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Gesture recognition in smart home using passive RFID technology

Bouchard Kévin, Bouzouane Abdenour et Bouchard Bruno. (2014). Gesture recognition in smart home using passive RFID technology. Dans Fillia Makedon (dir.), PETRA '14 : Proceedings of the 7th International conference on pervasive technologies related to assistive environments - PETRA '14. (p. 1-8). New York, NY, United States : Association for computing machinery.

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Gesture recognition is a well-establish topic of research that is widely adopted for a broad range of applications. For instance, it can be exploited for the command of a smart environment without any remote control unit or even for the recognition of human activities from a set of video cameras deployed in strategic position. Many researchers working on assistive smart home, such as our team, believe that the intrusiveness of that technology will prevent the future adoption and commercialization of smart homes. In this paper, we propose a novel gesture recognition algorithm that is solely based on passive RFID technology. This technology enables the localization of small tags that can be embedded in everyday life objects (a cup or a book, for instance) while remaining non intrusive. However, until now, this technology has been largely ignored by researchers on gesture recognition, mostly because it is easily disturbed by noise (metal, human, etc.) and offer limited precision. Despite these issues, the localization algorithms have improved over the years, and our recent efforts resulted in a real-time tracking algorithm with a precision approaching 14cm. With this, we developed a gesture recognition algorithm able to perform segmentation of gestures and prediction on a spatio-temporal data series. Our new model, exploiting works on qualitative spatial reasoning, achieves recognition of 91%. Our goal is to ultimately use that knowledge for both human activity recognition and errors detection.

Type de document:Chapitre de livre
Lieu de publication:New York, NY, United States
Identifiant unique:10.1145/2674396.2674405
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
Éditeurs:Makedon, Fillia
Mots-clés:applied computing, life and medical sciences, human-centered computing, software and its engineering, software organization and properties, gesture recognition, passive RFID, smart home, trilateration, spatio-temporal series, proceedings
Déposé le:12 févr. 2021 20:01
Dernière modification:12 févr. 2021 20:01
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