Smart Home Design for Disabled People based on Neural Networks

Hussein, Ali Zuhair; Adda, Mehdi; Atieh, Mirna; Fahs, Walid

doi:10.1016/j.procs.2014.08.020

Cited by 50 publications

(26 citation statements)

References 1 publication

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“…The neural network testing results of [7], show the importance of the NN for prediction activities in a smart home. Teich et al [8] build a Stable Neural Network (SNN) based on a feed forward neural network in learning step.…”

Section: Related Workmentioning

confidence: 96%

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A Genetic Neural Network Approach for Unusual Behavior Prediction in Smart Home

Liouane

Lemlouma

Roose

et al. 2017

Advances in Intelligent Systems and Computing

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Abstract. Detect efficiently the activities of daily living of elderly people at home in order to provide a secure life and to intervene in the necessary time is an important problem we propose here an improved artificial neural network model. As we need an efficient prediction model, we propose a recurrent output neural network model (RO-NN) combined with a genetic algorithm (GA) which surely monitors and predicts the state of the concerned elderly person. Furthermore, we propose a prediction algorithm "Unusual Behavior Algorithm (UBA)" dedicated to detect the unusual activities and hold us account in the dangerous state.

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Section: Related Workmentioning

confidence: 96%

“…To monitor disabled people in their life, Hussein et al [7] propose to use two types of Neural Network "the Feed-Forward Neural Networks and the Recurrent Neural Networks". The neural network testing results of [7], show the importance of the NN for prediction activities in a smart home.…”

Section: Related Workmentioning

confidence: 99%

A Genetic Neural Network Approach for Unusual Behavior Prediction in Smart Home

Liouane

Lemlouma

Roose

et al. 2017

Advances in Intelligent Systems and Computing

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“…As demonstrated in [89], ML is also used in conjunction with ECG spelling based BCI applications to minimize training times, although the conversational rates are still generally reported to be slow [90]. On the broader scale, research in [91] demonstrates that Neural Networks could be potentially used to learn, predict, and adapt to the events within a user’s environment to aid the people with disabilities.…”

Section: Sensing Modalities and Their Functionalitiesmentioning

confidence: 99%

Augmentative and Alternative Communication (AAC) Advances: A Review of Configurations for Individuals with a Speech Disability

Elsahar

Bouazza-Marouf

et al. 2019

Sensors

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High-tech augmentative and alternative communication (AAC) methods are on a constant rise; however, the interaction between the user and the assistive technology is still challenged for an optimal user experience centered around the desired activity. This review presents a range of signal sensing and acquisition methods utilized in conjunction with the existing high-tech AAC platforms for individuals with a speech disability, including imaging methods, touch-enabled systems, mechanical and electro-mechanical access, breath-activated methods, and brain–computer interfaces (BCI). The listed AAC sensing modalities are compared in terms of ease of access, affordability, complexity, portability, and typical conversational speeds. A revelation of the associated AAC signal processing, encoding, and retrieval highlights the roles of machine learning (ML) and deep learning (DL) in the development of intelligent AAC solutions. The demands and the affordability of most systems hinder the scale of usage of high-tech AAC. Further research is indeed needed for the development of intelligent AAC applications reducing the associated costs and enhancing the portability of the solutions for a real user’s environment. The consolidation of natural language processing with current solutions also needs to be further explored for the amelioration of the conversational speeds. The recommendations for prospective advances in coming high-tech AAC are addressed in terms of developments to support mobile health communicative applications.

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“…In [8], the authors proposed a Smart Home Design for Disabled People based on Neural Network to improve the daily life of handicap users. It uses two types of neural network, namely Feed-Forward and Recurrent, for adaptive learning.…”

Section: Related Workmentioning

confidence: 99%

Personalized ambience: An integration of learning model and intelligent lighting control

Yin

Keoh

2016

2016 IEEE 3rd World Forum on Internet of Things (WF-IoT)

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Abstract-The number of households and offices adopting automation system is on the rise. Although devices and actuators can be controlled through wireless transmission, they are mostly static with preset schedules, or at different times it requires human intervention. This paper presents a smart ambience system that analyzes the user's lighting habits, taking into account different environmental context variables and user needs in order to automatically learn about the user's preferences and automate the room ambience dynamically. Context information is obtained from Yahoo Weather and environmental data pertaining to the room is collected via Cubesensors to study the user's lighting habits. We employs a learning model known as the Reduced Error Prune Tree (REPTree) to analyze the users' preferences, and subsequently predicts the preferred lighting condition to be actuated in real time through Philips Hue. The system is able to ensure the user's comfort at all time by performing a closed feedback control loop which checks and maintains a suitable lighting ambience at optimal level.

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Smart Home Design for Disabled People based on Neural Networks

Cited by 50 publications

References 1 publication

A Genetic Neural Network Approach for Unusual Behavior Prediction in Smart Home

A Genetic Neural Network Approach for Unusual Behavior Prediction in Smart Home

Augmentative and Alternative Communication (AAC) Advances: A Review of Configurations for Individuals with a Speech Disability

Personalized ambience: An integration of learning model and intelligent lighting control

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