward robots: a software type to use everywhere by personal computing devices such as a PDA and a cellular phone, and a hardware type to provide tangible services with physical interaction by two robotic arms and a mobile base.

The use of Emotional Design Techniques in user oriented design of interfaces within a smart house environment: Case study

IBV is working in the emerging area of Emotional Engineering with application in the development of technology for the user wellness. A first prospective analysis is made in the field of smart house technologies for elderly people focused on clarifying the role of emotions in the interaction user-environment mediated by technology. The study was undertaken with elderly people within the age segment from 40 to 75 years old with no pathology that could affect cognitive processes. As a result, a clear emotional dimension is identified as design barrier that makes smart house environments be understood far from redeeming lack of abilities due to ageing. This paper also highlights the challenge of developing methodological approaches customized for the field of emotional design in interactive products.

A chemotactic-based model for spatial activity recognition

Spatial activity recognition in everyday environments is particularly challenging due to noise incorporated during video-tracking. We address the noise issue of spatial recognition with a biologically inspired chemotactic model that is capable of handling noisy data. The model is based on bacterial chemotaxis, a process that allows bacteria to survive by changing motile behaviour in relation to environmental dynamics. Using chemotactic principles, we propose the chemotactic model and evaluate its classification performance in a smart house environment. The model exhibits high classification accuracy (99%) with a diverse 10 class activity dataset and outperforms the discrete hidden Markov model (HMM). High accuracy (>89%) is also maintained across small training sets and through incorporation of varying degrees of artificial noise into testing sequences. Importantly, unlike other bottom–up spatial activity recognition models, we show that the chemotactic model is capable of recognising simple interwoven activities.

Multi-modal emotive computing in a smart house environment

We determine hazards within a smart house environment using an emotive computing framework. Representing a hazardous situation as an abnormal activity, we model normality using the concept of anxiety, using an agent based probabilistic approach. Interactions between a user and the environment are determined using multi-modal sensor data. The anxiety framework is a scalable, real-time approach that is able to incorporate data from a number of sources, or agents, and able to accommodate interleaving event sequences. In addition to using simple sensors, we introduce a method for using audio as a pervasive sensor indicating the presence of an activity. The audio data enabled the detection of activity when interactions between a user and a monitored device didn’t occur, successfully preventing false hazardous situations from being detected. We present results for a number of activity sequences, both normal and abnormal.