Dynamically Reconfigurable Online Self-organising Fuzzy Neural Network with Variable Number of Inputs for Smart Home Application

A self-organising fuzzy-neural network (SOFNN) adapts its structure based on variations of the input data. Conventionally in such self-organising networks, the number of inputs providing the data is fixed. In this paper, we consider the situation where the number of inputs to a network changes dynamically during its online operation. We extend our existing work on a SOFNN such that the SOFNN can self-organise its structure based not only on its input data, but also according to the changes in the number of its inputs. We apply the approach to a smart home application, where there are certain situations when some of the existing events may be removed or new events emerge, and illustrate that our approach enhances cognitive reasoning in a dynamic smart home environment. In this case, the network identifies the removed and/or added events from the received information over time, and reconfigures its structure dynamically. We present results for different combinations of training and testing phases of the dynamic reconfigurable SOFNN using a set of realistic synthesized data. The results show the potential of the proposed method

Design and realization of motion detector system for house security

In this paper, the design and realization of motion detector system for house security based GSM network is presents. The development of microcontroller carried out intruder detection that supports tracking techniques to provide vital security with control and alert operation inside and outside the home. The pivot of security on the integration the motion detector and cameras into web applications has become more interested. The smart surveillance Pi camera obtain the input from the motion detector and controller which is send the video to the web server allowing the homeowner to access this video by use web applications. An intrusion alert send to the owner by mean of message via mobile and buzzers alarms located at suitable distance. This system is typify proficient video camera for remote sensing and tracking with live video for succeeding play again to offers efficient and easy implementation with omnipresent surveillance solutio

Application of supervised learning methods to better predict building energy performance

Building energy consumption is shaped by a variety of factors which prompts a challenge of accurately predicting the building energy performance. Research findings disclosed a significant gap between the building’s predicted and actual energy performance. One of the key factors behind this gap is the occupant’s behavior during operation which includes a set of dependent and independent parameters generating a greater level of uncertainties. To accurately estimate the energy performance, we need to quantify the impact of any observed parameters and further detect its correlation with other parameters. Human behaviors are complex and quantifying the impact of all its interconnected parameters can be error prone and costly. To minimize the performance gap, more scalable and accurate prediction approaches, such as supervised machine learning methods, should be considered. This paper is devoted to investigate the most commonly used supervised learning methods which, when intertwined with conventional building energy performance prediction model, could potentially provide more accurate and reliable estimates. The paper will pinpoint the best use of each studied method in the relation to energy prediction in general and occupant’s behavior in specific and how it can be implemented to better predict building energy performance

Online Health Monitoring using Household Activity Patterns from Smart Meter Data

In recent years, people are migrating from rural areas to urban areas which became common. The people whoever suffering from ill-health must require health care services and providing those services to them is the most challenging aspect. Technological enhancements led to construct smart homes, which are equipped several sensor or smart meter for process automation of another electronic device. In addition to these smart meters are able to capture the patient�s routine activities and also monitors their health situations by frequent patterns mining and association rules formed from smart meters. We introduced a model in this work which is able to monitor the patient�s activities in home and could send routine activities to the respected doctor. We can retrieve frequent patterns and association rules from log data and can estimate the patient�s health situations and suggest them based on this prediction. Our work is partitioned into three stages. Initially we record the patients� routine activities by allocating particular time period with three regular intervals. In second stage, we applied the growth of frequent pattern in order to extract the association rules from log file. In final stage, we applied k-means clustering for input and applied Bayesian network model to guess the patient�s health behavior and suggest precautions accordingly

Major requirements for building Smart Homes in Smart Cities based on Internet of Things technologies

The recent boom in the Internet of Things (IoT) will turn Smart Cities and Smart Homes (SH) from hype to reality. SH is the major building block for Smart Cities and have long been a dream for decades, hobbyists in the late 1970s made Home Automation (HA) possible when personal computers started invading home spaces. While SH can share most of the IoT technologies, there are unique characteristics that make SH special. From the result of a recent research survey on SH and IoT technologies, this paper defines the major requirements for building SH. Seven unique requirement recommendations are defined and classified according to the specific quality of the SH building blocks

Sensor-based datasets for human activity recognition - a systematic review of literature

