Cross-environment activity recognition using a shared semantic vocabulary

Effectively recognizing activities in smart environments requires either matching sensors to semantic models or labeled training data from the target environment for machine learning. Combining knowledge-driven and data-driven approaches improves activity recognition (AR) by providing the benefits o...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Pervasive and mobile computing Ročník 51; s. 150 - 159
Hlavní autoři: Wemlinger, Zachary E., Holder, Lawrence B.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.12.2018
Témata:
Activity recognition
Semantics
Smart environments
Smart environments
Activity recognition
Semantics
ISSN:1574-1192, 1873-1589
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Effectively recognizing activities in smart environments requires either matching sensors to semantic models or labeled training data from the target environment for machine learning. Combining knowledge-driven and data-driven approaches improves activity recognition (AR) by providing the benefits of each while also mitigating their challenges. In this paper we present the Semantic Cross-Environment Activity Recognition (SCEAR) system which is a novel method for creating semantic feature spaces and enables data-driven AR systems to transfer AR models across environments. We evaluate SCEAR using 22 datasets from real-world smart environments. Transferred model performance is compared to models trained in the target environment and shown to provide a 39% average per-class improvement.
ISSN:1574-1192
1873-1589
DOI:10.1016/j.pmcj.2018.10.004