scholarly journals Smart Home Design for Disabled People based on Neural Networks

2014 ◽  
Vol 37 ◽  
pp. 117-126 ◽  
Author(s):  
Ali Hussein ◽  
Mehdi Adda ◽  
Mirna Atieh ◽  
Walid Fahs
Keyword(s):  
Neural Networks ◽  
Smart Home ◽  
Disabled People

Risk Management of Smart Homes for Disabled People Based on Neural Networks and IoTs

2021 ◽  
Author(s):  
Walid Fahs ◽  
Mohammad Jaafar Housseiny ◽  
Hiba Sbeity ◽  
Ali Mekdad ◽  
Jamal Haydar ◽  
...  
Keyword(s):  
Neural Networks ◽  
Risk Management ◽  
Smart Homes ◽  
Disabled People

scholarly journals Methodology for Ensuring a Comfortable Microclimate State in a Smart Home Using an Ensemble of Fuzzy Artificial Neural Networks

2021 ◽  
Vol 20 (6) ◽  
pp. 1418-1447
Author(s):  
Andrey Mozohin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Control System ◽  
Physical Environment ◽  
Air Conditioning ◽  
Smart Home ◽  
Practical Implementation ◽  
Climate Control ◽  
Artificial Neural ◽  
Hybrid Neural Networks

Analysis of the application of smart home technology indicates an insufficient level of controllability of its infrastructure, which leads to excessive consumption of energy and information resources. The problem of managing the digital infrastructure of human living space, is associated with a large number of highly specialized solutions for home automation, which complicate the management process. Smart home is considered as a set of independent cyber-physical devices aimed at achieving its goal. For coordinated work of cyber-physical devices it is proposed to provide their joint work through a single information center. Simulation of device operation modes in a digital environment preserves the resource of physical devices by making a virtual calculation for all possible variants of interaction of devices between themselves and the physical environment. A methodology for controlling the microclimate of a smart home using an ensemble of fuzzy artificial neural networks is developed, with the example of joint use of air conditioning, ventilation and heating. The neural network algorithm allows you to monitor the parameters of the physical environment, predict the modes of cyber-physical devices and generate control signals for each of them, ensuring the joint operation of devices with minimal resource consumption and information traffic. A variant of practical implementation of a smart home climate control system on the example of a multifunctional educational computer class is proposed. Hybrid neural networks of air conditioning, ventilation and heating systems were developed. The testing of the microclimate control system of a multifunctional university classroom using hybrid neural networks was carried out, a programmable logic controller of domestic production was used as a control device. The goal of management based on cooperating cyber-physical devices is to achieve a minimum of power and information traffic when they work together.

scholarly journals Indirect Recognition of Predefined Human Activities

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4829
Author(s):  
Ojan Majidzadeh Gorjani ◽  
Antonino Proto ◽  
Jan Vanus ◽  
Petr Bilik
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Artificial Neural Networks ◽  
Data Collection ◽  
Human Activities ◽  
Air Conditioning ◽  
Smart Home ◽  
Home Automation ◽  
Specific Activities ◽  
Conditioning Control

The work investigates the application of artificial neural networks and logistic regression for the recognition of activities performed by room occupants. KNX (Konnex) standard-based devices were selected for smart home automation and data collection. The obtained data from these devices (Humidity, CO2, temperature) were used in combination with two wearable gadgets to classify specific activities performed by the room occupant. The obtained classifications can benefit the occupant by monitoring the wellbeing of elderly residents and providing optimal air quality and temperature by utilizing heating, ventilation, and air conditioning control. The obtained results yield accurate classification.

A Health Smart Home System to Report Incidents for Disabled People

2015 ◽  
Author(s):  
Diulie J. Freitas ◽  
Tiago B. Marcondes ◽  
Luis H.V. Nakamura ◽  
Rodolfo I. Meneguette
Keyword(s):  
Smart Home ◽  
Disabled People ◽  
Health Smart Home

scholarly journals Smart Home System for Energy Saving using Genetic- Fuzzy-Neural Networks Approach

2016 ◽  
Vol 08 (1) ◽  
Keyword(s):  
Neural Networks ◽  
Energy Saving ◽  
Human Behavior ◽  
Smart Home ◽  
Cost Effective ◽  
Fuzzy Neural Networks ◽  
Behavior Patterns ◽  
Power Losses ◽  
Sustainable Improvement ◽  
Fuzzy Neural

Home energy saving is very important to realize sustainable improvement. This can be achieved by designing a smart home system that provides a productive and cost-effective environment through optimization of different factors that will be explained in this paper. In this paper, an adaptive smart home system for optimal utilization of power will be designed. The system is based on genetic-fuzzy-neural networks technique, which can capture a human behavior patterns and use it to predict the user's mood. This technique will improve the intelligence of the smart home control to minimize the power losses.

scholarly journals A DSL-Based Approach for Detecting Activities of Daily Living by Means of the AGGIR Variables

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5674
Author(s):  
José Manuel Negrete Ramírez ◽  
Philippe Roose ◽  
Marc Dalmau ◽  
Yudith Cardinale ◽  
Edgar Silva
Keyword(s):  
Activities Of Daily Living ◽  
Home Environment ◽  
Daily Living ◽  
Smart Home ◽  
Daily Activities ◽  
Domain Specific Language ◽  
Analysis Tool ◽  
Disabled People ◽  
Domain Specific ◽  
Event Information

In this paper, we propose a framework for studying the AGGIR (Autonomie Gérontologique et Groupe Iso Ressources—Autonomy Gerontology Iso-Resources Groups) grid model, with the aim of assessing the level of independence of elderly people in accordance with their capabilities of performing daily activities as well as interacting with their environments. In order to model the Activities of Daily Living (ADL), we extend a previously proposed Domain Specific Language (DSL), by defining new operators to deal with constraints related to time and location of activities and event recognition. The proposed framework aims at providing an analysis tool regarding the performance of elderly/disabled people within a home environment by means of data recovered from sensors using a smart-home simulator environment. We perform an evaluation of our framework in several scenarios, considering five of the AGGIR variables (i.e., feeding, dressing, toileting, elimination, and transfers) as well as health-care devices for tracking the occurrence of elderly activities. The results demonstrate the accuracy of the proposed framework for managing the tracked records correctly and, thus, generate the appropriate event information related to the ADL.

AN EVALUATION OF SOME FACTORS AFFECTING ACCURACY OF THE VIETNAMESE KEYWORD SPOTTING SYSTEM

2020 ◽  
pp. 33-43
Author(s):  
Xuan
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Call Center ◽  
Smart Home ◽  
Far Field ◽  
Keyword Spotting ◽  
Factors Affecting ◽  
Testing Environment ◽  
Call Routing ◽  
Voice Control

Keyword spotting (KWS) is one of the important systems on speech applications, such as data mining, call routing, call center, customer-controlled smartphone, smart home systems with voice control, etc. With the goals of researching some factors affecting the Vietnamese Keyword spotting system, we study the combination architecture of CNN (Convolutional Neural Networks)-RNN (Recurrent Neural Networks) on both clean and noise environments with 2 distance speaker cases: 1m and 2m. The obtained results show that the noise trained models are better performance than clean trained models in any (clean or noise) testing environment. The results in this far-field experiment suggest to us how to choose the suitable distance of the recording microphones to the speaker so that there is no redundancy of data with the contexts considered to be the same. 

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