The research area of ambient assisted living has led to the development of activity recognition systems (ARS) based on human activity recognition (HAR). These systems improve the quality of life and the health care of the elderly and dependent people. However, before making them available to end users, it is necessary to evaluate their performance in recognizing activities of daily living, using data set benchmarks in experimental scenarios. For that reason, the scientific community has developed and provided a huge amount of data sets for HAR. Therefore, identifying which ones to use in the evaluation process and which techniques are the most appropriate for prediction of HAR in a specific context is not a trivial task and is key to further progress in this area of research. This work presents a systematic review of the literature of the sensor-based data sets used to evaluate ARS. On the one hand, an analysis of different variables taken from indexed publications related to this field was performed. The sources of information are journals, proceedings, and books located in specialized databases. The analyzed variables characterize publications by year, database, type, quartile, country of origin, and destination, using scientometrics, which allowed identification of the data set most used by researchers. On the other hand, the descriptive and functional variables were analyzed for each of the identified data sets: occupation, annotation, approach, segmentation, representation, feature selection, balancing and addition of instances, and classifier used for recognition. This paper provides an analysis of the sensor-based data sets used in HAR to date, identifying the most appropriate dataset to evaluate ARS and the classification techniques that generate better results

Sensor-based datasets for human activity recognition - a systematic review of literature

The research area of ambient assisted living has led to the development of activity recognition systems (ARS) based on human activity recognition (HAR). These systems improve the quality of life and the health care of the elderly and dependent people. However, before making them available to end users, it is necessary to evaluate their performance in recognizing activities of daily living, using data set benchmarks in experimental scenarios. For that reason, the scientific community has developed and provided a huge amount of data sets for HAR. Therefore, identifying which ones to use in the evaluation process and which techniques are the most appropriate for prediction of HAR in a specific context is not a trivial task and is key to further progress in this area of research. This work presents a systematic review of the literature of the sensor-based data sets used to evaluate ARS. On the one hand, an analysis of different variables taken from indexed publications related to this field was performed. The sources of information are journals, proceedings, and books located in specialized databases. The analyzed variables characterize publications by year, database, type, quartile, country of origin, and destination, using scientometrics, which allowed identification of the data set most used by researchers. On the other hand, the descriptive and functional variables were analyzed for each of the identified data sets: occupation, annotation, approach, segmentation, representation, feature selection, balancing and addition of instances, and classifier used for recognition. This paper provides an analysis of the sensor-based data sets used in HAR to date, identifying the most appropriate dataset to evaluate ARS and the classification techniques that generate better results

A data-driven situation-aware framework for predictive analysis in smart environments

In the era of Internet of Things (IoT), it is vital for smart environments to be able to efficiently provide effective predictions of user’s situations and take actions in a proactive manner to achieve the highest performance. However, there are two main challenges. First, the sensor environment is equipped with a heterogeneous set of data sources including hardware and software sensors, and oftentimes complex humans as sensors, too. These sensors generate a huge amount of raw data. In order to extract knowledge and do predictive analysis, it is necessary that the raw sensor data be cleaned, understood, analyzed, and interpreted. Second challenge refers to predictive modeling. Traditional predictive models predict situations that are likely to happen in the near future by keeping and analyzing the history of past user’s situations. Traditional predictive analysis approaches have become less effective because of the massive amount of data that both affects data processing efficiency and complicates the data semantics. In this study, we propose a data-driven, situation-aware framework for predictive analysis in smart environments that addresses the above challenges

Monitorování lidského chování v Smart Home (SH) v rámci IoT

It is widely researched that last 20 years fall accidents increased dramatically. Most often it happens with elderly people who leave alone. Without timely assistance, fall accident may lead to unpredictable consequences. Nowadays smart home technologies allow not only monitor but collect and process human behavior data. This diploma thesis applies cutting edge technologies to collect data from smart home based on KNX technology with help of Home Assistant software, preprocess it with a machine learning model stored in Amazon Web Service and notify emergency about fall event via Telegram messenger.Je široce prozkoumáváno, že za posledních 20 let se nehody s poklesem dramaticky zvýšily. Nejčastěji se to stává u starších lidí, kteří odcházejí sami. Bez včasné pomoci může nehoda s pádem vést k nepředvídatelným následkům. Technologie inteligentní domácnosti dnes umožňují nejen sledovat, ale také shromažďovat a zpracovávat údaje o lidském chování. Tato diplomová práce využívá nejmodernější technologie ke sběru dat z inteligentní domácnosti založené na technologii KNX pomocí softwaru Home Assistant, jejich předzpracování pomocí modelu strojového učení uloženého ve službě Amazon Web Service a nouzové upozornění na událost pádu prostřednictvím telegramového posla.450 - Katedra kybernetiky a biomedicínského inženýrstvívelmi dobř