ADL Monitoring of Elderly People in Smart Homes Based on Task Models - Interactive Critical Systems Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

ADL Monitoring of Elderly People in Smart Homes Based on Task Models

Jose Manuel Negrete Ramírez
  • Fonction : Auteur
  • PersonId : 1191196
Célia Martinie
Philippe Palanque
Yudith Cardinale

Résumé

Monitoring activities of daily living (ADL) is a frequent practice to determine the independence of elderly people in their homes instrumented with a set of sensors. To do so, the representation of ADL is an issue that must be addressed. In this paper, ADL representation based on task models is presented. To this extent, we propose an approach for task modelling of ADL by means of HAMSTERS-XL, an extensible task notation and tool allowing the design, visualisation, and simulation of task models. The proposed approach consists of several steps aiming to determine parameters, such as time and location, of both activities and sensors, as well as the events that need to be identified. To demonstrate the suitability of our approach, we present the description of two ADL performed by a resident within a smart home environment by employing the AGGIR grid variables, a tool adopted by the French government for determining the autonomy of an elderly individual. The obtained results are analyzed with the purpose of indicating the advantages of using ADL representation by means of task modeling.
Fichier principal
Vignette du fichier
AISE-32-AISE230034.pdf (1.86 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-04151435 , version 1 (05-07-2023)

Licence

Paternité - Pas d'utilisation commerciale

Identifiants

Citer

Jose Manuel Negrete Ramírez, Célia Martinie, Philippe Palanque, Yudith Cardinale. ADL Monitoring of Elderly People in Smart Homes Based on Task Models. Workshop @ 19th International Conference on Intelligent Environments (IE 2023), Jun 2023, Mauritius, Mauritius. pp.216 - 225, ⟨10.3233/AISE230034⟩. ⟨hal-04151435⟩
52 Consultations
33 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More