Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pone.0221390
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dc.titleHierarchical multi-view aggregation network for sensor-based human activity recognition
dc.contributor.authorZhang, X.
dc.contributor.authorWong, Y.
dc.contributor.authorKankanhalli, M.S.
dc.contributor.authorGeng, W.
dc.date.accessioned2021-12-29T04:33:20Z
dc.date.available2021-12-29T04:33:20Z
dc.date.issued2019
dc.identifier.citationZhang, X., Wong, Y., Kankanhalli, M.S., Geng, W. (2019). Hierarchical multi-view aggregation network for sensor-based human activity recognition. PLoS ONE 14 (9) : e0221390. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pone.0221390
dc.identifier.issn19326203
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/212277
dc.description.abstractSensor-based human activity recognition aims at detecting various physical activities performed by people with ubiquitous sensors. Different from existing deep learning-based method which mainly extracting black-box features from the raw sensor data, we propose a hierarchical multi-view aggregation network based on multi-view feature spaces. Specifically, we first construct various views of feature spaces for each individual sensor in terms of white-box features and black-box features. Then our model learns a unified representation for multi-view features by aggregating views in a hierarchical context from the aspect of feature level, position level and modality level. We design three aggregation modules corresponding to each level aggregation respectively. Based on the idea of non-local operation and attention, our fusion method is able to capture the correlation between features and leverage the relationship across different sensor position and modality. We comprehensively evaluate our method on 12 human activity benchmark datasets and the resulting accuracy outperforms the state-of-the-art approaches. © 2019 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.publisherPublic Library of Science
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceScopus OA2019
dc.typeArticle
dc.contributor.departmentDEPT OF COMPUTER SCIENCE
dc.contributor.departmentDEAN'S OFFICE (SCHOOL OF COMPUTING)
dc.description.doi10.1371/journal.pone.0221390
dc.description.sourcetitlePLoS ONE
dc.description.volume14
dc.description.issue9
dc.description.pagee0221390
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