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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| Vydáno v: | Pervasive and mobile computing Ročník 51; s. 150 - 159 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.12.2018
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| ISSN: | 1574-1192, 1873-1589 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Holder, Lawrence B. Wemlinger, Zachary E. |
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| Cites_doi | 10.1145/2662870 10.1016/j.pmcj.2017.05.003 10.1109/TKDE.2011.51 10.1007/s00779-010-0331-7 10.1016/j.compeleceng.2015.03.012 10.1007/978-3-540-88564-1_51 10.1504/IJWGS.2010.035091 10.1109/JBHI.2015.2461659 10.3390/s150511725 10.1016/j.pmcj.2011.02.007 10.3414/ME0592 |
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Syst. – year: 2005 ident: 10.1016/j.pmcj.2018.10.004_b13 article-title: DomoML-env: an ontology for Human Home Interaction – volume: 7 start-page: 331 issue: 3 year: 2011 ident: 10.1016/j.pmcj.2018.10.004_b17 article-title: Activity knowledge transfer in smart environments publication-title: Pervasive Mob. Comput. doi: 10.1016/j.pmcj.2011.02.007 – volume: 12 start-page: 2825 year: 2011 ident: 10.1016/j.pmcj.2018.10.004_b27 article-title: Scikit-learn: Machine learning in {P}ython publication-title: J. Mach. Learn. Res. – volume: 48 start-page: 480 issue: 5 year: 2009 ident: 10.1016/j.pmcj.2018.10.004_b22 article-title: Assessing the quality of activities in a smart environment publication-title: Methods Inf. Med. doi: 10.3414/ME0592 |
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