Next Article in Journal
Optimal Threshold Determination for Discriminating Driving Anger Intensity Based on EEG Wavelet Features and ROC Curve Analysis
Previous Article in Journal
The Role of Physical Layer Security in IoT: A Novel Perspective
Previous Article in Special Issue
Investigation of Voltage Control at Consumers Connection Points Based on Smart Approach
Open AccessArticle

Smart Homes and Sensors for Surveillance and Preventive Education at Home: Example of Obesity

1
Laboratoire AGIM, Faculty of Medicine, University J. Fourier Grenoble, Domaine de la Merci, La Tronche 38700, France
2
Laboratoire RIADI, Ecole Nationale des Sciences de l’Informatique, Université de la Manouba, 2010 Manouba, Tunisia
3
Equipe de Biostatistiques, Institut Pasteur, 13 Place Pasteur, Tunis 1002, Tunisia
4
Escuela de Ingeniería Civil en Informática, Universidad de Valparaíso, General Cruz 222, Valparaíso, Chile
*
Author to whom correspondence should be addressed.
Academic Editors: Sugam Sharma and Anna Fensel
Information 2016, 7(3), 50; https://doi.org/10.3390/info7030050
Received: 26 February 2016 / Revised: 30 June 2016 / Accepted: 29 July 2016 / Published: 8 August 2016
(This article belongs to the Special Issue Smart Home)
(1) Background: The aim of this paper is to show that e-health tools like smart homes allow the personalization of the surveillance and preventive education of chronic patients, such as obese persons, in order to maintain a comfortable and preventive lifestyle at home. (2) Technologies and methods: Several types of sensors allow coaching the patient at home, e.g., the sensors recording the activity and monitoring the physiology of the person. All of this information serves to personalize serious games dedicated to preventive education, for example in nutrition and vision. (3) Results: We built a system of personalized preventive education at home based on serious games, derived from the feedback information they provide through a monitoring system. Therefore, it is possible to define (after clustering and personalized calibration) from the at home surveillance of chronic patients different comfort zones where their behavior can be estimated as normal or abnormal and, then, to adapt both alarm levels for surveillance and education programs for prevention, the chosen example of application being obesity. View Full-Text
Keywords: smart homes; tele-surveillance; preventive education at home; sensors; serious games; obesity determinants smart homes; tele-surveillance; preventive education at home; sensors; serious games; obesity determinants
Show Figures

Figure 1

MDPI and ACS Style

Demongeot, J.; Elena, A.; Jelassi, M.; Ben Miled, S.; Bellamine Ben Saoud, N.; Taramasco, C. Smart Homes and Sensors for Surveillance and Preventive Education at Home: Example of Obesity. Information 2016, 7, 50.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop