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Activity prediction based on time series forcasting

Moutacalli Mohamed Tarik, Bouchard Kévin, Bouzouane Abdenour et Bouchard Bruno. (2014). Activity prediction based on time series forcasting. Dans Artificial intelligence applied to assistive technologies and smart environments $b papers presented at the Twenty-Eighth AAAI Conference on artificial intelligence. (p. 32-37). Palo Alto, California : AAAI Press.

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URL officielle: https://www.aaai.org/ocs/index.php/WS/AAAIW14/pape...

Résumé

Activity recognition is a crucial step in automatic assistance for elderly and disabled people, such as Alzheimer’s patients. The large number of activities of daily living (ADLs) that these persons are used to performing as well as their inability, sometimes, to start an activity make the recognition process difficult, if not impossible. To adress such problems, we propose a timebased activity prediction approch as a preliminary step to activity recognition. Not only it will facilitate the recognition, but it will also rank activities according to their occurrence probabilities at every time interval. In this paper, after detecting activities models, we implement and validate an activity prediction process using a time series framework.

Type de document:Chapitre de livre
Date:2014
Lieu de publication:Palo Alto, California
Sujets:Sciences naturelles et génie > Sciences mathématiques > Informatique
Sciences de la santé
Département, module, service et unité de recherche:Départements et modules > Département d'informatique et de mathématique
Mots-clés:proceedings, smart homes, time series, activity prediction, activity recognition
Déposé le:15 févr. 2021 19:18
Dernière modification:15 févr. 2021 19:18
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