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Representation learning for minority and subtle activities in a smart home environment
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dc.contributor.author | Rosales Sanabria, Andrea | |
dc.contributor.author | Kelsey, Thomas William | |
dc.contributor.author | Ye, Juan | |
dc.date.accessioned | 2019-07-26T12:30:02Z | |
dc.date.available | 2019-07-26T12:30:02Z | |
dc.date.issued | 2019-07-22 | |
dc.identifier | 260282913 | |
dc.identifier | b1fb461f-760a-48bd-a4de-e99827c1abb6 | |
dc.identifier | 85070191649 | |
dc.identifier | 000492869600032 | |
dc.identifier.citation | Rosales Sanabria , A , Kelsey , T W & Ye , J 2019 , Representation learning for minority and subtle activities in a smart home environment . in Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom 2019) . , 8767417 , IEEE Computer Society , IEEE International Conference on Pervasive Computing and Communications (PerCom 2019) , Kyoto , Japan , 12/03/19 . https://doi.org/10.1109/percom.2019.8767417 | en |
dc.identifier.citation | conference | en |
dc.identifier.isbn | 9781538691496 | |
dc.identifier.isbn | 9781538691489 | |
dc.identifier.isbn | 9781538691472 | |
dc.identifier.other | ORCID: /0000-0002-8091-1458/work/59953680 | |
dc.identifier.other | ORCID: /0000-0002-2838-6836/work/68280966 | |
dc.identifier.uri | https://hdl.handle.net/10023/18174 | |
dc.description.abstract | Daily human activity recognition using sensor data can be a fundamental task for many real-world applications, such as home monitoring and assisted living. One of the challenges in human activity recognition is to distinguish activities that have infrequent occurrence and less distinctive patterns. We propose a dissimilarity representation-based hierarchical classifier to perform two-phase learning. In the first phase, the classifier learns general features to recognise majority classes, and the second phase is to collect minority and subtle classes to identify fine difference between them. We compare our approach with a collection of state-of-the-art classification techniques on a real-world third-party dataset that is collected in a two-user home setting. Our results demonstrate that our hierarchical classifier approach outperforms the existing techniques in distinguishing users in performing the same type of activities. The key novelty of our approach is the exploration of dissimilarity representations and hierarchical classifiers, which allows us to highlight the difference between activities with subtle difference, and thus allows the identification of well-discriminating features. | |
dc.format.extent | 7 | |
dc.format.extent | 509308 | |
dc.language.iso | eng | |
dc.publisher | IEEE Computer Society | |
dc.relation.ispartof | Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom 2019) | en |
dc.subject | Smart home | en |
dc.subject | Activity recognition | en |
dc.subject | Dissimilarity representation | en |
dc.subject | Representation learning | en |
dc.subject | QA75 Electronic computers. Computer science | en |
dc.subject | NDAS | en |
dc.subject.lcc | QA75 | en |
dc.title | Representation learning for minority and subtle activities in a smart home environment | en |
dc.type | Conference item | en |
dc.contributor.institution | University of St Andrews. School of Computer Science | en |
dc.contributor.institution | University of St Andrews. Centre for Interdisciplinary Research in Computational Algebra | en |
dc.identifier.doi | 10.1109/percom.2019.8767417 | |
dc.identifier.url | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8767417 | en |
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