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Activity recognition in smart homes using UWB radars

Bouchard Kévin, Maitre Julien, Bertuglia Camille et Gaboury Sébastien. (2020). Activity recognition in smart homes using UWB radars. Procedia Computer Science, 170, p. 10-17.

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In the last decade, smart homes have transitioned from a potential solution for aging-in-place to a real set of technologies being deployed in the real-world. This technological transfer has been mostly supported by simple, commercially available sensors such as passive infrared and electromagnetic contacts. On the other hand, many teams of research claim that the sensing capabilities are still too low to offer accurate, robust health-related monitoring and services. In this paper, we investigate the possibility of using Ultra-wideband (UWB) Doppler radars for the purpose of recognizing the ongoing ADLs in smart homes. Our team found out that with simple configuration and classical features engineering, a small set of UWB radars could reasonably be used to recognize ADLs in a realistic home environment. A dataset was built from 10 persons performing 15 different ADLs in a 40 square meters apartment with movement on the other side of the wall. Random Forest was able to attain 80% accuracy with an F1-Score of 79%, and a Kappa of 77%. Those results indicate the use of Doppler radars can be a good research avenue for smart homes.

Type de document:Article publié dans une revue avec comité d'évaluation
Pages:p. 10-17
Version évaluée par les pairs:Oui
Identifiant unique:10.1016/j.procs.2020.03.004
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:smart home, activity recognition, ambient intelligence, radars, machine learning, proceedings
Déposé le:12 févr. 2021 19:40
Dernière modification:12 févr. 2021 19:40
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