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 A french corpus of audio and multimodal interactions in a health smart home

2012 ◽  
Vol 7 (1-2) ◽  
pp. 93-109 ◽  
Author(s):  
Anthony Fleury ◽  
Michel Vacher ◽  
François Portet ◽  
Pedro Chahuara ◽  
Norbert Noury
Keyword(s):  
Smart Home ◽  
Multimodal Interactions ◽  
Health Smart Home ◽  
French Corpus

scholarly journals Genetic algorithm–optimized support vector machine for real-time activity recognition in health smart home

2020 ◽  
Vol 16 (11) ◽  
pp. 155014772097151
Author(s):  
Yan Hu ◽  
Bingce Wang ◽  
Yuyan Sun ◽  
Jing An ◽  
Zhiliang Wang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Real Time ◽  
Activity Recognition ◽  
Smart Home ◽  
Feature Representation ◽  
Support Vector ◽  
Typical Application ◽  
Time Activity ◽  
Health Smart Home

Health smart home, as a typical application of Internet of things, provides a new solution for remote medical treatment. It can effectively relieve pressure from shortage of medical resources caused by aging population and help elderly people live at home more independently and safely. Activity recognition is the core of health smart home. This technology aims to recognize the activity patterns of users from a series of observations on the user’ actions and the environmental conditions, so as to avoid distress situations as much as possible. However, most of the existing researches focus on offline activity recognition, but not good at online real-time activity recognition. Besides, the feature representation techniques used for offline activity recognition are generally not suitable for online scenarios. In this article, the authors propose a real-time online activity recognition approach based on the genetic algorithm–optimized support vector machine classifier. In order to support online real-time activity recognition, a new sliding window-based feature representation technique enhanced by mutual information between sensors is devised. In addition, the genetic algorithm is used to automatically select optimal hyperparameters for the support vector machine model, thereby reducing the recognition inaccuracy caused by manual tuning of hyperparameters. Finally, a series of comprehensive experiments are conducted on freely available data sets to validate the effectiveness of the proposed approach.

Health "Smart" Home: Information Technology for Patients at Home

2002 ◽  
Vol 8 (4) ◽  
pp. 395-409 ◽  
Author(s):  
Vincent Rialle ◽  
Florence Duchene ◽  
Norbert Noury ◽  
Lionel Bajolle ◽  
Jacques Demongeot
Keyword(s):  
Information Technology ◽  
Smart Home ◽  
Health Smart Home ◽  
At Home

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

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.

Data fusion in health smart home: primary results of individual evaluation of two sensors

2008 ◽  
Vol 7 (2) ◽  
Author(s):  
A. Fleury ◽  
M. Vacher ◽  
H. Glasson ◽  
N. Noury ◽  
J-F. Serignat
Keyword(s):  
Data Fusion ◽  
Smart Home ◽  
Individual Evaluation ◽  
Health Smart Home
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