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Recognizing activities of daily living from UWB radars and deep learning

Maitre Julien, Bouchard Kévin, Bertuglia Camille et Gaboury Sébastien. (2021). Recognizing activities of daily living from UWB radars and deep learning. Expert Systems with Applications, 164, p. 113994.

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Since years, the number of seniors increases while, at the same time, we observe a diminution of the potential support ratio. In order to overcome this limitation, solutions emerged, such as smart homes and wearable devices. Smart homes integrate sensors, actuators, and artificial intelligence to assist seniors in their everyday life. One of the objectives is to recognize the activities of everyday life. This recognition aims to provide the right assistance at the right moment and gives some autonomy to seniors. However, it is a complex task (a significant quantity of different sensors, hardware implementation), and the number of solutions (combinations between approaches, for example, video-based HAR and wearable sensors-based HAR) that exist is important. In this paper, we propose to perform the activity recognition from three ultra-wideband (UWB) radars, deep learning models, and a voting system. Also, all the experiments have been conducted in a real apartment and are composed of 15 different activities. The presented solution is simple compared to the literature since we exploit only one type of sensor. Finally, we obtained promising results with our approach. Indeed, the classification rate reaches 90% and more in some cases.

Type de document:Article publié dans une revue avec comité d'évaluation
Pages:p. 113994
Version évaluée par les pairs:Oui
Identifiant unique:10.1016/j.eswa.2020.113994
Sujets:Sciences naturelles et génie > Sciences mathématiques > Informatique
Département, module, service et unité de recherche:Départements et modules > Département d'informatique et de mathématique
Mots-clés:everyday activities, classification, recognition, UWB radar, deep learning
Déposé le:09 févr. 2021 00:01
Dernière modification:09 févr. 2021 00:01
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