Undernutrition Prevention for Disabled and Elderly People in Smart Home with Bayesian Networks and RFID Sensors
Identifieur interne : 000143 ( Hal/Corpus ); précédent : 000142; suivant : 000144Undernutrition Prevention for Disabled and Elderly People in Smart Home with Bayesian Networks and RFID Sensors
Auteurs : Nathalie CisloSource :
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Abstract
Undernutrition prevention or detection for disabled or elderly people must be performed rapidly to avoid irremediable consequences. In this paper a classification of uncertainties centered on a meal notion is first proposed. Two of these uncertainties are developed in a smart home and homecare context. Meal preparation probability is evaluated by a simulation based on Naïve Bayesian Networks. To determine if a person is at risk of malnutrition or undernutrition, and to supervise prepared meal quality and quantity in terms of nutrients, the use of RFID tags is discussed, bringing many open issues for which additional sensors are proposed. This research work was initiated in a collaborative project called CaptHom.
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<front><div type="abstract" xml:lang="en">Undernutrition prevention or detection for disabled or elderly people must be performed rapidly to avoid irremediable consequences. In this paper a classification of uncertainties centered on a meal notion is first proposed. Two of these uncertainties are developed in a smart home and homecare context. Meal preparation probability is evaluated by a simulation based on Naïve Bayesian Networks. To determine if a person is at risk of malnutrition or undernutrition, and to supervise prepared meal quality and quantity in terms of nutrients, the use of RFID tags is discussed, bringing many open issues for which additional sensors are proposed. This research work was initiated in a collaborative project called CaptHom.</div>
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<notesStmt><note type="commentary">We especially thank all our partners involved in the CAPTHOM project of the S2E2 cluster (Sciences and Systems of Electrical Energy), and the French Industry Ministry and locale authorities for their financial help</note>
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<abstract xml:lang="en">Undernutrition prevention or detection for disabled or elderly people must be performed rapidly to avoid irremediable consequences. In this paper a classification of uncertainties centered on a meal notion is first proposed. Two of these uncertainties are developed in a smart home and homecare context. Meal preparation probability is evaluated by a simulation based on Naïve Bayesian Networks. To determine if a person is at risk of malnutrition or undernutrition, and to supervise prepared meal quality and quantity in terms of nutrients, the use of RFID tags is discussed, bringing many open issues for which additional sensors are proposed. This research work was initiated in a collaborative project called CaptHom.</abstract>
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