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...

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Bibliographic Details
Published in:Pervasive and mobile computing Vol. 51; pp. 150 - 159
Main Authors: Wemlinger, Zachary E., Holder, Lawrence B.
Format: Journal Article
Language:English
Published: Elsevier B.V 01.12.2018
Subjects:
Activity recognition
Semantics
Smart environments
Smart environments
Activity recognition
Semantics
ISSN:1574-1192, 1873-1589
Online Access:Get full text
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Summary: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